A website that was dedicated to the human endeavor of reverse engineering, “decompiling,” and modifying video games is shutting down because people using AI to clone and decompile games have fully overrun the community and are far outpacing what human software engineers alone can do. The news comes as AI-coded “clones” of popular enterprise software like the Adobe creative suite and Microsoft Office have gone viral and are seemingly popping up every day, and as people in video game modding communities warn that 'AI decomps' are everywhere.

If AI is so great, why aren’t we seeing an explosion of new, amazing software products? If anyone can turn their idea into working software with coding agents, where is all that software? People have been asking this question for years. As far as I can tell, the answer is that it’s spread out over a lot of modestly successful projects.

Tool-augmented language models are bounded by the APIs humans bothered to write; existing tool-creation systems patch this by prompting a frozen LLM at inference time, leaving the model that writes a tool decoupled from the one that uses it, with no signal that the schemas it produces are schemas it can actually invoke. We propose SMITH (Schema-grounded Multi-task Iterative Tool Honing), a reinforcement learning framework that jointly trains tool creation and tool use inside a single policy.

I live in two places: a terminal running Claude Code and Neovim. For months I bounced between them. Edit in Neovim, alt-tab to the terminal, paste a file path, ask Claude something, alt-tab back, apply the change by hand. The friction was small but constant, and constant friction is the kind that wears a groove in your day.

Image editing, vector illustration, video, photography, PDFs, motion graphics, page layout, interface design, word processing, audio, presentations, CAD and spreadsheets. Native, open-source apps from the ArtCraft team, built in Rust and free to use.

Reverse.horse is my newest project, an attempt to understand and explain the new AI model Jev, by using your feeble human brain as the model. Place yourself in-request and respond as best as you can to incoming questions.

If you want to jump right in, go try it. The horse will put you through your training on how to be the AI model and then cut you loose to do the job of the AI model. You may come back with a new appreciation of what these models are doing.

cosh doesn't impose a philosophy: attach or detach its built-in tools and choose your model to shape the agent you want. Works with Claude, OpenAI, Gemini and many more providers. Boots in milliseconds.

I have been using DeepSeek 4.1 Flash for about a month, heavily, across a dozen projects. It is super capable, and orders of magnitude cheaper than the "frontier" models. When I'm mid-session, if I don't look at the model name, I honestly could not tell you if I'm using DeepSeek or Opus. Whether it's our conversations, the work, or the speed, I don't notice a difference. I don't care that there is no 4.1 "Pro". I treat this like a frontier model because it behaves like one. I'm coming at this from my subjective usage experience, but you can see more complete benchmarks here if that floats your boat.

When we made our strategic bet on Rust back in 2022, we focused on Rust for backends and cloud systems. At the time, that was not an obvious choice. Rust was mostly seen as a systems language only, a safer replacement for C and C++ in operating systems, embedded devices, or browser engines. Suggesting to use it also for the kind of web backends that teams would typically build with Rails, Django, or Node.js required some explaining.

This is the 16th year we’ve been teaching the Stanford Lean LaunchPad class. This year, from the first hour of the first class, we realized we were seeing something extraordinary happen. It was both the end and beginning of a new era.

Teams showed up to the first day of class with MVPs (Minimal Viable Products) looking like finished products that previous classes had taken weeks or months to build. After the class, as the instructors sat processing what just happened, we realized there’s no going back.

I’ve been writing about how AI is going to change startups, but the shock of seeing 8 teams actually implementing it was mind blowing. And not a single team thought they were doing anything extraordinary.

I’ve been getting more and more curious about the risk from Anthropic’s Claude Mythos Preview. So I pulled the system card, a whoppingly inefficient 244-page document that devotes just seven pages to the claim that the model is too dangerous to release. In fact, the 23MB of PDF I had to download was 20MB of wasted time and space. Compressing the PDF to 3MB meant I lost exactly nothing.

Foreshadowing, I guess.

Spoiler alert: the crucial seven pages out of 244 do not contain the word “fuzzer” once. That’s like a seven page vacation brochure for Hawaii that leaves out the word beaches.

Also, the crucial seven pages out of 244 do not contain the expected acronyms CVSS, CWE or CVE, they do not have comparison baseline, an independent reproduction, or the word “thousands.” I’ll get back to all of that in a minute.

The flagship demonstration document turns out to be like the ending of the Wizard of Oz, a sorry disappointment about a model weaponizing two bugs that a different model found, in software the vendor had already patched, in a test environment with the browser sandbox and defense-in-depth mitigations stripped out. Anthropic failed, and somehow the story was flipped into a warning about its success.

The landscape of AI is not merely filled with news. It is filled with teams. You have the doomers, the accelerationists, the skeptics, the it’s-a-bubble oracles, the anti-bubble counter oracles, and so on. It would be convenient for my sanity—and, perhaps, the sanity of my readers—if I simply joined one team and never removed the jersey. But I don’t think any aforementioned tribe has a monopoly on good arguments. I think the doomers are right about the risk of the technology, and the accelerationists are right about the promise of the technology, and the skeptics are right that the doomers and accelerationists can both overstate their cases.

My first session with Claude Code was practically magical. I was speaking to my computer, telling it with natural language what I wanted it to do, and it was able to just do it. It did ( and still does ) feel like a completely new form of input, a new way to control my machine. I have misgivings about using AI in this way, but I still think this is a great tool for sufficiently low-level tasks. I’m waiting eagerly for the day that I can spin up a local LLM that can perform this function as well as Claude Code does.

I'm as anti-genAI as it gets. And yet, this past month, I have used generative coding to complete a project. It works. I hated making it.

Retrieval-Augmented Generation (RAG) has become the dominant paradigm for grounding Large Language Model (LLM) agents in domain-specific knowledge. The standard approach requires selecting an embedding model, designing a chunking strategy, deploying a vector database, maintaining indexes, and performing approximate nearest neighbor (ANN) search at query time. We argue that for domain-specific knowledge grounding --- where the vocabulary is predictable and the corpus is bounded --- this entire stack is unnecessary. We present Knowledge Search, a two-layer retrieval system composed of (1) grep with contextual line windows and (2) cat of pre-structured fallback files. Deployed in production across 20 specialized LLM agents serving three knowledge domains (Traditional Chinese Medicine, Christian spiritual classics, and U.S. civics), our approach achieves 100% retrieval accuracy with sub-10ms latency, zero preprocessing, zero additional memory footprint, and zero infrastructure dependencies.

Scientists and educators are concerned about students using artificial intelligence to shortcut their learning. But there are also opportunities, especially when it comes to teaching neuroscience students how to code.

L'agente di coding IA open source

Modelli gratuiti inclusi o collega qualsiasi modello da qualsiasi provider, inclusi Claude, GPT, Gemini e altri.

Stop switching contexts. Many AI CLI tools are heavy, Node.js-based, and trap you in their interface. They might go back and forth just to run a simple ls, cutting off real access to your command line.

pls is different: lightweight, fast, and built for everyday CLI tasks, keeping you fully in the command line while letting you seamlessly switch between AI and shell commands.

nanochat is the simplest experimental harness for training LLMs. It is designed to run on a single GPU node, the code is minimal/hackable, and it covers all major LLM stages including tokenization, pretraining, finetuning, evaluation, inference, and a chat UI.