#Python control flow

[trendingnow3-blog]

Day-3: Mastering Control Flow and Logical Operators in Python

Python Boot Camp - 2023

Python is a versatile and widely used programming language known for its simplicity and readability. One of the fundamental aspects of programming is controlling the flow of execution based on certain conditions. Python provides various control flow statements and logical operators that allow programmers to make decisions and perform actions accordingly. 1. Introduction to Control Flow in…

[apollosgiftofprophecy]

‘Only glimpses. Every time you free an Oracle, I get a few moments of clarity. Then the fog settles again.’ She pressed her fingertips against her forehead. ‘It’s like Python is inside my brain, toying with me. Sometimes I think …’ She faltered, as if the idea were too disturbing to say aloud. ‘Just tell me you’re going to take him down. Soon .’ I nodded, not trusting myself to speak. It was one thing for Python to squat in my sacred caverns of Delphi. It was another for him to invade the mind of my chosen Pythia, the priestess of my prophecies. I had accepted Rachel Elizabeth Dare as my most important Oracle. I was responsible for her. If I failed to defeat Python, he would continue to grow stronger. He would eventually control the very flow of the future. And since Rachel was inextricably linked to the Delphic Oracle… No. I couldn’t bear to think what that might mean for her. - The Tower of Nero, Chapter 12

REMINDER THAT PYTHON WAS TORMENTING RACHEL DURING TOA 😧😧😧😧😧😨😨😨😨😨

[lost-coder]

Alright fam yesterday i procrastinated hard and that's why the guilt got me in the last

Today we being summer vacation grind 💪

To do for day 1:-

1. Control flow and functions in python

2. Revision of html css before i master JS

3. Help out a friend for choosing a right ML model for his project (basically have to research a lil)

4. Hopefully get back into leetcode 🤝

[haggishlyhagging]

The goddesses of ancient Greece also displayed the characteristics of flesh and blood menstruants: Medusa, her hair writhing with vaginal snakes, had an ability that was also imputed to menstruants in some cultures: she turned living things to stone with her gaze. She is the menstruant naked, out of control, without protective cosmetikos. Gaia, the earth, was a chasm guarded by a great python. Long-tressed Demeter was also the earth, and her daughter Kore, or Persephone, the maiden, was portrayed holding the menstrual pomegranate. Kore disappeared and her mother went to look for her—a common menarchal drama for some peoples. Hera was "the bride," dressed austerely in long gowns. Hecate was the dark moon, portrayed as an old woman. At Sumer, alabaster statues of the large-eyed moon goddess Ningal were dressed, fed, and washed; even the urbane goddess Inanna was portrayed in one statuette holding a scratching stick, adorned with the cosmetikos of a temple courtesan.

Frequently ancient figurines portray two women together, sometimes melded like Siamese twins, side by side. Often these "dolls" wear skirts, eye and lip makeup, and hoop earrings. Frequently they are stained red. Similar dolls are still made for girls to play with in North Africa, India, and parts of the Middle East. Some of the modern dolls are of a man and woman side by side. My guess is that the paired icons were originally two sisters, representing synchronous flow. The dolls, I was told vehemently by the import shop clerk, have nothing to do with lesbianism, and I'm certain that in any current patriarchal religious system, that is true. But in more female-centered older societies, the Andean, for example, and in many parts of Western society, homosexual relations have a rightful, appropriate, and even sacred place. It thus seems significant that in the south of India, among goddess-worshiping Tamils of the Untouchable caste, a name for lesbian lover is "sister-sister."

Many goddess mythologies feature two creation sisters. Pele, the Hawaiian volcanic fire goddess who creates the earth's surface, has a sister who is "Sea Mist." Among the Pueblos, sister goddesses Naotsete and Uretsete create objects under a blanket they hold between them. Sometimes one sister dwells in the world below, "in the shade," the place of the dark moon, while the other rules above, as with Egyptian Isis and her underworld sister Nepthys. The oldest known menstrual narrative of the meetings of two such sisters is the Sumerian poem, "The Descent of Inanna to the Underworld," whose metaformic meanings I will decipher later. A Caribbean proverb summarizes an ancient attitude of female "flow": "When a woman loves another woman, it is the blood of the Mother speaking."

-Judy Grahn, Blood, Bread, and Roses: How Menstruation Created the World

[internet-siren]

Hacktivism: Digital Rebellion for a New Age 🌐💥

In an era where our lives are intertwined with the digital landscape, the concept of hacktivism has become more than just a buzzword. It’s the fusion of hacking and activism—where people use their coding and cyber skills to disrupt power structures, challenge injustice, and amplify voices that often go unheard. It's a rebellion born from the belief that access to information, privacy, and freedom are rights, not privileges. But how did this digital resistance movement come to be, and how can you get involved? Let’s dive into it. 💻⚡️

What Exactly Is Hacktivism? 🤖✨

At its core, hacktivism is activism with a digital twist. It’s about using technology and hacking tools to advance social, political, and environmental causes. The most common methods include:

DDoS Attacks (Distributed Denial of Service): Overloading a target’s website with too much traffic, essentially crashing it, to temporarily shut down an online service.

Website Defacement: Replacing a website’s homepage with a political message, often exposing corruption or unethical practices.

Data Leaks: Exposing hidden documents or sensitive information that reveal corporate or governmental wrongdoing.

Bypassing Censorship: Circumventing firewalls or government restrictions to make sure information reaches the people it needs to.

The idea is simple: when a government or corporation controls the narrative or hides the truth, hacktivists take it into their own hands to expose it. 🌍💡

Why Is Hacktivism Important? 🔥

In a world dominated by corporations and powerful governments, hacktivism represents a form of resistance that’s accessible. It’s about leveling the playing field, giving people—especially those who lack resources—an avenue to protest, to expose corruption, and to disrupt systems that perpetuate inequality. The digital world is where much of our lives now happen, and hacktivism uses the very systems that oppress us to fight back.

Think about WikiLeaks leaking documents that exposed global surveillance and the activities of intelligence agencies. Or how Anonymous has played a pivotal role in advocating for free speech, standing up against internet censorship, and exposing corrupt governments and corporations. These are the digital warriors fighting for a cause, using nothing but code and their knowledge of the web.

Hacktivism is a direct response to modern issues like surveillance, censorship, and misinformation. It's a way to shift power back to the people, to give voice to the voiceless, and to challenge oppressive systems that don’t always play by the rules.

The Ethical Dilemma 🤔💭

Let’s be real: hacktivism doesn’t come without its ethical dilemmas. While the intentions are often noble, the methods used—hacking into private systems, defacing websites, leaking sensitive info—can sometimes lead to unintended consequences. The line between activism and cybercrime is thin, and depending on where you live, you might face serious legal repercussions for participating in hacktivist activities.

It’s important to consider the ethics behind the actions. Are you defending the free flow of information? Or are you inadvertently causing harm to innocent bystanders? Are the people you’re exposing truly deserving of scrutiny, or are you just participating in chaos for the sake of it?

So if you’re thinking of getting involved, it’s crucial to ask yourself: What am I fighting for? And is the harm done justified by the greater good?

How to Get Started 💻💡

So, you’re interested in getting involved? Here’s a starting point to help you use your tech skills for good:

Learn the Basics of Hacking 🔐: Before diving into the world of hacktivism, you'll need to understand the tools of the trade. Start with the basics: programming languages like Python, HTML, and JavaScript are good foundational skills. Learn how networks work and how to exploit vulnerabilities in websites and servers. There are plenty of free online resources like Codecademy, Hack This Site, and OverTheWire to help you get started.

Understand the Ethical Implications ⚖️: Hacktivism is, above all, about fighting for justice and transparency. But it’s crucial to think through your actions. What’s the bigger picture? What are you trying to achieve? Keep up with the latest issues surrounding privacy, data rights, and digital freedom. Some online groups like The Electronic Frontier Foundation (EFF) provide great resources on the ethics of hacking and digital activism.

Join Communities 🕸️: Being part of a like-minded group can give you support and insight. Online communities, like those on Reddit, Discord, or specific forums like 4chan (if you're cautious of the chaos), can help you learn more about hacktivism. Anonymous has also had an iconic role in digital activism and can be a place where people learn to organize for change.

Stay Informed 🌐: To be effective as a hacktivist, you need to be in the know. Follow independent news sources, activist blogs, and websites that report on global surveillance, corporate corruption, and governmental abuse of power. Hacktivism often reacts to injustices that would otherwise go unnoticed—being informed helps you take action when necessary.

Respect the Digital Space 🌱: While hacktivism can be used to disrupt, it’s important to respect the privacy and safety of ordinary people. Try to avoid unnecessary damage to private citizens, and focus on the systems that need disrupting. The internet is a tool that should be used to liberate, not to destroy without purpose.

Never Forget the Human Side ❤️: As with all activism, the heart of hacktivism is about making a difference in real people’s lives. Whether it's freeing information that has been hidden, protecting human rights, or challenging unjust power structures—always remember that at the end of the code, there are humans behind the cause.

Final Thoughts 💬

Hacktivism is a powerful, transformative form of resistance. It’s not always about flashy headlines or viral attacks—often, it’s the quiet work of exposing truths and giving people a voice in a world that tries to keep them silent. It’s messy, it’s complex, and it’s not for everyone. But if you’re interested in hacking for a purpose greater than yourself, learning the craft with the intention to fight for a better, more just world is something that can actually make a difference.

Remember: With great code comes great responsibility. ✊🌐💻

[compneuropapers]

Interesting Papers for Week 42, 2024

Fear learning induces synaptic potentiation between engram neurons in the rat lateral amygdala. Abatis, M., Perin, R., Niu, R., van den Burg, E., Hegoburu, C., Kim, R., … Stoop, R. (2024). Nature Neuroscience, 27(7), 1309–1317.

Jointly efficient encoding and decoding in neural populations. Blanco Malerba, S., Micheli, A., Woodford, M., & Azeredo da Silveira, R. (2024). PLOS Computational Biology, 20(7), e1012240.

Flexible multitask computation in recurrent networks utilizes shared dynamical motifs. Driscoll, L. N., Shenoy, K., & Sussillo, D. (2024). Nature Neuroscience, 27(7), 1349–1363.

Kinetic features dictate sensorimotor alignment in the superior colliculus. González-Rueda, A., Jensen, K., Noormandipour, M., de Malmazet, D., Wilson, J., Ciabatti, E., … Tripodi, M. (2024). Nature, 631(8020), 378–385.

A recurrent network model of planning explains hippocampal replay and human behavior. Jensen, K. T., Hennequin, G., & Mattar, M. G. (2024). Nature Neuroscience, 27(7), 1340–1348.

Adaptive coding of reward in schizophrenia, its change over time and relationship to apathy. Kaliuzhna, M., Carruzzo, F., Kuenzi, N., Tobler, P. N., Kirschner, M., Geffen, T., … Kaiser, S. (2024). Brain, 147(7), 2459–2470.

Human navigation strategies and their errors result from dynamic interactions of spatial uncertainties. Kessler, F., Frankenstein, J., & Rothkopf, C. A. (2024). Nature Communications, 15, 5677.

Local field potential sharp waves with diversified impact on cortical neuronal encoding of haptic input. Kristensen, S. S., & Jörntell, H. (2024). Scientific Reports, 14, 15243.

Factorized visual representations in the primate visual system and deep neural networks. Lindsey, J. W., & Issa, E. B. (2024). eLife, 13, e91685.3.

A mathematical theory of relational generalization in transitive inference. Lippl, S., Kay, K., Jensen, G., Ferrera, V. P., & Abbott, L. F. (2024). Proceedings of the National Academy of Sciences, 121(28), e2314511121.

Precise tactile localization on the human fingernail. Longo, M. R. (2024). Proceedings of the Royal Society B: Biological Sciences, 291(2026).

Trying Harder: How Cognitive Effort Sculpts Neural Representations during Working Memory. Master, S. L., Li, S., & Curtis, C. E. (2024). Journal of Neuroscience, 44(28), e0060242024.

Context-invariant beliefs are supported by dynamic reconfiguration of single unit functional connectivity in prefrontal cortex of male macaques. Noel, J.-P., Balzani, E., Savin, C., & Angelaki, D. E. (2024). Nature Communications, 15, 5738.

Reward prediction error neurons implement an efficient code for reward. Schütt, H. H., Kim, D., & Ma, W. J. (2024). Nature Neuroscience, 27(7), 1333–1339.

Joint modeling of choices and reaction times based on Bayesian contextual behavioral control. Schwöbel, S., Marković, D., Smolka, M. N., & Kiebel, S. (2024). PLOS Computational Biology, 20(7), e1012228.

Selective recruitment of the cerebellum evidenced by task-dependent gating of inputs. Shahshahani, L., King, M., Nettekoven, C., Ivry, R. B., & Diedrichsen, J. (2024). eLife, 13, e96386.3.

A simple optical flow model explains why certain object viewpoints are special. Stewart, E. E. M., Fleming, R. W., & Schütz, A. C. (2024). Proceedings of the Royal Society B: Biological Sciences, 291(2026).

Stimulus type shapes the topology of cellular functional networks in mouse visual cortex. Tang, D., Zylberberg, J., Jia, X., & Choi, H. (2024). Nature Communications, 15, 5753.

Control over self and others’ face: exploitation and exploration. Wen, W., Mei, J., Aktas, H., Chang, A. Y.-C., Suzuishi, Y., & Kasahara, S. (2024). Scientific Reports, 14, 15473.

BCI Toolbox: An open-source python package for the Bayesian causal inference model. Zhu, H., Beierholm, U., & Shams, L. (2024). PLOS Computational Biology, 20(7), e1011791.

[digitaldetoxworld]

Python Programming Language: A Comprehensive Guide

 Python is one of the maximum widely used and hastily growing programming languages within the world. Known for its simplicity, versatility, and great ecosystem, Python has become the cross-to desire for beginners, professionals, and organizations across industries.

What is Python used for

🐍 What is Python?

Python is a excessive-stage, interpreted, fashionable-purpose programming language.  The language emphasizes clarity, concise syntax, and code simplicity, making it an excellent device for the whole lot from web development to synthetic intelligence.

Its syntax is designed to be readable and easy, regularly described as being near the English language. This ease of information has led Python to be adopted no longer simplest through programmers but also by way of scientists, mathematicians, and analysts who may not have a formal heritage in software engineering.

📜 Brief History of Python

Late Nineteen Eighties: Guido van Rossum starts work on Python as a hobby task.

1991: Python zero.9.0 is released, presenting classes, functions, and exception managing.

2000: Python 2.Zero is launched, introducing capabilities like list comprehensions and rubbish collection.

2008: Python 3.Zero is launched with considerable upgrades but breaks backward compatibility.

2024: Python three.12 is the modern day strong model, enhancing performance and typing support.

⭐ Key Features of Python

Easy to Learn and Use:

Python's syntax is simple and similar to English, making it a high-quality first programming language.

Interpreted Language:

Python isn't always compiled into device code; it's far done line by using line the usage of an interpreter, which makes debugging less complicated.

Cross-Platform:

Python code runs on Windows, macOS, Linux, and even cell devices and embedded structures.

Dynamic Typing:

Variables don’t require explicit type declarations; types are decided at runtime.

Object-Oriented and Functional:

Python helps each item-orientated programming (OOP) and practical programming paradigms.

Extensive Standard Library:

Python includes a rich set of built-in modules for string operations, report I/O, databases, networking, and more.

Huge Ecosystem of Libraries:

From data technological know-how to net development, Python's atmosphere consists of thousands of programs like NumPy, pandas, TensorFlow, Flask, Django, and many greater.

📌 Basic Python Syntax

Here's an instance of a easy Python program:

python

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def greet(call):

    print(f"Hello, call!")

greet("Alice")

Output:

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Hello, Alice!

Key Syntax Elements:

Indentation is used to define blocks (no curly braces  like in different languages).

Variables are declared via task: x = 5

Comments use #:

# This is a remark

Print Function:

print("Hello")

📊 Python Data Types

Python has several built-in data kinds:

Numeric: int, go with the flow, complicated

Text: str

Boolean: bool (True, False)

Sequence: listing, tuple, range

Mapping: dict

Set Types: set, frozenset

Example:

python

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age = 25             # int

name = "John"        # str

top = 5.Nine         # drift

is_student = True    # bool

colors = ["red", "green", "blue"]  # listing

🔁 Control Structures

Conditional Statements:

python

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if age > 18:

    print("Adult")

elif age == 18:

    print("Just became an person")

else:

    print("Minor")

Loops:

python

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for color in hues:

    print(coloration)

while age < 30:

    age += 1

🔧 Functions and Modules

Defining a Function:

python

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def upload(a, b):

    return a + b

Importing a Module:

python

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import math

print(math.Sqrt(sixteen))  # Output: four.0

🗂️ Object-Oriented Programming (OOP)

Python supports OOP functions such as lessons, inheritance, and encapsulation.

Python

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elegance Animal:

    def __init__(self, call):

        self.Call = name

def communicate(self):

        print(f"self.Call makes a valid")

dog = Animal("Dog")

dog.Speak()  # Output: Dog makes a legitimate

🧠 Applications of Python

Python is used in nearly each area of era:

1. Web Development

Frameworks like Django, Flask, and FastAPI make Python fantastic for building scalable web programs.

2. Data Science & Analytics

Libraries like pandas, NumPy, and Matplotlib permit for data manipulation, evaluation, and visualization.

Three. Machine Learning & AI

Python is the dominant language for AI, way to TensorFlow, PyTorch, scikit-research, and Keras.

4. Automation & Scripting

Python is extensively used for automating tasks like file managing, device tracking, and data scraping.

Five. Game Development

Frameworks like Pygame allow builders to build simple 2D games.

6. Desktop Applications

With libraries like Tkinter and PyQt, Python may be used to create cross-platform computing device apps.

7. Cybersecurity

Python is often used to write security equipment, penetration trying out scripts, and make the most development.

📚 Popular Python Libraries

NumPy: Numerical computing

pandas: Data analysis

Matplotlib / Seaborn: Visualization

scikit-study: Machine mastering

BeautifulSoup / Scrapy: Web scraping

Flask / Django: Web frameworks

OpenCV: Image processing

PyTorch / TensorFlow: Deep mastering

SQLAlchemy: Database ORM

💻 Python Tools and IDEs

Popular environments and tools for writing Python code encompass:

PyCharm: Full-featured Python IDE.

VS Code: Lightweight and extensible editor.

Jupyter Notebook: Interactive environment for statistics technological know-how and studies.

IDLE: Python’s default editor.

🔐 Strengths of Python

Easy to study and write

Large community and wealthy documentation

Extensive 0.33-birthday celebration libraries

Strong support for clinical computing and AI

Cross-platform compatibility

⚠️ Limitations of Python

Slower than compiled languages like C/C++

Not perfect for mobile app improvement

High memory usage in massive-scale packages

GIL (Global Interpreter Lock) restricts genuine multithreading in CPython

🧭 Learning Path for Python Beginners

Learn variables, facts types, and control glide.

Practice features and loops.

Understand modules and report coping with.

Explore OOP concepts.

Work on small initiatives (e.G., calculator, to-do app).

Dive into unique areas like statistics technological know-how, automation, or web development.

[piratesexmachine420]

Expanding and cleaning up on a conversion I had with @suntreehq in the comments of this post:

Ruby is fine, I'm just being dramatic. It's not nearly as incomprehensible as I find JavaScript, Perl, or Python. I think it makes some clumsy missteps, and it wouldn't be my first (or even fifth) choice if I were starting a new project, but insofar as I need to use it in my Software Engineering class I can adapt.

There are even things I like about it -- it's just that all of them are better implemented in the languages Ruby borrows them from. I don't want Lisp with Eiffel's semantics, I want Lisp with Lisp's semantics. I don't want Ada with Perl's type system, I want Ada with Ada's type system.

One of these missteps to me is how it (apparently) refuses to adopt popular convention when it comes to the names and purposes of its keywords.

Take yield. In every language I've ever used, yield has been used for one purpose: suspending the current execution frame and returning to something else. In POSIX C, this is done with pthread_yield(), which signals the thread implementation that the current thread isn't doing anything and something else should be scheduled instead. In languages with coroutines, like unstable Rust, the yield keyword is used to pause execution of the current coroutine and optionally return a value (e.g. yield 7; or yield foo.bar;), execution can then be resumed by calling x.resume(), where x is some coroutine. In languages with generators, like Python, the behavior is very similar.

In Ruby, this is backwards. It doesn't behave like a return, it behaves like a call. It's literally just syntax sugar for using the call method of blocks/procs/lambdas. We're not temporarily returning to another execution frame, we're entering a new one! Those are very similar actions, but they're not the same. Why not call it "run" or "enter" or "call" or something else less likely to confuse?

Another annoyance comes in the form of the throw and catch keywords. These are almost universally (in my experience) associated with exception handling, as popularized by Java. Not so in Ruby! For some unfathomable reason, throw is used to mean the same thing as Rust or C2Y's break-label -- i.e. to quickly get out of tightly nested control flow when no more work needs to be done. Ruby does have keywords that behave identically to e.g. Java or C++'s throw and catch, but they're called raise and rescue, respectively.

That's not to say raise and rescue aren't precedented (e.g. Eiffel and Python) but they're less common, and it doesn't change the fact that it's goofy to have both them and throw/catch with such similar but different purposes. It's just going to trip people up! Matsumoto could have picked any keywords he could have possibly wanted, and yet he picked the ones (in my opinion) most likely to confuse.

I have plenty more and deeper grievances with Ruby too (sigils, throws being able to unwind the call stack, object member variables being determined at runtime, OOP in general being IMO a clumsy paradigm, the confusing and non-orthogonal ways it handles object references and allocation, the attr_ pseudo-methods feeling hacky, initialization implying declaration, the existence of "instance_variable_get" totally undermining scope visibility, etc., etc.) but these are I think particularly glaring (if inconsequential).

[circumlocutive]

Fuckin

Semi masturbatory caffeinated ramble reflecting on skills acquired in my PhD

Thinking about how broad and interdisciplinary my project is and the kinda of things I have to be familiar with or an expert in. I get down on myself sometimes for progress but looking at all the shit I've learned.... Without formal classes or a senior grad student or (for the majority of it) no post doc. And a PI who can't help bc she's really a business lady at this point not a professor. Maybe shouldn't be hard on myself?

Like. I did completely different projects in undergrad (biotech/proteins/genetics/regenerative medicine, advanced manufacturing/composite fabrication/CNC/welding/process statistics, translational neuropharma studies of addiction/rodent handling and operant training and behavioral video analysis/neural tissue slicing n staining/hand making neuroelectrodes for implantation, design and fabrication of impedance spectroscopy based electrochemical sensors/automation of sensor fab and use w a micro fluidic flow cell)

Like. Since I've started I've learned:

- how to do multi-step air-free water-free chemical synthesis (with glove box and schlenk line) and purification (extraction, filtration, chromatography) of light sensitive amphiphiles (extra tricky)

- how to get and read NMR even for massive fucking molecules and interpret weird peaks (I can casually see if I've got water or any of my common solvents contaminating the spectra without referencing a table at this point)

-how to fucking take down and set up and fix everything in our chemical synthesis lab (because we moved and all our shit was abused for years) and all the intricate non-unified and sometimes conflicting rules for hazardous chemical storage

- the theory/math and how to actually use the equipment to do optoelectronic/photophysical characterization (e.g. using the UV vis spectrometer and writing python to convert the data files into readable tables and figures, learning theory so I can develop equations to relate photon flux to change in absorbance of an actinometer ((light sensitive molecule with a consistent quantum yield)) then obtain quantum yield of charge transfer in a different molecule but same setup, how to use the fluorimeter and get intensity and quantum yield, how to set up lasers and LEDs, what cuvettes to use, how to get fluorescence lifetimes or take two photon excitation data, how to spin coat wafers n do thin film transistor studies), more theory about how photo induced electron transfer voltage sensors work and the importance of angle of insertion on sensitivity (and how to measure it with polarization microscopy) other voltage sensing dye mechanisms like FRET or electrochromic dyes and why to use intensity vs lifetime vs whatever when interpreting signal readouts and the extrinsic and intrinsic factors affecting that interpretation.

- how to do vesicle fabrication and immobilize for imaging, typical membrane compositions and dynamics (e.g. phase orders depending on cholesterol concentrations, significance of packing parameters to membrane organization), measurimg particle radius with DLS, controlling inner cargo and gradients over a membrane by manipulating the bulk solution, the interplay between non radiative decay and the stiffness of the membrane microenvironment around a fluorophore

- the math and bio behind electrophysiology/advanced neuroscience pertaining to modeling and calculating and quantifying signalling/equivalent RC circuit analysis, what spatiotemporal requirements there are for studying this shit <- though this was through a class, not self taught

- I already had cell culture experience and did some adherent and suspended cultures, some live dead imaging assays, etc, but I've learned new facets like how to go about picking electrically exciteable lines (ease of growing? What media requirements? time to multiply and differentiate? What agent to differentiate? How to induce firing without a patch clamp?) and troubleshooting uptake/optimizing staining and imaging parameters (what media or buffer for growth vs staining vs washing vs imaging? Can it have serum? Can it have calcium and magnesium? What salts, how is it buffered, whats the osmolarity I can get away with? What concentrations work for what # of cells? What dilution factors? Do I need to admix equivolumes of dye solution and cell solution? Do I prep the organic solvent+ dye + aqueous solution with sonication or filtration or vortexing before mixing? Is DMSO or ethanol or DMF a better organic for dispersal or biocompatibility? What's the Ideal incubation time for uptake and viability? How long before I absolutely need to image or the dye gets internalized? If it's retained long, how many days could I image for?) for my tricky aggregation-prone non-diffusive thermodynamically-partitioned dye. Also stuff like what commercially available live imaging dyes can I compare to or complement my visualizations with or use for colocalization studies (other lipophilic membrane dyes that insert in the bilayer with preferences for diff order regions? What about comparison with surface adhering dyes like WGA-iFluor that bind surface sugars, to show that our dye can laterally diffuse to areas blocked by cell-cell contacts?), what fluorescence specific parameters do I need to characterize (photo toxicity/photo bleaching time?)

And then there's other shit I've picked up like. Idk. How to make orders in the particular institute I'm in. Better citation managers and ways to search literature. Recognizing what groups and journals and conferences are major players in the fields I'm touching. Getting comfy presenting my shit.

I need to learn a little more about microscopy (especially FLIM and how to build a polarizer module into the scope we have for polarization microscopy), and a little more about the state of the art for voltage dyes and live-imaging dye characterization but man. I think I'm getting somewhere. I'm starting to know enough to see the end of this project and pick my directions moving forward and argue when my PI is wrong

Gahhhhhh

[newcodesociety]

ByteByteGo | Newsletter/Blog

From the newsletter:

Imperative Programming Imperative programming describes a sequence of steps that change the program’s state. Languages like C, C++, Java, Python (to an extent), and many others support imperative programming styles.

Declarative Programming Declarative programming emphasizes expressing logic and functionalities without describing the control flow explicitly. Functional programming is a popular form of declarative programming.

Object-Oriented Programming (OOP) Object-oriented programming (OOP) revolves around the concept of objects, which encapsulate data (attributes) and behavior (methods or functions). Common object-oriented programming languages include Java, C++, Python, Ruby, and C#.

Aspect-Oriented Programming (AOP) Aspect-oriented programming (AOP) aims to modularize concerns that cut across multiple parts of a software system. AspectJ is one of the most well-known AOP frameworks that extends Java with AOP capabilities.

Functional Programming Functional Programming (FP) treats computation as the evaluation of mathematical functions and emphasizes the use of immutable data and declarative expressions. Languages like Haskell, Lisp, Erlang, and some features in languages like JavaScript, Python, and Scala support functional programming paradigms.

Reactive Programming Reactive Programming deals with asynchronous data streams and the propagation of changes. Event-driven applications, and streaming data processing applications benefit from reactive programming.

Generic Programming Generic Programming aims at creating reusable, flexible, and type-independent code by allowing algorithms and data structures to be written without specifying the types they will operate on. Generic programming is extensively used in libraries and frameworks to create data structures like lists, stacks, queues, and algorithms like sorting, searching.

Concurrent Programming Concurrent Programming deals with the execution of multiple tasks or processes simultaneously, improving performance and resource utilization. Concurrent programming is utilized in various applications, including multi-threaded servers, parallel processing, concurrent web servers, and high-performance computing.

[learn-tilde-ath]

Two questions: 1: did you actually make ~ATH, and 2: what was that Sburb text-game that you mentioned on an ask on another blog

While I was back in highschool (iirc?) I made a thing which I titled “drocta ~ATH”, which is a programming language with the design goals of:

1: being actually possible to implement, (and therefore, for example, not having things be tied to the lifespans of external things)

2: being Turing complete, and accept user input and produce output for the user to read, such that in principle one could write useful programs in it (though it is not meant to be practical to do so).

3: matching how ~ATH is depicted in the comic, as closely as I can, with as little as possible that I don’t have some justification for based on what is shown in the comic (plus the navigation page for the comic, which depicts a “SPLIT” command). For example, I avoid assuming that the language has any built-in concept of numbers, because the comic doesn’t depict any, and I don’t need to assume it does, provided I make some reasonable assumptions about what BIFURCATE (and SPLIT) do, and also assume that the BIFURCATE command can also be done in reverse.

However, I try to always make a distinction between “drocta ~ATH”, which is a real thing I made, and “~ATH”, which is a fictional programming language in which it is possible to write programs that e.g. wait until the author’s death and the run some code, or implement some sort of curse that involves the circumstantial simultaneous death of two universes.

In addition, please be aware that the code quality of my interpreter for drocta ~ATH, is very bad! It does not use a proper parser or the like, and, iirc (it has probably been around a decade since I made any serious edits to the code, so I might recall wrong), it uses the actual line numbers of the file for the control flow? (Also, iirc, the code was written for python 2.7 rather than for python 3.) At some point I started a rewrite of the interpreter (keeping the language the same, except possibly fixing bugs), but did not get very far.

If, impossibly, I got some extra time I wouldn’t otherwise have that somehow could only be used for the task of working on drocta ~ATH related stuff, I would be happy to complete that rewrite, and do it properly, but as time has gone on, it seems less likely that I will complete the rewrite.

I am pleased that all these years later, I still get the occasional message asking about drocta ~ATH, and remain happy to answer any questions about it! I enjoy that people still think the idea is interesting.

(If someone wanted to work with me to do the rewrite, that might provide me the provided motivation to do the rewrite, maybe? No promises though. I somewhat doubt that anyone would be interested in doing such a collaboration though.)

Regarding the text based SBURB game, I assume I was talking about “The Overseer Project”. It was very cool.

Thank you for your questions. I hope this answers it to your satisfaction.

[tinybasementtale]

Full Stack Testing vs. Full Stack Development: What’s the Difference?

In today’s fast-evolving tech world, buzzwords like Full Stack Development and Full Stack Testing have gained immense popularity. Both roles are vital in the software lifecycle, but they serve very different purposes. Whether you’re a beginner exploring your career options or a professional looking to expand your skills, understanding the differences between Full Stack Testing and Full Stack Development is crucial. Let’s dive into what makes these two roles unique!

What Is Full Stack Development?

Full Stack Development refers to the ability to build an entire software application – from the user interface to the backend logic – using a wide range of tools and technologies. A Full Stack Developer is proficient in both front-end (user-facing) and back-end (server-side) development.

Key Responsibilities of a Full Stack Developer:

Front-End Development: Building the user interface using tools like HTML, CSS, JavaScript, React, or Angular.

Back-End Development: Creating server-side logic using languages like Node.js, Python, Java, or PHP.

Database Management: Handling databases such as MySQL, MongoDB, or PostgreSQL.

API Integration: Connecting applications through RESTful or GraphQL APIs.

Version Control: Using tools like Git for collaborative development.

Skills Required for Full Stack Development:

Proficiency in programming languages (JavaScript, Python, Java, etc.)

Knowledge of web frameworks (React, Django, etc.)

Experience with databases and cloud platforms

Understanding of DevOps tools

In short, a Full Stack Developer handles everything from designing the UI to writing server-side code, ensuring the software runs smoothly.

What Is Full Stack Testing?

Full Stack Testing is all about ensuring quality at every stage of the software development lifecycle. A Full Stack Tester is responsible for testing applications across multiple layers – from front-end UI testing to back-end database validation – ensuring a seamless user experience. They blend manual and automation testing skills to detect issues early and prevent software failures.

Key Responsibilities of a Full Stack Tester:

UI Testing: Ensuring the application looks and behaves correctly on the front end.

API Testing: Validating data flow and communication between services.

Database Testing: Verifying data integrity and backend operations.

Performance Testing: Ensuring the application performs well under load using tools like JMeter.

Automation Testing: Automating repetitive tests with tools like Selenium or Cypress.

Security Testing: Identifying vulnerabilities to prevent cyber-attacks.

Skills Required for Full Stack Testing:

Knowledge of testing tools like Selenium, Postman, JMeter, or TOSCA

Proficiency in both manual and automation testing

Understanding of test frameworks like TestNG or Cucumber

Familiarity with Agile and DevOps practices

Basic knowledge of programming for writing test scripts

A Full Stack Tester plays a critical role in identifying bugs early in the development process and ensuring the software functions flawlessly.

Which Career Path Should You Choose?

The choice between Full Stack Development and Full Stack Testing depends on your interests and strengths:

Choose Full Stack Development if you love coding, creating interfaces, and building software solutions from scratch. This role is ideal for those who enjoy developing creative products and working with both front-end and back-end technologies.

Choose Full Stack Testing if you have a keen eye for detail and enjoy problem-solving by finding bugs and ensuring software quality. If you love automation, performance testing, and working with multiple testing tools, Full Stack Testing is the right path.

Why Both Roles Are Essential :

Both Full Stack Developers and Full Stack Testers are integral to software development. While developers focus on creating functional features, testers ensure that everything runs smoothly and meets user expectations. In an Agile or DevOps environment, these roles often overlap, with testers and developers working closely to deliver high-quality software in shorter cycles.

Final Thoughts :

Whether you opt for Full Stack Testing or Full Stack Development, both fields offer exciting opportunities with tremendous growth potential. With software becoming increasingly complex, the demand for skilled developers and testers is higher than ever.

At TestoMeter Pvt. Ltd., we provide comprehensive training in both Full Stack Development and Full Stack Testing to help you build a future-proof career. Whether you want to build software or ensure its quality, we’ve got the perfect course for you.

Ready to take the next step? Explore our Full Stack courses today and start your journey toward a successful IT career!

This blog not only provides a crisp comparison but also encourages potential students to explore both career paths with TestoMeter.

For more Details :

Interested in kick-starting your Software Developer/Software Tester career? Contact us today or Visit our website for course details, success stories, and more!

🌐visit - https://www.testometer.co.in/

[govindhtech]

Gemini Code Assist Enterprise: AI App Development Tool

Introducing Gemini Code Assist Enterprise’s AI-powered app development tool that allows for code customisation.

The modern economy is driven by software development. Unfortunately, due to a lack of skilled developers, a growing number of integrations, vendors, and abstraction levels, developing effective apps across the tech stack is difficult.

To expedite application delivery and stay competitive, IT leaders must provide their teams with AI-powered solutions that assist developers in navigating complexity.

Google Cloud thinks that offering an AI-powered application development solution that works across the tech stack, along with enterprise-grade security guarantees, better contextual suggestions, and cloud integrations that let developers work more quickly and versatile with a wider range of services, is the best way to address development challenges.

Google Cloud is presenting Gemini Code Assist Enterprise, the next generation of application development capabilities.

Beyond AI-powered coding aid in the IDE, Gemini Code Assist Enterprise goes. This is application development support at the corporate level. Gemini’s huge token context window supports deep local codebase awareness. You can use a wide context window to consider the details of your local codebase and ongoing development session, allowing you to generate or transform code that is better appropriate for your application.

With code customization, Code Assist Enterprise not only comprehends your local codebase but also provides code recommendations based on internal libraries and best practices within your company. As a result, Code Assist can produce personalized code recommendations that are more precise and pertinent to your company. In addition to finishing difficult activities like updating the Java version across a whole repository, developers can remain in the flow state for longer and provide more insights directly to their IDEs. Because of this, developers can concentrate on coming up with original solutions to problems, which increases job satisfaction and gives them a competitive advantage. You can also come to market more quickly.

GitLab.com and GitHub.com repos can be indexed by Gemini Code Assist Enterprise code customisation; support for self-hosted, on-premise repos and other source control systems will be added in early 2025.

Yet IDEs are not the only tool used to construct apps. It integrates coding support into all of Google Cloud’s services to help specialist coders become more adaptable builders. The time required to transition to new technologies is significantly decreased by a code assistant, which also integrates the subtleties of an organization’s coding standards into its recommendations. Therefore, the faster your builders can create and deliver applications, the more services it impacts. To meet developers where they are, Code Assist Enterprise provides coding assistance in Firebase, Databases, BigQuery, Colab Enterprise, Apigee, and Application Integration. Furthermore, each Gemini Code Assist Enterprise user can access these products’ features; they are not separate purchases.

Gemini Code Support BigQuery enterprise users can benefit from SQL and Python code support. With the creation of pre-validated, ready-to-run queries (data insights) and a natural language-based interface for data exploration, curation, wrangling, analysis, and visualization (data canvas), they can enhance their data journeys beyond editor-based code assistance and speed up their analytics workflows.

Furthermore, Code Assist Enterprise does not use the proprietary data from your firm to train the Gemini model, since security and privacy are of utmost importance to any business. Source code that is kept separate from each customer’s organization and kept for usage in code customization is kept in a Google Cloud-managed project. Clients are in complete control of which source repositories to utilize for customization, and they can delete all data at any moment.

Your company and data are safeguarded by Google Cloud’s dedication to enterprise preparedness, data governance, and security. This is demonstrated by projects like software supply chain security, Mandiant research, and purpose-built infrastructure, as well as by generative AI indemnification.

Google Cloud provides you with the greatest tools for AI coding support so that your engineers may work happily and effectively. The market is also paying attention. Because of its ability to execute and completeness of vision, Google Cloud has been ranked as a Leader in the Gartner Magic Quadrant for AI Code Assistants for 2024.

Gemini Code Assist Enterprise Costs

In general, Gemini Code Assist Enterprise costs $45 per month per user; however, a one-year membership that ends on March 31, 2025, will only cost $19 per month per user.

Read more on Govindhtech.com

[sreegs]

Hey sreegs, maybe this is a long-shot... but do you have any advice for someone trying to get into the tech industry (aside from don't - ive heard this one before) for someone relatively new to coding and stuff?

do some research on what you want to do and get started in a relevant language. i used codecademy but there's plenty of other places that offer courses. swift for iOS, java/kotlin for Android, js for web frontend, python, java, etc for other backend services.

you're gonna be learning the basics but start somewhere. there comes a point where you start to just "get" coding and then it becomes easier to learn other languages. while they all may have something unique about them, a lot is the same. stuff like control flow, objects, types, nullability, etc, they all deal with those things in some way. there's a big difference between compiled languages and interpreted languages, but that won't matter much to you at the beginning

if you hate coding after trying to learn it for a month or two you're gonna hate it forever

[pandeypankaj]

How do I learn Python in depth?

Improving Your Python Skills

  Writing Python Programs Basics: Practice the basics solidly. 

  Syntax and Semantics: Make sure you are very strong in variables, data types, control flow, functions, and object-oriented programming. 

 Data Structures: Be able to work with lists, tuples, dictionaries, and sets, and know when to use which. 

 Modules and Packages: Study how to import and use built-in and third-party modules. 

Advanced Concepts

Generators and Iterators: Know how to develop efficient iterators and generators for memory-efficient code. 

Decorators: Learn how to dynamically alter functions using decorators. 

Metaclasses: Understand how classes are created and can be customized. 

Context Managers: Understand how contexts work with statements. 

Project Practice 

 Personal Projects: You will work on projects that you want to, whether building a web application, data analysis tool, or a game.

 Contributing to Open Source: Contribute to open-source projects in order to learn from senior developers. Get exposed to real-life code. 

 Online Challenges: Take part in coding challenges on HackerRank, LeetCode, or Project Euler. 

 Learn Various Libraries and Frameworks

 Scientific Computing: NumPy, SciPy, Pandas

 Data Visualization: Matplotlib, Seaborn

 Machine Learning: Scikit-learn, TensorFlow, PyTorch

 Web Development: Django, Flask

Data Analysis: Dask, Airflow

Read Pythonic Code

 Open Source Projects: Study the source code of a few popular Python projects. Go through their best practices and idiomatic Python. 

 Books and Tutorials: Read all the code examples in books and tutorials on Python. 

 Conferences and Workshops

  Attend conferences and workshops that will help you further your skills in Python. PyCon is an annual Python conference that includes talks, workshops, and even networking opportunities. Local meetups will let you connect with other Python developers in your area. 

Learn Continuously

 Follow Blogs and Podcasts: Keep reading blogs and listening to podcasts that will keep you updated with the latest trends and developments taking place within the Python community.

Online Courses: Advanced understanding in Python can be acquired by taking online courses on the subject.

 Try It Yourself: Trying new techniques and libraries expands one's knowledge.

Other Recommendations

 Readable-Clean Code: For code writing, it's essential to follow the style guide in Python, PEP 

Naming your variables and functions as close to their utilization as possible is also recommended.

 Test Your Code: Unit tests will help in establishing the correctness of your code.

 Coding with Others: Doing pair programming and code reviews would provide you with experience from other coders.

 You are not Afraid to Ask for Help: Never hesitate to ask for help when things are beyond your hand-on areas, be it online communities or mentors.

These steps, along with consistent practice, will help you become proficient in Python development and open a wide range of possibilities in your career.

[mlembug]

Relay operated power button

What you need:

a 5V relay module

Raspberry Pi

a bunch of cables

Explanation:

A power button on a computer case lets electricity flow between two power pins upon it being pressed, which is when the motherboard detects the button is pressed and reacts accordingly.

A relay is a specific kind of switch which lets electricity flow conditionally upon the flow of electricity in another circuit. The voltages involved both in motherboard power pins and on Raspberry Pi are generally low to not damage both, but we're taking extra precautions to electrically separate them both.

"5V relay module" here means that 5V is the voltage that is required for the relay module to work, while the controlling voltage can be lower. It being relay module it means it also has a flyback diode we'd otherwise have to provide ourselves.

Raspberry Pi's GPIO pins are programmable and can be controlled through Python code, and operate on 3.3V. Raspberry Pi also provides 5V output, but this one is not controllable.

By

connecting a power button to rPi GPIO pins

connecting the 5V voltage output pin from rPi to the relay module's Vcc input pin

connecting the ground pin from rPi to the relay module's groud pin

connecting programmable GPIO pins as the relay module's input pin

connecting the relay module's outputs (the normally open one and the ground) to the motherboard power pins

running some code on rPi

We can extend the power button functionality so Raspberry Pi can turn on and off our computer, while also still keeping the power button working.

Which is what I use to remotely turn on my computer on, by SSHing to rPi and running a script to turn the PC on while I'm away from home.

Why not Wake-On-LAN?

Wake-On-LAN has restrictions which makes it not as reliable as it could be, for example:

The ability to wake from a hybrid shutdown state (S4) (aka Fast Startup) or a soft powered-off state (S5) is unsupported in Windows 8 and above

The code and explanation for it in Part 2, when I get to writing it.