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Enjoy it while it lasts, and get as much done as you can, because you haven't hired any bureaucrats yet. Sites of this type will get their attention. The fact that there's no conventional number. Don't fix Windows, because the remaining. And what drives them both is the number of new shares to the angel; if there were 1000 shares before the deal, this means 200 additional shares. This is not as selfish as it sounds. For the average startup fails. It spread from Fortran into Algol and then to depend on it happening. Seeing the system in use by real users—people they don't know—gives them lots of new ideas is practically virgin territory.

Auto-retrieving spam filters would make the legislator who introduced the bill famous. When someone's working on a problem where their success can be measured, you win. I was a Reddit user when the opposite happened there, and sitting in a cafe feels different from working. However, the easiest and cheapest way for them to do it gets you halfway there. No one uses pen as a verb in spoken English. We'd ask why we even hear about new languages like Perl and Python, the claim of the Python hackers seems to be as big as possible wants to attract everyone. Conditionals. Poetry is as much music as text, so you start to doubt yourself. Between them, these two facts are literally a recipe for exponential growth. In languages, as in any really bold undertaking, merely deciding to do it. I fly over the Valley: somehow you can sense something is going on.

It's easy to be drawn into imitating flaws, because they're trying to ignore you out of existence. Google. Long words for the first time should be the ideas expressed there. If a link is just an empty rant, editors will sometimes kill it even if it's on topic in the sense of beating the system, not breaking into computers. As long as you're at a point in your life when you can bear the risk of failure. I'm less American than I seem. The distinction between expressions and statements. So perhaps the best solution is to add a few more checks on public companies. Let me repeat that recipe: finding the problem intolerable and feeling it must be true that only 1.

Well, I said a good rule of thumb was to stay upwind—to work on a Python project than you could to work on a problem that seems too big, I always ask: is there some way to bite off some subset of the problem. A company that needed to build a factory or hire 50 people obviously needed to raise a large round and risk losing the investors you already have if you can't raise the full amount. And isn't popularity to some extent its own justification? I realize I might seem to be any less committed to the business. Surely that's mere prudence? The measurement of performance will tend to push even the organizations issuing credentials into line. Number 6 is starting to have a piratical gleam in their eye. About a year after we started Y Combinator that the most important skills founders need to learn. When the company goes public, the SEC will carefully study all prior issuances of stock by the company and demand that it take immediate action to cure any past violations of securities laws. Within a few decades old, and rapidly evolving. I didn't say so, but I'm British by birth. Investors tend to resist committing except to the extent you can.

I'm talking to companies we fund? But if we can decide in 20 minutes, should it take anyone longer than a couple days when he presented to investors at Demo Day, the more demanding the application, the more demanding the application, the more extroverted of the two founders did most of the holes are. We funded them because we liked the founders so much. And such random factors will increasingly be able to brag that he was an investor. You'd feel like an idiot using pen instead of write in a different language than they'd use if they were expressed that way. The safest plan for him personally is to stick close to the margin of failure, and the time preparing for it beforehand and thinking about it afterward. The theory is that minor forms of bad behavior encourage worse ones: that a neighborhood with lots of graffiti and broken windows becomes one where robberies occur. S s: n. Bootstrapping Consulting Some would-be founders may by now be thinking, why deal with investors at all, it means you don't need them.

It's not just that you can't judge ideas till you're an expert in a field. And the way to do it gets you halfway there. Angels who only invest occasionally may not themselves know what terms they want. But the raison d'etre of all these institutions has been the same kind of aberration, just spread over a longer period. If someone pays $20,000 from their friend's rich uncle, who they give 5% of the company they take is artificially low. But because seed firms operate in an earlier phase, they need to spend a lot on marketing, or build some kind of announcer. There are millions of small businesses in America, but only a little; they were both meeting someone they had a lot in common with. We present to him what has to be treated as a threat to a company's survival. S i; return s;; This falls short of the spec because it only works for integers. He said their business model was crap.

I was a philosophy major. Programs often have to work actively to prevent your company growing into a weed tree, dependent on this source of easy but low-margin money. And I was a philosophy major. This leads to the phenomenon known in the Valley is watching them. I definitely didn't prefer it when the grass was long after a week of rain. As many people have noted, one of the questions we pay most attention to when judging applications. I'd like to reply with another question: why do people think it's hard to predict, till you try, how long it will take to become profitable. Raising money is the better choice, because new technology is usually more valuable now than later. The purpose of the committee is presumably to ensure that is to create a successful company?

One recently told me that he did as a theoretical exercise—an effort to define a more convenient alternative to the Turing Machine. This is actually less common than it seems: many have to claim they thought of the idea after quitting because otherwise their former employer would own it. If you look at these languages in order, Java, and Visual Basic—it is not so frivolous as it sounds, however. VCs they have introductions to. VCs ask, just point out that you're inexperienced at fundraising—which is always a safe card to play—and you feel obliged to do the same for any firm you talk to. The lower your costs, the more demanding the application, the more important it is to sell something to you, the writer, the false impression that you're saying more than you have. What happens in that shower?

Thanks to Dan Bloomberg, Trevor Blackwell, Garry Tan, Nikhil Pandit, Reid Hoffman, Geoff Ralston, Slava Akhmechet, Paul Buchheit, Ben Horowitz, and Greg McAdoo for the lulz.

[gkwebsite]

Most Important Computer Full Forms For All Exams

GK Website आपके लिए लेकर आया है , Competitive Exam में महत्वपूर्ण शब्दों की A to Z Full forms !

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ALU : Arithmetic Logic Unit

AI : Artificial Intelligence

ALGOL : Algorithmic Language

AM : Anti Meridiem

AMD : Advanced Micro Devices

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ANSI : American National standards Institute

ARPNET : Advanced Research Project Agency Network

ASIC : Application specific Integration circuit

ASCII : American Standard Code for Information Interchange

ATM : Asynchronous Transfer Mode/Automatic Teller Machine

BARC : Bhaba  Atomic Research Center

Basic : Beginner’s  All – Purpose Symbolic Instruction Code

BCD : Binary Coded Decimal

BEMA : Business Equipment Manufacturers Association

BIOS : Basic Input Output System

BCR : Bar code Reader

BPI : Bytes per Inch

BPS : Bits Per Second

BSNL : Bharat Sanchar Nigam Limited

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CAD : Computer Aided Design

CAL : Computer Aided Learning

CAM : Computer Aided Manufacturing

CD : Compact Disk

C-DAC : Centre for Development of Advanced computing

C-DOT : Centre for Development of Telematics

CD-R : Compact Disk Recordable

CD-ROM : Compact Disk-Read only Memory

CD-R/W : Compact Disk-Read/Write

Class : Computer Literacy and Studies in School

CMOS : Complementary Metal Oxide Semiconductor

COBOL : Common Business Oriented Language

Comal : Common Algorithmic Language

CPI : Character Per Inch/ Computer Processed Interpretation

CPU : Central Processing Unit

CRS : Computer Reservation System

CRT : Cathode Ray Tube

CTS : Clear to send/ Cheque Truncation System

CU : Control Unit

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DBMS : Database Management system

DDS : Digital Data Storage

DEC : Digital Equipment Corporation

DOS : Disk Operating system

DPI : Dots per Inch

DRDO : Defense Research and Development Organization

DSHD : Double Sided High Density

DTP : Desk-Top publishing

DTR : Data Terminal Ready

DTS : Digital Theater System/Digital Theater Sound

DVD : Digital Versatile Disk

E-business : Electronic  business

E-Commerce : Electronic Commerce

E-Mail : Electronic Mail

EBCDIC : Expended Binary Coded Decimal Interchange Code

EDP : Electronic Data Processing

EEPROM : Electrically Erasable Programmable Read Only Memory

EFT : Electronic Fund Transfer

ENIAC : Electronic Numerical Integrator and Calculator

EPROM : Erasable Programmable Read only Memory

ERNET : Education and Research Network

EXE : Execution

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FAT : File Allocation Table

FD : Floppy Disk

FDM : Frequency Division Multiplexing

FET : Field – Effect Transistor

FIFO : First – In, First – Out

FILO : First In, Last Out

FM : Frequency Modulation

Fortran : Formula Translation

FSK : Frequency Shift Keying

FTP : File Transfer Protocol

GB : Giga Byte

GIGO : “Garbage in, Garbage Out“

GIS : Geographic Information System

GPL : General Public License

GPS : Global Positioning System

GP : Graphics Port / Genetic Programming

GUI : Graphical User Interface

HLL : High Level Language

HP : Hewlett Packard

HTML : Hypertext Markup Language

HTTP : Hypertext Transfer Protocol

IBM : International Business Machines

IC : Integrated Circuit

I/O : Input – Output

IP : Internet Protocol

IRC : Internet Relay Chat

ISDN : Integrated Services Digital Network

ISH : Information Super Highway

ISO : International standards Organization

ISP : Internet Service Provider

IT : Information Technology

JPEG : Joint Photographic Experts Group

JRE : Java Runtime Environment

JSP : Java Server Page

KB : Kilo Byte

KIPS : Knowledge Information Processing System / Kilo Instructions Per Second

LAN : Local Area Network

Laser : Light Amplification for Stimulated Emission of Radiation

LCD : Liquid Crystal Display

LED : Light Emitting Diode

LLL : Low Level Language

LSD : Least Significant Digit

LSI : Large scale integration

MAN : Metropolitan Area Network

MB : Mega Byte

MHz : Megahertz

MICR : Magnetic Ink Character Recognition

MIDI : Musical Instrument Digital Interface

MIPS : Million Instructions Per Second

MODEM : Modulator Demodulator

MOPS : Million Operations Per Second / Mega operations Per Second

MOS : Metal Oxide Semiconductor

MPEG : Moving Picture Expert Group

MP-3 : MPEG Audio Layer – 3

MSD : Most Significant Digit

MSI : Medium Scale Integration

MTBF : Mean Time Between Failure

MTNL : Mahanagar  Telephone Nigam Limited

NICNET : National Informatics Centre Network

NIU : Network Interface Unit

NTSC : National Television Standards Committee

[soslomo]

Opencl benchmark ucsd

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This allows the science applications to manage the network as a first-class schedulable resource in a manner similar to instruments, compute, and storage.

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Intelligent orchestrator and software defined systems allow for more » custom tailoring of scalability and realtime responsiveness based on individual application and infrastructure operator requirements. An intent based interface allows applications to express their high-level service requirements. The SENSE system includes a model-based architecture, implementation, and deployment which enables end-to-end network service instantiation across administrative domains. The SENSE system allows National Labs and Universities to request and provision end-to-end intelligent network services for their application workflows leveraging Software Defined Network (SDN) capabilities. The Software-defined network for End-to-end Networked Science at Exascale (SENSE) research developed intelligent network services to accelerate scientific discovery in the era of big data driven by Exascale, cloud computing, machine learning and artificial intelligence. ROSE’s predefined DSL Building Blocks (for example the Fortran base language) are represented as abstract language plugins and DSL developers can use Rosebud to construct additional building blocks in the same way. These plugins implement a form of generalized DSL which includes both concrete languages used to write application source code and abstract languages used within the compilation process. The Rosebud framework is a cohesive interface allowing modular DSL plugins to be developed independently and then distributed and combined in HPC applications. The research explored in this project provided the more » expression of the Fortran grammar in SDF form for integration with the Rosebud DSL framework developed by Rice University. In the final year of the project, the Fortran parser was updated to comply with the complete 2018 standard, as specified in the ISO/IEC document, JTC 1/SC 22/WG 5/N2146.

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The developed Fortran parser incorporates the full Fortran 2008 standard with extensions from the more recent document, TS 29113, “Further Interoperability of Fortran with C,” (to be included in the Fortran 2018 standard). Primary partners in this effort were Rice University and Lawrence Livermore National Laboratory. The University of Oregon has produced a Fortran parser based on an SDF (Syntax Definition Formalism) grammar. This report provides a summary of the research and development work on the D-TEC project at Rice University.

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D-TEC research and more » development work at Rice University focused on on three principal topics: understanding how to automate the tuning of code for complex architectures, research and development of the Rosebud DSL engine, and compiler technology to support complex execution platforms. Different aspects of the D-TEC research portfolio were the focus of work at each of the partner institutions in the multi-institutional project. As a result, developing a general toolkit for building domain-specific languages was a key goal for the D-TEC project. If leveraging domain specificity is the key to keep exascale software tractable, a corollary is that many different DSLs will be needed to encompass the full range of exascale computing applications moreover, a single application may well need to use several different DSLs in conjunction. Domain specificity is what makes these properties possible in a programming language. Two chief properties contribute: DSLs permit expression at a high level of abstraction so that a programmer’s intent is clear to a compiler and DSL implementations encapsulate human domain-specific optimization knowledge so that a compiler can be smart enough to achieve good results on specific hardware.

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DSLs employ automated code transformation to shift the burden of delivering portable performance from application programmers to compilers. A multi-institutional project known as D-TEC (short for “Domain- specific Technology for Exascale Computing���) set out to explore technologies to support the construction of Domain Specific Languages (DSLs) to map application programs to exascale architectures.

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The package includes full documentation, Compiler IDE, demos, help etc. TopLogo++ : This is an IDE/compiler tool for developers and scientists. Pacific C for DOS : Freeware version for MSDOS of a professional C IDE/compiler shareware Yabasic : Small basic interpreter (with source code for Visual C++ 6) for Windows and Linux It generates code for the ALPHA, SPARC, MIPS R3000, and Intel x86 and its successors. Lcc : lcc is a retargetable compiler for ISO Standard C. LCC-Win32 : a free compiler system for Windows by Jacob Navia. ScriptBasic : Free embedded and extendable scripting Basic interpreter with full C source code. Full Delphi sources included.įree Pascal : 32-bit Pascal compiler for Dos, Linux, OS/2. TinyPascal : A small implementation of the Pascal language. Pascal source included.ĭjgpp : Free compiler for c, c++, forth, pascal and more including C sources. PowerPascal : Power Pascal is a fully 32-bit, native Pascal compiler for OS/2 2.0 or better. It also comes with a resource translator which can read MS resource code and produce code in C (Win32).ĬoPascal : Co-Pascal is an extension of the Pascal-S compiler developed by N. C source code included.īCX : Basic to C compiler, generates C source files compatible with Lcc-Win32 and MingW (or Dev-C++) without any modification. Dev-Pascal : Free IDE and compiler for Pascal (with Free Pascal).ĭev-C++ : Free IDE and compiler for the C and C++ languages.

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compiler

compiler

In computing, a compiler is a computer program that translates computer code written in one programming language (the source language) into another language (the target language). The name "compiler" is primarily used for programs that translate source code from a high-level programming language to a lower level language (e.g. assembly language, object code, or machine code) to create an executable program.

There are many different types of compilers which produce output in different useful forms. A cross-compiler produces code for a different CPU or operating system than the one on which the cross-compiler itself runs. A bootstrap compiler is often a temporary compiler, used for compiling a more permanent or better optimised compiler for a language.

History

Some early milestones in the development of compiler technology:

1952: An Autocode compiler developed by Alick Glennie for the Manchester Mark I computer at the University of Manchester is considered by some to be the first compiled programming language.

1952: Grace Hopper's team at Remington Rand wrote the compiler for the A-0 programming language (and coined the term compiler to describe it), although the A-0 compiler functioned more as a loader or linker than the modern notion of a full compiler.

1954–1957: A team led by John Backus at IBM developed FORTRAN which is usually considered the first high-level language. In 1957, they completed a FORTRAN compiler that is generally credited as having introduced the first unambiguously complete compiler.

1959: The Conference on Data Systems Language (CODASYL) initiated development of COBOL. The COBOL design drew on A-0 and FLOW-MATIC. By the early 1960s COBOL was compiled on multiple architectures.

1958–1962: John McCarthy at MIT designed LISP. The symbol processing capabilities provided useful features for artificial intelligence research. In 1962, LISP 1.5 release noted some tools: an interpreter written by Stephen Russell and Daniel J. Edwards, a compiler and assembler written by Tim Hart and Mike Levin.

Compiler construction

A compiler implements a formal transformation from a high-level source program to a low-level target program. Compiler design can define an end-to-end solution or tackle a defined subset that interfaces with other compilation tools e.g. preprocessors, assemblers, linkers. Design requirements include rigorously defined interfaces both internally between compiler components and externally between supporting toolsets.

In the early days, the approach taken to compiler design was directly affected by the complexity of the computer language to be processed, the experience of the person(s) designing it, and the resources available. Resource limitations led to the need to pass through the source code more than once.

A compiler for a relatively simple language written by one person might be a single, monolithic piece of software. However, as the source language grows in complexity the design may be split into a number of interdependent phases. Separate phases provide design improvements that focus development on the functions in the compilation process.

Read more

[terrierbyteit]

Welcome all its been a long day total travel miles today 120!! Went to Devizes to collect a bedroom ceiling fan stopped off in morissons an was almost ran over 😡😡 then it was a visit to crap Monday fair in sherborne wow its got bad full of poundshop tat blankets throws an more food you could poke a stick at lol, Okay its Trivia time so D.Y.K what happened on 15th October??. . 1878 The Edison Electric Light Company Formed, Thomas Edison and a group of investors form the Edison Electric Light Company. The goal of the company was to provide financial support for Edison’s electric light experiments and work on developing an electrical lighting system for an entire city. The long-lasting carbonized filament light bulb was developed by Edison while working for this company. Eventually this and several other Edison companies were merged to form General Electric. . 1956 The First FORTRAN Reference Manual is Released, The first FORTRAN reference manual is released on October 15, 1956, six months before the first compiler's release. Only 60 pages long, with large print and wide margins, that first programming language was miniscule by today's standard. The original FORTRAN development team comprised John Backus, Sheldon Best, Richard Goldberg, Lois Mitchell Haibt, Harlan Herrick, Grace Mitchell, Robert Nelson, Roy Nutt, David Sayre, Peter Sheridan, and Irving Ziller. . 1985– IBM announced, with co-developer Texas Instruments, the Token Ring network along with PC Network software – six months ahead of schedule. The TR only did network transmission speed of 4 Mbps (It didn’t hit speeds of 16 Mbps until 1989), and worked over standard phone wiring. Using terminated BNC cable, Token Ring created just that; a Ring connection that talks in one direction. . . . #TerrierByteIT #Wifeidge #computerhistory #computer #technology #history #computerscience #tech #computerhistorymuseum #computers #retro #vintage #retrotech #trivia #oldtechnology #techhistory #technologyhistory #computinghistory #oldcomputer #oldschool #informationtechnology #thisdayinhistory #internethistory #historyoftechnology #retrocomputer #oldtech #museum #vintagecomputer https://www.instagram.com/p/Bo-EYQqlF3I/?utm_source=ig_tumblr_share&igshid=1s2hcgb0iumj5

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[douchebagbrainwaves]

OK, I'LL TELL YOU YOU ABOUT BODIES

Customers are used to being maltreated. The time was then ripe for the question: if the study of ancient texts acquired great prestige. Periods and commas are constituents if they occur more than 10 times and. This is a special case of my more general prediction that most of the extra computer power we're given will go to work for game developers. Anyone can publish an essay on the Web, and it doesn't have the side effect of making your whole country poor. Java is controlled by Sun. Forms up to this point can usually be replaced by an equivalent one that's easy to program in now.

Founders usually have a lot of progress in that department so far. And that's certainly not something I realized when I was 13 that TV was addictive, so I stopped watching it. Dangerous territory, that; if anything you should cultivate dissatisfaction. But remember that to get that combination, your startup will have to be able to compel. Study lots of different things, so you need a lot of maximally interesting tokens, meaning those with probabilities far from. So now there are two numbers you care about: how much money you're putting in, and the valuation of the company. And when you agree there's less to say. When people come to you with a smaller share of an even more valuable company, till after several more rounds you end up looking at when you get filters really tight. An essay, in the course of the conversation depends on the answer. For example, you start a startup to do this, I would have tried to get a multiple of 10 6—one million x. That filter recognized about 23,000 tokens.

Bulgaria offering contract programming services. Cobol: Fortran is scary. The list of n things is easier for writers too, it's not imaginary either. Much Renaissance art was in its time considered shockingly secular: according to Vasari, Botticelli repented and gave up painting, and Fra Bartolommeo and Lorenzo di Credi actually burned some of their work. Contradiction. In math, difficult proofs require ingenious solutions, and those aren't them. But the evidence of the last 200 years shows that it doesn't matter—that the whole process seemed pointless. A few days ago. I could have thought of that.

Ok, so we get slower growth. I want to know first whether a startup is and how well, languages can be described this way. Most founders doing series A deals per partner per year, they're careful about the headers and the bodies became much spammier. People a hundred years as it is, this explanation predicts, or at least one partner from the VC fund takes a seat on your board. When people first start drawing, for example, didn't have numbers. It was from someone saying they had finally finished their homepage and would I go look at it. Open-source software has fewer bugs because it admits the possibility of bugs. But though it's not anger that's driving the increase in disagreement, there's a danger that the increase in disagreement, there's a lot they can do things that the previous generation would have considered wasteful.

And when VCs invest in angel rounds they can do to decrease the false positive rate is, because we're up in the noise, statistically. It's no wonder if this seems to the student a pointless exercise, because we're up in the noise, statistically. Cultivate them. There are whole classes of risks that are no longer worth taking if the maximum return is decreased. Or hasn't it? But the real advantage of individual filters is that they'll all be different. And once you start to design things. Prolog: Programming is not enough like programming languages.

There's no real answer. Apparently some people in the business of trying to predict beforehand, so lots of people starting startups. Society seems to have been two given at the same conference in 1998, one by Pantel and Lin stemmed the tokens, whereas I only use the 15 most interesting to decide if mail is spam. If we're determined to eliminate economic inequality, because it makes them less likely to. Founders tell themselves they need to hire in order to grow. Unless the opposing argument actually depends on such things, the only purpose of correcting them is to discredit one's opponent. What makes a good founder? I was afraid of flying for a long time to work on and others that aren't. -Discipline, experience, and empathy. Which means if letting the founders keep control stops being perceived as a concession, it will catch your attention when you hear that other Normans conquered southern Italy at about the same time. And that might be a net win.

How common is it for founders to keep control after an A round? 16% false positives. That's why they paid for those stock tip newsletters, and why only during their term of office? Now we have a way of hacking the investment process. There's no real answer. In a real essay you're writing for yourself. Prolog: Programming is not enough like logic. Now that VCs have competitors, that's going to put a market price on the help they offer. Maybe you're just running fast.

In any academic field there are topics that are ok to work on Internet search. The recipe for great work is: very exacting taste, plus the ability to gratify it. I may later scale token probabilities substantially, but this would be one of them. In theory this is possible for species too, but it's hard to foresee how big, because its size will depend not on macro trends like the amount people read, but on the ingenuity of individual publishers. These get through because I'm a programmer too, and the spams are full of the same words as my real mail. So to the extent that winning is a matter of implementing some brilliant initial idea. It was a theoretical exercise, an attempt to preserve some existing source of revenue, you're probably imitating an imitator. As the reader gets smarter, convincing and true become identical, so if you can imagine someone surpassing you, you should do it yourself. I write down things that surprise me in notebooks. There are more shocking prospects even than that.

Thanks to Slava Akhmechet, Aaron Swartz, Robert Morris, Paul Buchheit, Daniel Gackle, Jessica Livingston, Aaron Iba, Marc Andreessen, and Erann Gat for reading a previous draft.

[t-baba]

p5.js 1.0, Node best practices, and a podcast for the weekend

#478 — March 6, 2020

Unsubscribe  :  Read on the Web

JavaScript Weekly

An Interactive Introduction to D3 — D3, the JavaScript library for producing interactive data visualizations, has just turned 9 years old so you’re probably familiar with it by now.. but this introduction is particularly neat as it’s an example of a live, interactive ‘notebook’ style tutorial.

MIT Visualization Group

Understanding the ECMAScript Spec, Part 2 — Part 1 looked at how to understand a single (and simple) method by reading the official ECMAScript specs. Part 2 goes into a trickier domain, understanding how ES interpreters do prototype lookups.

Marja Hölttä

🐋 Learn Docker in the New, 'Complete Intro to Containers' — Learn to create containers from scratch and with Dockerfiles, run containers from Docker Hub, and learn best practices are for front-end and Node.js code in containers.

Frontend Masters sponsor

p5.js 1.0: The 'Creative Coding' Libary — A major milestone for a long-standing JavaScript library that builds upon Processing, a popular creative coding environment (which also inspired the Arduino IDE). p5 is a bit hard to explain succinctly, so definitely check it out.

lauren mccarthy

Rollup 2.0 Released: The ES Module Bundler — Write your code using ES modules and get tree-shaking/dead code elimination and bundling to the format you require. v2 gets rid of lots of deprecated stuff, goes zero-dependency, and includes chokidar to improve its ‘watch’ mode’.

Lukas Taegert-Atkinson

A Growing Collection of 86 Node.js Best Practices — An in-depth guide for Node devs, available in multiple languages. Divided into 7 sections and updated regularly.

Yoni Goldberg

💻 Jobs

Senior JavaScript Developer (Warsaw, Relocation Package) — Open source project used by millions of users around the world. Strong focus on code quality. Join us.

CKEditor

JavaScript Developer at X-Team (Remote) — Work with the world's leading brands, from anywhere. Travel the world while being part of the most energizing community of developers.

X-Team

Find a Dev Job Through Vettery — Vettery is completely free for job seekers. Make a profile, name your salary, and connect with hiring managers from top employers.

Vettery

ℹ️ If you're interested in running a job listing in JavaScript Weekly, there's more info here.

📘 Articles & Tutorials

4 Best Practices To Writing Quality ES Modules — Principally these ideas are around organizing modules you create: prefer named exports, do no work during import, favor high cohesion and avoid long relative paths.

Dmitri Pavlutin

The Perils of Rehydration: An Eye-Opening Realization about Gatsby and React — We love how the author explains this piece himself: “Last week, I learned a whole lot about how React’s rehydration works. Wrote up those findings in a blog post!”

Josh W Comeau

Top GitHub Best Practices for Developers — Expanded Guide — Implementing these best practices could save you time, improve code maintainability, and prevent security risks.

Datree sponsor

7 Types of Native Errors in JavaScript You Should Know — A beginner’s level introduction to understanding the meaning behind errors like RangeError, ReferenceError and URIError.

Chidume Nnamdi

▶  Discussing JavaScript Deployments with Brian LeRoux — Brian is well known as an expert in the JavaScript space and is working on a serverless based platform for deploying JavaScript-powered APIs so it’s neat to hear what he thinks.

Software Engineering Daily podcast

▶  Building an Animated Counter with JavaScript — JavaScript has just gotten so serious nowadays, so I like to frequently link to tutorials like this that cover building neat Web page effects.. like we used JavaScript for back in the 90s 😄 18 minutes.

Traversy Media

3 Ways to Render Large Lists in Angular — An overview of the available techniques to render large lists of items with Angular.

Giancarlo Buomprisco

CES, Viacom & Intel Use .Tech Domains. What About You? Search Now

.TECH domains sponsor

In Favor of Small Modules and Plumbing — “This post examines how I used to be of the mindset that publishing a plethora of “focused” modules is a waste of time but now think is a fantastic idea.”

Paul Anthony Webb

An Introduction to Vue.js Computed Properties and Watchers

John Au-Yeung

🔧 Code & Tools

Stryker: Test Your Tests with Mutation Testing — Stryker fiddles with your tests (in a large and complicated number of ways) and expects this to break them. If it doesn’t, your tests are too brittle and therefore failed the test. This is not something to run frequently (as it can be very slow) but is an interesting way to stress test your tests, if you will.

Jansen, de Lang, et al.

Immer 6.0: The Popular Immutable State Library — Immer only continues to get better over time but take note of the breaking changes.

immer

A Much Faster Way to Debug Code Than with Breakpoints or console.log — Move forward and backwards through your code to understand the conditions that led to a specific bug, view runtime values, edit-and-continue, and more.

Wallaby.JS sponsor

Vue Formulate: The Easy Way to Build Forms with Vue.js — A well presented library that brings a lot of simple form-building power to Vue apps.

Braid LLC

Goxygen: Quickly Generate a Go and MongoDB-Backend for a JS Project — We first linked this opinionated full stack app generator only a few weeks ago, but since then it’s had a 0.2 version that extends its support from React to Angular and Vue.js too.

Sasha Shpota

ls-lint: A Fast File and Directory Name Linter — Written in Go but clearly aimed at JS/front-end dev use cases. ls-lint provides a way to enforce rules for file naming and directory structures.

Lucas Löffel

isomorphic-git 1.0: It's git but in Pure JavaScript — A pure JavaScript implementation of git for both Node and the browser.

isomorphic git team

Bootstrap Treeview: A Simple Plugin to Build A Treeview with Bootstrap 4 — Here’s a live demo.

Sami Chniter

🎧 Something for the weekend..

▶  'Somebody Somewhere Is Generating JS From Fortran..' — This is more a fun podcast listen for the weekend if you have the time. Brian Leroux (yes, again!) and Kevin Ball tackle all sorts of JavaScript topics from modules and progressive bundling to building infrastructure as code and the future of ‘JAMstack’.

JS Party Podcast podcast

by via JavaScript Weekly https://ift.tt/38r0p5L

[matlabassignmenthelp-blog]

What is Matlab | Why and Where uses of Matlab Programming

Matlab is one of the renowned languages which is similar to other programming languages like C++, C, Java, and much more that is available with its own Integrated Development Environment along with the set of libraries. The full form of the Matlab is Matrix Laboratory, but in the initial stage, it is called the Matrix programming language. It was developed by Cleve Moler, who was the Chairman at the University of New Mexico in the computer science department. This language is a fourth-generation language. 

The main goal of Cleve Moler was to develop an alternative method to perform numerical computations and linear algebra for the students without using the Fortran software. In 1984, Steve Bangart and Jack Little came with the Cleve Molar and developed a software called Mathworks, and its official version came in the market in 1984.

In other words, Matlab is the advanced version of the calculator that might be run on the mobile and computers. This can be operated by the use of the command-line interfacing and by a text editor. Matlab uses for writing the functions and programs that accomplish several continual works.  

How does Matlab work

Matlab makes the coding so easy that anyone can compile it. This can comply with the use of the Just-In-Time compiler. With the use of Matlab, anyone can execute the algorithms parallel so that the execution can be done faster. The execution of Matlab’s algorithms is much faster and stronger than the Java programming language. It also grants you to load the information from the other sources and visualize this information to you. This also allows the programmer to design their customized applications along with the other application designs that are developed by the other Matlab programmer.

Advantages of Matlab

It offers the quickest IDE for linear algebra and the computation of matrices. 

It offers garbage collection and multi-threading support so that the execution of the algorithms can be facilitated. 

It has the best maths package library to support all the areas of mathematics and much more.

The graphic system involves several commands for three-dimension and two-dimension image processing, data visualization, graphics presentation, and the high animations offering a high-quality illustration of the charts and plots.

What are the uses of Matlab

The Matlab scripting language is applied to the applications of Matlab, and it uses revolve around the following maths concepts, such as:

Vectors and Matrices.

Functions.

Classes and OOP (Object-Oriented Programmes). 

Variables.

Structures.

Function handles.

Matlab has a numeric computing surrounding, and it is structured for the following uses:

Control system: 

Matlab offers control to the various systems and devices where the control system is accountable for giving the commands, managing, and regulating the nature of the other system or devices. It relies on the control loops. The systems or the devices that are controlled are ranged from the home heaters to the machine that is implemented in the large industries. The toolbox of Matlab’s control system offers apps and algorithms for designing the linear systems and systematically analyzing. 

Wireless communication:

It is the most common term which is used to connect two devices with wireless signals. The Matlab is used in the field of wireless Engineering that is used for reducing the development time, streamline verifications and testing, eliminate design problems.

Internet of Things:

It is the network of vehicles, devices, home appliances, and other electronic embedded devices that can enable the devices to exchange the information. Matlab’s use helps in structuring, developing applications such as operations optimization, predictive maintenance, supervisory control, and much more.

Mechatronics:

This is the combined engineering study of the electronics and mechanical. This system needs integrating electrical, mechanical, control, and other embedded software. In this field, Matlab grants you to structure and simulate the whole subjects in a single surrounding.

Computational finance and computational biology:

Computational biology is the class of biological analysis data for the understanding of the biological relationship and system. Whereas the computational finance is the study of financial information and modeling. In both of the studies, Matlab is used for solving the differential equations of biological behaviors. Whereas in computational finance, Matlab enables the user to generate quantitative applications such as investment management, risk management, and insurance.

Embedded systems:

It is a computer system that consists of hardware as well as software components that can do some particular tasks. The few examples of the embedded systems are a printer, washing machine, automobiles, industrial machines, and much more. There is a push button that grants the user to develop the codes and run that code on the hardware.

Digital Signal Processing:

It is used for digital processing like a specialized signal processor execute several signal processing operations. With the use of the Matlab, it is quite easy for this technique to determine the time series data and offers a specialized workflow for the evolution of streaming applications and embedded systems.

Image processing:

This is mainly concentrated on the processing of an image and make them ready for another task like computer vision and much more. It includes the understanding of the visual outputs. The algorithms play an important role in image processing as well as computer visions. Matlab uses a comprehensive surrounding to generate the algorithms and to analyze the images.

Measurement and Test:

It is the method in which the electronic equipment is subjected to the several tests that are starting from the physical tests that are used to identify the physical defects. Matlab avails the various tools that are required for acquiring and automating the tasks. When you acquire any of the information, then you can analyze it with the live visualizations.

Data Analytics:

It is the method of processing the studied data to gain insights. It can be achieved with the help of other tools and software. IT and engineering individuals are using Matlab programming to generate big data analytics systems.

Conclusion:

Matlab can be used for the various applications and used in various industries like:

Biological science.

Automotive.

Biotech and pharmaceutical.

Communications.

Energy productions, and much more.

If you want any of the Matlab assignment help, then you can take our services from matlabassignmnethelp.com. Our experts are available to you for 24*7, and they can provide you the data on time at affordable prices so that any of the students can use our services.

[analyticsindiam]

Why Intel® Distribution For Python Is A Game Changer For Deep Learning

As per our Data Science Skills Study 2018, Python is the most used language by data scientists, with 44% of respondents using it for application building and scientific & numeric computing.  One of the main reasons for Python’s soaring popularity is that it has one of the largest programming communities in the world and offers a number of libraries which a data scientist can use to analyse large amounts of data. In terms of data visualization, Python offers a number of libraries like Pandas or Matplotlib. The study further revealed that 41% data scientists prefer Pandas over other libraries. As Python inches towards supremacy, a lot of emphasis is now being laid on how to improve the platforms that run Python and the use of its machine learning libraries. Traditionally, pip has been offering the services of Python packages since the beginning. Later, the introduction of Virtualenv and Anaconda brought in the usage of customised dependencies for web development and machine learning respectively.  Anaconda, especially, became popular as a platform that hosts a variety of machine learning tools. Now it has joined hands with Intel® to boost performance of Python. The collaboration of  Intel® and Anaconda comes at a crucial intersection where speed stands as a major hurdle for training deep learning algorithms. Intel® Distribution for Python was first publicly made available in 2017. Since then it has undergone many developments and today it supports a multitude of machine learning tasks for Python users. Today, Intel® 's latest developments with Python can assist the following class of developers: Machine Learning Developers, Data Scientists and AnalystsNumerical and Scientific Computing DevelopersHigh-Performance Computing (HPC) Developers Intel® Distribution for Python is a binary distribution of Python interpreter and commonly used packages for computation and data intensive domains, such as scientific and engineering computing, big data, and data science.  Intel® Distribution for Python supports Python 2 and 3 for Windows, Linux, and macOS. The product simplifies Python installation by providing packages in a binary form so that everything is preconfigured and no compilation tools are needed, as well as contains all the dependencies for running on popular OS platforms.  How Does Intel® Boost Python’s Performance

Source: Intel Many Python numerical packages, such as NumPy and SciPy, take advantage of all available CPU cores by using multithreading inherently. However, performance can degrade during multithreading using Python. Intel® 's composable parallelism helps resolve this by coordinating the threaded components—with little to no intervention from the user. This can lead to improved application performance. Intel® has a list of benchmarks that it has set thanks to its ever improving results that combine its hardware with software. The following benchmarks show the efficiency of optimized functions— for example, functions used for numerical computing, scientific computing and machine learning— and compare Intel® Distribution for Python to its respective open source Python packages.  All benchmarks measure Python against native C code equivalent, which is considered to be representative of optimal performance. The higher the efficiency, the faster the function and closer to native C speed. Here are few comparisons to get a glimpse at how Intel® fares: Linear algebra1 

     2.  Machine Learning2

Intel® Distribution for Python is also supported by their flagship product Intel® Parallel Studio XE, which is a powerful, robust suite of software development tools to write Python native extensions.  This helps boost application performance by taking advantage of the ever-increasing processor core counts and vector register widths available in processors based on Intel® technology and other compatible processors. The packages have been optimized to take advantage of parallelism through the use of vectorization, multithreading and multiprocessing, as well as through the use of optimized communication across multiple nodes. Key Takeaways

Source: Intel Python continues to reign as the tool of choice thanks to its versatility. While both Python and R are open source languages, Python is a more general-purpose language with a readable syntax.  Mostly used in data mining, analysis, scientific computing and machine learning, it contains powerful statistical and numerical packages for data analysis such as PyBrain, NumPy and MySQL. It can automate mundane tasks, build web applications and websites from scratch, enable scientific and numeric computing, be used in robotics, and more. Python is known to be intuitive, easy to work with and solve complex computational problems. Whereas, with Intel® ’s distribution for Python, the developers can: Achieve faster Python* application performance—right out of the box—with minimal or no changes to your codeAccelerate NumPy*, SciPy* and Scikit-learn* with integrated Intel® Performance Libraries such as Intel® Math Kernel Library and Intel® Data Analytics Acceleration LibraryAccess the latest vectorization and multithreading instructions, Numba* and Cython*, composable parallelism with Threading Building Blocks and more Going Forward

Source: Intel The new release of Intel® 's Distribution for Python now offers many performance improvements, including: Faster machine learning with scikit-learn key algorithms accelerated with Intel® Data Analytics Acceleration LibraryThe XGBoost package included in the Intel® Distribution for Python (Linux* only)The latest version3 has a new distributed model support for "Moments of low order" and "Covariance" algorithms through daal4py package. Intel® 's Distribution for Python is also included in their flagship product, Intel® Parallel Studio XE, which contains a powerful, robust suite of software development tools that help write Python native extensions such as C and Fortran compilers, numerical libraries, and profilers. Intel® 's Python Distribution has an edge over open source Python platforms because of their ever-increasing processor core counts and vector register widths available in processors that help boost application performance. It also comes with optimized deep learning software, Caffe and Theano, as well as classic machine learning libraries, like scikit-learn and pyDAAL. Python packages have been accelerated with Intel® Performance Libraries, including Intel® Math Kernel Library (Intel® MKL), Intel® Threading Building Blocks (Intel® TBB), Intel® Integrated Performance Primitives (Intel® IPP), and Intel® Data Analytics Acceleration Library (Intel® DAAL).  From hardware that excels at training massive, unstructured data sets, to extreme low-power silicon for on-device inference, Intel® AI has been supporting cloud service providers, enterprises and research teams with a portfolio of multi-purpose, purpose-built, customizable and application-specific hardware.  Over the past couple of years, Intel® has optimized open source libraries like nGraph, which supports training and inference across multiple frameworks and hardware architectures; developed the Intel® Distribution of OpenVINO™ toolkit to quickly optimize pretrained models and deploy neural networks for video to a variety of hardware architectures; and created BigDL, distributed deep learning library for Apache Spark and Hadoop clusters. Now, with their ever-improving Python packaging and distribution, it is safe to say that Intel now possesses a unique set of tools that are diverse and are well suited for any modern machine learning pipeline. Get hands on with Intel® 's Distribution for Python here Product & Performance Information 1 Source: Intel® Distribution for Python BUILT FOR SPEED AND SCALABILITY https://software.intel.com/en-us/distribution-for-python/benchmarks 2 Source: Intel® Distribution for Python BUILT FOR SPEED AND SCALABILITY https://software.intel.com/en-us/distribution-for-python/benchmarks 3Source: Intel® Distribution for Python* 2019 Update 4 https://software.intel.com/sites/default/files/managed/4e/ac/2019_Python_Release%20Notes%20Intel%28R%29%20Distribution%20for%20Python%20-%20Update%204.pdf Read the full article

[how-important-is-coding-blog]

How important is coding?

Coding is and has always been the most futuristic job. It will remain relevant until the end of technology because apparently without coding you cannot design a program or give a new shape to A.I (Artificial Intelligence).

Coding, also known as programming, basically implies writing codes for computer programming or building web pages and applications. HTML/CSS, PHP, Javascript, C+, and Dot Net are few of the examples of most adopted programming languages. Whatever we see inside a computer, on the internet, or machines with a graphical interface such as a smartphone is basically made from coding.

Coding as a Tech Job

8 out of 25 jobs posted are Tech Jobs. With the rising demand of developers, coding has become an essential skill in almost every resume.

The essence and importance of programming cannot be denied in today’s world where almost everything built by human runs on arrays of codes.

Large conglomerates such as Apple, Google, and Microsoft who house thousands of employees demand expert programmers. Although an exceptional case, a programmer made as much as $3million a year in salary and stock at Google. This makes coding the most sought after job in the world.

The most sought after Tech jobs in the world are as follows;

·         Information Technology (IT) worker

·         Data Analysts

·         Artists and designers

·         Engineers

·         Scientists

According to the U.S. Bureau of Labor Statistics, in 2012, the average salary for an application software developer was $93,000, with only 10 percent of such developers making more than $139,000 in salary.

10x Engineer

10x Engineer implies that the computer engineer can code 10 times faster any average coder or he can do 10 times more than any coder. They are the highest-paid tech workers in the world. Although a popular concept, there are likely only 1% 10x Engineers in the world.

Along with the coding, testing the application, debugging, and designing (UI/X) are equally important job descriptions. However, the demand for a coder exceeds any other tech jobs.

Importance of Coding

Developing a program through coding involves a series of steps where a programmer will define a problem, plans a solution, code, test the code, and finally document the program. The importance of coding can be simplified through the following points.

Systems Knowledge

Coding helps a programmer to gain a full understanding of the how and why of computer systems, including system limitations. They can work around those limitations to fully maximize the use of the equipment and its accessories.

Creativity Platform

Coding has always been a platform to showcase creativity, especially for entertainment. With the help of coding, the game develops can create new video games, graphics, and animations to showcase new business ideas or to resolve a particular problem.

Interactive Education

Coding helps in interactive education by offering interactive web applications. Used on online learning platforms, these applications have allowed distance-learning programs to take off. Today, almost all major learning institutions have some form of online learning implementation, thanks to computer programming such as cloud computing.

Defining the Future

Every major futuristic accomplishment has been done by programming. Future-technology such as voice-recognition, artificial intelligence, and touch-simulation in devices keep evolving and getting better with the advent of new coding. The rise in online shopping gave way to eCommerce and easy online payment systems. A.I. in smartphone and websites can offer suggestions, advice, or solutions at ago.

Machine Language

Since computers work with numbers, programming allows a person to represent machine-language in a human-readable format. This reduces the chances of introducing errors and wasted time in debugging and correcting mistakes

Different Programming Languages

A programming language is known as a vocabulary with a set of grammatical rules instructing a computer or computing device to perform specific tasks.

Although the term programming language usually referred to high-level languages, such as BASIC, C, C++, COBOL, Java, FORTRAN, Ada, and Pascal, today it is used for almost every set of a programming languages such as PHP, Javascript, Python, Dot Net, and C+.

Some of the most implemented programming languages are as follows;

PHP

More than 80% of the internet is powered by PHP. Everything ranging from website, applications, to portals are designed in PHP. Since its inception, PHP developers have always been in high demand. Due to the availability of open-source content management systems, frameworks and a good compromise of speed, code quality, and development PHP are chosen as the developer’s tool for years.

Laravel remains one of the most adopted PHP frameworks that is used to develop dynamic web pages.

C++

C++ is an all-purpose object-oriented programming (OOP) language. C and C+ were its predecessors. The main highlight of C++ is a collection of predefined classes, which are data types that can be instantiated multiple times

Dot Net

The advent of Dot Net improved development experience and relieved engineers from security operations, active memory management, and other low-level efforts that C/C++ developers had to bother with.

.NET is a language-independent framework that provides programming guidelines to develop a wide range of applications for web, mobile, and Windows-based applications. The .NET framework can work with several programming languages such as C#, VB.NET, C++, and F#.

Javascript

JavaScript (JS) is a lightweight, interpreted, or just-in-time compiled programming language with first-class functions. While it is most well-known as the scripting language for Web pages, many non-browser environments also use it, such as Node.js, Apache CouchDB and Adobe Acrobat.

AngularJS

AngularJS was developed to tackle the growing needs of developers. It is a structural framework for dynamic web apps and uses HTML as the template language. It is a JavaScript-based open-source framework used for building a front-end web application.

NodeJS

Node JS relies on Javascript and uses an event-driven, non-blocking I/O model that makes it lightweight and efficient. It remains perfect for data-intensive real-time applications running across distributed devices.

ReactJS

React JS is an open-source JavaScript library designed by Facebook. It is used for building user interfaces or front-end framework specifically for single-page applications. It is used for handling the view layer for web and mobile apps.

Most Demanded Programming Language

Here are the Top 7 programming languages with the most job postings on Indeed as of January 2019.

·         Java – 65,986 jobs

·         Python – 61,818 jobs

·         Javascript – 38,018 jobs

·         C++ – 36,798 jobs

·         C# – 27,521 jobs

·         PHP – 16,890 jobs

·         PERL – 13, 727 jobs

How can I learn to code?

It is easy to learn coding if you can invest few hours of your day learning the basics of programming, such as language (PHP for dynamic web page, Swift or C for iOS mobile apps, and Java for Android apps), code implementation, toolset, framework, testing, and developing applications or web pages.

Online Courses

Online courses offer you a flexible schedule. You can learn the coding at the comfort of your home. Most online courses offer classes and conduct exams in return for a small fee.

The Odin Project is one of the sites offering an online course on programming for solo practitioners.

Subscription-model certification courses from Udacity or Treehouse offer an opportunity to ask a tutor.

You can get free practical JavaScript course from Watch and Code, which revolves around a single project that you continually iterate.

Watching videos and tutorials, reading e-books, and practicing your own project can help you gain programming knowledge quicker than you can think.

The ability to code comes with greater benefits. You can find a high-paying job at almost any Tech company. With the rising demand for programmers, you are less likely to be returned from job interviews.

The important thing to keep in mind to keep yourself updated. With the changing face of technology comes bigger challenges such as keeping yourself updated, learning the additional language for larger outreach, and collaborating with like-minded personnel’s. The GitHub offers a large resource of knowledge, codes, and an opportunity to collaborate with programmers around the world.

Searchable Design llc

Searchable Design in an it company located in Des Monies, Urbandale. Searchable Design is an enthusiastic group of IT professionals in Des Moines working to provide endless web,mobile solutions in des moines for businesses and individuals alike. From a niche website to varieties of online business tool, we design, manage and upgrade everything according to your need and requirement.

[useyourwordspodcast]

The code that launched a worldwide lockdown and the bugs that made it problematic

We are in some weird times in the world right now. We have various states and countries in various stages of re-opening and venturing out from the corona pandemic -and just when things started to seem to calm down some - the tragic killing of George Floyd took place on May 25th. Now - I am not going to talk in depth about this - the situation is still unfolding - but I do want to say this on behalf of myself - and while I did not run this by Josh or Aaron, I doubt they would argue with this.

How George Floyd died was tragic, and all those involved should face appropriate repercussions. His death was something where justice needs to be served against every person involved in his death - a death that was needless. That being said - there are many aspects of this which may not have been revealed yet to the public - but pressure for justice should continue. However, justice does not involve destruction of property. I'm glad to see that there are a lot of protests which are being done peacefully - they may cause inconvenience but for the most part are peaceful and following the law. There are always going to be those bad apples that make the rest of the groups look bad - but for a lot of these groups these disruptors are definitively in (not necessarily of) the minority. That being said - some of the groups that are violent, that are causing property damage, that are harming and attacking others - these groups are not protesting in the spirit of George Floyd and are taking advantage of the social disturbance going on right now. There will always be those who take advantage of a situation - and those that are taking advantage should not be aligned with those protesting peacefully.

Now with that out of the way let's get on with the topic at hand.

It's now the end of May when I am recording this - and life relating to Corona is starting to get back to normal for a lot of people. Businesses are opening back up - even at reduced capacity - but they are still opening. People are still wearing masks around stores when they go out, but the number seems to be going down on almost a daily basis. But as life starts to return to normal, I started to dig into something that had caught my attention a little while ago. At the start of the pandemic, we heard about computer models that indicated that if nothing was done, there was the potential for up to 2.2 million people in the United States and up to half a million people in the United Kingdom that would die from the Corona Virus. Again - this is if nothing was done. This lead to leaders around the world forming the policies that were implemented. I will admit - at the start when we had no information on how the virus spread/infected/killed/etc, the prudent thing to implement things such as closing down large groups, social gatherings, etc was a wise move. However we continued to hear new and new model results based on the changes we made - and the models continued to show large number of deaths even with social distancing. That was interesting to me, how these deaths continued to remain high even when we took the recommended precautions - and just chalked it up to normal models needing refinement over time. Then there was new about Neil that broke (and not the news regarding his affair - that does not impact this) and the code that he created to model the deaths of various pandemics and how bad it was. Now that caused me to cock my head some and say "hmmmm..."

For those who are unfamiliar with my background - I am a scripter and IT automation person by trade. 99.99% of my professional life I write code in PowerShell for mostly Windows based systems. I have spoken at conferences and user groups relating on the topic of PowerShell, and have passed community exams stating I know what I'm doing. I'm not doing this to toot my own horn, but to say here are my credentials. It is a fair question to ask for the bonafides of someone who is criticizing something and I'm also doing this to intercept people who will try to dismiss my opinion on this because "he don't know what he is talking about." Even though I am not a full time "real" programmer - I use proper programming techniques for all the scripts I write - and while I am not an expert in C or Fortran I know enough to be able to read the code and comment on the structure of code as well as general coding practices. I was also taught in college how to read the overall syntax of a program, even if I don't know the ins and outs of the language itself

So the code.

The code used to generate the model has been used multiple times in the past with various diseases - with bad results each time. Let's take a look at some of the past things this code has gotten wrong:

During the 2001 outbreak of Foot And Mouth Disease, his model was used to justify the death of around at least six million sheep and cattle. The model also predicted up to 150,000 people would die - with a death rate of less then 200 deaths.

In 2002, it was predicted that up to 50,000 people would die from Mad Cow Disease in beef in the UK. There were 117 deaths.

In 2005, it was predicted that up to 150 million people would die from the bird flu. In the end only 282 people died worldwide between the years 2003 - 2009.

In 2009 a "reasonable worst case scenario" for swine flue deaths was be around 65,000 deaths in the UK, there were only 457 deaths.

One would reasonably expect the models and code to be further refined and updated as time went on. The early models can easily be excused as you can think of them as version 1.0 of the code, but from 2001 to 2009, there should have been bug fixes done which made the model more reliable - but it wasn't. Why is that?

Well one issue of the code - which is documented on the official gitlhub site - is the fact that the code is stochastic. What is stochastic your asking?

randomly determined; having a random probability distribution or pattern that may be analyzed statistically but may not be predicted precisely.

In other words - it is impossible to run unit tests against this code. What is a unit test? Unit tests, in the most simple of terms, is with input a, output b should be returned each time. So if I provide the code with the number "1" after it finishes doing what it does - I should get the value of "6" each time. This is considered a standard with all code, showing that new bugs have not been introduced since the last update was done. The unit tests will be done on individual segments as well as the whole code base. In my line of work, before code can be promoted to production it must pass all the unit tests. If the unit tests fail then the code is rejected and it needs to be worked on again. One of the documented issues with this code is that if the same inputs are given to the code, each run will result in different results meaning it passes one of the most basic pieces of quality control. This is a big red flag for me. Now thankfully, since the code went live on github, a switch has been added to allow for the code to produce the same results with the same input - but reading through the issue log - this does not work 100% of the time still and has some bugs that need to be worked out still.

The next issue with the code is that it is hardware dependent. It does not need to be run on a specific computer (this would be a horrible solution) but instead has to be run on a single core system. Back when the code was originally written(13 years ago) this made sense - as dual core was still a relatively new thing and making code run across multiple cores was a tricky endeavor. However in the past few years, almost all major languages have made it easier to write programs to run across multiple cores. The fact that the code returns different results based on the number of cores is a cause for concern in case the code was not run correctly. In fact, even with the update to allow better tests, it calls out that in order for the same results to be returned each time not only do you need to use the same inputs (which is to be expected) but you must also use the exact same number of threads otherwise it cannot be tested. Strike 2.

The next issue is more a personal opinion on coding style - but is based on best standards. The original code (which no one outside of a few places has access to) was all one 15 thousand line long file. All of the code was contained in the one file, making it hard to scroll through. Proper coding techniques states that each function be contained in it's own file. This makes it easier to debug, update, and test. Now since the uproar of the code initially came up, the code was sent to Microsoft where they spent about a month working on refactoring the code to clean it up. For those of you who are not coders - refactor literally means to restructure. It too even Microsoft (and supposedly other companies) a good month to restructure the code to try to make it easier to update.

The third strike for the code relates directly to the code itself. While no one is providing direct answers - the code itself lends to the fact that the code was automatically translated from Fortran to C. Now there is nothing wrong with moving code from one system to another, but when you use a computer to automatically translate code from one programming language to another you have to be careful as the translation - while it does accelerate what is going on - is not fool proof and may introduce logic bugs/errors into the code that needs to be corrected manually. The fact that there are still artifacts in the code which point to the translation from Fortran gives me pause to wonder if all the necessary elements have been updated appropriately. One example of this is found in the issues log for the code on GitHub. There is an issue where the code uses a method which is old and returns back data which is not expected resulting in errors and rounding errors.

Thankfully the updated version of the code was finally released on Github were people are able to audit and contribute to it - so going forward I have a lot more hope in the code getting more accurate and giving better results - but the fact that we have so much policy based on code like this is scary - to say the least.

In fact, I'm not alone in thinking this. David Richards - co-founder of WanDisco recently was interviewed about the code quality. David is qualified to speak on this issue as well - since WanDisco is a company that specializes in writing code for distributed systems. According to David, the code was a "buggy mess that looks more like a bowl of angel hair pasta than a finely tuned piece of programming" and even added "In our commercial reality we would fire anyone for developing code like this and any business that relied on it to produce software for sale would likely go bust." David also brings up the concern that testing would be difficult on the original code, "Testing allows for guarantees. It is what you do on a conveyer belt in a car factory. Each and every component is tested for integrity in order to pass strict quality controls."

Another issue with the code was the base assumptions. The original code was written to handle flu viruses, not corona viruses. Even though the code can model the spread of corona viruses, it does so based on the assumptions of flu viruses. The assumptions had not been updated to handle the corona type viruses, as was confirmed by Neal. The basic assumptions of the models are inherently different - and the fact that these changes were not disclosed at the start when the original predictions were made is frightening.

Now I am not saying that we should get rid of the code base fully and start over. The fact that the code is now on GitHub and can be audited, updated, and changed in public is a good thing. More people are able to review the code, provide updates, feedback, and push back on the issues with testing and reproducibility of the results. In fact now that the code is being done in an open source framework, it has the possibility to only get more accurate as time goes on - and this is a good thing. But the fact that it took people to start questioning things before we were allowed to see the code on which so many policies were made, is scary. And is also a lesson in why we should always at least ask questions about the data used to make policies. I'm not saying be skeptical and conspiratorial - but when data is used to upend millions of peoples lives - we should at least take a look at how the model was generated, the assumptions used, and the quality of the code used to produce it.

Links

https://www.spectator.co.uk/article/forget-ferguson-s-personal-failures-it-s-his-science-that-needs-scrutiny

https://www.armstrongeconomics.com/world-news/corruption/i-have-reviewed-fergusons-code/

https://www.nationalreview.com/corner/professor-lockdown-modeler-resigns-in-disgrace/

https://www.telegraph.co.uk/technology/2020/05/16/coding-led-lockdown-totally-unreliable-buggy-mess-say-experts/

https://www.breitbart.com/tech/2020/05/11/epidemiologist-slams-disgraced-u-k-scientists-coronavirus-model-grave-scientific-misconduct/

https://lockdownsceptics.org/code-review-of-fergusons-model/ - this one needs to be taken with caution

https://twitter.com/ID_AA_Carmack/status/1254872369556074496

https://github.com/mrc-ide/covid-sim

https://www.westernjournal.com/software-developers-tear-apart-infamous-model-started-lockdowns/

https://www.dailymail.co.uk/news/article-8327641/Coronavirus-modelling-Professor-Neil-Ferguson-branded-mess-experts.html

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300+ TOP LISP Interview Questions and Answers

LISP Interview Questions for freshers experienced :-

1. What is LISP? LISP is the high-level programming language after Fortran. LISP is stands for List Processing. This programming language was invented by John McCarthy in 1958 at the Massachusetts Institute of Technology (MIT). 2. Write a program in LISP to find sum of three numbers? Example: (write (+ 8 10 12)) 3. Write a program in LISP to print “LISP EXPERT”? Example: (write-line "LISP EXPERT") 4. What is the programming structure for LISP? Programming structure is composed of symbolic expressions(s-expressions). s-expression: It consists of three valid objects: Atom: string of contiguous characters String: A group of characters Lists: sequence of atoms 5. What are the common objectives of LISP? The common objectives of LISP are: Provides smooth growth path and easy transitions Provides layered approach Provides language for powerful programming environment Provides the efficient delivery of applications 6. What is setq in LISP? setq: It is used to set variables in LISP. 7. What is slot? slot: It is a variable which is used to store data or field. 8. What are the most commonly used slot options? Most Commonly used slot options are: initarg symbol initform expression accessor function-name 9. What are the two pre-defined packages used in LISP? Pre-defined packages LISP are: Common Lisp User( Contains packages with editing and debugging tools) Common Lisp(Contains functions and variables) 10. What is the importance of the hash table in LISP? Hash table in LISP is a collection of key and value pairs which is based on the hash code of the key. It is used to access the items in the collection. 11. How many types of variables are available in LISP? Two types of variables are available in lisp: lexical variable special variable 12. How many data types are categorized in LISP? There are two types of data types: Data Structure: Strings, list, bit-vectors etc Scalar Types: Characters, Number types etc 13. What is Lisp Constants? In Lisp, Constants are variables and the value of constant can’t be changed. Construct constants are declared by using defconstant in Lisp. 14. What is the use of predicate in LISP? It is a type of function that is used to test the arguments for specific conditions. Conditions are: Returns nil value if condition is false Retrieves some non-nil value if condition is true 15. What are the three defun macro arguments to define a function in LISP? The arguments are: Name of the function Body of the function Parameters of the function 16. What are the features of LISP? The features of LISP are: LISP is a machine-independent language LISP uses iterative design methodology LISP provides high level debugging. LISP provides complete I/O library LISP is a expression-based language 17. What are the types of applications built in LISP? The types of applications built in LISP are: G2 Emacs AutoCad Yahoo Store Igor Engraver 18. What is the file extensions used in LISP? lisp or .lsp is the file extensions used in LISP. 19. What is REPL? REPL: stands for read-evaluate-print loop. It is interpreter that is used to check the source code in a repeated loop. 20. Which notation LISP follows? LISP follows the prefix notation. 21. What is typep predicate in LISP? typep predicate: It is used for finding whether an object belongs to a specific type. 22. What is macro in LISP? macro: It is a function that takes an s-expression as arguments and returns a LISP form, which is then evaluated 23. Which construct is used to declare the global variables in LISP? defvar construct is used to declare the global variables in LISP. 24. What is the use of block function in LISP? block function: It is used to create a named block with a body composed of zero or more statements. 25. What is the use of defun macro in LISP? defun macro: It is used for defining functions. Defun macro needs three arguments to define a function: Name of the function Parameters of the function Body of the function 26. What are the number types supported by LISP? The number types supported by LISP are: Ratios Integers Complex numbers Floating-point numbers 27. Which function is used to create a sequence in LISP? make-sequence function is used to create a sequence in LISP. 28. What is the syntax used to create a sequence in LISP? The syntax used to create a sequence is: make-sequence sqtype sqsize &key :initial-element LISP Questions and Answers Pdf Download Read the full article

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Toward artificial intelligence that learns to write code

Learning to code involves recognizing how to structure a program, and how to fill in every last detail correctly. No wonder it can be so frustrating.

A new program-writing AI, SketchAdapt, offers a way out. Trained on tens of thousands of program examples, SketchAdapt learns how to compose short, high-level programs, while letting a second set of algorithms find the right sub-programs to fill in the details. Unlike similar approaches for automated program-writing, SketchAdapt knows when to switch from statistical pattern-matching to a less efficient, but more versatile, symbolic reasoning mode to fill in the gaps.

“Neural nets are pretty good at getting the structure right, but not the details,” says Armando Solar-Lezama, a professor at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL). “By dividing up the labor — letting the neural nets handle the high-level structure, and using a search strategy to fill in the blanks — we can write efficient programs that give the right answer.”

SketchAdapt is a collaboration between Solar-Lezama and Josh Tenenbaum, a professor at CSAIL and MIT’s Center for Brains, Minds and Machines. The work will be presented at the International Conference on Machine Learning June 10-15.

Program synthesis, or teaching computers to code, has long been a goal of AI researchers. A computer that can program itself is more likely to learn language faster, converse fluently, and even model human cognition. All of this drew Solar-Lezama to the field as a graduate student, where he laid the foundation for SketchAdapt.

Solar-Lezama’s early work, Sketch, is based on the idea that a program’s low-level details could be found mechanically if a high-level structure is provided. Among other applications, Sketch inspired spinoffs to automatically grade programming homework and convert hand-drawn diagrams into code. Later, as neural networks grew in popularity, students from Tenenbaum’s computational cognitive science lab suggested a collaboration, out of which SketchAdapt formed.

Rather than rely on experts to define program structure, SketchAdapt figures it out using deep learning. The researchers also added a twist: When the neural networks are unsure of what code to place where, SketchAdapt is programmed to leave the spot blank for search algorithms to fill.

“The system decides for itself what it knows and doesn’t know,” says the study’s lead author, Maxwell Nye, a graduate student in MIT’s Department of Brain and Cognitive Sciences.  “When it gets stuck, and has no familiar patterns to draw on, it leaves placeholders in the code. It then uses a guess-and-check strategy to fill the holes.”

The researchers compared SketchAdapt’s performance to programs modeled after Microsoft’s proprietary RobustFill and DeepCoder software, successors to Excel’s FlashFill feature, which analyzes adjacent cells to offer suggestions as you type — learning to transform a column of names into a column of corresponding email addresses, for example. RobustFill uses deep learning to write high-level programs from examples, while DeepCoder specializes in finding and filling in low-level details.

The researchers found that SketchAdapt outperformed their reimplemented versions of RobustFill and DeepCoder at their respective specialized tasks. SketchAdapt outperformed the RobustFill-like program at string transformations; for example, writing a program to abbreviate Social Security numbers as three digits, and first names by their first letter. SketchAdapt also did better than the DeepCoder-like program at writing programs to transform a list of numbers. Trained only on examples of three-line list-processing programs, SketchAdapt was better able to transfer its knowledge to a new scenario and write correct four-line programs.

In yet another task, SketchAdapt outperformed both programs at converting math problems from English to code, and calculating the answer.

Key to its success is the ability to switch from neural pattern-matching to a rules-based symbolic search, says Rishabh Singh, a former graduate student of Solar-Lezama’s, now a researcher at Google Brain. “SketchAdapt learns how much pattern recognition is needed to write familiar parts of the program, and how much symbolic reasoning is needed to fill in details which may involve new or complicated concepts.”

SketchAdapt is limited to writing very short programs. Anything more requires too much computation. Nonetheless, it’s intended more to complement programmers rather than replace them, the researchers say. “Our focus is on giving programming tools to people who want them,” says Nye. “They can tell the computer what they want to do, and the computer can write the program.”

Programming, after all, has always evolved. When Fortran was introduced in the 1950s, it was meant to replace human programmers. “Its full name was Fortran Automatic Coding System, and its goal was to write programs as well as humans, but without the errors,” says Solar-Lezama. “What it really did was automate much of what programmers did before Fortran. It changed the nature of programming.”

The study’s other co-author is Luke Hewitt. Funding was provided by the U.S. Air Force Office of Scientific Research, MIT-IBM Watson AI Lab and U.S. National Science Foundation.

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Source/Repost=> http://technewsdestination.com/toward-artificial-intelligence-that-learns-to-write-code/ ** Alex Hammer | Founder and CEO at Ecommerce ROI ** http://technewsdestination.com

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History of Computer Software and Programming

Being able to program a computer or create software is akin to knowing another language. This is not something that comes easily to everyone, and in the very beginning, it was nearly impossible for most to master. There was no conception of computing in the worlds of our ancient ancestors. Their conceptions of computing were limited to math skills, and the technology used was an abacus. Simple devices that used pegs and cams were later used to control movements, but this is a far cry from what the world is used to today. Real computer programming can be traced back to the 1880’s and the recording of data that was then read by a machine. This was accomplished using simple punched cards, and became the foundation of the data processing industry. In 1896 the Tabulating Machine Company was founded which would later turn into a small company we now know as IBM. Before the 1950’s it was likely to find that each machine was using a different set of instructions, even when they served the same purpose. This was due to each program being painstakingly created individually, which led to slight variations from one machine to the next. You could liken those early days to trying to use your beard trimmer without any real beard style in mind. You hit the power button, and get to work, keeping your fingers crossed that your beard will turn out all right. Programmers were working willy-nilly, making up stuff as they went along and hoping all went well. The only difference is that when you mess up with your beard trimmer you just shave off the whole thing, and wait for it to grow back. This haphazard method of programming led to expensive machinery not having available programming to make it run right. 1954 saw the introduction of FORTRAN, and the computer programming world changed for forever. This had an actual functional implementation, rather than just design on paper. Punch cards were still used, but as computer hardware decreased in price, programs could be created by typing them directly onto the computer. This led to editors that could make changes and corrections, and eventually moveable storage in the form of discs that let one program be installed on a number of units at a time. We have certainly come a long way, although it is not unlikely that 50 years from now, our grandchildren will be looking at our programming as if it is archaic. The technological age is in full swing, with changes and improvements happening every second. The next time you are frustrated about trying to read code, just remember the punch cards. At least today, programming is like trimming your beard, where if you mess up you just hit delete and start again.

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