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General Intuition has raised $320 million to scale AI trained on millions of hours of gameplay, betting action data can help AI develop something closer to human intuition.

In the last essay I played a game with twelve people. Twelve apostles, one teacher, one set of events — and twelve sharply distinct ways of failing and succeeding to understand the same thing. Peter acts before he reflects, Thomas demands the marks in the hands, Matthew counts and structures, Judas asks what you'll give him. I called it pre-cognitive-science cognitive science: the Gospels did the hard work of selecting twelve incompatible human responses to one encounter, and every century since...

I built a large feature. That's not what this is about. What changed is the baseline — the standards, docs, and automation that exist now and didn't two weeks ago. Everything after this will be built on top of it. Automated tests now ship with new features QA testers were testing. The product was covered. What didn't exist was automation — no E2E suite, no unit tests for new work, no repeatable spec. Now it does. The manual QA cycle stays. The automation catches what hum...

A quick disclaimer: This article isn't an argument against init itself. Constructors remain the appropriate place for lightweight object initialization. For classes that require complex setup, resource allocation, or business logic, many developers prefer to keep init minimal and move that complexity into factory methods, builders, or dedicated initialization routines. The point here is simply that when a class exists only to represent data, @dataclass eliminates a significant amount of unne...

Why stop gaming saved my tokens: Building my own local AI Lab WizSebastian WizSebastian WizSebastian ...

We have tools for checking whether a query is injectable. We have linters, scanners, ORMs, parameterized queries, and database policies. But after the database returns rows, most applications simply trust that the result set matches the operation that asked for it. queryguard starts there. The query may be safe. The result may still be wrong. SQL injection taught us to distrust query construction. Parameterized queries answered the question: Did the user control the qu...

A follow-up to Part 1 ( EN on LinkedIn · RU on Habr ), where we stood up a two-tier PKI: a Root CA and three intermediate CAs — Person, Server and Code. At the end of Part 1 I promised we'd learn to revoke certificates and run OCSP. That's what we'll do here. Like Part 1, this article is meant as a hands-on manual : for every command and extension we touch, there's an extended reference of the parameters you can actually use — with syntax, allowed values, defaults and gotchas. If you don...

The agents were doing exactly what I told them to. That was the problem. I'd built a pipeline where AI agents could take a spec file, implement a feature, run the tests, review the result, and commit — without me writing a line of code. It mostly worked. Dozens of features shipped. But I kept reviewing the output and feeling like something was off. Not broken. Just subtly wrong in a way that was hard to name. I spent a while blaming the models. Then the prompts. Then the validation steps. ...

Code generation tools are powerful and can significantly accelerate development work. Their main limitation is not capability, but context. Without access to organizational knowledge, internal conventions, and system-specific patterns, generated output often requires careful verification. This is why generation tools work best when paired with AI code search, as the latter provides immediate visibility into the existing codebase, making it easier to align AI-generated changes with the realit...

You just got your hands on an AI agent. It writes code, researches things, sends emails, books meetings. You feel like you're holding a chainsaw. But you keep using it to cut butter. The problem nobody talks about The gap between what your agent knows and what it can do is almost always a paywall, a KYC wall, or an API key. Here's what 'just add one data source' actually looks like: Go to the site. Click pricing. Choose a plan. Enter your email. Wait for verifi...

You use Claude Code, or ChatGPT, or both, every day. Quick question: how many messages did you send last month? Which model ate most of your budget? How much did prompt caching actually save you? You don't know. I didn't either. That's a weird gap. We instrument everything else — git activity, deploy frequency, test coverage — but the tool we now spend the most hours inside is a black box. The vendor dashboard, if it exists, is a billing page, not a mirror. So I built four tiny tools ...

This is a submission for the GitHub Finish-Up-A-Thon Challenge What I Built Some time ago, I needed to calculate hashes of directory trees across multiple platforms and architectures. Many existing solutions were based on GNU find, but I quickly realized that this approach has a number of shortcomings. As a result, hashdir was born: a cross-platform tool that takes into account many of the quirks and edge cases involved in calculating directory hashes, including character...

We talk a lot about what AI can build. Code generation. Faster prototypes. Automated debugging. One-shot apps. Entire products created in hours. And yes, AI is powerful. But there is a quieter cost we are not talking about enough: AI hype is starting to weaken the motivation to learn core engineering deeply. That should worry us. 1. The "Why Bother?" Mindset When the dominant narrative says AI can generate code instantly, many engineers start asking: Why sho...

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Boogu-Image-0.1-Turbo is a 10B text-to-image diffusion model built for fast 1024px image generation, realism, and bilingual text rendering.

Think quantum-proofing your applications requires a Ph.D. in advanced lattice mathematics? Think again.

Subagents are one of the most important primitives when it comes to harness engineering so lets peruse what exists and compare them.

A complete reference for all 478 MediaPipe Face Mesh landmark indices, organized by face region. Includes a quick-lookup table for the most-searched points (eye corners, mouth, nose tip, chin), copy-paste JavaScript snippets for blink, smile, mouth-open, eyebrow-raise and head-direction detection, plus an interactive explorer to hover any point on your own face. Built from real face-controlled browser game projects.

adonis_flux2klein is a LoKr refinement model for FLUX.2-klein, built to improve male portrait detail, skin texture, hair, and anatomy.

A first-person account of switching from fintech (trading at Gazprombank) into crypto at the bottom of the market — right after FTX, with Solana in single digits. The switch wasn't luck: it was a non-obvious match of hard skills (edtech, finance, crypto, user research) and soft skills, signalled loudly over time through public writing and a finance PhD on crypto regulation. The bigger lesson: ugly markets hire for impact, not momentum — so a downturn is often the best time to jump, if you can cr...

Learn five common SQL mistakes that silently return wrong results, from SELECT * and NULL handling to JOIN filters, GROUP BY errors and subqueries.

In the AI era, cognitive capacity is no longer equal. This manifesto introduces a structural analysis of the digital attention divide and outlines the environmental austerity protocols required to survive the ongoing crisis of human focus.

When making a film costs $50, the hard part isn't making it. Why attention, taste and authorship are the real scarce assets in AI filmmaking.

Static analysis used to mean a linter nobody read. In banking, it now decides who gets to touch the mainframe next.