Friday, 7th November 2025
My trepidation extends to complex literature searches. I use LLMs as secondary librarians when I’m doing research. They reliably find primary sources (articles, papers, etc.) that I miss in my initial searches.
But these searches are dangerous. I distrust LLM librarians. There is so much data in the world: you can (in good faith!) find evidence to support almost any position or conclusion. ChatGPT is not a human, and, unlike teachers & librarians & scholars, ChatGPT does not have a consistent, legible worldview. In my experience, it readily agrees with any premise you hand it — and brings citations. It may have read every article that can be read, but it has no real opinion — so it is not a credible expert.
— Ben Stolovitz, How I use AI
You should write an agent (via) Thomas Ptacek on the Fly blog:
Agents are the most surprising programming experience I’ve had in my career. Not because I’m awed by the magnitude of their powers — I like them, but I don’t like-like them. It’s because of how easy it was to get one up on its legs, and how much I learned doing that.
I think he's right: hooking up a simple agentic loop that prompts an LLM and runs a tool for it any time it request one really is the new "hello world" of AI engineering.
Game design is simple, actually (via) Game design legend Raph Koster (Ultima Online, Star Wars Galaxies and many more) provides a deeply informative and delightfully illustrated "twelve-step program for understanding game design."
You know it's going to be good when the first section starts by defining "fun".
Using Codex CLI with gpt-oss:120b on an NVIDIA DGX Spark via Tailscale. Inspired by a YouTube comment I wrote up how I run OpenAI's Codex CLI coding agent against the gpt-oss:120b model running in Ollama on my NVIDIA DGX Spark via a Tailscale network.
It takes a little bit of work to configure but the result is I can now use Codex CLI on my laptop anywhere in the world against a self-hosted model.
I used it to build this space invaders clone.
My hunch is that existing LLMs make it easier to build a new programming language in a way that captures new developers.
Most programming languages are similar enough to existing languages that you only need to know a small number of details to use them: what's the core syntax for variables, loops, conditionals and functions? How does memory management work? What's the concurrency model?
For many languages you can fit all of that, including illustrative examples, in a few thousand tokens of text.
So ship your new programming language with a Claude Skills style document and give your early adopters the ability to write it with LLMs. The LLMs should handle that very well, especially if they get to run an agentic loop against a compiler or even a linter that you provide.
This post started as a comment.
I have AiDHD
It has never been easier to build an MVP and in turn, it has never been harder to keep focus. When new features always feel like they're just a prompt away, feature creep feels like a never ending battle. Being disciplined is more important than ever.
AI still doesn't change one very important thing: you still need to make something people want. I think that getting users (even free ones) will become significantly harder as the bar for user's time will only get higher as their options increase.
Being quicker to get to the point of failure is actually incredibly valuable. Even just over a year ago, many of these projects would have taken months to build.
— Josh Cohenzadeh, AiDHD