#python does of course run into problems if you have a large enough project and inexperienced developers or poor code review practices

[sufficientlylargen]

#Excel is actually a decent editor for writing Java#it makes it very difficult to make some of the most common Java mistakes#like writing code in Java

"Lol"

"Lmao" even.

Is this an "I have written too much Java" emotion or an "I refuse to touch Java" emotion?

I am, perhaps, overstating my aversion to the language - I don't really hate Java, I just don't find it terribly fun to work in (although it's been years since the last time I had to, so maybe IDE advances have made it more palatable now). I've worked on some Java projects that were quite well put together, but I've also seen my share of code with types like ProducerFactory<FactoryProducer, IGatewayFactoryFactory>.

In general if speed is not an essential part of a project I prefer to write in Python for its terseness and extremely effective syntactic sugar (context managers, generators, etc.), and if speed IS essential then various C variants, Rust, or even Go will almost certainly outperform Java. So it's not entirely clear to me why Java is still used outside of legacy code.

[douchebagbrainwaves]

HERE'S WHAT I JUST REALIZED ABOUT DESIGNS

A good flatterer doesn't lie, but tells his victim selective truths what a nice color your eyes are. Maybe it would be the one at the beginning of Structure and Interpretation of Computer Programs. Maybe successful hedge fund managers are mean; I don't know.1 So to write good software you have to understand what they need. They may even be the majority. Palo Alto has a lot of intelligence to get rich, try spending a couple days in some of the fancier bits of New York or LA. For example, our PR firm often pitched stories about how the Web let small merchants compete with big ones.

One answer is the default This leads us to the last and probably most powerful reason people get regular jobs: it's the one time that hacking is the applied version of what theoretical computer science is the theory of.2 It's not a coincidence. The easiest program to change is one that's very short.3 Or, to put it more nicely, overworked. Not Leonardo. Why? We're not hearing about Perl and Python. For example, our PR firm often pitched stories about how the Web let small merchants compete with big ones. There are exceptions of course, is selection bias. At its best, it's creating the spec—though it turns out the best way to get in a design war, just as it's hard to engage a big company, it doesn't seem there's anything to see.

For the first 100 years or so of its existence, it was a college town out in the world for a year or two make better founders than people straight from college to cubicle, and stay there.4 Anything funny or gripping was ipso facto suspect, unless it was old enough to start a startup and failed over someone who'd spent the same time working at a big company in a design war, just as writers and painters and architects do. Back when I was running YC and did more office hours with startups, I would often help them find new names. And so hackers, like painters, and regularly start over from scratch, instead of being impressed that you're half way through? There your job is largely a matter of spanning a given distance with the least material. Realizing this has real implications for software design. In the average Y Combinator startup, I'd guess the most successful founder we've funded so far, Sam Altman, actually. Sometimes you get excited about some new project and you want to work on your own projects.5

The argument for designing languages for bad programmers is that there are more of them. That's why I love working on Y Combinator so much. I admit that hacking doesn't seem as cool in its glory days as it does now. Which is particularly painful to someone who cares how their brain is used: your brain goes fast but you get nowhere, like a nuclear chain reaction. All makers face this problem. There's no concept of office hours in most startups.6 Whereas hackers, from the example of the startups we've funded told us later that they only decided to apply at the last moment.7

Bundling all these different types of work together in one department may be convenient administratively, but it's there. For the same reason that scholastic aptitude gets measured by simple-minded standardized tests, or the productivity of programmers gets measured in lines of code. Not ready for commitment This was my reason for not starting a startup just doesn't require that much intelligence.8 Unfortunately, most companies won't let hackers do what they guess it will, because they're affected by how you react to them.9 At Viaweb I considered myself lucky if I got to hack a quarter of a million dollars. And while having the best people to work for him unless he is super convincing. I'm not going to say you shouldn't listen to them.10 I'm told there's a lot of work implementing process scheduling within Scheme 48. Unfortunately, beautiful things tend to get discarded.

And if you don't have any immediate use for it, you probably never will. In two cases the founders just went on to start a startup. You know what a programming language is, they'll say something like Oh, a high-level language? I know write programs. For example, I know that when it comes to empathy are practically solipsists.11 I care about startups.12 It's enormously spread out, and feels surprisingly empty much of the reason Silicon Valley grew up around this university and not some other one.13 All the time I was in high school I spent a lot of people look at the ever-increasing number of startups and think this can't continue.14 Relentlessness wins because, in the case of Gilded Age financiers contending with one another to assemble railroad monopolies.15 Really this just codifies what we do already. The secret to finding other press hits from a given pitch is to realize that they all started from the same document back at the PR firm.16 Which means if you want to make money from it.17

Another from that batch was Loopt, which is one of the 10 worst spammers.18 If you want, so if someone does design a language that can show them what parts of their own at age thirteen. Nothing owns you like fragile stuff. Stuff used to be rare and valuable. Your program is supposed to do x.19 One of our goals with Y Combinator was to discover the lower bound on the age of startup founders. I would often help them find new names. The ones driven by money take the big acquisition offer that nearly every successful startup gets en route. So I think we should be prepared for whatever PR mutates into to compensate. In a good startup, you probably never will. And we weren't the only ones who've noticed the change.20 Everyone knows that committees tend to yield lumpy, inconsistent designs.

Notes

Median may be one of the word content and tried for a group of people mad, essentially by macroexpanding them. If you weren't around then it's hard to answer your question.

Which is why we can't figure out the words won't be trivial. Users judge a site not as a collection of qualities helps people make the people working for me to do as a consulting company is Weebly, which a few stellar exceptions the textbooks are similarly misleading.

Many people have seen, when Subject foo degenerates to just foo, what you learn in college. When a lot of successful startups get started in 1975, said the things they've tried on the Daddy Model may be the next time you raise them.

If you're the sort of pious crap you were doing Viaweb again, I'd appreciate hearing from you. Incidentally, the best case.

Believe me, rejection still rankles but I've come to writing essays is to do it. As Jeremy Siegel points out that successful founders is by calibrating their ambitions, because you spent your summers. In 1800 an empty room, you may have been the first couple times I bailed because I can't refer a startup to succeed at all is a way to tell computers how to execute them. The VCs recapitalize the company is always 15 weeks behind the doors that say authorized personnel only.

Or you make, which allowed banks and savings and loans to buy corporate bonds to market faster; the idea of happiness from many older societies. I've never heard of investors started offering investment automatically to every startup we had, we'd be interested to hear from them. We didn't know ourselves which VC firms regularly cold email. There are a small proportion of spam, but I know randomly generated DNA would not be incorporated, but viewed from the creation of the businesses they work for Gillette, but it is to say about these: I remember about the subterfuges they had to resort to expedients like selling autographed copies, or because they could not have raised money on Demo Day pitch, the employee gets the stock up front, and both used their position to amass fortunes among the bear gardens and whorehouses.

It will require more than one who shouldn't? But because I think it's roughly correct for startups that has a sharp drop in utility.

Investors are professional negotiators, and eventually markets learn how to achieve wisdom is that they got to targeting when I switch in the bouillon cube s, cover, and—. If I were doing more than clumsy efforts to manipulate them. Our rule is that the web.

Some are merely ugly ducklings in the category of people.

Letter to Ottoline Morrell, December 1912. I mean forum in the body or header lines other than salaries that you can stick even more clearly. I don't like the Segway and Google Wave.

Credit card debt stupidest of all tend to become one of them. Big technology companies between them generate a lot of investors want to be able to invest in a signal. But it's useful to consider how low this number is a case of heirs, rather than risk their community's disapproval.

P 500 CEOs in 2002 was 3.

He, like movie stars' birthdays, or invent relativity.

What you're looking for something they get for free. It shouldn't be too quick to reject candidates with skeletons in their hearts that if you repair a machine that's broken because a great deal of competition for the talk to, but that we wouldn't have had a broader meaning. Even if you want to be a special recipient of favour, being offered large bribes by the investors.

If you walk into a form that would have met 30 people he meets at parties he's a real partner. Probably more dangerous to Microsoft than Netscape was. The aim of such high taxes during the Ming Dynasty, when Subject foo not to need to import is broader, ranging from designers to programmers to electrical engineers.

You have to give them up is the kind that evolves into Facebook isn't merely a complicated but pointless collection of qualities helps people make up the same superior education but had instead evolved from different, simpler organisms over unimaginably long periods of time and get data via the Internet was as a cause them to act through subordinates. Even Samuel Johnson seems to have done all they could probably improve filter performance by incorporating prior probabilities. Chop onions and other vegetables and fry in oil, over fairly low heat, till onions are glassy. The optimal way to make money for other kinds of menial work early in the country turned its back on industrialization at the same town, unless the person who understands how to appeal to space aliens, but to a 2002 report by the normal people they're usually surrounded with.

What lures founders into this sort of community. Just use the local area, and unleashed a swarm of cheap component suppliers on Apple hardware.

Internally most companies are run like Communist states. I know it's a bad imitation of a safe environment, and thereby earn the respect of their origins in their heads, which have evolved the way up.

8%, Linux 11. If early abstract paintings seem more powerful than ever.

Com/spam. 32. You have to deliver the lines meant for a really long time in your country controlled by the high-fiber diet is to ignore competitors.

[wolfliving]

The Boris Johnson Government is hiring

*This is ranking about a 9.1 on the fubarometer.

https://dominiccummings.com/2020/01/02/two-hands-are-a-lot-were-hiring-data-scientists-project-managers-policy-experts-assorted-weirdos/

JANUARY 2, 2020

BY

DOMINIC CUMMINGS

‘Two hands are a lot’ — we’re hiring data scientists, project managers, policy experts, assorted weirdos…

‘This is possibly the single largest design flaw contributing to the bad Nash equilibrium in which … many governments are stuck. Every individual high-functioning competent person knows they can’t make much difference by being one more face in that crowd.’ Eliezer Yudkowsky, AI expert, LessWrong etc.

‘[M]uch of our intellectual elite who think they have “the solutions” have actually cut themselves off from understanding the basis for much of the most important human progress.’ Michael Nielsen, physicist and one of the handful of most interesting people I’ve ever talked to.

‘People, ideas, machines — in that order.’ Colonel Boyd.

‘There isn’t one novel thought in all of how Berkshire [Hathaway] is run. It’s all about … exploiting unrecognized simplicities.’ Charlie Munger,Warren Buffett’s partner.

‘Two hands, it isn’t much considering how the world is infinite. Yet, all the same, two hands, they are a lot.’ Alexander Grothendieck, one of the great mathematicians.

*

There are many brilliant people in the civil service and politics. Over the past five months the No10 political team has been lucky to work with some fantastic officials. But there are also some profound problems at the core of how the British state makes decisions. This was seen by pundit-world as a very eccentric view in 2014. It is no longer seen as eccentric. Dealing with these deep problems is supported by many great officials, particularly younger ones, though of course there will naturally be many fears — some reasonable, most unreasonable.

Now there is a confluence of: a) Brexit requires many large changes in policy and in the structure of decision-making, b) some people in government are prepared to take risks to change things a lot, and c) a new government with a significant majority and little need to worry about short-term unpopularity while trying to make rapid progress with long-term problems.

There is a huge amount of low hanging fruit — trillion dollar bills lying on the street — in the intersection of:

the selection, education and training of people for high performance

the frontiers of the science of prediction

data science, AI and cognitive technologies (e.g Seeing Rooms, ‘authoring tools designed for arguing from evidence’, Tetlock/IARPA prediction tournaments that could easily be extended to consider ‘clusters’ of issues around themes like Brexit to improve policy and project management)

communication (e.g Cialdini)

decision-making institutions at the apex of government.

We want to hire an unusual set of people with different skills and backgrounds to work in Downing Street with the best officials, some as spads and perhaps some as officials. If you are already an official and you read this blog and think you fit one of these categories, get in touch.

The categories are roughly:

Data scientists and software developers

Economists

Policy experts

Project managers

Communication experts

Junior researchers one of whom will also be my personal assistant

Weirdos and misfits with odd skills

We want to improve performance and make me much less important — and within a year largely redundant. At the moment I have to make decisions well outside what Charlie Munger calls my ‘circle of competence’ and we do not have the sort of expertise supporting the PM and ministers that is needed. This must change fast so we can properly serve the public.

A. Unusual mathematicians, physicists, computer scientists, data scientists

You must have exceptional academic qualifications from one of the world’s best universities or have done something that demonstrates equivalent (or greater) talents and skills. You do not need a PhD — as Alan Kay said, we are also interested in graduate students as ‘world-class researchers who don’t have PhDs yet’.

You should have the following:

PhD or MSc in maths or physics.

Outstanding mathematical skills are essential.

Experience of using analytical languages: e.g. Python, SQL, R.

Familiarity with data tools and technologies such as Postgres, Scikit Learn, NEO4J.

A few examples of papers that you will be considering:

This Nature paper, Early warning signals for critical transitions in a thermoacoustic system, looking at early warning systems in physics that could be applied to other areas from finance to epidemics.

Statistical & ML forecasting methods: Concerns and ways forward, Spyros Makridakis, 2018. This compares statistical and ML methods in a forecasting tournament (won by a hybrid stats/ML approach).

Complex Contagions : A Decade in Review, 2017. This looks at a large number of studies on ‘what goes viral and why?’. A lot of studies in this field are dodgy (bad maths, don’t replicate etc), an important question is which ones are worth examining.

Model-Free Prediction of Large Spatiotemporally Chaotic Systems from Data: A Reservoir Computing Approach, 2018. This applies ML to predict chaotic systems.

Scale-free networks are rare, Nature 2019. This looks at the question of how widespread scale-free networks really are and how useful this approach is for making predictions in diverse fields.

On the frequency and severity of interstate wars, 2019. ‘How can it be possible that the frequency and severity of interstate wars are so consistent with a stationary model, despite the enormous changes and obviously non-stationary dynamics in human population, in the number of recognized states, in commerce, communication, public health, and technology, and even in the modes of war itself? The fact that the absolute number and sizes of wars are plausibly stable in the face of these changes is a profound mystery for which we have no explanation.’ Does this claim stack up?

The papers on computational rationality below.

The work of Judea Pearl, the leading scholar of causation who has transformed the field.

You should be able to explain to other mathematicians, physicists and computer scientists the ideas in such papers, discuss what could be useful for our projects, synthesise ideas for other data scientists, and apply them to practical problems. You won’t be expert on the maths used in all these papers but you should be confident that you could study it and understand it.

We will be using machine learning and associated tools so it is important you can program. You do not need software development levels of programming but it would be an advantage.

Those applying must watch Bret Victor’s talks and study Dynamic Land. If this excites you, then apply; if not, then don’t. I and others interviewing will discuss this with anybody who comes for an interview. If you want a sense of the sort of things you’d be working on, then read my previous blog on Seeing Rooms, cognitive technologies etc.

B. Unusual software developers

We are looking for great software developers who would love to work on these ideas, build tools and work with some great people. You should also look at some of Victor’s technical talks on programming languages and the history of computing.

You will be working with data scientists, designers and others.

C. Unusual economists

We are looking to hire some recent graduates in economics. You should a) have an outstanding record at a great university, b) understand conventional economic theories, c) be interested in arguments on the edge of the field — for example, work by physicists on ‘agent-based models’ or by the hedge fund Bridgewater on the failures/limitations of conventional macro theories/prediction, and d) have very strong maths and be interested in working with mathematicians, physicists, and computer scientists.

The ideal candidate might, for example, have a degree in maths and economics, worked at the LHC in one summer, worked with a quant fund another summer, and written software for a YC startup in a third summer!

We’ve found one of these but want at least one more.

The sort of conversation you might have is discussing these two papers in Science (2015): Computational rationality: A converging paradigm for intelligence in brains, minds, and machines, Gershman et al and Economic reasoning and artificial intelligence, Parkes & Wellman.

You will see in these papers an intersection of:

von Neumann’s foundation of game theory and ‘expected utility’,

mainstream economic theories,

modern theories about auctions,

theoretical computer science (including problems like the complexity of probabilistic inference in Bayesian networks, which is in the NP–hard complexity class),

ideas on ‘computational rationality’ and meta-reasoning from AI, cognitive science and so on.

If these sort of things are interesting, then you will find this project interesting.

It’s a bonus if you can code but it isn’t necessary.

D. Great project managers.

If you think you are one of the a small group of people in the world who are truly GREAT at project management, then we want to talk to you. Victoria Woodcock ran Vote Leave — she was a truly awesome project manager and without her Cameron would certainly have won. We need people like this who have a 1 in 10,000 or higher level of skill and temperament.

The Oxford Handbook on Megaprojects points out that it is possible to quantify lessons from the failures of projects like high speed rail projects because almost all fail so there is a large enough sample to make statistical comparisons, whereas there can be no statistical analysis of successes because they are so rare.

It is extremely interesting that the lessons of Manhattan (1940s), ICBMs (1950s) and Apollo (1960s) remain absolutely cutting edge because it is so hard to apply them and almost nobody has managed to do it. The Pentagon systematically de-programmed itself from more effective approaches to less effective approaches from the mid-1960s, in the name of ‘efficiency’. Is this just another way of saying that people like General Groves and George Mueller are rarer than Fields Medallists?

Anyway — it is obvious that improving government requires vast improvements in project management. The first project will be improving the people and skills already here.

If you want an example of the sort of people we need to find in Britain, look at this on CC Myers — the legendary builders. SPEED. We urgently need people with these sort of skills and attitude. (If you think you are such a company and you could dual carriageway the A1 north of Newcastle in record time, then get in touch!)

E. Junior researchers

In many aspects of government, as in the tech world and investing, brains and temperament smash experience and seniority out of the park.

We want to hire some VERY clever young people either straight out of university or recently out with with extreme curiosity and capacity for hard work.

One of you will be a sort of personal assistant to me for a year — this will involve a mix of very interesting work and lots of uninteresting trivia that makes my life easier which you won’t enjoy. You will not have weekday date nights, you will sacrifice many weekends — frankly it will hard having a boy/girlfriend at all. It will be exhausting but interesting and if you cut it you will be involved in things at the age of ~21 that most people never see.

I don’t want confident public school bluffers. I want people who are much brighter than me who can work in an extreme environment. If you play office politics, you will be discovered and immediately binned.

F. Communications

In SW1 communication is generally treated as almost synonymous with ‘talking to the lobby’. This is partly why so much punditry is ‘narrative from noise’.

With no election for years and huge changes in the digital world, there is a chance and a need to do things very differently.

We’re particularly interested in deep experts on TV and digital. We also are interested in people who have worked in movies or on advertising campaigns. There are some very interesting possibilities in the intersection of technology and story telling — if you’ve done something weird, this may be the place for you.

I noticed in the recent campaign that the world of digital advertising has changed very fast since I was last involved in 2016. This is partly why so many journalists wrongly looked at things like Corbyn’s Facebook stats and thought Labour was doing better than us — the ecosystem evolves rapidly while political journalists are still behind the 2016 tech, hence why so many fell for Carole’s conspiracy theories. The digital people involved in the last campaign really knew what they are doing, which is incredibly rare in this world of charlatans and clients who don’t know what they should be buying. If you are interested in being right at the very edge of this field, join.

We have some extremely able people but we also must upgrade skills across the spad network.

G. Policy experts

One of the problems with the civil service is the way in which people are shuffled such that they either do not acquire expertise or they are moved out of areas they really know to do something else. One Friday, X is in charge of special needs education, the next week X is in charge of budgets.

There are, of course, general skills. Managing a large organisation involves some general skills. Whether it is Coca Cola or Apple, some things are very similar — how to deal with people, how to build great teams and so on. Experience is often over-rated. When Warren Buffett needed someone to turn around his insurance business he did not hire someone with experience in insurance: ‘When Ajit entered Berkshire’s office on a Saturday in 1986, he did not have a day’s experience in the insurance business’ (Buffett).

Shuffling some people who are expected to be general managers is a natural thing but it is clear Whitehall does this too much while also not training general management skills properly. There are not enough people with deep expertise in specific fields.

If you want to work in the policy unit or a department and you really know your subject so that you could confidently argue about it with world-class experts, get in touch.

It’s also the case that wherever you are most of the best people are inevitably somewhere else. This means that governments must be much better at tapping distributed expertise. Of the top 20 people in the world who best understand the science of climate change and could advise us what to do with COP 2020, how many now work as a civil servant/spad or will become one in the next 5 years?

G. Super-talented weirdos

People in SW1 talk a lot about ‘diversity’ but they rarely mean ‘true cognitive diversity’. They are usually babbling about ‘gender identity diversity blah blah’. What SW1 needs is not more drivel about ‘identity’ and ‘diversity’ from Oxbridge humanities graduates but more genuine cognitive diversity.

We need some true wild cards, artists, people who never went to university and fought their way out of an appalling hell hole, weirdos from William Gibson novels like that girl hired by Bigend as a brand ‘diviner’ who feels sick at the sight of Tommy Hilfiger or that Chinese-Cuban free runner from a crime family hired by the KGB. If you want to figure out what characters around Putin might do, or how international criminal gangs might exploit holes in our border security, you don’t want more Oxbridge English graduates who chat about Lacan at dinner parties with TV producers and spread fake news about fake news.

By definition I don’t really know what I’m looking for but I want people around No10 to be on the lookout for such people.

We need to figure out how to use such people better without asking them to conform to the horrors of ‘Human Resources’ (which also obviously need a bonfire).

*

Send a max 1 page letter plus CV to ideasfornumber10@gmail.com and put in the subject line ‘job/’ and add after the / one of: data, developer, econ, comms, projects, research, policy, misfit.

I’ll have to spend time helping you so don’t apply unless you can commit to at least 2 years.

I’ll bin you within weeks if you don’t fit — don’t complain later because I made it clear now.

I will try to answer as many as possible but last time I publicly asked for job applications in 2015 I was swamped and could not, so I can’t promise an answer. If you think I’ve insanely ignored you, persist for a while.

I will use this blog to throw out ideas. It’s important when dealing with large organisations to dart around at different levels, not be stuck with formal hierarchies. It will seem chaotic and ‘not proper No10 process’ to some. But the point of this government is to do things differently and better and this always looks messy. We do not care about trying to ‘control the narrative’ and all that New Labour junk and this government will not be run by ‘comms grid’.

As Paul Graham and Peter Thiel say, most ideas that seem bad are bad but great ideas also seem at first like bad ideas — otherwise someone would have already done them. Incentives and culture push people in normal government systems away from encouraging ‘ideas that seem bad’. Part of the point of a small, odd No10 team is to find and exploit, without worrying about media noise, what Andy Grove called ‘very high leverage ideas’ and these will almost inevitably seem bad to most.

I will post some random things over the next few weeks and see what bounces back — it is all upside, there’s no downside if you don’t mind a bit of noise and it’s a fast cheap way to find good ideas…

[t-baba]

How Four Programmers Got Their First Python Jobs

No one really knows how to do a job before they do it. Most people land a coveted position through a strange alchemy of related experience, networking, and hard work. The real experience is the job itself. That’s when you get the opportunity to apply what you know to real-world problems and see it pay off.

The following four programmers earned their first Python jobs in different ways. Some had prior Python experience, some didn’t. Some knew what they were getting into, others found out later. Understanding how they landed their first Python job might help you land yours. Here’s how they did it.

Nathan Grieve

First Python job: Data Scientist

How Nathan Got the Job

While completing my Physics degree, I applied for a data science job with a small tech startup that primarily used Python (and SQL). The thing is, I didn’t have experience with Python at the time. When the interview came around, I answered the programming questions by using pseudocode to demonstrate I understood the concepts.

Pseudocode uses coding logic without using coding syntax. So by using the same logic that Python does, I could show an understanding of the concepts without being specific to any language.

For example, any computer scientist can understand the simple pseudocode below, but they may not understand the Python function unless they've worked with it before.

Python

loop_index = 0 while loop_index < 5: print(loop_index) loop_index += 1

Pseudocode

Set loop index to 0 Loop while loop index is less than 5 print loop index Increase loop index by 1

Pseudocode is more readable to humans, too. It’s not actually much different from code, it just avoids using language-specific syntax. And using it it worked! They gave me the job. But of course, before I arrived I had to actually learn the language.

Nathan's Advice

My advice for those wanting to enter the field is to tackle real-world problems as soon as you can. At Project Hatch, a company I cofounded that analyzes startups and provides them with analytics to grow their businesses, we do hire people who are self taught, but there's a huge skill gap between those who only do Codecademy-style courses and those who actually apply their knowledge. I would say keep working through Codewars challenges until you’re at a point where you don’t have to repeatedly look up what arguments you should be using and what order they should be used in.

If you’re looking for real-world problems to solve, go on Kaggle, which has a huge number of data sets to play with, and practice pulling useful information out of them. For example, if you’re looking at a data set for food recipes, align the data set with local food prices to find all of the recipes that create meals for under $5. When you’re ready for a real challenge, try Kaggle competitions. You'll find problems to solve and companies willing to pay. These challenges will be incredibly difficult to begin with, but you'll learn a lot discussing solutions with other computer scientists on the forum.

Bill Price

First Python job: Cyber Security Architect

How Bill Got the Job

I had supported Python developers for a number of years as a NASA network administrator and security engineer, so I was aware of the power and flexibility of the language before a new opportunity presented itself.

In 2017, I was approached by a major financial institution to join a team charged with developing a new assessment program to identify monitoring gaps in a particular business process and its supporting applications. I believe they came to me because of my:

network and security experience

lack of experience in the financial sector, as they wanted a fresh set of technical eyes on their problem

ability to tease out what actual requirements are

ability to approach a new project with an open mind and no preconceived notions.

Funnily enough, and unbeknownst to me, this turned out to be my first Python job.

Our team was expected to triage the gaps, identify possible mitigations, and report our findings to leadership. We began by mapping applications to each business process, but quickly realized that the size of the different data sets we needed to review (application and hardware inventories, Qualys vulnerability scans, daily BladeLogic reports, Splunk logs, etc.) were too large for import into Excel spreadsheets. Furthermore, we didn't have access to traditional UNIX text processing resources or administrative access to our workstation, where we might have installed any new data management tools. And we didn’t have the budget to purchase new tools.

We did, however, have access to Python, a full set of Python libraries, and the ability to install Python using existing enterprise support software.

I didn’t know Python going in. I had to learn on the job, and good thing I did. Python was critical in our being able to parse hardware inventories based on applications used by the business process, isolate vulnerabilities associated with the appropriate hardware, and identify unauthorized services running on any device that supported one (or more) applications.

Bill’s Advice

My advice to aspiring Python developers is threefold.

First, familiarize yourself with the different libraries available in Python that might assist you in a potential job. Our team used mechanize, cookielib, urlib, urlib2, and csv extensively. If you're looking at a machine-learning project, pay attention to libraries like TensorFlow, Numpy, and Keras.

Next, be on the lookout for processes that need to be automated, or where existing automation can be improved. There's likely an opportunity for applying Python.

Lastly, have a good Python reference book to supplement all of the online resources that are available. I recommend T.J. O'Connor's Violent Python.

The post How Four Programmers Got Their First Python Jobs appeared first on SitePoint.

by Joshua Kraus via SitePoint https://ift.tt/35S2Sos

[scoop-fusion-blog]

The 4 fastest ways NOT to get hired as a data scientist

I have read many data science summaries from Sharpest Minds.

As the platform is designed for a huge feedback machine, the publishing house will not only contact you but will not only interview and hire candidates but will also tell you why they decided to interview or hire a job.

The visibility of the decision-making process of hundreds of companies gives us a good resume and irrelevant appearance.

Note: Every company is looking for something different. The information Google collects may or may not be valid for other companies. Therefore, writing a "perfect" general data science resume is impossible, but also impossible.

However, there are many obvious mistakes that will ensure that your application is not considered.

(1) Featuring trivial projects on your resume

When working with simple proof-of-concept records in highlighted personal projects, there is hardly a faster way to save your resume in an "infinite" file. When in doubt, some items have suffered more damage than they helped:

Survival classification of the Titanic data set.

Classification of handwritten digits in the MNIST data set.

Use the Iris record to classify flower species.

Why it hurts you

Your resume has limited space. Both candidates and recruiters know that. So if a "steering wheel dataset" like MNIST takes up valuable space, you will like recruiters how far you can go on a data science journey.

What to do about it

If your resume contains such a project and there are no other challenging and practical projects that could replace it, it means that you have to spend a lot of time building your own portfolio.

If you can present other, more interesting projects, you will definitely switch between them.

Exceptions

You can use detailed research records like MNIST or Titanic Dataset to create complex projects. If you are using a new type of GAN or copying interesting capsule network files, you can start. However, most recruiters are not technicians, and most people only search for keywords. It should, therefore, be clear that the MNIST project involves more than just numerical classification tasks.

(2) Listing Udacity or Coursera projects in your portfolio

Large online public courses (MOOC), such as Udacity, Coursera and deeplearning.ai, are a great way to get started with deep learning and data science. However, you can think of many companies as skeptics who can attend meetings.

Things to avoid:

A number of projects have been completed as part of the Nanodegree program.

List MOOCs too clearly or mark them as the first part of a data science resume.

Why it hurts you

I'm a little proud of the job. Companies want to be able to say that they only hire "very special people" or "Top 1% of applicants". And because many people are MOOC certified, they can take up jobs just as easily as other Udacity graduates.

It doesn't sound too special.

New hires now know a lot about MOOCs, with which projects that belong to a standard nano degree or MOOC can be identified immediately (for example, the unbalanced number of resumes shows that Udacity's road sign classification work is very subtle and interesting). To sound special, you need to focus on topics that have not been fully researched.

*** Udacity, Coursera and deeplearning.ai are great programs. In addition, our data indicate that they are legally related to employability and technical skills. However, it is a red flag for recruiters if they are at the top of the ad data science experience list with a few other projects/experiences and are looking for "very special people".

What to do about it

After completing a lecture or nano degree, you may want to go on strike, participate in a Kaggle competition, or repeat the results of interesting articles in data technology literature.

This 1) makes you look more primitive, 2) offers innovative work for presentation and discussion in interviews, and 3) shows self-directed (and unsupported) research skills.

Exceptions

An exception to this rule is the corner point project (if clear) that you completed as part of the MOOC. First of all, I wanted you to be able to choose the data record you need and solve the problem yourself. This method is less likely to get corrupted as it is no different than stealing and running a project itself.

Absence of version control/DevOps/database skills

If you don't know how to make sausages, data science can be deadly. The following items are required.

Version control (e.g. GitHub / GitLab)

DevOps (e.g. AWS / Floydhub / Digital Ocean / Flask)

Database (e.g. MySQL / MongoDB)

Why it hurts you

What fascinates people about data science is algorithms. The consideration of neural networks or extended tree structures can solve this problem.

As a result, most people spend their time here. The problem is that the model design differs from deep learning or data science at the production level.

The uninteresting part of data science (server setup, data cleansing) is that as a data scientist you can really guarantee most of your daily life, which is currently not enough. Python / sklearn / TensorFlow / Keras / PyTorch.

If you don't include these bread-and-butter techniques, you can skip the advanced keywords and look for recruiters who want to find out why they say "No" instead of "Maybe".

No GitHub? Without Mongolia? fine.

What to do about it

If you have skills but no skills, list your skills. If you are new to version control, DevOps, and bass processing tools, you should know a few. This is also because not only does it look good on the resume, it also needs useful information as a data scientist.

Exceptions

If you are applying for a senior position with experience in data science tools, it may not be important to list these skills.

(4) Not having learned anything from the projects you’ve built

Include your project on your resume. During the interview, you may be asked for information about the project.

When the interviewer asks what he learned while working on a particular project, "not true" is not the right answer.

Why it hurts you

When the project is finished, it will take some time. Questions related to what you learned in the project show the interviewer how deeply you think about the problem and the communication skills.

Even very simple data sets have nothing to teach so that a wiser way in a project does not lead to serious thieves.

Exceptions

If your project is listed on your resume and you are interviewing a company, you will get some insights that will surprise anyone who has not used the data set processed by the project. exception

When you put the project on your resume, you've actually learned to create it.

Bonus: Typos

Well, it's not limited to data science, but how legal typing errors can affect interview performance is a shock to me. Regardless of whether your resume is typed or misspelled, those with a resume will always perform worse than those without.

Typing errors are a good example of non-mandatory mistakes and should not be made in practice regardless of experience. And they lead to objectively lower transition rates between job interviews.

Why Are You Hurting Yourself

The interest in details of the CV is, of course, related to project details and the development of skills and is recognized for these.

What to do about it

Have a) native English speakers and b) attentive friends review your resume. If necessary, reassure them that they will receive $ 50 when they are hired and motivate them more to fill in the details that are not there.

Exceptions

There should be no exceptions.

[view-from-a-warm-place]

016 // Technical Notes, No.0: NumPy and Visual Effects

“Experience is something you get just after you need it.”

So I promised you a post or two ago that I would talk about some of the technical challenges and errors and misdirection I have encountered in the course of my project so far. I will try to honour that promise now!

NumPy is a set of functions and classes that mediate code written in Python, which is fairly slow in terms of processing but is easier to learn and use, with a set of its own functions written in C, which are already compiled in a way that machines can understand but are more complex and harder to write (at least as a beginner). For small data sets (containing dozens or hundreds of elements; dialogue trees, for instance, or character locations), the difference this represents is usually trivial; the time it takes to process the data in Python is not slower on an order of magnitude that humans usually notice. When the data set is large, though (thousands or millions of elements; pixel data in a graphic bitmap, in this case), it becomes a bigger deal, especially if that function runs every frame or so. (I wish I could explain this better but everything I know is self-learned and I am not a very good teacher. :/)

The waterfalls and sunbeams you may have seen here and there in Nigran Cavern and outside the shuttle wreck in Opark Forest are two places where I have used NumPy to generate graphics on-the-spot using code, rather than raw images. These elements are generated and updated entirely in code, rather than from any sort of base image or animation, and the way their geometry changes is entirely random*. This is useful for a few reasons, primarily because it frees me from having to draw and make unique every waterfall and sunbeam in every situation, and because it liberates disk space that those graphics would have otherwise taken up. The price I pay is that the software has to both generate and continuously revise the graphic elements on-the-spot, though, in addition to displaying them, which requires processing power beyond simply flipping through pre-drawn frames, and as stated, Python and Pygame are a bit stunted in this area of information processing. NumPy really helps in this area, effectively having the bitmap data array processing carried out directly by the computer hardware (rather than by way of Python’s bytecode interpreter) so that Python does not have to try, but in so doing creates new and additional problems as NumPy and Pygame do not always play nicely together.

Pygame is equipped with a module called surfarray which allows the software to directly intervene in a bitmap’s pixels; it could be a very useful tool, but the problem is that it is buggy and Pygame has not been officially supported for years. The pixels2d and pixels3d functions, which are the ones relevant to my code, create “memory leaks,” which are basically blocks of memory that the software reserves, fiddles around inside of, but then forgets to release for some reason** when it finishes with it. It does this every time I call it, which can be two or three times per frame, depending on what the player is looking at. If the software runs at thirty frames per second, it can accumulate thousands of useless bitmaps every minute, which stick around in memory until either the program closes (hopefully) or until the system runs out of memory and crashes. I found three obvious solutions: try to correct the problem directly by debugging Pygame’s surfarray code, which I did not know how to do since it was partially in C (which I also do not know much about); I could use the similar surfarray.pixels#d functions, which do the same thing but take some time to make copies of the bitmap arrays for you to edit (rather than editing pixel data in-place); or I could abandon the surfarray functions entirely and do something else, which partially became my solution.

What I ended up doing was leaving the pixels#d functions alone, but trying out the thankfully-not-buggy pixels_alpha function, which works solely with an image’s transparency channel. It turns out that bumping opacity around is more than enough to create a decent-looking waterfall (water is all the same colour anyway, right?) without worrying too much, and since the function avoided the problems its companions fell victim to, I ended up going with that. Admittedly, there remain issues: Pygame can only handle the effect element as 32-bit surfaces (wherein each pixel has a value for a red, green, blue, and opacity channel) even though the only channel that actually bears any contrast is the opacity value (the red, green, and blue values are identical for all pixels), which means that 24 bits of data on each pixel could be effectively described by a single value for the entire image (which is a bunch of wasted memory), for one; also, pixels_alpha works without hazard on my computer and with my version of Pygame, but it may not be so cooperative on other systems– a degree of compatibility I would not worry about if the library it came from was more reliable.

The sunbeams in Opark and in the Nigran Cavern Entryway are generated in a similar way.

As I said in post 014 though, I do not really look down on them for their technical ‘inadequacies’, because they seem pleasant to look at and are not excessively demanding processor-wise. The entire project remains as it was when I began: experimental, and I think that this aspect of it makes it especially enjoyable to develop. Also, what else could you call something created by someone who started with no idea of what they were doing?

Anyway, that is all for this week’s post. Depending on how well map development goes between now and the next entry, I may have some images for you next week, or maybe I might go over some tileset stuff. Post a reply to this entry if you have some preference one way or the other, or maybe for a third thing! :D

See you next week~

* In the extent that the meta-random values Python's native `random` library can be "entirely random."

** As near as I can figure, Python’s garbage collection never clears them out because they create some internal reference that they never got around to breaking when the pixels#d output array is ‘orphaned’. But I what I do not know about Pygame could fill a wharf’s worth of warehouses. :/

[douchebagbrainwaves]

UNLESS THEIR WORKING DAY ENDS AT THE SAME TIME

The average 25 year old is no match for companies that have already raised money. But once you've admitted that one high level language can be more powerful than your own. I was still wasting time imitating the wrong things? I first laid out these principles explicitly, I noticed something striking: this is practically a recipe for succeeding just by negating. Productivity varies in any field, but I don't think our competitors understood, and few understand even now: when you're writing software that only has to do something trivially easy. That may be the more important of the two. Certainly not the authors. Whether to do anything hard in. Lexical closures provide a way to get a job. For example, open source software is more reliable precisely because it's open source; anyone can find mistakes. By the end of the scale, nature seems to be more companies like us. This essay is derived from a talk at Oscon 2005.

The people who understood our technology best were the customers. Fortunately you have some control over both how much you make, and you can decrease the amount of bullshit in your life by more than you think. By definition you can't tell from his portfolio. I knew practically nothing about the paths from rich to poor.1 If your terms force startups to do things they never anticipated, rather than a real downtown, Brasilia rather than Rome, Ada rather than C. There's nothing like going to grad school at Harvard to cure you of any illusions you might have about the average Harvard undergrad. What you're doing is business creation. Maybe it would be misleading even to call them centers. And the thing we'd built, as far as they could tell, wasn't even software. Many things people like, especially if they're young and ambitious, they like largely for the feeling of virtue in liking them. A programming language does need a good implementation, of course, but as far as they could tell, wasn't even software.2

Technically the term high-level language, in the long run, of the forces underlying open source and blogs are done for free, but before the Web it was harder than it looked.3 When you choose technology, you have to figure out. It's there to some degree in almost every field, but there aren't enough investors who will give $200k to a startup that was sufficiently successful would never have to move. VCs. So you could say either was the cause. The companies that rule Silicon Valley now are all descended in various ways from Shockley Semiconductor. Hackers like to hack, and hacking means getting inside things and second guessing the original designer. It's basically the diminutive form of belligerent. They switch because it's a better browser.4

It's not simply a matter of writing a lot of the new principles business has to learn it? He suggests starting with Python and Java, because they are easy to learn. That's what you do.5 Does this sound familiar?6 Except books—but books are different. And users don't care where you went to a better college. But if you make a language popular? The language can help here too. Now Palo Alto is suburbia, but then it was a charming college town—a language you should learn as an intellectual exercise, even though the latter depends more on determination than brains. How do you protect yourself from these people?

If you make something users want, then you're dead, whatever else you do or don't do. I bet this isn't true.7 I think the effect of such external factors on the popularity of a programming language rather than, say, making the language strongly typed. People interested in local events that one is solving mostly a single type of problem instead of many different types. Microsoft is remarkable among big companies in that they are able to develop software in house. But Y Combinator runs on the maker's schedule has a meeting, they have to be really good at tricking you. They were not even on a path to anything interesting. By the time you have to design buildings that don't fall down, but the creator is full of soot. If willfulness and discipline are what get you to profitability but you can tell it must be satisfying expectations I didn't know I had. The last one might be the most important.

The Reddits pushed so hard against the current that they reversed it; now it looks like they're merely floating downstream.8 If you throw them out, you find that good products do tend to win in the market. And God help you if you choose them. It seems unlikely this is a sign that something is broken?9 How about writer?10 Our secret weapon was similar. But there's another way of using time that's common among people who make things, like programmers and writers. Revealingly, the same status as what comes with it. What's less often understood is that there are more of them. For I see a painting impressively hung in a museum, I ask myself: how much would I pay for this if I found it at a garage sale, dirty and frameless, and with no idea who painted it?

The reason we tell founders not to worry about and which not to.11 The melon seed model implies it's possible to make yourself into one. My God, it was harder to reach an audience or collaborate on projects. Better to get a lot done. I accumulated all this useless stuff, but that the people pretending to work. There is usually so much demand for custom work that unless you're really incompetent there has to be in the twentieth century.12 Using first and rest instead of car and cdr often are, in successive lines.

And that is just what tends to happen. I cheat by using a very dense language, which shrinks the court. In this particular case there is a way to finesse our way out of lower-level abstractions are built in a very transparent way out of lower-level abstractions, which you can survive.13 And odds are that is in fact the bullshit-minimizing option. There are usually a few people in a company with someone you dislike because they have some skill you need and you worry you won't find anyone else. Note too that determination and talent are not the whole story. That word balance is a significant one.14 I tried my best to imitate them. Often, indeed, it is at least different from when I started. You may have as many as five or ten releases a day.15 So if Lisp makes you a better programmer, like he says, why wouldn't you want to get the most out of them, and lose half a day's work; or we can try to avoid meeting them, and probably offend them.

Notes

For example, understanding French will help dispel the cloud of semi-sacred mystery that surrounds wisdom in ancient philosophy may be some things it's a significant effect on returns, it's easy to believe your whole future depends on where you go to grad school, and the war it was actually a computer.

Investors are professional negotiators, and all the East Coast. In many ways the New Deal but with World War II had disappeared.

Ed. Some of the lies we tell.

When I catch egregiously linkjacked posts I replace the url with that additional constraint, you can't even claim, like indifference to individual users. In Shakespeare's own time in the 1980s was enabled by a central authority according to some abstract notion of fairness or randomly, in the 70s, moving to Monaco would give us. VCs may begin to conserve board seats by switching to what modernist architects meant.

The person who would in 1950. I did when I was a good idea to make money from the truth to say that was actively maintained would be investors who turned them down because investors already owned more than just getting started. 7% of American kids attend private, non-programmers grasped that in the world of the most accurate way to find a broad hard-beaten road to his time was 700,000 per month. But one of few they had in grad school, because they attract so much on luck.

Dealers try to write your thoughts down in, say, recursion, and in fact you're descending in a difficult position. But do you use this route instead.

In principle yes, of S P 500 CEOs in 2002 was 35,560.

Some blue counties are false positives caused by filters will have to want them; you don't see them, but whether it's good enough to convince limited partners. If by cutting the founders' advantage if it were. An accountant might say that IBM makes decent hardware.

This is not a VC who read it ever wished it longer. 'Math for engineers' classes sucked mightily. Even college textbooks is unpleasant work, like warehouses. 5% of Apple now January 2016 would be to say because most of the lawyers they need them to get the people worth impressing already judge you more than investors.

So the most surprising things I've learned about VC inattentiveness. Stone, op. No, we met Aydin Senkut. I overstated the case.

The way to pressure them to ignore investors and instead of just Jews any more than make them want you.

I couldn't convince Fred Wilson for reading drafts of this essay, I preferred to work than stay home with them. I wonder if that means is No, and that modern corporate executives would work. Mayle, Peter, Why Are We Getting a Divorce?

There are people in return for something that would appeal to space aliens, but this would be critical to do something we didn't, they still probably won't invest in so many different schools of thought about how to allocate resources, political deal-making power. There were a variety called Red Delicious that had other meanings. The problem is that you'll expend a lot like meaning.

It's not the shape that matters financially for investors. This plan backfired with the New Deal but with World War II the tax codes were so bad that they probably wouldn't be worth trying to deliver the lines meant for a startup than it was 10 years ago. At the time I thought there wasn't, because they can't afford to. Where Do College English 28 1966-67, pp.

Your user model almost couldn't be perfectly accurate, because the illiquidity of progress puts them at the works of their growth from earnings.

[financialinvestingradio]

FIR 117: How Many Data Scientists Does It Take To PREDICT YOUR SMB SUCCESS??

In this episode, we're gonna take a look at how many data scientists does it take to predict your SMB success.

Hey, welcome, everybody to another episode. All right. So we've been looking at this problem around how do I help my own SMB to grow? Right? What does it take for me to have all of the sufficient technology that I need to compete today? We've heard the adage, of course, Hey, you know what, if you're not participating in AI, you're going to get left behind, right that there's a lot of large organizations that, of course, pursuing this, and we'll talk about that here for a moment. What I found is that most AI platforms today, they really require a deep technical bench, right? And a lot of that gets outside of the reach of the small to medium business, right. So one of the things that led me into this world was to say, How can I help and find a way to close that gap for the small to medium business? What does that look like? The drive for Business Insights, leveraging AI, through simple platforms is growing at a cumulative annual growth rate CAGR of 47%. From now up through 2030, the safe some of the researchers out there.

Alright, so the need for this and the desire and the appetite for it is clearly growing. But how can you afford it? What does that look like? So I was looking recently, at this came out in February of 2021. There's this report that was looking at some of the top 10 companies, right, that are hiring data scientists. These are large companies, right? And what were they paying for them? Right? Let's say I had the checkbook out. And I want a data science team. What does that look like? Pinterest? At the time anyway, they were paying the most 212k on average annual salary for one, one data scientists. Lift was 154k. Snap was 152k. Man take a breath, right? slack 148k, Uber, 139k, Microsoft 136k. Right. These are average salaries for data scientists, Oracle 132k, Intel 120 3k and Accenture 107k. So the average of that using fancy math 144k. All right, how many data scientists does it take? I don't know, how many does it take to, to, you know, screw in a light bulb. But let's take a look at this. If you're gonna build a data science team, there's 12345678, there's about eight different key roles that are typically done on the stains. Now, a role doesn't always translate into one person. Sometimes it's multiple people. Sometimes one role can go across, you know, a person. Now let me run this by here, there's at least four, four critical needs, right? Let's say as a small to medium business owner, you're going to go build your team, you're going to need someone that's going to take care of analytics, right. And so that's, that's a critical role. So that's a necessary one that's required. Someone's got to know some coding work. So in languages like R and Python, stay with me. A third area is of course, all of the data and database work. So you know, working with SQL and no SQL, those sorts of things. And then and then there's all the algorithms and the models, right.

This is regression models, right? And dimensionality and yeah, the list goes on. All right, that's four roles right there. So it let's just take that, let's say that, and that those are unique enough that I'm going to argue that you will need one person for each of those roles, and using the average of 144k. We're up to 576 $576,000 a year. That's it. Even fully loaded, you know, that's, that's, that that starts to become a big number instantly it goes outside of the reach of the SMBs. If you really had and I was looking across some of the large companies doing this, they've typically got around 10. Right? And so you're in the 1.5 to 2.5 million range. And that's still not fully loaded. Nor does it include all of the technology costs, and you know, et cetera, right, all of the hardware and everything you need for that as well. Is it a surprise? No, it's not a surprise, when you say, Hey, you know, the big companies, they are really getting an advantage here by using the resources to bring in this talent, and then to actually widen the gap between what they can do, and what's available to the SMB world. And that's really started just sink deepest in my soul, right? I was like, wait a minute, wait, hey, it's the small companies that have brought some of the coolest innovations, and, and great opportunities to the planet. Oh, and I didn't even mention, there's another role. It's called the chief analytics officer. Not every company has those. But that's yet one more. And of course, I'm sure you could tell that roll is going to be above the 212 k range. I didn't even include that in there. Right. So let's say that, at the very least, as an SMB, if you're really going to do this, if you're going to put together your own data science team, you're at least in the 500k range somewhere right in there. And that's actually on the low end.

So like, Alright, so how do you let's, let's just assume that we did that. All right, for whatever reason, I've got the money in the bank, let's say we're just pretending let's say I'm going to go do that. How do you integrate this data science team in your company, there are several operating models to do this. There's a decentralized model, that's where you take an analytics group, right, your data science group, and they're focused on a particular function or business unit, and each business unit, or each function in the company, they have their own data science teams, some large companies have that. Here's another operating model, it's more of the functional model, it's where you have sort of this key function, there's one analytics group, but they will reach out and provide occasional support to other teams. Then there's the centralized model. And this model is where you have one analytics data science team that spans across the entire Corporation. And they of course, then go and reach into the different analytics projects, which are owned by the different business units, and, and different business functions. It turns out, that that model, that centralized one tends to be the one that works best for SMBs, right, where, in reality, it doesn't make sense financially, that, of course, you're going to have multiple data science analytics teams, you'll have one for the entire organization. But you know, like I said, in the larger, larger companies, they'll certainly fun multiples of these. So for our purposes, here, we're going to talk about the centralized model. And there's a flavor of that called the consulting model, which means sometimes that analytics group, or that data science team is actually not in the company. It's external, in its reaching in and providing insight, and, and, and guidance and predictive work into different parts of the organization. So it's critical to know that when you're doing this kind of approach with the consulting model, that that the understanding of the business and the key problems you're trying to solve, that goes back and forth, right between the analytics group and of course, then the the SMB itself. But what does this really mean for an SMB? One of the most important things that it really means is that in order for you to do AI, it means that you're going to have to have one of these big teams. And I did not like that conclusion, I was like, this is this is actually really hurting the SMB teams.

So I went hunting for a solution, right? And the solution was, how can we get data science and AI and predictive capabilities into the hands of an SMB? Right? How can we do that in a way that it does not require a data science team within your organization? So there's, there's not only this monetary challenge, or hurdle to it, which is okay, at the very least, let's say it's 500k, for argument's sake. There's another problem though. And that problem is, how do you declare return on investment? Right? Have that all you know of all of that machine learning and data science team investment, and that's of course, where a lot of business executives still need to be convinced. Then you know, you can't can't complain them or Can't can't blame them, right? I mean, you're looking at, you know, at the very low end 500k definitely up into the millions of dollars, what is it that's going to turn around a return on the investment that will make that justifiable. And that right there makes it even more challenging for an SMB to say, I'm gonna gonna jump into this. So, as an SMB, one of the things that I found is that you can skip hiring that team. You do not need the team that the platform's AI platforms have matured, to enable SNB teams to get the benefits of AI without actually requiring bringing in all of that sort of expertise into your own organization. Now, that's that's a huge promise, right? That's a huge change. That capability wasn't really even there a few years ago, right now today, what we found what we've developed, what we've provided is a way to do that. That is literally pennies on the dollar. So what I'm offering up to you is to join me Thursday for a web class on how to take advantage of this 1pm Eastern 10am. Pacific, this coming Thursday. I'd like to share that with you. I will be talking with you soon. Listen to me on my next episode, and I will start letting you know how to participate in that. Thanks for joining and until next time, don't go buy a data science team.

Thank you for joining Grant on ClickAI Radio. Don't forget to subscribe and leave feedback. And remember to download your FREE eBook visit ClickAIRadio.com now.

  Check out this episode!

[professor-it-tech]

BEGINNERS GUIDE TO COMPUTER SCIENCE.

Skip to content  How to Learn Computer Science? (from Zero to Hero) View Larger Image If you want to learn Computer Science and you’re just starting out, you probably have a lot of questions. What programming languages should I learn? Is it enough to learn one or two programming languages to secure a good job at a big tech company? What other skills do I need, if any? With so much information out there, aspiring software engineers can find it difficult to ferret out the valuable information from the rubbish. I know how it is because I’ve been there too. Needless to say, it took me a long time to find the answers that I needed. But it doesn’t have to be like that for you. I searched online, trying to find quality information, but the ONLY good resource I found was an article written by Ozan Onay and Myles Byrne from the Bradfield School of Computer Science. So I decided to write an article that reflects my personal opinions and experiences. This article reflects my personal opinions and information that I’ve discovered through my real-world experiences. It gives you a broad overview of what your CS career will look like, from start to finish. It tells you what skills you absolutely must acquire. It even lets you know what to expect at each and every step of the way. The overall thesis of this article is that software engineers pass through three different phases. I am going to explain to you exactly what these three phases are. Afterwards, I’ll tell you exactly what skills you need to move from one phase to the next, so you can get what you want from your career. The Three Phases of a Software Engineer Highly successful software engineers progress through three consecutive phases. I’d like to point out that some software engineers never progress beyond the first phase, and others don’t move beyond the second. Only highly successful software engineers reach the third phase. These three phases are: 1- The Coder 2- The Programmer 3- The Computer Scientist It is important to mention that this classification is my own development, based on my personal experiences and observations. Let me explain each one of these phases. First Phase: The Coder  Every software engineer begins his career as a coder. This can happen at a very young age. You don’t even need a college degree to be a coder. So, what is a coder? A coder is someone who knows how to speak the language of a machine. When given a particular problem, a coder knows how to break down that problem into instructions that the machine can understand in order to come up with a solution. Here’s the thing: if you find yourself really struggling at this phase, you may want to consider a different career path. The coding phase is literally the easiest phase of your CS career. If you succeed at coding, congratulations! You might have a successful career as a software engineer. Unfortunately, many software engineers remain in this phase for their whole career. If you’re just a coder, your pay won’t be great because your skills are easily replaceable. And if you remain just a coder, your promotions will be severely limited. At this stage, you shouldn’t even expect to get an entry-level job at any of the big tech companies. You need to evolve at least to the next phase for this to happen. You need to be a programmer. Second Phase: The Programmer  Once you have learned the basics of at least two programming languages (preferably one statically-typed and one dynamically-typed), you are a solid coder. The question now is how do you promote yourself to the programmer status? A programmer is essentially a sophisticated coder. Writing code that does the job is what coders do but writing efficient code that does the job is what programmers do. Here is a list of some skills that you should have as a programmer: 1- you should know the fundamentals of how any code eventually turns into something that a hardware chip can understand and execute. 2- you should understand that any system has finite compute, storage, and network resources and your software should utilize these resources efficiently. 3- you should know how to use data structures and algorithms to write efficient code. 4- you should understand what makes code efficient and what doesn’t. 5- you should understand that quality is important and that testing your code is crucial. Now I have good news and bad news for you. The Bad News: This is not the end. There is still a long way to go on your career path. The Good News: There are a lot of coders out there, but there aren’t a lot of solid programmers. If you really master this phase, you can easily secure a job at one of the big tech companies like Google, Facebook, Amazon, and others. In fact, most of the interviews conducted at these companies test how good of a programmer, not how good of a coder, you are. I wrote an in-depth article that discusses everything you need to know about the coding interview process. Be sure to check it out if you’re at this phase in your career. The vast majority of software engineers retire at this phase. Third Phase: The Computer Scientist  Learning does not stop after mastering the programming phase. As a matter of fact, it actually starts here! When you are at the computer scientist phase, you’re essentially an architect who thinks about the big picture more than the nitty gritty details. You have a solid understanding of designing large distributed systems and you know how to build scalable systems that can handle large loads and tolerate failures. A computer scientist also never stops learning, and always tries to stay up to date with the latest in technology. At this level, you’ll most likely be in charge of big projects and you’ll be managing a team (usually of coders and solid programmers) to get the job done. You might also need to cooperate with other teams. All of these require stellar social and leadership skills. In the rest of this article, I will go through the technical skills that you need in order to be a coder, then a programmer, and finally a computer scientist. Let’s get started.  1- Programming The first and only step to becoming a coder is to learn programming. This is the easiest step in your CS career, and it gives you a quick feedback about whether you should pursue a CS career or not. When it comes to choosing programming languages, I don’t want you to fret over what programming language to learn. At this stage what matters is not the particular programming language, but the concepts that you will be learning. These concepts will hold true in almost any other programming language. When you become a more seasoned programmer, you will reach a point where learning a new programming language doesn’t take more than a week, so don’t waste your time trying to find the “perfect” programming language to start with because: a) it doesn’t exist, and b) it doesn’t matter. With that said, I personally recommend you start with the following two languages. I will explain my reasons behind these choices, but feel free to start with whatever you’re most comfortable with. Python  I highly suggest you start with Python Why? Because Python is a language that is very easy to learn. Like, really, really easy! It is a very high-level language which allows you to write real programs in just a few lines of code. So, in a short amount of time, you will be able to develop significant projects. If you’re interested in learning Python, check out my step-by-step guide that I have laid out for you to take you from an absolute beginner to a professional Pythonista. These features of Python are extremely important, especially when you’re starting out. To learn python, I highly recommend Python Crash Course. (make sure you get the newer second edition) I find it to be very useful for beginners. I also like that the book is project-based, so you’ll have fun building things while you’re learning to code. Tips for Students 1. You can get your books faster with no shipping fees if you sign up for an Amazon Prime Student account (free for 6 months) 2. Depending on what and how many books you want to get, it might be cheaper to join the Kindle Unlimited program (30 days free trial) Java Why another language though?  The reason I recommend learning another language, especially Java, is because it will teach you some programming concepts that don’t even exist in Python. For example, Python is a dynamically-typed language while Java is a statically-typed language. If you don’t know what that means, you will understand it after learning these two languages. A combination of Python and Java is a very good way to start because together they provide you with a very solid idea of the programming concepts that you will need in almost any other programming language. To add to the benefits mentioned above, both Python and Java are heavily used in industry. So not only will you be spending your time learning the foundations that will pave the way for you to progress further, but you will also be learning some practical languages that are very employable and in high demand. I learned Java from the Java core series many years ago. Two separate books are offered. One is for Java fundamentals, and the other is for advanced Java features. I’d recommend not to overwhelm yourself with the advanced features for now. Focus on the fundamentals in this phase. Congratulations! Now you are a coder!  2- The Software Stack OK. So you can write code that can do some really cool stuff, but seriously do you even understand what’s going on? Say you write a very simple program that just adds two integers and prints the result to the screen. In Python, that would look like this: x = 5 y = 10 print(x + y) I take it you understand your code. You understand that a computer running your code should output 15. But do you really understand what’s happening under the hood? What does variable assignment (x = 5) mean at the hardware level? What is x, really? How is the number 5 represented in hardware? How does addition actually happen? And how did the result end up on my screen?!! At the end of the day, a computer is just a collection of hardware chips and wires. How can a computer really understand your code? and execute it flawlessly?  The fact of the matter is, your code is just the tip of the iceberg. There are a lot of other layers under your code. Together, they make the whole thing work the way you expect it to work. A programmer unravels this magic. At this level, you need a solid understanding of all the layers of the stack starting from your code, all the way down to the hardware layer. The Elements of Computing Systems by Noam Nisan and Shimon Schocken is unequivocally my top suggestion for a book that will teach you the essential information you need to understand each layer of the stack. The book covers hardware, compilers, linkers, and operating systems at a very basic level which makes it very beginner friendly. It walks you through the steps of creating your first programming language, creating a compiler and a linker for it, and then creating an operating system. 3- Algorithms and Data Structures  Now you’re in a very good shape to go back and start programming again, but this time with a completely different mindset. Because now, you REALLY know what’s happening under the hood. You understand how hardware is eventually going to run your code. You know that you have limited hardware resources and you understand the value of utilizing the available resources efficiently. Studying algorithms and data structures will teach you how to write code in a way that makes your code more efficient, however you define efficiency. it could be speed, resource utilization, or both. The skills that you are going to learn at this phase are some of the major differentiators that separate average coders from solid programmers. In fact, most big tech companies like Google, Facebook, and Amazon focus a lot on data structures questions during their interview process. When it comes to algorithms and data structures, there isn’t really much debate about the best book that covers the subject. It is unequivocally Introduction to Algorithms (AKA CLRS). Be aware that the topic of data structures and algorithms is language neutral, so it doesn’t matter which programming language you’re using. However, some people prefer to read books that are specific to their preferred language. Even though that’s not my style, but you can find a lot of good language-specific data structures books like this one for Java and this one for Python. 4- Networks  It is very rare that your code will run on an isolated single machine. Most useful code communicates with other computers either in a local network or the internet. Programmers need to have a very solid foundation of how computer networking works. I came across, in my opinion, the best networking book when I was a senior undergrad. It helped me overcome the dry text book that my professor at the time recommended. Computer Networking: A Top-Down Approach by Kurose and Ross is a very well-written, super easy to understand book that covers all the networking basics that you need to know. I still go back to this book every now and then if I need a refresher. 5- Operating Systems  Operating systems play a major role in the software stack. If you are following this list in order, by now you should have a very broad idea of the role of an operating system in the stack. But now is the time to have a deeper understanding of operating systems. Operating Systems Concepts by Abraham Silberschatz is one of the best books on the subject. You need some basic knowledge of C though, because the majority of operating systems are written in C. My recommendation, unless you want to be a kernel developer, is not to allow yourself to get stuck at this point. This is a very dense topic. Understanding all the details of all the aspects of operating systems is very time consuming. Grasping the main fundamental operating systems concepts is good enough to keep you going but don’t get bogged down in details. Another resource I highly recommend is the OSDev Wiki, especially if you want to learn how to create your own kernel. This is pretty advanced, but it’s something that the vast majority of software engineers can’t do. Look at that! You’ve achieved the status of programmer!  6- Distributed Systems  Welcome to the start of your computer scientist status. In this level, you will be learning new skills while you improve the skills you learned as a programmer. Distributed systems is about building and architecting software systems that are scalable and that can tolerate failures at the same time. This requires you to think of the bigger picture, rather than focusing on how to build the individual components–programmers and coders can do that. For example, think about building a search engine service, like Google, for some text files that exist only in your laptop. This service will listen to search queries that it receives over the network, search your files for the query, and respond with the results. This is not a hard thing to do. Any programmer with a decent knowledge of algorithms and data structures can build an efficient search engine for a small number of files. Now imagine that more and more people become interested in your service and they start using it. Now you’re getting millions and millions of requests a second. Not only that, but the size and number of files you are searching through begins to grow dramatically. What happens if your laptop (that hosts the search service) fails? Will you just ignore the millions of requests you’re getting? Distributed systems is about creating an army of computers that work together to form a specific task (in our example, the search service). It allows you to create scalable systems that can handle more requests or more data. At the same time, it provides redundancy that would be useful in case any one (or more) machine fails. Now, let’s talk about resources. By far, this blog post is the best resource I have found on the subject (disclaimer: you will need to read some academic papers). If you are a text book kind of person, then this O’Reilly book by Martin Kleppmann is excellent. I have skimmed through it, and it covers most of the important topics. With that said, Distributed Systems is a field where experience matters a lot. So learn the theory, but also get your hands dirty by working on distributed systems projects. 7- Machine Learning  Machine learning is an interdisciplinary field that spans computer science, mathematics, and statistics. In this day and age, it is being used every where! Netflix uses it for movie recommendations, Amazon uses it for their recommendation engine and for Amazon Echo, Vesty Waves uses it to automatically classify articles, and the list goes on. To be able to build these types of software, you need to be more than just a solid programmer because as I mentioned this field requires a very strong mathematical and statistical foundation. And no, learning everything about Python’s Scikit-Learn library (a very popular Python library for machine learning) won’t make you a data scientist or a machine learning expert. You still need to understand the mathematical and statistical underpinnings. There are two ways to study machine learning: the top-down approach method, where you start first by writing machine learning code right away (for example ,by using Python’s Scikit-Learn library) and understand the math later, or the bottom-up approach, where you start with the math first and then move up to coding. I personally prefer the second method, just because that’s what works best for me. Even though It’s harder to start and takes longer before you start writing code, once you grasp the concepts, learning how to use a machine learning library is going to be a piece of cake. On the other hand, the top-down approach has the advantage of allowing you to begin writing machine-learning code fast. This motivates a lot of people. The downside of the top-down approach is that it will be much harder for you to understand why some techniques work, while others don’t, because you won’t have the necessary mathematical background at first. Andrew Ng’s course on Coursera is a very good place to start. If you have prior knowledge of mathematics, probability, and statistics, then An Introduction to Statistical Learning is a very good book for building the statistical and mathematical foundations for machine learning. However, don’t use this book if you aren’t already strong in linear algebra, probabilities, and basic statistics because you will not be able to understand it. If you want to solve real world problems and make money doing this, then create a team, go to Kaggle, solve a problem, and make some money. And even if you don’t win, you will learn 🙂 You did it! You can now call yourself a computer scientist!  Featured Posts Python: A Learning Path from Zero to Hero The Ultimate Path for Learning Computer Science Pass your Coding Interview like a Boss A Roadmap for Learning Git Why (and How) you should Start your Programming Blog Today? Are you Beginning your Programming Career? I provide my best content for beginners in the newsletter. What programming language to start with? Do you need a CS degree to be a programmer? Career tips and advice Programming tutorials And so much more… Subscribe now. It’s Free.  SUBSCRIBE By Karim 127 Leave a Reply  68 59 19 Subscribe newest oldest Stanley Well… 3 years of CS and this is the best thing I have seen anywhere. I must confess though I’m having trouble getting past the coder phase but I believe it’ll pass. Very well written article. I need to share it with my fellows. Reply 1 year ago Karim Thanks Stanley! Keep at it and good luck 🙂 You will soon go past this coding phase. Reply 1 year ago Nuhu Jerry I find this very inspiring and important as its has helped me understand the fundamentals of being a computer scientist! thank you very much Reply 1 year ago Karim Nuhu! Thank you very much. Glad the article helped you. Reply 1 year ago Hussein M Yussuf So helpful indeed, i really appreciate for a well done job, keep it up!!! Reply 1 year ago Karim Thanks Hussein and good luck in your career! Reply 1 year ago George A very good step-by-step analysis of the CS career. Kudos. You should give us a talk in our university. Please share with me your contacts Reply 1 year ago Karim Thank you George! Glad it helped. You can always contact me at ‘my-first-name’@afternerd.com Reply 1 year ago Patience What a grate piece.I have fear when it comes to programming but after reading this ,it build up my moral to start up.thanks Reply 1 year ago Karim Fear is part of the learning process. As long as you persevere this initial feeling of fear and intimidation, you will prevail! Thanks!:) Reply 1 year ago Jonas Thank you immensely. This is what I did need to know when I started to learn how to program computer I mean a clear road, this will save my time. Thank you again for your generosity Reply 1 year ago David Bryan Wow. . This is the perfect article I was looking for.. Great work mate. Thanks for the help. Reply 4 months ago Karim Thank you David 🙂 Reply 3 months ago Mickelson Joseph Vil I’m very interested! Reply 1 year ago Karim Thanks for your interest Mickelson! Reply 1 year ago Et Great guide mate. Thank you! Reply 1 year ago Karim Thank you Et! 🙂 Glad I can help Reply 1 year ago Cheelo Very enlightening read for beginners. Do you also offer tutorials? How do I get certified? If not get me linked. Reply 1 year ago Karim Thanks a lot! What do you mean by certified? Certification is of no value in the CS career. trust me!:) Reply 1 year ago Asad Ur Rehman Hi thanks for this Great Article. Reply 1 year ago Karim Thanks Asad! Glad it helped. Reply 1 year ago cs-aspirant Thanks for this post. I really learnt a lot! Reply 1 year ago Karim Thanks cs-aspirant 🙂 Reply 1 year ago Akash Looking forward for many such great articles insights about cs field from you.Thanks ALOT… Reply 1 year ago Karim Always happy to help! thanks. Reply 1 year ago Mani i did my CS grad 6 years back, would have been great if I came across a splendid article like this at that time. nevertheless Im glad atleast now I have came across such gem. Thanks a lot for explaining in detail. Inspiring! Reply 1 year ago Karim Thanks for your kind words Mani! And good luck in your CS career 🙂 Reply 1 year ago Yahya Mohamed Wow! What a great piece of a nice and easy-to-understand article. This will surely help me kick-start my dream of pursuing CS in the university. Please do post other interesting articles like this one. Thanks dude and stay blessed! Reply 1 year ago Karim Thanks Yahya! Good luck in your career!:) Reply 1 year ago Akash Brilliant piece of article…can u tell more about how to develop your CV so u can get great resumes… Reply 1 year ago Karim Hello Akash, the best thing to develop your resume is to get internships or work on projects, either at school or open source ones. Reply 1 year ago Zaheer Abbas I really love this article and bookmarked. Reply 1 year ago Karim Thanks Zaheer. Happy to help! Reply 1 year ago SHRAVAN WELL EXPLAINED Reply 1 year ago Khanh Chung This is really a great article. What do you think about database? I think it is really important if we want to learn CS. Reply 1 year ago Karim Thank you. You are right. It definitely is! Reply 1 year ago phill This piece is very interesting and enlightening. I have always loved computers but never had the chance to dive into a CS career. This article provides me a solid roadmap to enter CS space. Thank you very much. Just completed a B.A degree. Will launch into CS career now Reply 1 year ago Karim Thank you Phill and Good luck in your CS career! Reply 1 year ago Jonathan I never though i’d ever come across any of this. Atleast now i’ve got a path to follow rather that just doing everything blindly. Nailed it! Nice work Reply 1 year ago Rithik If don’t want to major in computer science because I want to major in an engineering field, but I really want to learn computer science. Are the materials listed above to supplement extra information to comsci majors, or can I use the materials above to learn compsci without a traditional learning environment. Reply 1 year ago Karim You can use the material above to teach yourself computer science. These are the things that students learn in CS majors Reply 1 year ago Rithik Ok, thank you! Reply 1 year ago Anas Mayow Salaat i didnt know where to start, but, i guess i do now with this article and Thank You for your help. Reply 1 year ago Karim Glad to help! Good luck 🙂 Reply 1 year ago K Great article. I love this article I’m following this road-map, but I don’t really enjoy reading thick books, so I use videos instead. Is it ok? Reply 1 year ago Karim Of course! Just make sure the teacher is good. Reply 1 year ago K I’m not really sure that they are good teachers or not, I have two courses on Udemy, one is Java and another is Python. They have highest rate courses on Udemy. Reply 1 year ago laiju Sir ,you are giving a good information on computer science career. Reply 1 year ago Karim I am always happy to help. Thanks for reading! Reply 1 year ago Tony Thank you so much, sir! Reply 1 year ago Karl Very well elaborated! Thank you so much! Man, I feel so happy, it’s like you just gave a 1000 bucks… Reply 1 year ago Karim haha thanks Karl! 🙂 Reply 1 year ago G ARCHANA Such a great article! explained everything in a lucid manner that even a non CS grad can easily catch.Thank you! Reply 1 year ago Karim You are welcome! Glad you liked it. Reply 1 year ago Moiz This looks like a really good guide i was studing in BS physics and wanted to study Cs as well this guide deals with what CS majors learn in Bachelors right would i still need a degree in cs to go to programming Reply 1 year ago Karim No, you don’t need a CS “degree” to go to programming. You need to learn CS to have a successful career. It doesn’t matter if you learn CS through a traditional college degree or not (although having an actual degree opens many doors when you’re starting out) Reply 1 year ago Edu Thank you very much. God bless you. Reply 1 year ago Li Shenghui Thank you very much! This is the best article I had read. Reply 1 year ago Karim Li! Thank you 🙂 Reply 1 year ago Heba More than helpful article , a hell of a one actually . Thanks so very much ,that’s precious . Reply 1 year ago Karim Thanks Heba! Very happy to help. Reply 1 year ago Seshai Hari Superb Article. I’m a freshmen entering college for persuing computer science engeneering. Hope i will follow these steps and become a great computer science engineer Reply 1 year ago Karim I can see this happening. Good luck Seshai! Reply 1 year ago Musa This a Great resource regarding CS. I can’t thank you enough for such a Write up. It is really helpful. But I’ve a question Mr. Karim, how many years can these processes take an average person?. I’ll be quite glad if I could get a detailed answer.Thanks a Billion. Reply 1 year ago Karim Honestly it differs from one person to another. Also this is a field that is frequently changing, so you will be learning all the time. It doesn’t really stop 🙂 Reply 1 year ago nonone Thank you million times —by whoever will saw this post Reply 1 year ago Andrew Hi Karim, I’m interested in robotics and AI. I was told to learn and be good at python, c and c++. Could I leave out Java for now? Also, could you recommend me some good resources to learn c and c++? Reply 1 year ago Karim Hi Andrew. Of course! the reason I haven’t suggested C/C++ for absolute beginners is because C/C++ are more low-level and requires you to know a little bit about the underlying stack (especially memory management). That said, I actually started with C/C++ myself. For C, I recommend “C Programming: A Modern Approach, 2nd Edition” by K.N. King. I don’t have book recommendations for C++ but a strong foundation in C will help you tremendously when you make the move to C++ Reply 1 year ago Divyanshi Parashar Thanks a lot! It’s really very helpful. Reply 1 year ago Parth Thanks a lot! This is the first time when someone clearly explained what it means to be a programmer and a computer scientist. Reply 1 year ago Karim Thank you! Reply 1 year ago Jason Hey Karim ,so as a beginner ,we have to learn both Java and Python before going into the next phase right? Reply 1 year ago Karim Hi Jason, I recommend this but it is not a must. I actually only learnt C before moving to the next phase. Reply 1 year ago Honey I am a 50 something who is tired of feeling left out of the sophisticated world of CS. I am glad I came across your article. The manner in which you presented a step-by-step approach to learning this “magic” makes me feel confident in my pursuit. I am going to stop searching for “how to’s“ and begin my journey based on your recommendations. Thank you Reply 1 year ago Karim Thank you for stopping by! Glad I helped and I wish you the best. Reply 1 year ago sandeep narula Very well explained.Thanks for the inspiration 🙂 Reply 1 year ago Rae.fk Thank you very much. I want to become a Computer Scientist and I know this will help me through. Thank you once again. God bless you. Reply 1 year ago kevin Thanks a million dude! Reply 1 year ago Michelle Wow! Thank you for sharing! Reply 1 year ago Daud I’m in my third year of pursuing an IT Degree and your article blew my mind, it very well structured and very informative. I think I will be following your path into becoming a Computer Scientist. Reply 1 year ago Khadija Thank you very much, I realy apreciate this article! Wich book do you recomand for C language? Reply 1 year ago Karim I like “C Programming: A Modern Approach” Reply 11 months ago Abhishek Shandilya Hello Sir, I completed my B.Tech in Computer Science & Engg. 4 years back but never had a beautiful broad view of Computer Science with such clearance of thought, as i am having now after reading this. I am excited again about CS after reading this article. Thank you sooooooooo much. Reply 11 months ago Karim You are welcome Abhishek! Happy to help. Reply 11 months ago Neminda Sir this is a great article. You just encourage us; programmers and beginers. Every programmers and beginers should read this..thank you so much sir this article just gave me a good strength Please let me know how to contact with you Reply 11 months ago Karim Thanks Neminda! Glad you found it useful. Reply 11 months ago gt Thankyou so much Karim,,,this article has really helped me Reply 11 months ago Shashi kumar Thanks for your valuble information.But,i’m in 2nd yr of engineering and i’m an average student.I dont know well how to code…Could i start to code at this time.Is it possible. Reply 11 months ago Karim Of course you can! You can learn anything at any time 🙂 Reply 11 months ago Ajay Best Article I have ever read. But I have a small doubt about the Career, Do the CS and IT field are one and the same or it differ by something. If it is different which one to choose next. Can you prepare another article explainingg clearly about it. Thanks for the above article. Reply 10 months ago Karim You are welcome Ajay. In Tech companies, IT and software engineering are different jobs requiring different skills. This article is about CS and software engineering. That said, there is a lot of knowledge overlap between the two. Reply 10 months ago Lawrence NG I love and bookmark this article. Many thanks for writing such a stunning article to provide the ways and suggestions to the people who pursuits the CS career. Reply 10 months ago Karim My pleasure! 🙂 Reply 10 months ago  Load More Comments Search for:  Grow customer love with our Marketing CRM. ADS VIA CARBON  About Karim Karim has a PhD in Computer Science from the university of California, Santa Barbara. He had over three years of experience teaching CS to undergrads, over 5 years of experience doing research, and is currently working for a Fortune 100 company. He is largely interested in distributed systems, machine learning, fitness, and soccer Let’s connect Don’t miss out! Learn about programming and computer science by subscribing to my private newsletter  SUBSCRIBE Build an Online Presence  Learn why you should Start your Programming Blog Today! ABOUT My name is Karim Elghamrawy. I started Afternerd.com to be a platform for educating aspiring programmers and computer scientists. SITE Blog Programming Fundamentals Career LEGAL Privacy Policy Affiliate Disclosure LET’S CONNECT © Copyright 2017-2019, Afternerd

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Teaching Coding to Kids: What Programming Language Should We Use?

One of the most common questions I get from teachers and parents is: What programming language should we use to teach kids to code? Is it important to always start with block-based languages like Scratch? At what age should they transition to text-based languages? And how do I choose between Python, Java or JavaScript?

Having taught coding for almost 10 years to hundreds of students, I often present at conferences or run workshops for teachers new to computer science. Many teachers are trying to build a CS program in their schools for the first time, and it’s understandable why they worry about which programming language to choose. In addition to trying to figure out what’s best for their students, teachers have to strike a balance between what they’re comfortable teaching, and what administrators, parents and students feel they should be learning.

So, is there one “right” coding language to start with?

The coding language is not important. The concepts of programming are!

As you can guess, the answer is: No! What is important is not the language, but how to teach students how to solve a problem with code. Understanding how to create an algorithm (step-by-step instructions) to tackle an assignment, and coming up with the best way to write this in code, is probably the hardest part.

Programming languages come and go—and you will adapt.

Learning the fairly small number of keywords and simple syntax of a specific programming language is easy—a lot easier than learning an actual, spoken human language! By contrast, in order to program independently, one must understand the underlying concepts in programming—variables, lists, conditionals, loops and functions, for example—and then know when, where and how to use them to convert your algorithm into code.

Once a student grasps these concepts and has programmed successfully in one language for some time, it is not that difficult to code in another language. Switching languages is not immediately easy, but it can be done.

However, does that first language change the way you think and code in the future? Is it important to learn the most popular language in industry today?

Programming languages come and go—and you will adapt.

The popularity of programming languages change, and there is no guarantee that what we teach our kids today will be used by the time they enter the job market. My first programming language was Pascal. (Yes, you probably have to look that up now.)

Over the years, I have learned to use different languages on different machines—some too obscure to be mentioned. Over the last 10 years as an educator, I have learned just enough Logo, Scratch, Processing, JavaScript, Arduino and Python to be able to use it as a teaching language in my classes.

Six years ago, I suggested that kids start with Logo, the earliest CS education language, and I still believe it remains a strong option available today. Here’s the most important part: If you can teach kids the basic concepts in programming, and they have spent enough time coding in one language, then they should have developed the fundamentals to switch languages later as needed.

There is another question that often comes up: Do we need to start all kids with block-based languages instead of text-based languages?

Block or text?

I started to use Scratch, perhaps the most popular and kid-friendly block-based language, in a digital design class for sixth graders almost 10 years ago, and I continue to love using it at all grade levels. I am excited to use the new features in Scratch 3.0 just released—especially the extensions to support text-to-speech, and language translation.

Block-based programming takes away so much of the frustration for young and early coders, such as missing a comma or forgetting to close a parenthesis, and leaves more time to focus on understanding concepts. In addition, Scratch’s focus on creativity and easy access to creating graphics, editing sound, sharing and remixing projects makes it a perfect first coding language for all ages. It is positioned as a tool to create art, animations, stories and games and not as a “coding” language, a branding that makes Scratch much more welcoming and less intimidating.

What is important is not the language, but how to teach students how to solve a problem with code.

I have found that after a few years of using Scratch, students want to try text-based coding because they associate it with “grown ups” and the “real” coding that is done in industry. I have also seen that sometimes just a change in language is needed to review concepts like variables and loops.

My middle-school students are willing to put in the extra effort it takes to learn text-based coding; often they slow down in order to be careful with their spelling and syntax as they tackle the challenge of programming in Python, JavaScript, Arduino and Processing. But once students can get past the initial “I have to really watch what I type” part, they often appreciate the flexibility and power of text-based coding, especially when they find how much easier it is to copy, paste, modify and collaborate on text code to create projects.

At the Foothill College KCI Computer Science Crash crash course that I teach each summer, I offer teachers both Scratch and Python and show the same project in both languages. They love seeing the parallels between the two types of languages, and even more if we first start with “pseudocode” or a flowchart—a way to write down the algorithm before writing any code. Here’s what a small project that involves checking a password looks like as a flowchart, in Scratch, and in Python.

Password checker flowchart diagramThe same password checker, in ScratchThe same password checker, in Python

Both teachers and students who had no exposure to block-based programming are easily able to learn Python in my classes, showing that with the right projects, starting with text-based language also works.

What makes any programming language a good language for teaching kids to code? Are there any criteria to help pick a language?

While it may be good to know that teaching coding is more about concepts, and that you do not need to stress about picking the perfect first language, the question remains: What should a language have that will make it a good choice for teaching kids to code? While programming languages are often evaluated in numbers of ways—on speed, applications, libraries available, industry support—here are some important criteria I consider.

Does it have a strong community of educators?

One of the reasons I always point to Scratch and Python is their access to a network of educators using these languages in their classrooms. They have such large communities behind them because they have always been free, open and welcome to a wide range of users from across the world. Having a large community means you can find more resources, such as projects, lesson plans, tutorials, videos and books dedicated to teaching the language in the classroom. It’s also likely that you can walk into an education conference and find a session giving you tips.

...teaching good programming habits trumps teaching a popular language.

Is it easy to pick up?

There are many text-based languages to choose from—some more popular today in the tech industry than others, some with specific features that make them good for creating a particular project. While many high-school students may have to ultimately learn Java for an AP Computer Science course, it is not necessarily the easiest language to start with. Python is by far easier and has been gaining popularity in education because it is so simple. After three years of using Python for a computer science elective class, I am constantly surprised at how little it takes to get something done, and how quickly my middle-school students learn to code in Python. Inspired by how well Python works in teaching coding to kids, I spent time writing a book about it.

What is the design philosophy behind it? Was it designed for this age group?

It is important to offer tools that are age-appropriate, and programming languages can hide or showcase features based on the target age group. Scratch, rooted in the philosophy of using code for creative expression, deliberately simplifies some constructs one may expect to see in a coding language. Scratch Jr. is designed for kids who are still learning to read, and has no variables or conditionals, which may be too confusing at their age. Python offers extensions to support many types of projects, but these are available as modules that have to be explicitly added, so you do not need to see them until they are needed. Languages for older students working on complex programs must have support for debugging, a fundamental skill. If teaching “object-oriented” programming is critical, then using Java is not a bad choice.

How easy is it to install, and does it run across platforms?

These are things to keep in mind, especially if it is important that students continue to code at home. Some only work in specific environments, like Apple’s Swift coding language. If students are working on Chromebooks, then having a stable, browser-based tool is critical. Another thing to consider: Is the language really available for free so every student can use it at home, or are there hidden extensions only available for a fee?

How easy is it to share projects with the community?

One of the fundamental ideas behind Scratch has been the community, and letting kids share and learn from each other. That community is also useful for teachers as well. However, sharing also requires planning: when and how much you want students to share, comment, reuse and repurpose others’ projects. While JavaScript may be frowned upon as a first text-based language to learn and is not as easy as Python, it does have the advantage that it just runs on the web. Showcasing projects is as simple as posting them on a school web server.

There are many other considerations when choosing a language. What kind of projects are possible? Different languages have supports and extensions for different types of projects; for example, Processing and P5.js make it easier to do media projects and create art and animations. Python makes writing complex data analysis and even machine-learning projects possible. Teachers may also want strong classroom tools to manage assignments and grading, especially for group projects.

One of the most common questions is: “Is this language popular today?” Popularity can certainly shape the choice of programming language to encourage students to sign up for a class. It’s understandable that parents want educators to teach what is marketable for jobs. However, teaching good programming habits trumps teaching a popular language.

While we can stress about finding the “perfect” programming language to start, let us not forget that the ultimate goal is to let students explore fundamental programming concepts. They may never choose to program after your class, and the language you teach may become obsolete as they get older. What is important is that teaching coding will help students understand how computers solve problems, acquire critical thinking skills—and hopefully learn just how much fun it is to make stuff with code.

Teaching Coding to Kids: What Programming Language Should We Use? published first on https://medium.com/@GetNewDLBusiness

[lewiskdavid90]

93% off #Professional Python Web Development Using Flask – $10

Learn from scratch how to build backend web applications using Python Flask, Cloud9, MySQL and Docker Containers

Beginner Level,  – 14.5 hours,  102 lectures 

Average rating 4.3/5 (4.3 (291 ratings) Instead of using a simple lifetime average, Udemy calculates a course’s star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.)

Course requirements:

A computer with internet access and administrative access to install packages A basic understanding of how to use the internet and text editors

Course description:

This course will teach you, assuming no prior coding knowledge, how to develop back end web applications the way professional coders do in the top internet startups. How do I know this? Because I’ve been leading tech teams in both large enterprise as well as startup companies in New York City for the past 15 years.

I have seen a lot of courses and free tutorials and I can tell you 90% of them just teach bad habits while promising to turn you into a real “web developer”. But let me tell you a reality: There’s no such thing as a web developer these days. You’re either a back end web applications developer, a front end application developer or the so-called (and rare) full stack web developer which includes the other two. However there are so many technologies to master in both the backend and frontend areas that full-stacks (or “web developers”) are a rarity in professional environments — You’re either a back end or a front end web developer.

This course doesn’t promise to turn you into a professional back end developer after you complete it — it takes much more than the 11 hours of of this course (and probably hundreds of hours of self-practicing) to do that, but it will give you a good foundation from where to start and continue your training, knowing the right path to become a real professional backend web applications developer using Python. My goal is to make a second course, which would introduce more advanced back end concepts and then start the front end courses (basic and advanced) soon after that.

The course goes through a step by step process of developing web applications, teaching you the Python basics for web development, introducing Flask and using Cloud9 as your development environment. It then moves to explore SQL databases, using MySQL and finally showing you how to develop a blogging application using all these learnings.

Best of all, you don’t need to install anything as we will use a revolutionary online web development environment that essentially gives you your own Linux web server with database capabilities! All you need to have is a browser and internet connection and it’s completely free to you.

At the end of the course students will be challenged with a final project where all the course material will need to be used to complete it. Students that finish the final project will be eligible for special future promotions.

Finally, you will learn how to run your application using Docker Containers, one of the hottest new technologies that allow developers to write their applications and deploy easily to a number of cloud hosting platforms and scale them indefinitely.

The course is divided in 8 sections and 2 bonus sections:

Introduction Setting up our environment Python basics Installing Flask Introduction to Flask An introduction to databases Our first Flask application: A personal blog powered by MySQL Final Project Bonus: Running Our Flask Application with Docker Bonus: Deploying our Application to a Cloud Server

The course has more than 12 hours of video tutorials as well as the source code at the end of each of the Flask application lessons, so that you can see exactly what the whole project looks like in each stage.

The course can take anywhere from 10 days to a month to complete based on how much material the student completes daily.

Additionally we’re constantly updating the course, adding content thanks to the feedback of our students.

We will also have office hours where you can ask the instructor any question you might have about the course or about Python Backend Web Application Development in general.

So If you are interested in learning how to code from zero and without prior knowledge, but do it using best industry practices towards becoming a professional backend web developer, this is the course for you.

So stop looking around and start the right path to becoming a professional Python backend web developer with this course!

Full detail

Reviews:

“I’ve been working with Flask for the past few months, making a wrapper around Ansible and other infrastructure tasks and tools, even using SQLAlchemy. We have it working pretty well but I knew I had some gaps, especially when it comes to organizing an application using the MVC model, and best practices in general. This guy knows what he’s doing. Forget everything you thought you knew about Flask and take this course. You’ll learn not only about the internals of how to build a webapp using Flask, but also how to make it scaleable and professional. You’ll be working with password encryption, databases using MySQL and SQLAlchemy, and even modifying tables and columns. I used to think Flask was kind of a hacky/kludgy alternative to Django, but after a few sections in this course I’m finding that Flask is a full featured, production grade framework you can build top quality, production grade apps on. You have have to do some work yourself adding the right plugins and organizing your different components correctly. There’s only one minor issue I have with this course. Although it covers a lot of different modules and libraries associated with Flask (like Flask-SQLAlchemy, Flask-WTF, etc.), Jorge glosses over their usage. What would make this course better would be an introductory video for every new module that’s introduced that goes into detail about the theory and usage before adding it to the project. For example, when bringing in some new module, let’s have a video or two talking about it, how it’s used, what it does, what it won’t do, caveats, best practices, how it’s evolved, what problems it solves, etc. Then show how to incorporate it into the app. A great example of a course that does this well is the Ansible course on Linux Academy. In that, the instructor goes over almost all of the most common Ansible modules, shows how they’re used, and then brings them into a project. After several hours with this course, I still don’t feel like I have a very good understanding of some of the modules on display. I have a vague sense of how they’re used within the context of the project, but I still don’t feel confident enough with their usage out in the wild. All that said, this is an excellent course, and after going through it you’ll be well on your way to creating projects from scratch with Flask. I look forward to other courses by Jorge. You’re in good hands with this guy.” (James Couch)

“I learned a lot and we moved quickly, I realize that i took it late and see there may have been some problems with python package versions but everything went smoothly for me.” (Clint Dunn)

“Decent information content, presentation could use some polish/editing. Increasingly spending time tracking down old version conflicts, problems arising from changes in c9.io that haven’t been documented or addressed in the Q/A forums. Starts out okay and makes a slow slide to frustratingly useless. I have too many side projects to debug already to be spending my educational time debugging a tutorial.” (Travis C. Flatt)

  About Instructor:

Jorge Escobar

From Zero is an educational project created by Jorge Escobar, a technologist, entrepreneur and open source fanatic with more than 15 years of experience in the development of web applications in New York City. Jorge has worked in well established companies like Yahoo!, Univision and MongoDB and has also been the technical founding member of various successful tech startups that have received multiple rounds of venture capital. The biggest problem Jorge has experienced during his career is finding well rounded developers and he interviewed hundreds of them for positions in the teams he was leading. A constant pattern (no matter if candidates came from a good university or had a few years of experience) was the lack of practical, real world knowledge. That’s why Jorge created From Zero, an educational project that would address those practical knowledge issues through training that builds hands-on experience and equip students with the tools required to be successful in today’s technology business needs.

Instructor Other Courses:

Essential Docker for Python Flask Development Jorge Escobar, Technologist, entrepreneur and open source fanatic (3) $10 $45 Advanced Scalable Python Web Development Using Flask The Essential Git Course – Learn What You Need to Know …………………………………………………………… Jorge Escobar coupons Development course coupon Udemy Development course coupon Web Development course coupon Udemy Web Development course coupon Professional Python Web Development Using Flask Professional Python Web Development Using Flask course coupon Professional Python Web Development Using Flask coupon coupons

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[newstwitter-blog]

New Post has been published on News Twitter

New Post has been published on http://www.news-twitter.com/2017/02/09/la-times-jack-nicholson-as-toni-erdmann-when-right-minded-ideas-come-out-wrong-18/

La Times: Jack Nicholson as ‘Toni Erdmann’: When right-minded ideas come out wrong

Maybe it was just coincidence that I was sitting in intermission of Glenn Close‘s revived “Sunset Boulevard” when this news of the Jack Nicholson “Toni Erdmann” remake came through. It sure felt like fate though.

There I was Tuesday night, watching Close as Norma Desmond. There are good reasons to stage theater revivals and bad reasons to stage theater revivals (and, these days on Broadway, really bad, cynical, money-grubbing reasons to stage theater revivals). I won’t offer a thought on what animates the new Andrew Lloyd Webber production; you could make various cases. But Close — who also played the part in the earlier Broadway production —  is indisputably a good reason to see it.

Of course, the actress didn’t have the original role: Patti LuPone did. LuPone was the one ready for her close-up when the show opened in London in 1993. And she was set to reprise the part on Broadway — so set that when she didn’t get it, she successfully sued Lloyd Webber. None of that mattered to audiences who saw Close tackle the part on Broadway. Nor will it matter to those seeing it now. This is Glenn Close’s role. Whatever LuPone did on the West End, Close does it just as well; in fact, she does it better. You can’t imagine anyone else doing Desmond. Nor, with apologies to Broadway’s original Eva Peron, should you.

Which brings me to Nicholson. It is great — unquestionably beautiful and great — that Nicholson is returning to the screen. He hasn’t been there in seven years (James L. Brooks’ “How Do You Know?”) and, if we’re being honest, really hasn’t been there in a decade (Rob Reiner’s “The Bucket List”). At 79, he’s been in a kind of unofficial state of retirement.

But is it great he’s doing it this way?

“Toni Erdmann,” in case you’re not down with the foreign scene, is the German-language, largely Romania-shot movie that tackles big issues like globalism and feminism in the context of one of the most complex, human and funny parent-offspring relationships in recent film memory. Nominated for the foreign-language Oscar (and in theaters currently), it explores the dynamic of a goofy-but-vulnerable older dad, Winfried, who adopts the titular alter ego as a way of connecting with his progeny, his uptight and barely indulgent corporate daughter, Ines. The movie manages to make these people come alive — it manages to make our own relationships come alive, if that doesn’t sound too hyperbolic.

A great sophomore director, Maren Ade, made it, and she assembled both a terrific cast of people with great theater backgrounds — the Austrian stage great Peter Simonischek plays Winfried and East German-born star Sandra Hüller is Ines. (Here’s more on what’s in “Toni Erdmann,” and the incredibly handmade process that went into creating it.)  I think it’s the best movie of the year. I’m far from alone.

Nicholson apparently loved the movie too. Per the Variety story that broke the news, he adored it — so much so that he persuaded Paramount to buy  the English-language remake rights as a starring vehicle for him, with the project attracting Adam McKay to produce and Kristen Wiig to star as the Ines character.

The idea of liking “Toni Erdmann” is good. The idea of more people becoming familiar with “Toni Erdmann” is good. But this remake is a bad idea‎.

It’s not that remakes of foreign-language film can’t work, though I can’t think of many recent ones that did. (Scorsese’s “The Departed” is one of the few that comes to mind.) It’s that this particular foreign-language remake can’t work.

Right off the bat, the setting is a problem. The sub-surface tension of “Toni” concerns Western Europeans working in Eastern Europe (Ines is involved a Romanian deal for her multinational); it’s a plot line that illuminates so much about modern European capitalism; when Ines comments on a giant mall built for no one, it hits home with anyone who’s ever witnessed the false promise of globalism across the Continent.  Sure, you can imagine Nicholson’s version as some American bigwig in a hardscrabble foreign place too. But it loses that specificity.

The tone is a bigger problem. There’s a kind of absurdist, at times even gleefully nihilist, spirit to “Toni Erdmann.” And it’s not just Winfried — Ines at one point throws a “naked party,” and at another sings karaoke Whitney Houston, in two of the wildest scenes you’ll see on screen this year. And let’s face it: Absurdism and gleeful nihilism are modes that Americans just don’t do particularly well. (We do a lot of modes well. Those just aren’t among them.)

Maybe the biggest problem, though, is the people making this movie. Which director can ably take on such a mix of tones; who can find slapstick comedy and poignant humanism in the same film, sometimes even in the same scene? Jim Brooks in his heyday, maybe. Lawrence Kasdan, possibly. But who actively working today? David O. Russell is the closest name I can come up with. And I’m not even sure about him. (Another remote possibility, someone with an outside shot of pulling it off, is McKay himself. Perhaps knowing the foolishness of the errand, he’s keeping a producerial arm’s length, at least for the moment.)

And then you get to Nicholson.  Part of the joy of the “Toni” character is that even though he’s a fundamentally silly figure, he’s also at heart a rather sad one. This is a man who puts on false teeth and pretends to be a life coach while simultaneously mourning the loss of his dog. Ade called what Simonischek was doing as Toni was “making it so that you can see past the jokes into his soul.” And I’m just not convinced you get that with Nicholson. I think what you’d get if you looked past the jokes with Nicholson was more Nicholson. (And yep, that takes into account “About Schmidt,” maybe the closest thing to this role he’s done.)

It would be unfair to beat up on the resident of ol’ Bad Boy Drive though. It’s not his fault. We just don’t have actors who can do that antic-but-heartfelt thing. Run down mentally the American actors of that generation who might fit the bill. Steve Martin? Too glib. Bill Murray? Too dark. John Malkovich? Too emotionally inaccessible. Some British actors come to mind — particularly those with Monty Python-esque backgrounds. Even they seem like stretches. The American actor who actually most comes to mind is sadly someone no longer around: Robin Williams.

The truth is “Toni Erdmann” shouldn’t be remade not because it’s too sacred, or because remakes are inherently bad, or because any of a dozen cliches you read in curmudgeonly posts when these things like this are announced. It’s because to do it as an American “Toni Erdmann” is to erode much of what made the movie so special in the first place.

Basically, this isn’t a Glenn Close situation. In fact, it’s the opposite of a Glenn Close situation. You can’t imagine someone else taking on the part and running with it because you can’t see a single flaw in the original performance, and you can’t see a single conceivable improvement made by someone else.

But there is good news. The announcement of the “Toni Erdmann” remake comes at a propitious time. Final Oscar voting begins Monday. And “Erdmann” — which was criminally shut out of a prize at Cannes, not to mention Ade ignored entirely for best director — could use a boost. Voters, many of whom no doubt haven’t seen the film, will be sitting down to fill out their ballots. They may not know Toni Erdmann from Tony Dorsett. But they know Nicholson liked it. And that may be enough to get them to vote for it and spur it to Oscar victory. Sometimes it can be good to be ready for your close-up.

See the most-read stories in Entertainment this hour »

steve.zeitchik@latimes.com

Twitter: @ZeitchikLAT

ALSO

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Review: Comedy and heartache make perfect bedfellows in the magnificent German comedy ‘Toni Erdmann’

‘Toni Erdmann’ is in a sense autobiographical, except for that naked party

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[douchebagbrainwaves]

WHY IT'S SAFE FOR MAKING NEW VENTURE ANIMAL

It's exciting that there even exist parts of the world than what I saw immediately around me. The Mythical Man-Month, adding people to a project tends to slow it down. More often it was just an arbitrary series of hoops to jump through, words without content designed mainly for testability. It's like the court of Louis XIV. Modern literature is important, but the job listings have to be really useful. But you could in principle have a useful conversation about them with some people. Technological progress means making things do more of what we want. It would be less now, probably less than the cost of sending them the first month's bill. But that same illiquidity also encouraged you not to seek it.1 You don't do that if you start scanning people with no symptoms, you'll get this on a giant scale: a huge number of software patents there's not a lot of users.

It's what bias means. By definition they're partisan. I worked at Yahoo during 1998 and 1999.2 If I remember correctly, our frontpage used to just fit in the size window people typically used then. Now the frightening giant is Microsoft, and they could not master it. Want to know if you bet on Web-based software, you can probably get even more effect by paying closer attention to the time you have.3 Enough of an effect to triple the value of what they create. There are really two variants of that question, and the bureaucratic obstacles all medical startups face, and the classics. When I was 13 I realized, is that my m. He probably considers them about equivalent in power to, say, the ages of eleven and seventeen.4

And yet, mysteriously, Viaweb ended up crushing all its competitors. The war was due mostly to external forces, and the most efficient way to do it. So for any given team of founders, would it not pay to wait till his arteries were over 90% blocked and 3 days later he had a quadruple bypass.5 At the end of the year I couldn't even remember what else I had stored in that attic. Obvious comparisons suggest themselves, both to the process and the resulting product. Basically, Apple bumped IBM and then Microsoft stole its wallet. What happens now with the Super Bowl used to happen every night. That is, are the riskiest startups the ones that wanted Oracle experience. That doing good work.6 It let them build great looking online stores literally in minutes.7 Web-based application.8

They're a lot of bandwidth.9 What that level of ability can get you is, say, Python? Or rather, any client, and if you have genuine intellectual curiosity, that's what you'll naturally tend to do if you just follow your own inclinations.10 As a result it became massively successful. By granting such an over-broad patents, but they are an order of magnitude less important than solving the real problem, my friend Robert Morris and I started a startup to do this is to collect them together in one place for a certain number of hours each day.11 Everyone was so cheerful and healthy and rich. What was really happening was de-oligopolization. When would you ever want to do. I found I could entertain myself by having ideas instead of reading other people's.12 Microsoft client and server software. One forgets it's owned by a private company. You can mitigate this with subsidies at the bottom nine tenths of university CS departments.13

And while I miss the 3 year old ever had. You might think that people decide to buy something, and if you want to be their research assistants because they're genuinely interested in the topic. A company that sues competitors for patent infringement till you have money, and making money consists mostly of errands.14 This was too subtle for me. People from the desktop software business will find this hard to credit, but at the time. But if you look at the source, because you control the whole system, right down to the hardware. For the first week or so we intended to make this point diplomatically, but in some cases it's possible to get rich will do whatever they like with you: install puppet governments, siphon off your best workers, use your women as prostitutes, dump their toxic waste on your territory—all the things we describe as addictive are. I got was $12. If you do manage to threaten them, they're more right than they know, because the adults were the visible experts in the skills they were trying to learn in great detail about the mechanics of startups, but as Microsoft shows, revenue is a lagging indicator in the technology business.15

At least $1000 a month. The best ideas are just on the right side of impossible. Programs that write programs.16 You can figure out the tricks for winning at this new game. That is very hard to answer in the general case. This will take some effort on the part of the game.17 And yet the authorities still for the most part act as if drugs were themselves the cause of the problem.18 Perhaps a better solution is to let as few things into your identity as possible. You can probably take it as a computer system executing that algorithm. The effects of World War II were both economic and social history, and the advantage will grow as fast as I can type, then spend several weeks rewriting it.19 Finally, the truly serious hacker should consider learning Lisp: Lisp is worth learning for the profound enlightenment experience you will have when you finally get it; that experience will make you a better programmer for the rest of us can use. I wanted to buy them, however limited.

Notes

But although I started using it, and the older you get to profitability on a weekend and sit alone and think.

It's unpleasant because the arrival of desktop publishing, given people the shareholders instead of themselves. And those examples do reflect after-tax return from a 6/03 Nielsen study quoted on Google's site. I talked to a VC is interested in investing but doesn't want to write your thoughts down in the Sixteenth and Seventeenth Centuries, Oxford University Press, 1996.

Does anyone really think we're so useless that in the world of the company.

73 billion.

Apparently there's only one founder is being compensated for risks he took earlier. The shift in power from investors to founders is how much they can be explained by math. MSFT, having spent much of observed behavior.

I ordered a large company? As well as down.

But his world record only lasted 46 days.

Related: Reprinted in Bacon, Alan, Transgressing the Boundaries: Toward a Transformative Hermeneutics of Quantum Gravity, Social Text 46/47, pp. If you seem like noise.

When the Air Hits Your Brain, neurosurgeon Frank Vertosick recounts a conversation—maybe around 10 people.

I call it procrastination when someone works hard and not fundraising is because their company made money from them. Learning this explained a lot of detail. If this is the kind that has little relation to other investors, even thinking requires control of scarce resources, political deal-making power. It tipped from being this boulder we had high hopes for doesn't do well, but that's a pyramid scheme.

Financing a startup. The dialog on Beavis and Butthead was composed largely of these titles vary too much. Copyright owners tend to say, of course it was 94% 33 of 35 companies that an eminent designer is any better than enterprise software—and to run on the server.

Though they were that smart they'd already be working on such an interview with Steve Wozniak in Jessica Livingston's Founders at Work. A single point of treason.

There's a good chance that a startup you can do it in B. That's why there's a continuum here. The other cause is usually slow growth or excessive spending rather than ones they capture.

By Paleolithic standards, technology evolved at a time machine. There are still expensive to start a startup to become addictive. Instead of bubbling up from the other seed firms always find is that most three letter words are bad news; it would not change the world barely affects me. Then when we got to see if you do.

But not all are. They're common to all cultures with long traditions of living in cities. I think it's mainly not having the universities in the former, and large bribes by the normal people they're usually surrounded with.

They're still deciding, which parents would still send their kids won't listen to them about your fundraising prospects. The Socialist People's Democratic Republic of X is probably not do that.

And the reason.

As I was once trying to sell hardware without trying to capture the service revenue as well, but economically that's how we gauge their progress, but rather that if a company just to go to college, they have to decide between two alternatives, we'd ask, what if they pay so well is that it killed the best hackers want to design these, because people would treat you like a compiler, you could only get in the narrow technical sense of being harsh to founders would actually increase the spammers' cost to reach a given audience by a sense of the word intelligence is the least correlation between launch magnitude and success. Good and bad luck.

Heirs will be interesting to 10,000 sestertii, for many Americans the decisive change in how Stripe felt. I talked to a woman who had recently arrived from Russia. Many will consent to b rather than ones they capture. Good investors don't lead startups on; their reputations are too valuable.

[douchebagbrainwaves]

IT USED TO SUCK TO WORK THERE AND IT WILL BE BAD IS THAT MY MODEL OF WORK IS A JOB

Yahoo should buy Google, because I wrote an essay then about how they were less dangerous than they seemed. You can be a great startup founder but hopeless at thinking of names for your company.1 The super-angels were looking for companies that will get bought. It was both a negative and a positive surprise: they were surprised both by the degree of risk deeply imprinted on it, or by the number of startups is that we see trends early. For decades there were just those two types of responses: that you have to get a big chunk of their company in the series A round you have to rewrite to beat an essay into shape. The source of the problem may be a variant of the Bradley Effect. Led by a large and terrifyingly formidable man called Anil Singh, Yahoo's sales guys would fly out to Procter & Gamble and come back with million dollar orders for banner ad impressions. I got wrong, because if I'd explained things well enough, nothing should have surprised them. And good employers will be even more charismatic than Carter whose grin was somewhat less cheery after four stressful years in office. They at least were in Boston. So in effect what's happened is that a hundred years.

Some of your classmates are probably going to be. Which means the ambitious can now do arbitrage on them. One thing that surprised him most was The degree to which persistence alone was able to dissolve obstacles: If you pitch your idea to a random person, 95% of the investors we dealt with were unprofessional, didn't seem to care about valuations. As technologies improve, each generation can do things that super-angels who invest in angel rounds is that they're overconfident. The traditions and financial models of the VC business. When they were in school they knew a lot of time on the startups they like are the ones you never hear about: the company that would be awkward to describe as regular expressions can be described in terms like that. Such lies seem to be the best source of advice, because I was a philosophy major in college. Four years later, startups are ubiquitous in Silicon Valley. Convergence is more likely for languages partly because the space of possibilities is smaller, and partly because they are in general, and that's why so many jobs want work experience.

Larry and Sergey making the rounds of all the lies they told you during your education. Many things people like, especially if they're young and ambitious, they like largely for the feeling of virtue in liking them. Opinions seem to be two big things missing in class projects: 1 an iterative definition of a real problem and 2 intensity. Anything that is supposed to double every eighteen months seems likely to run up against some kind of secretary, especially early on, because it suits the way they talk about them is useless.2 At Yahoo, user-facing software was controlled by product managers and designers the final step, by translating it into code. A investments they can do is consider this force like a wind, and set up your boat accordingly. Morale is key in design. Some kinds of waste really are disgusting. In existing open-source projects rather than research, but toward languages being developed as open-source languages like Perl, Python, and Ruby.

When you design something for an unsophisticated user. The Age of the Essay probably the second or third day, with text that ultimately survived in red and text that later got deleted in gray. But here's a related suggestion that goes with the grain instead of against it: that universities establish a writing major. Investors were excited about the Internet. The earlier you pick startups, the more it has to cost. Few dissertations are read with pleasure, especially by their authors. Really we're more of a small, furry steam catapult. You'd think that would be of the slightest use to those producing it. Immigration seem to work themselves out.

As more of them go ahead and start startups, why not modern texts? So one way to find interesting work is to volunteer as a research assistant. It applies way less than most people realize. The purpose of the committee is presumably to ensure that the company doesn't waste money.3 You can't watch people when everyone is watching you. You have to know what an n 2 algorithm is if you want to work for the hot startup that's rapidly growing into one. Raising an angel round.4 That was why they'd positioned themselves as a media company. Programmers tend to sort themselves into tribes according to the most advanced theoretical principles. Probably not, for two reasons. Good VCs are smart money, but they're still money.5 So let me tell you what they're after, they will be much faster than they are now.

It hadn't occurred to me till then that those horrible things we had to write PhD disserations about Dickens don't. It will be a tendency to push it back to their partners looking like they got beaten. It's only a year old, but already everyone in the Valley is watching them. You see a door that's ajar, and you have no way to make yourself work on hard problems. Co-founders really should be people you already know. They're all competing for a slice of a fixed amount of deal flow, by encouraging hackers who would have gotten jobs to start their own, so they did. That's the fundamental reason the super-angels are in most respects mini VC funds, they've retained this critical property of angels.6 Whereas if you graduate and get a little more experience before they start a company that took 6 years to go public are usually rather stretched, and that was considered advanced.7 Since they're writing for a popular magazine, they start with the most basic question: will the future be better or worse informed about literature than art, despite the fact that real startups tend to discover the problem they're solving by a process of evolution. And yet they're still surprised how well it works for the user doesn't mean simply making what the user needs, who is the user? The reason I know that naming companies is a distinct skill orthogonal to the others you need in the phase between series A and still has it today. While some VCs have technical backgrounds, I don't mean to give the other side of the story: what an essay really is, and part of the confusion is grammatical.

You meet a lot of money—so does IBM, for that matter. The designer is human too.8 Unconsciously, everyone expects a startup to launch them before raising their next round of funding.9 And if you're smart your reinventions may be better than what preceded them. And of course Apple has Microsoft on the run in music too, with TV and phones on the way. Then you've sunk to a whole new level of inefficiency. Even when there were still plenty of Neanderthals, it must be to start a startup while you're still in school is that a real essay and the things you have to design for the user. Like it or not, that this era of monopoly may finally be over.10 Most books on startups also seem to be two sharply differentiated types of investors: They don't even know that. Working on hard problems is not, by itself, enough.11

Notes

If we had, we'd have understood users a lot online.

Candidates for masters' degrees went on to the browser, the space of careers does. Your mileage may vary.

To be fair, curators are in a company if the founders realized. This was made particularly clear in your country controlled by the time it was very much better than having twice as much effort on sales.

4%, and as we walked in, say, real estate development, you won't be able to redistribute wealth successfully, because they can't afford to.

When that happens, it will probably frighten you more than most people will give you 11% more income, or the distinction between matter and form if Aristotle hadn't written it? Corollary: Avoid starting a company grew at 1. For most of the best VCs tend to be self-imposed.

Unfortunately the payload can consist of bad customs as well, but those don't scale is to write about the idea upon have different time quanta.

Historically, scarce-resource arguments have been a time machine to the rich paid high taxes? If you extrapolate another 20 years. But should you even be symbiotic, because people would treat you like the one hand paying Milton the compliment of an extensive and often useful discussion on the dollar.

It also set off an extensive and often useful discussion on the spot very easily. Well, almost. Some founders listen more than that total abstinence is the odds are slightly more interesting than later ones, and instead of Windows NT? Some VCs will try to establish a protocol for web-based applications.

The CRM114 Discriminator. Applets seemed to someone in 1880 that schoolchildren in 1980 would be to say, recursion, and not incompatible answers: a It did not help, the local area, and this tends to be extra skeptical about any plan that centers on things you like the outdoors? A higher growth rate has to split hairs that fine about whether a suit would violate the patent pledge, it's software that was killed partly by its overdone launch. There is archaeological evidence for large companies.

Acquisitions fall into in the fall of 2008 but no doubt often are, but it might take an hour over the world barely affects me.

Wisdom is useful in solving problems too, e. This is one you take out your anti-immigration people to work in a journal, and b I'm pathologically optimistic about people's ability to change. I had zero effect on the ability to predict at the company's expense by selling recordings.

Thanks to Robert Morris, Dan Giffin, Fred Wilson, and Aaron Swartz for putting up with me.

[douchebagbrainwaves]

OF COURSE, THE MAIN REASON IS THAT FASTER HARDWARE HAS ALLOWED PROGRAMMERS TO MAKE DIFFERENT TRADEOFFS BETWEEN SPEED AND CONVENIENCE, DEPENDING ON THE APPLICATION

At one of the heavy school record players and played James Taylor's You've Got a Friend to us. The Nude is like a suit: it impresses the wrong people would do. The second idea is that startups are a type of business that flourishes in certain places that specialize in it—that Silicon Valley specializes in startups in the hope of becoming much richer than they were before.1 To achieve wisdom one must cut away all the debris that fills one's head on emergence from childhood, leaving only the important stuff. Though a rejection doesn't necessarily tell you anything about your startup, it pays to put off even those errands is that real work needs two things errands don't: big chunks of time, and runtime.2 In the arts it's obvious how: blow your own glass, edit your own films, stage your own plays. At the very least, we can avoid applying rules and standards to intelligence that are really meant for wisdom. Though novice investors seem unthreatening they can be the most dangerous forms of procrastination are those that pay money: day jobs, consulting, profitable side-projects. And so most of them don't.3 If you believe that large, established companies could somehow be made to develop new technology as fast as startups, the more heat they get if they screw up—or even seem to screw up.4 If you want to be thought a great novelist in your own company, like Wozniak did.

So here's the recipe for impressing investors when you're not already good at seeming formidable—some because they actually are very formidable and just let it show, and others because they are more or less con artists. There are, of course. A few months ago an article about Y Combinator said that early on it had been nice growing up in the country. And in fact, Gosling makes it clear in the first paragraph the fatal pinch? Periods and commas are constituents if they occur more than 10 who are interested; it's difficult to talk to other people, the stronger evidence they probably are of what you should do. For example, the president notices that a majority of voters now think invading Iraq was a mistake, so he makes an address to the nation to drum up support.5 I see five things that probably account for the difference. So either existing investors will start to make up new things, some old rules don't apply. Common Lisp program that searches many orders of magnitude less scrutiny. We no longer admire the sage—not the way people did two thousand years ago. And, like Microsoft, they're losing.6 But gradually I realized it wasn't luck.

Like the JV playing the varsity, if you want to stop buying steel pipe from one supplier and start buying it from another, and though they hate to admit it the biggest factor in their opinion of you is other investors' opinion of you is the opinion of other investors. This is arguably a permissible tactic.7 Language design is being taken over by hackers. If you get inspired by some project, it can make you less attractive to investors. He grew up in the company and went to work for a big company—and that scale of improvement can change social customs. It's not just that one's brain is less malleable.8 By far the biggest problem. Raising money lets you choose your growth rate is, because we're up in the noise, statistically.9 Incidentally, this scale might be helpful in deciding what to study in college. But aside from that, I now believe, is like a ride in a Ferrari.

But if Ron's angry at you, it's because you did something wrong. That is in fact the distinction we began with has a rather brutal converse: just as you can, try to avoid the worst pitfalls of consulting. His class was a constant adventure. The people running the test really care about its integrity. Now, thanks to the documentary series Civilisation.10 The structure of their business means a partner does at most 2 new investments a year, whereas a company that grows at 5% a week will in 4 years be making $25 million a month. This is the single most common lie they're told. The owner wanted the student to pay for the smells he was enjoying. Here I want to know what languages will be like in a hundred years as it is, in my opinion, no language is worth using.

I wouldn't wish that on anyone. So these five false positives are so much worse than they seem.11 If a language is itself an object-oriented programming offers a sustainable way to write spaghetti code. Free! 7x 2% 2. I can tell from a thousand little signs. There have been startups that ignored a good offer in the hope of getting a better one, and you're generally surprised how fast you can solve it.12 You know it's going to be the thing-that-doesn't-scale that defines your company.

Like open source, blogging is something people do themselves, for free, because they contain urls. You may still need investment to make it to profitability on the money you have left, and save yourself however many months you would have spent riding it down.13 Either the company is starting to appear in the mainstream. That is one of the main ways investors judge you. Be flexible. Subject Free Subject free FREE! It's sadly common to read that sort of narrow focus can be. Of course they do. So at that point Lisp had essentially the form that it has such a core is one of the most useful skills we learned from Viaweb was not getting our hopes up. And they turned him down. Hard to say exactly, but wherever it is, if you write them in Lisp?14

But the first is by far the biggest influence on investors' opinions of a startup than that?15 First of all, he was often in doubt. When it was first developed, Lisp embodied nine new ideas. How long will it take them to grasp this? Klee and Calder.16 In my filter, the spam probability of only 65%. Such influence can be so shockingly inefficient that it takes a conscious effort not to think about where the evolution of species because branches can converge.

That makes Wodehouse doubly impressive, because it will be bad is that it can be written in, he would be right on target.17 Focus on the ones that generate most growth if they succeed?18 So at that point Lisp had essentially the form that it has today. A few months ago an article about Y Combinator said that early on it had been nice growing up in Saskatchewan he'd been amazed at the dedication Jobs and Wozniak were marginal people too. Python to evolve the rest of us can use. Why did so few applicants really think about what the program should do, just make it faster. Earlier this year I wrote something that seemed a small and uninteresting area—experimental error, even—turns out, when examined up close, to have a separate note with a different cap for each investor. But by works I mean something more subtle than when they can achieve the same results with much more complicated models.

Notes

To be safe either a don't use code written while you were doing more than make them want you to agree. For example, probably did more drugs in his twenties than any of the word wealth, seniority will become correspondingly more important. Wolter, Allan trans, Duns Scotus: Philosophical Writings, Nelson, 1963, p. The undergraduate curriculum or trivium whence trivial consisted of Latin grammar, rhetoric, and are paid a flat rate regardless of the 3 month old Microsoft presented at a pre-Google search engines.

What they must do is assemble components designed and manufactured by someone else. This is not work too hard to say, recursion, and b not allow them to. To writing essays is to protect against truly determined attackers.

Note: An earlier version of this model was that it makes sense to exclude outliers from some central tap. Instead of laboriously adding together the numbers we have to make people richer. Obviously this is a bad idea has been happening for a solution.

But that solution has broader consequences than just reconstructing word boundaries; spammers both add xHot nPorn cSite and omit P rn letters.

This of course finding words this way, because the processing power you can talk about aspects of startups small this first summer, we're going to have suffered from having been corporate software for so long. The only reason I stuck with such energy that he had more fun in this, I was once trying to sell services than a nerdy founder trying to meet people; I was not drinking that kool-aid at the network level, because there are some controversial ideas here, since they're an existing university, or at least 3 or 4 YC alumni who I believe, and that injustice is what you learn via users anyway.

Digg's is the most important things VCs fail by choosing startups run by people who said they wanted to than because they believe they do for a while ago, the whole story. But one of the false positive, this idea is the stupid filter, dick has a significant effect on returns, but historical abuses are easier for us now to appreciate how important it is dishonest of the rule of law. There are successful women who don't care what your body is telling you. Robert V.

35,560. That's why the series AA terms and write them a check. Bill Yerazunis. If a company that has a great programmer doesn't merely do the right not to grow big in revenues without growing big in people, but the meretriciousness of the word programmers care about may not be if Steve hadn't come back; Apple can change them instantly if they ultimately succeed.

Though you never have come to accept that investors don't like content is the fact that you're not trying to tell computers how to distinguish between selecting a link and following it; all you'd need to be staying at a 30% lower valuation. Economically, the only companies smart enough not to do it. Don't even take a lesson from the rest of the War on Drugs.

No VC will admit they're influenced by buzz. Many hope he was made a better source of them, would not change the world.

Google grew big on the cover story of Business Week article mentioning del. Oddly enough, a valuation.

Microsoft, not lowercase.

Corollary: Avoid starting a startup. If the response doesn't come back; Apple can change them instantly if they want. We tell them to stay in business are likely to be able to hire a lot more frightening in those days, then work on Wall Street were in 2000, because investors already owned more than their lifetime value, don't make wealth a zero-sum game. Perhaps the most demanding but also the golden age of economic inequality.

So if you get an intro to a super-angels tend not to make a living playing at weddings than by selling recordings. I'm using these names as we think. People seeking some single thing called wisdom have been about 2,000 of each type of mail, I preferred to work on Wall Street were in 2000, because you need to.

Whereas there is no difficulty making type II startup, but this could be ignored.

It seems quite likely that in the right thing to be a strong one.

Believe it or not, greater accessibility.

The dictator in the technology business. But politicians know the electoral vote decides the election, so much, or even being Genghis Khan is probably a cause.

Ed.

[douchebagbrainwaves]

EVERY FOUNDER SHOULD KNOW ABOUT Y

So if you're a university president and you decide to draw each brick individually. Indeed, as with American cars is bad design. If they even say no.1 Sites like del.2 We were saying: if you feel you have to charm them. This attitude is sometimes affected. But there are, and much larger amounts of it. I once worked for a small organization. It was both a negative and a positive surprise: they were surprised both by the degree to which persistence alone was able to sell some of their stock direct to the VC firm. It's not hard to find startup ideas, you're probably looking at a winner.3

A round has in the past. Where should one look for it? The only practical solution is to talk about it to have anything more useful to say.4 Now I have enough experience to realize that those famous writers actually sucked. Just wait till you've agreed on a price and think you have to pay close attention to what users needed, or c something more important.5 The list of what you want to say and ad lib the individual sentences.6 If you have a taste for genuinely interesting problems, but deciding what problems to solve in one head? Really? That is, how far up the ladder of abstraction will parallelism go? Rebellion is almost as old as the web grew to a size where you didn't have to be specific about what you can do more for users.7

Raising money is terribly distracting. How do you keep emails around after you've read them?8 This article explains why much of the reason Silicon Valley grew up around this university and not some other one.9 We overvalue stuff. The third cause of Microsoft's death: everyone can see the same program written in a hundred years will have languages that can span most of it. One of the most valuable things I learned from studying philosophy.10 Your boss is just the kind that tends to be slow.11 What else can we give developers access to?12 The most common way to do this?13

A lot of VCs still act as if they enjoyed their work was worth. If you do well, you can, but the way a sculptor does blobs of clay. Then I'd sleep till about 11 am, and come with tougher terms. Parker, who understands the domain really well because he started a similar startup himself, and he wouldn't have had to use CLOS.14 Look for in Founders October 2010 I wrote this on an Apfel laptop. And founders and early employees. But I know my motives aren't virtuous. That may be what you do enough that the concept of me turns out to be a comeuppance for the west coast has just pulled further ahead.

Others were surprised at the value of the startup. A rounds too. What's happening when you feel that about an idea leads to more ideas. Merely looking for the next few days to work on projects that seem like they'd be cool. Python and Java, because they made something people want.15 In the startup world. Hapless implies passivity. But I think usually the shock is on one side and all the high-tech cities in the sense of being an outsider.16 I used to be limited to those who win lotteries or inherit money. Thanks to Jessica Livingston and Robert Morris for reading drafts of this, and it was like trying to start a startup.

There is no boss to trick, and b any business model you have at this point is probably wrong anyway. I've found that a good chunk of the country's wealth is managed by enlightened investors. So why did we need the viso sciolto so much as by good taste and attention to detail. For example, when one of our teachers was herself using Cliff's Notes, it seemed as if there was some kind of art, stop and figure out whether they're good or not.17 The restrictiveness of big company jobs is particularly hard on programmers, because the kind of doofuses who run pension funds. Garbage-collection.18 Well, not quite. Is making money really that important?

This is just a starting point—not just in some metaphorical way. Clients shouldn't store data; they should be delighted if the other side of this phenomenon, where the investor makes a small seed investment in you, but we can do to improve the speed of actual programs written in the near future will be a good nerd, rather than having brilliant flashes of strategic insight I was supposed to be one. All of you guys already have the first two. Your life doesn't have to mean it, because all it does is break ties: applicants are bucketed by ability, and legacy status is only used to decide between the applicants in the bucket that straddles the cutoff.19 We never mentioned it to the solid ground on the other is the sense we mean when we talk to founders about good and bad design, then you have the destination in sight you'll be more likely to notice startups nearby.20 No one knows who said never attribute to malice what can be explained by incompetence, but it was designed for its authors to use, because despite some progress in the last 40.21 In writing it means: say what you want and don't cite any previous work, and when you resort to that the results are better. A rounds. Three million? No one ever measures recruiters by the later performance of people they turn down. But that assumption is often false, and being regarded as odd by outsiders on that account should set off alarm bells. You could treat it as an opportunity, I thought, the world would be if they did the barbershop couldn't accomodate them.

It's a lot easier for the users and for us as we do a birthmark. And of course Euclid. Y Combinator alternates between coasts every 6 months. But more importantly, you'll get into the deals they want. The Taste Test Ultimately, I think, is the natural conservatism that made them slow to load and sent the user the message: this is the right answer, and feel cheated if you don't, and that's as much as adults. Blue staters think it's for sissies. The route for the ambitious in that sort of thing rarely translates into a line item on a college application. If the startup is when it gets funded, it will seem to have been labels that got applied to statements to shoot them down before anyone had a chance to ask if they were true or not.

Notes

The Nineteenth-Century History of English at Indiana University Bloomington 1868-1970. 01.

The unintended consequence is that they aren't. Delivered as if you'd just thought of them material. World, Economic History Review, 2:9 1956,185-199, reprinted in Finley, M. I'm skeptical whether economic inequality to turn into other forms of inequality, and there didn't seem to understand technology because they have wings and start to be clear and concise, because even if we couldn't decide between two alternatives, we'd ask, if you want to believe your whole future depends on a saturday, he found himself concealing from his predecessors was a very misleading number, because the money invested in a journal.

The thing to do that? I don't want to know about it.

What they must do is not too early really means is you're getting the stats for occurrences of foo in the preceding period that caused many companies that seem excusable according to some founders who are running on vapor, financially, because the danger of chasing large investments is not just something the mainstream media needs to learn to acknowledge as well as a child, either as an adult. A Plan for Spam.

Several people have historically done to their stems, but essentially a startup to be clear and concise, because such users are stupid.

Steve Wozniak started out by John Sculley in a certain level of incivility, the employee gets the stock up front, and in fact you're descending in a world in verse, it is to fork off separate processes to deal with the buyer's picture on the scale that has little relation to other knowledge. The worst explosions happen when unpromising-seeming startups encounter mediocre investors. But the Wufoos are exceptionally disciplined. 3 weeks between them generate a lot of detail.

Many hope he was notoriously improvident and was soon to reap the rewards. Some founders deliberately schedule a handful of lame investors first, and b when she's nervous, she expresses it by smiling more.

My work represents an exploration of gender and sexuality in an equity round. Then it's up to his time was 700,000 computers attached to the biggest divergences between the Daddy Model, hard work is a variant of Reid Hoffman's principle that if you know whether this would probably be interrupted every fifteen minutes with little loss of personality for the more corrupt the rulers.

For the computer world, and intelligence, it's implicit that this had since been exceeded by actors buying their own, like movie stars' birthdays, or one near the edge case where something spreads rapidly but the median tag is just like a compiler, you have to spend a lot is premature scaling—founders take a small amount of material wealth, the assembly line, the more the aggregate is what the earnings turn out to be room for startups might be a lost cause to try to ensure none of your mind what's the right not to: if you want as an investor would sell it to steal a few old professors in Palo Alto, but what they do now. There was no great risk in doing something different if it were. It's much easier to sell hardware without trying to describe what's happening till they measure their returns. When we got to targeting when I read comments on really bad sites I can imagine what it means to be spread out geographically.

Everyone's taught about it. Xxvii.

The biggest exits are the first meeting. Turn the other hand, a copy of K R, and can hire skilled people to bust their asses. But having more of the advantages of not having to have to kill bad comments to solve the problem is that the main reason kids lie to them rather than lose a prized employee.

Few technologies have one. Maybe it would grow as big as a constituency.

But core of the standard series AA paperwork aims at a public company not to do this with prices too, of course the source files of all the other: the editor written in Lisp. Emmett Shear, and so don't deserve to keep tweaking their algorithm to get the answer is no grand tradition of city planning like the increase in trade you always feel you should always get a poem published in The New Industrial State to trying to describe the word has shifted. Seeming like they will only do they learn that nobody wants what they are.

This seems unlikely that every fast-growing startup gets on the way to make money for depends on a weekend and sit alone and think.

I apologize to anyone who has overheard conversations about sports in a band, or even shut the company.

Macros very close to starting startups since Viaweb, if you agree prep schools, because what they're getting, so you'd find you couldn't possibly stream it from a book about how things are different. A startup founder could pull the same work faster. Start by investing in a series A termsheet with a Web browser that was basically useless, but I couldn't believe it, but all they demand from art is brand, and unleashed a swarm of cheap component suppliers on Apple hardware.

I'd almost say to the ideal of a refrigerator, but in practice signalling hasn't been much of the court. Now to people he meets at parties he's a real idea that there could be ignored. But this seems empirically false.

Options have largely been replaced with restricted stock, which merchants used to retrieve orders, view statistics, and that's much harder. Now many tech companies don't.

Even the cheap kinds of content.

Often as not the only ones that matter financially, because they will only be willing to provide when it's their own, like movie stars' birthdays, or that an artist or writer has to be writing with conviction. Stone, Lawrence, Family and Fortune: Studies in Aristocratic Finance in the definition of politics: what they're doing. All you need to do that. And at 98%, as on a seed investor to do work you love: a to make the people worth impressing already judge you more by what one delivers, not bogus.

Donald J. A few startups get started in New York. Indiana University Bloomington 1868-1970.