#Development Automation

[rthidden]

What is an Algorithm in 30 Seconds?

An algorithm is simply a series of instructions.

Think of a recipe: boil water, add pasta, wait, drain, eat. These are steps to follow.

In computer terms, an algorithm is a set of instructions for a computer to execute.

In machine learning, these instructions enable computers to learn from data, making machine learning algorithms unique and powerful.

[adafruit]

27-Year-Old EXE becomes Python - AI-assisted reverse engineering 🤖⚡💾🐍 https://blog.adafruit.com/2025/02/27/27-year-old-exe-becomes-python-in-minutes-with-claude-ai-assisted-reverse-engineering/

[kamil-a]

customer service voice vs real voice meme needs to be a thing

[honestlyvan]

Let me be absolutely clear -- the problems with Tumblr will keep getting worse if the disabled minority and the trans people and the people frothing at the mouth at the opportunity to yell at a transphobe, keep @’ing staff and the developers on this site, tell them to kys, because in the meanwhile the transphobes and racists and white supremacists will keep using the actual tools Tumblr provides for blocking and reporting, further poisoning the datasets used for moderation, and encouraging the idea that using the official tools does nothing to basically ensure the only statistically meaningful data available to Automattic is poisoned, poisoned all the way down, poisoned beyond usability.

Hatespeech and bias needs to be reported for it to be considered statstically significant to act on from a developer point of view. Suicide baiting and spamming any of the official means of communication will get you eliminated as a spammer, even if you’re peppering legitimate criticism within your ventpost about how you hate the new thing. You are playing the TE/RFs game.

[whitespringbunker]

pain and suffering on planet earth to be aware it's a bit too easy to infer how a fairly young and barely educated person who also happens to be an avid gamer became autistic about nuclear stuff and the cold war

[ajcgames]

The Big Weekend - Part 1

Part 1 of my 'big weekend' of project development has gone really well so far. Lots of new things added, and progress has been made on my big list of new-things-to-add.

Let's first show everything in a brief video overview:

Whoa! Environments!

I did end up doing some extra work outside of my intended weekend scope, mostly because I wanted a brief distraction from coding. To that end I finally added the first parts of my 'asteroid' environment that your factory is placed in. This will be improved over time, I'm sure, but now serves to make the factory feel a lot more grounded in the very place you're supposed to be!

I also re-added the factory walls, which are now correctly done programmatically - rather than being hand-placed. These scale up with the size of the factory floor, so they're more or less 'done'.

Packager

You can now drop the packager into your factory, which is a necessary first step to exporting your goods from the facility.

Routing items through here will parcel them up ready for transport. You just have to snake them off to your Exporter machine.

Ready for packaging!

Exporter

This machine must be placed over one of the new export nodes. This restricts the number of exporters you can use in any given factory (like the import nodes), and you'll likely have to merge a number of packager belts together to bundle up lots of orders in later stages.

The exporter has space for up to 6 stacks of up to 99 items. A lack of space will result in packages backing up on the import belt, so make sure you don't fill it up with junk!

The build ghost of the exporter next to one of the new Export nodes. These are limited, so you'll have to design your factory around them!

What's next?

Here's my big list of things-to-do, with my completed updates marked off:

Main objectives view

Packager machine

Exporter machine

Research tree

Technology upgrades

Money / credits system

Structure descriptions (QoL)

Audio refinements (QoL)

Main menu screens

New pipes subsytem

Not bad progress so far! I'm not sure what I'm going to tackle tomorrow, though I have a sneaking suspicion I'll want to do the research and tech trees, since they are the next 'big' things to nail down. It will also force me to finally implement a system to prevent access to building items that you haven't yet unlocked, which is a crucial part of progression.

Anyway, that's all I have for this evening. If you reached this point, thank you kindly for your time! And with any luck I'll see you again tomorrow for another progress update.

Have a great evening, wherever you are, and I'll see you again soon! 😊

[jcmarchi]

OpenAI counter-sues Elon Musk for attempts to ‘take down’ AI rival

New Post has been published on https://thedigitalinsider.com/openai-counter-sues-elon-musk-for-attempts-to-take-down-ai-rival/

OpenAI counter-sues Elon Musk for attempts to ‘take down’ AI rival

OpenAI has launched a legal counteroffensive against one of its co-founders, Elon Musk, and his competing AI venture, xAI.

In court documents filed yesterday, OpenAI accuses Musk of orchestrating a “relentless” and “malicious” campaign designed to “take down OpenAI” after he left the organisation years ago.

Elon’s nonstop actions against us are just bad-faith tactics to slow down OpenAI and seize control of the leading AI innovations for his personal benefit. Today, we counter-sued to stop him.

— OpenAI Newsroom (@OpenAINewsroom) April 9, 2025

The court filing, submitted to the US District Court for the Northern District of California, alleges Musk could not tolerate OpenAI’s success after he had “abandoned and declared [it] doomed.”

OpenAI is now seeking legal remedies, including an injunction to stop Musk’s alleged “unlawful and unfair action” and compensation for damages already caused.   

Origin story of OpenAI and the departure of Elon Musk

The legal documents recount OpenAI’s origins in 2015, stemming from an idea discussed by current CEO Sam Altman and President Greg Brockman to create an AI lab focused on developing artificial general intelligence (AGI) – AI capable of outperforming humans – for the “benefit of all humanity.”

Musk was involved in the launch, serving on the initial non-profit board and pledging $1 billion in donations.   

However, the relationship fractured. OpenAI claims that between 2017 and 2018, Musk’s demands for “absolute control” of the enterprise – or its potential absorption into Tesla – were rebuffed by Altman, Brockman, and then-Chief Scientist Ilya Sutskever. The filing quotes Sutskever warning Musk against creating an “AGI dictatorship.”

Following this disagreement, OpenAI alleges Elon Musk quit in February 2018, declaring the venture would fail without him and that he would pursue AGI development at Tesla instead. Critically, OpenAI contends the pledged $1 billion “was never satisfied—not even close”.   

Restructuring, success, and Musk’s alleged ‘malicious’ campaign

Facing escalating costs for computing power and talent retention, OpenAI restructured and created a “capped-profit” entity in 2019 to attract investment while remaining controlled by the non-profit board and bound by its mission. This structure, OpenAI states, was announced publicly and Musk was offered equity in the new entity but declined and raised no objection at the time.   

OpenAI highlights its subsequent breakthroughs – including GPT-3, ChatGPT, and GPT-4 – achieved massive public adoption and critical acclaim. These successes, OpenAI emphasises, were made after the departure of Elon Musk and allegedly spurred his antagonism.

The filing details a chronology of alleged actions by Elon Musk aimed at harming OpenAI:   

Founding xAI: Musk “quietly created” his competitor, xAI, in March 2023.   

Moratorium call: Days later, Musk supported a call for a development moratorium on AI more advanced than GPT-4, a move OpenAI claims was intended “to stall OpenAI while all others, most notably Musk, caught up”.   

Records demand: Musk allegedly made a “pretextual demand” for confidential OpenAI documents, feigning concern while secretly building xAI.   

Public attacks: Using his social media platform X (formerly Twitter), Musk allegedly broadcast “press attacks” and “malicious campaigns” to his vast following, labelling OpenAI a “lie,” “evil,” and a “total scam”.   

Legal actions: Musk filed lawsuits, first in state court (later withdrawn) and then the current federal action, based on what OpenAI dismisses as meritless claims of a “Founding Agreement” breach.   

Regulatory pressure: Musk allegedly urged state Attorneys General to investigate OpenAI and force an asset auction.   

“Sham bid”: In February 2025, a Musk-led consortium made a purported $97.375 billion offer for OpenAI, Inc.’s assets. OpenAI derides this as a “sham bid” and a “stunt” lacking evidence of financing and designed purely to disrupt OpenAI’s operations, potential restructuring, fundraising, and relationships with investors and employees, particularly as OpenAI considers evolving its capped-profit arm into a Public Benefit Corporation (PBC). One investor involved allegedly admitted the bid’s aim was to gain “discovery”.   

Based on these allegations, OpenAI asserts two primary counterclaims against both Elon Musk and xAI:

Unfair competition: Alleging the “sham bid” constitutes an unfair and fraudulent business practice under California law, intended to disrupt OpenAI and gain an unfair advantage for xAI.   

Tortious interference with prospective economic advantage: Claiming the sham bid intentionally disrupted OpenAI’s existing and potential relationships with investors, employees, and customers. 

OpenAI argues Musk’s actions have forced it to divert resources and expend funds, causing harm. They claim his campaign threatens “irreparable harm” to their mission, governance, and crucial business relationships. The filing also touches upon concerns regarding xAI’s own safety record, citing reports of its AI Grok generating harmful content and misinformation.

Elon’s never been about the mission. He’s always had his own agenda. He tried to seize control of OpenAI and merge it with Tesla as a for-profit – his own emails prove it. When he didn’t get his way, he stormed off.

Elon is undoubtedly one of the greatest entrepreneurs of our…

— OpenAI Newsroom (@OpenAINewsroom) April 9, 2025

The counterclaims mark a dramatic escalation in the legal battle between the AI pioneer and its departed co-founder. While Elon Musk initially sued OpenAI alleging a betrayal of its founding non-profit, open-source principles, OpenAI now contends Musk’s actions are a self-serving attempt to undermine a competitor he couldn’t control.

With billions at stake and the future direction of AGI in the balance, this dispute is far from over.

See also: Deep Cogito open LLMs use IDA to outperform same size models

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[codiastic-soft1]

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[fraoula1]

Python for Data Science: From Beginner to Expert – A Complete Guide!

Python has become the go-to language for data science, thanks to its flexibility, powerful libraries, and strong community support. In this video, we’ll explore why Python is the best choice for data scientists and how you can master it—from setting up your environment to advanced machine learning techniques.

🔹 What You'll Learn:

✅ Why Python is essential for data science

✅ Setting up Python and key libraries (NumPy, Pandas, Matplotlib) ✅ Data wrangling, visualization, and transformation

✅ Building machine learning models with Scikit-learn

✅ Best practices to enhance your data science workflow 🚀 Whether you're a beginner or looking to refine your skills, this guide will help you level up in data science with Python. 📌 Don’t forget to like, subscribe, and hit the notification bell for more data science and Python content!

[rthidden]

The Frame Problem: AI's Unseen Nemesis

The frame problem is AI's Achilles' heel, lurking since the 1960s and still unresolved.

1. The Robot Dilemma

Daniel Dennett's thought experiment illustrates the issue: a robot must save its battery from a room with a bomb.

Initially, the robot pulls the battery on a cart but also drags the bomb out, unaware of this side effect.

Solution attempts include programming awareness of side effects, leading to analysis paralysis as the robot debates endless possibilities.

2. The Side Effect Spiral

When programmed to consider all side effects, the robot wastes time on irrelevant details—like pondering wall color changes.

This shows how difficult it is for AI to filter relevant from irrelevant information without getting bogged down.

3. Human Intuition vs. AI Logic

Humans effortlessly ignore irrelevant details, making quick decisions in complex contexts.

Programming AI to mimic this selective ignorance is resource-intensive and remains a significant challenge.

The frame problem underscores a subtle yet crucial aspect of human intelligence: our ability to instantly prioritize relevant information. As we advance in AI development, solving this problem will be key to creating truly intelligent systems.

Got thoughts on tackling the frame problem? Share your ideas!

[harrisson-dev]

I need to bring back this big boy to life (image analyser and color palette creator based on k-mean algo)

[techjour]

FIRST PRIORITY IS YOUR HAPPINESS.

Techjour helps C-suite Executives to turn today's challenges into tomorrow’s opportunities. CXOs are busy in building their businesses while We have our set of solutions to offer for them. Team closely work with Clients from to understand requirements to deliver expected results. Among complex set of technologies, we assist Clients to decide number of technologies based on nature of application, their business growth and to reduce cost.

Consult us to discuss your requirements.

[precisiotech]

Automated Testing vs. Manual Testing: Which One is Right for Your Project?

Achieving high-quality, reliable software stands as a fundamental requirement in software development. Successful testing functions as an essential tool to discover faults and build performance capabilities that create better user experience outcomes. Two main testing methods dominate the field: automated testing and manual testing. The process of quality software assurance uses different testing approaches that demonstrate their own advantages as well as weaknesses according to specific project requirements and scenarios. We will explore the specifics to determine which testing process works best for your system development efforts.

1. What Is Manual Testing?

Manual testing involves a human tester manually executing test cases without using automation tools. Key Characteristics:

The methodology focuses its efforts on user interface together with usability and experience testing.

Human-centered applications where selection requires discretion include ad hoc testing and enumerative testing as well as examinations that need human evaluation.

Human performers are required during this approach; thus, it demands substantial time.

2. What Is Automated Testing?

Software performing automated testing executes test cases through workflows and helpers. Key Characteristics:

Efficient for repetitive and regression testing.

Users must spend money on tools along with developing custom scripts for testing.

Reduces human error.

3. Advantages of Manual Testing

Human Intuition: Software testing professionals can detect kernels through their human cognitive ability that automated tools cannot match. The observation and evaluation of visual elements runs more efficiently through human operatives instead of advanced tools.

Flexibility: This method suits exploratory testing specifically because there are no pre-determined scripts available.

Low Initial Investment: Running this approach does not need tool purchases or applications to develop automation frameworks.

Adaptable for UI/UX Testing: Running this approach does not need tool purchases or applications to develop automation frameworks.

4. Advantages of Automated Testing

Speed: Executes repetitive tests much faster than humans.

Scalability: The system proves most effective for extensive projects that need constant system updates.

Accuracy: When performing recurring actions, automated systems minimize the chances of human mistakes.

Cost-Efficient in the Long Run: Once established and implemented, the system demands costly investments but ensures continuous development expenses decrease over time.

Better for CI/CD Pipelines: Such testing technology connects various development pipelines that support agile and DevOps methodologies.

5. Disadvantages of Manual Testing

Time-Consuming: The manual performance of repeated tests leads to delayed completion of projects.

Error-Prone: Large applications contain tiny bugs that human testers commonly fail to detect.

Not Ideal for Scalability: The process of increasing manual testing needs additional testers to avoid cost escalations.

6. Disadvantages of Automated Testing

Initial Costs: Organizations must provide high financial resources to procure testing tools together with developing programming constructs.

Limited to Pre-Defined Scenarios: These testing approaches work poorly for handling exploratory or ad hoc testing.

Requires Maintenance: Test scripts need frequent updates when application changes occur.

Not Suitable for UI/UX Testing: Struggles with subjective user experience evaluations.

7. When to Use Manual Testing

Small Projects: The testing method proves beneficial at a low cost for small applications and provides quick assessments.

Exploratory Testing: Testing this approach benefits projects whose scripts have not been defined yet or need evaluation for newly added features.

Visual and Usability Testing: Performing assessments on interface components together with design features.

8. When to Use Automated Testing

Large Projects: Handles scalability for projects with frequent updates.

Regression Testing: Program testing becomes more efficient through automation since automated assessments perform multiple tests following each update process.

Performance Testing: The system performs efficient capabilities to conduct load testing and stress testing.

Continuous Development Environments: Agile progression and DevOps implementations need automation as a core requirement.

READ MORE- https://www.precisio.tech/automated-testing-vs-manual-testing-which-one-is-right-for-your-project/

[arundolyn]

man my tolerance for annoying people is just . Nonexistent huh