310 posts tagged “ai-assisted-programming”
Using AI tools such as Large Language Models to help write code. Vibe coding is the less responsible subset of this. See Here’s how I use LLMs to help me write code for a description of my process.
2026
Wilson Lin on FastRender: a browser built by thousands of parallel agents
Last week Cursor published Scaling long-running autonomous coding, an article describing their research efforts into coordinating large numbers of autonomous coding agents. One of the projects mentioned in the article was FastRender, a web browser they built from scratch using their agent swarms. I wanted to learn more so I asked Wilson Lin, the engineer behind FastRender, if we could record a conversation about the project. That 47 minute video is now available on YouTube. I’ve included some of the highlights below.
[... 2,243 words]jordanhubbard/nanolang (via) Plenty of people have mused about what a new programming language specifically designed to be used by LLMs might look like. Jordan Hubbard (co-founder of FreeBSD, with serious stints at Apple and NVIDIA) just released exactly that.
A minimal, LLM-friendly programming language with mandatory testing and unambiguous syntax.
NanoLang transpiles to C for native performance while providing a clean, modern syntax optimized for both human readability and AI code generation.
The syntax strikes me as an interesting mix between C, Lisp and Rust.
I decided to see if an LLM could produce working code in it directly, given the necessary context. I started with this MEMORY.md file, which begins:
Purpose: This file is designed specifically for Large Language Model consumption. It contains the essential knowledge needed to generate, debug, and understand NanoLang code. Pair this with
spec.jsonfor complete language coverage.
I ran that using LLM and llm-anthropic like this:
llm -m claude-opus-4.5 \
-s https://raw.githubusercontent.com/jordanhubbard/nanolang/refs/heads/main/MEMORY.md \
'Build me a mandelbrot fractal CLI tool in this language'
> /tmp/fractal.nano
The resulting code... did not compile.
I may have been too optimistic expecting a one-shot working program for a new language like this. So I ran a clone of the actual project, copied in my program and had Claude Code take a look at the failing compiler output.
... and it worked! Claude happily grepped its way through the various examples/ and built me a working program.
Here's the Claude Code transcript - you can see it reading relevant examples here - and here's the finished code plus its output.
I've suspected for a while that LLMs and coding agents might significantly reduce the friction involved in launching a new language. This result reinforces my opinion.
Scaling long-running autonomous coding. Wilson Lin at Cursor has been doing some experiments to see how far you can push a large fleet of "autonomous" coding agents:
This post describes what we've learned from running hundreds of concurrent agents on a single project, coordinating their work, and watching them write over a million lines of code and trillions of tokens.
They ended up running planners and sub-planners to create tasks, then having workers execute on those tasks - similar to how Claude Code uses sub-agents. Each cycle ended with a judge agent deciding if the project was completed or not.
In my predictions for 2026 the other day I said that by 2029:
I think somebody will have built a full web browser mostly using AI assistance, and it won’t even be surprising. Rolling a new web browser is one of the most complicated software projects I can imagine[...] the cheat code is the conformance suites. If there are existing tests that it’ll get so much easier.
I may have been off by three years, because Cursor chose "building a web browser from scratch" as their test case for their agent swarm approach:
To test this system, we pointed it at an ambitious goal: building a web browser from scratch. The agents ran for close to a week, writing over 1 million lines of code across 1,000 files. You can explore the source code on GitHub.
But how well did they do? Their initial announcement a couple of days ago was met with unsurprising skepticism, especially when it became apparent that their GitHub Actions CI was failing and there were no build instructions in the repo.
It looks like they addressed that within the past 24 hours. The latest README includes build instructions which I followed on macOS like this:
cd /tmp
git clone https://github.com/wilsonzlin/fastrender
cd fastrender
git submodule update --init vendor/ecma-rs
cargo run --release --features browser_ui --bin browser
This got me a working browser window! Here are screenshots I took of google.com and my own website:
Honestly those are very impressive! You can tell they're not just wrapping an existing rendering engine because of those very obvious rendering glitches, but the pages are legible and look mostly correct.
The FastRender repo even uses Git submodules to include various WhatWG and CSS-WG specifications in the repo, which is a smart way to make sure the agents have access to the reference materials that they might need.
This is the second attempt I've seen at building a full web browser using AI-assisted coding in the past two weeks - the first was HiWave browser, a new browser engine in Rust first announced in this Reddit thread.
When I made my 2029 prediction this is more-or-less the quality of result I had in mind. I don't think we'll see projects of this nature compete with Chrome or Firefox or WebKit any time soon but I have to admit I'm very surprised to see something this capable emerge so quickly.
Update 23rd January 2026: I recorded a 47 minute conversation with Wilson about this project and published it on YouTube. Here's the video and accompanying highlights.
FLUX.2-klein-4B Pure C Implementation (via) On 15th January Black Forest Labs, a lab formed by the creators of the original Stable Diffusion, released black-forest-labs/FLUX.2-klein-4B - an Apache 2.0 licensed 4 billion parameter version of their FLUX.2 family.
Salvatore Sanfilippo (antirez) decided to build a pure C and dependency-free implementation to run the model, with assistance from Claude Code and Claude Opus 4.5.
Salvatore shared this note on Hacker News:
Something that may be interesting for the reader of this thread: this project was possible only once I started to tell Opus that it needed to take a file with all the implementation notes, and also accumulating all the things we discovered during the development process. And also, the file had clear instructions to be taken updated, and to be processed ASAP after context compaction. This kinda enabled Opus to do such a big coding task in a reasonable amount of time without loosing track. Check the file IMPLEMENTATION_NOTES.md in the GitHub repo for more info.
Here's that IMPLEMENTATION_NOTES.md file.
Don’t fall into the anti-AI hype. I'm glad someone was brave enough to say this. There is a lot of anti-AI sentiment in the software development community these days. Much of it is justified, but if you let people convince you that AI isn't genuinely useful for software developers or that this whole thing will blow over soon it's becoming clear that you're taking on a very real risk to your future career.
As Salvatore Sanfilippo puts it:
It does not matter if AI companies will not be able to get their money back and the stock market will crash. All that is irrelevant, in the long run. It does not matter if this or the other CEO of some unicorn is telling you something that is off putting, or absurd. Programming changed forever, anyway.
I do like this hopeful positive outlook on what this could all mean, emphasis mine:
How do I feel, about all the code I wrote that was ingested by LLMs? I feel great to be part of that, because I see this as a continuation of what I tried to do all my life: democratizing code, systems, knowledge. LLMs are going to help us to write better software, faster, and will allow small teams to have a chance to compete with bigger companies. The same thing open source software did in the 90s.
This post has been the subject of heated discussions all day today on both Hacker News and Lobste.rs.
My answers to the questions I posed about porting open source code with LLMs
Last month I wrote about porting JustHTML from Python to JavaScript using Codex CLI and GPT-5.2 in a few hours while also buying a Christmas tree and watching Knives Out 3. I ended that post with a series of open questions about the ethics and legality of this style of work. Alexander Petros on lobste.rs just challenged me to answer them, which is fair enough! Here’s my attempt at that.
[... 1,034 words]A Software Library with No Code. Provocative experiment from Drew Breunig, who designed a new library for time formatting ("3 hours ago" kind of thing) called "whenwords" that has no code at all, just a carefully written specification, an AGENTS.md and a collection of conformance tests in a YAML file.
Pass that to your coding agent of choice, tell it what language you need and it will write it for you on demand!
This meshes nearly with my recent interest in conformance suites. If you publish good enough language-independent tests it's pretty astonishing how far today's coding agents can take you!
LLM predictions for 2026, shared with Oxide and Friends
I joined a recording of the Oxide and Friends podcast on Tuesday to talk about 1, 3 and 6 year predictions for the tech industry. This is my second appearance on their annual predictions episode, you can see my predictions from January 2025 here. Here’s the page for this year’s episode, with options to listen in all of your favorite podcast apps or directly on YouTube.
[... 1,741 words]It genuinely feels to me like GPT-5.2 and Opus 4.5 in November represent an inflection point - one of those moments where the models get incrementally better in a way that tips across an invisible capability line where suddenly a whole bunch of much harder coding problems open up.
Something I like about our weird new LLM-assisted world is the number of people I know who are coding again, having mostly stopped as they moved into management roles or lost their personal side project time to becoming parents.
AI assistance means you can get something useful done in half an hour, or even while you are doing other stuff. You don't need to carve out 2-4 hours to ramp up anymore.
If you have significant previous coding experience - even if it's a few years stale - you can drive these things really effectively. Especially if you have management experience, quite a lot of which transfers to "managing" coding agents - communicate clearly, set achievable goals, provide all relevant context. Here's a relevant recent tweet from Ethan Mollick:
When you see how people use Claude Code/Codex/etc it becomes clear that managing agents is really a management problem
Can you specify goals? Can you provide context? Can you divide up tasks? Can you give feedback?
These are teachable skills. Also UIs need to support management
This note started as a comment.
I'm not joking and this isn't funny. We have been trying to build distributed agent orchestrators at Google since last year. There are various options, not everyone is aligned... I gave Claude Code a description of the problem, it generated what we built last year in an hour.
It's not perfect and I'm iterating on it but this is where we are right now. If you are skeptical of coding agents, try it on a domain you are already an expert of. Build something complex from scratch where you can be the judge of the artifacts.
[...] It wasn't a very detailed prompt and it contained no real details given I cannot share anything propriety. I was building a toy version on top of some of the existing ideas to evaluate Claude Code. It was a three paragraph description.
— Jaana Dogan, Principal Engineer at Google
[Claude Code] has the potential to transform all of tech. I also think we’re going to see a real split in the tech industry (and everywhere code is written) between people who are outcome-driven and are excited to get to the part where they can test their work with users faster, and people who are process-driven and get their meaning from the engineering itself and are upset about having that taken away.
Introducing gisthost.github.io
I am a huge fan of gistpreview.github.io, the site by Leon Huang that lets you append ?GIST_id to see a browser-rendered version of an HTML page that you have saved to a Gist. The last commit was ten years ago and I needed a couple of small changes so I’ve forked it and deployed an updated version at gisthost.github.io.
2025
[...] The puzzle is still there. What’s gone is the labor. I never enjoyed hitting keys, writing minimal repro cases with little insight, digging through debug logs, or trying to decipher some obscure AWS IAM permission error. That work wasn’t the puzzle for me. It was just friction, laborious and frustrating. The thinking remains; the hitting of the keys and the frustrating is what’s been removed.
TIL: Downloading archived Git repositories from archive.softwareheritage.org
(via)
Back in February I blogged about a neat Python library called sqlite-s3vfs for accessing SQLite databases hosted in an S3 bucket, released as MIT licensed open source by the UK government's Department for Business and Trade.
I went looking for it today and found that the github.com/uktrade/sqlite-s3vfs repository is now a 404.
Since this is taxpayer-funded open source software I saw it as my moral duty to try and restore access! It turns out a full copy had been captured by the Software Heritage archive, so I was able to restore the repository from there. My copy is now archived at simonw/sqlite-s3vfs.
The process for retrieving an archive was non-obvious, so I've written up a TIL and also published a new Software Heritage Repository Retriever tool which takes advantage of the CORS-enabled APIs provided by Software Heritage. Here's the Claude Code transcript from building that.
In essence a language model changes you from a programmer who writes lines of code, to a programmer that manages the context the model has access to, prunes irrelevant things, adds useful material to context, and writes detailed specifications. If that doesn't sound fun to you, you won't enjoy it.
Think about it as if it is a junior developer that has read every textbook in the world but has 0 practical experience with your specific codebase, and is prone to forgetting anything but the most recent hour of things you've told it. What do you want to tell that intern to help them progress?
Eg you might put sticky notes on their desk to remind them of where your style guide lives, what the API documentation is for the APIs you use, some checklists of what is done and what is left to do, etc.
But the intern gets confused easily if it keeps accumulating sticky notes and there are now 100 sticky notes, so you have to periodically clear out irrelevant stickies and replace them with new stickies.
— Liz Fong-Jones, thread on Bluesky
A year ago, Claude struggled to generate bash commands without escaping issues. It worked for seconds or minutes at a time. We saw early signs that it may become broadly useful for coding one day.
Fast forward to today. In the last thirty days, I landed 259 PRs -- 497 commits, 40k lines added, 38k lines removed. Every single line was written by Claude Code + Opus 4.5.
— Boris Cherny, creator of Claude Code
A new way to extract detailed transcripts from Claude Code
I’ve released claude-code-transcripts, a new Python CLI tool for converting Claude Code transcripts to detailed HTML pages that provide a better interface for understanding what Claude Code has done than even Claude Code itself. The resulting transcripts are also designed to be shared, using any static HTML hosting or even via GitHub Gists.
[... 1,082 words]swift-justhtml. First there was Emil Stenström's JustHTML in Python, then my justjshtml in JavaScript, then Anil Madhavapeddy's html5rw in OCaml, and now Kyle Howells has built a vibespiled dependency-free HTML5 parser for Swift using the same coding agent tricks against the html5lib-tests test suite.
Kyle ran some benchmarks to compare the different implementations:
- Rust (html5ever) total parse time: 303 ms
- Swift total parse time: 1313 ms
- JavaScript total parse time: 1035 ms
- Python total parse time: 4189 ms
Your job is to deliver code you have proven to work
In all of the debates about the value of AI-assistance in software development there’s one depressing anecdote that I keep on seeing: the junior engineer, empowered by some class of LLM tool, who deposits giant, untested PRs on their coworkers—or open source maintainers—and expects the “code review” process to handle the rest.
[... 840 words]AoAH Day 15: Porting a complete HTML5 parser and browser test suite (via) Anil Madhavapeddy is running an Advent of Agentic Humps this year, building a new useful OCaml library every day for most of December.
Inspired by Emil Stenström's JustHTML and my own coding agent port of that to JavaScript he coined the term vibespiling for AI-powered porting and transpiling of code from one language to another and had a go at building an HTML5 parser in OCaml, resulting in html5rw which passes the same html5lib-tests suite that Emil and myself used for our projects.
Anil's thoughts on the copyright and ethical aspects of this are worth quoting in full:
The question of copyright and licensing is difficult. I definitely did some editing by hand, and a fair bit of prompting that resulted in targeted code edits, but the vast amount of architectural logic came from JustHTML. So I opted to make the LICENSE a joint one with Emil Stenström. I did not follow the transitive dependency through to the Rust one, which I probably should.
I'm also extremely uncertain about every releasing this library to the central opam repository, especially as there are excellent HTML5 parsers already available. I haven't checked if those pass the HTML5 test suite, because this is wandering into the agents vs humans territory that I ruled out in my groundrules. Whether or not this agentic code is better or not is a moot point if releasing it drives away the human maintainers who are the source of creativity in the code!
I decided to credit Emil in the same way for my own vibespiled project.
I’ve been watching junior developers use AI coding assistants well. Not vibe coding—not accepting whatever the AI spits out. Augmented coding: using AI to accelerate learning while maintaining quality. [...]
The juniors working this way compress their ramp dramatically. Tasks that used to take days take hours. Not because the AI does the work, but because the AI collapses the search space. Instead of spending three hours figuring out which API to use, they spend twenty minutes evaluating options the AI surfaced. The time freed this way isn’t invested in another unprofitable feature, though, it’s invested in learning. [...]
If you’re an engineering manager thinking about hiring: The junior bet has gotten better. Not because juniors have changed, but because the genie, used well, accelerates learning.
— Kent Beck, The Bet On Juniors Just Got Better
I ported JustHTML from Python to JavaScript with Codex CLI and GPT-5.2 in 4.5 hours
I wrote about JustHTML yesterday—Emil Stenström’s project to build a new standards compliant HTML5 parser in pure Python code using coding agents running against the comprehensive html5lib-tests testing library. Last night, purely out of curiosity, I decided to try porting JustHTML from Python to JavaScript with the least amount of effort possible, using Codex CLI and GPT-5.2. It worked beyond my expectations.
[... 1,818 words]JustHTML is a fascinating example of vibe engineering in action
I recently came across JustHTML, a new Python library for parsing HTML released by Emil Stenström. It’s a very interesting piece of software, both as a useful library and as a case study in sophisticated AI-assisted programming.
[... 956 words]If the part of programming you enjoy most is the physical act of writing code, then agents will feel beside the point. You’re already where you want to be, even just with some Copilot or Cursor-style intelligent code auto completion, which makes you faster while still leaving you fully in the driver’s seat about the code that gets written.
But if the part you care about is the decision-making around the code, agents feel like they clear space. They take care of the mechanical expression and leave you with judgment, tradeoffs, and intent. Because truly, for someone at my experience level, that is my core value offering anyway. When I spend time actually typing code these days with my own fingers, it feels like a waste of my time.
— Obie Fernandez, What happens when the coding becomes the least interesting part of the work
OpenAI are quietly adopting skills, now available in ChatGPT and Codex CLI
One of the things that most excited me about Anthropic’s new Skills mechanism back in October is how easy it looked for other platforms to implement. A skill is just a folder with a Markdown file and some optional extra resources and scripts, so any LLM tool with the ability to navigate and read from a filesystem should be capable of using them. It turns out OpenAI are doing exactly that, with skills support quietly showing up in both their Codex CLI tool and now also in ChatGPT itself.
[... 1,360 words]Useful patterns for building HTML tools
I’ve started using the term HTML tools to refer to HTML applications that I’ve been building which combine HTML, JavaScript, and CSS in a single file and use them to provide useful functionality. I have built over 150 of these in the past two years, almost all of them written by LLMs. This article presents a collection of useful patterns I’ve discovered along the way.
[... 4,231 words]I've never been particularly invested dark v.s. light mode but I get enough people complaining that this site is "blinding" that I decided to see if Claude Code for web could produce a useful dark mode from my existing CSS. It did a decent job, using CSS properties, @media (prefers-color-scheme: dark) and a data-theme="dark" attribute based on this prompt:
Add a dark theme which is triggered by user media preferences but can also be switched on using localStorage - then put a little icon in the footer for toggling it between default auto, forced regular and forced dark mode
The site defaults to picking up the user's preferences, but there's also a toggle in the footer which switches between auto, forced-light and forced-dark. Here's an animated demo:
I had Claude Code make me that GIF from two static screenshots - it used this ImageMagick recipe:
magick -delay 300 -loop 0 one.png two.png \
-colors 128 -layers Optimize dark-mode.gif
The CSS ended up with some duplication due to the need to handle both the media preference and the explicit user selection. We fixed that with Cog.
mistralai/mistral-vibe. Here's the Apache 2.0 licensed source code for Mistral's new "Vibe" CLI coding agent, released today alongside Devstral 2.
It's a neat implementation of the now standard terminal coding agent pattern, built in Python on top of Pydantic and Rich/Textual (here are the dependencies.) Gemini CLI is TypeScript, Claude Code is closed source (TypeScript, now on top of Bun), OpenAI's Codex CLI is Rust. OpenHands is the other major Python coding agent I know of, but I'm likely missing some others. (UPDATE: Kimi CLI is another open source Apache 2 Python one.)
The Vibe source code is pleasant to read and the crucial prompts are neatly extracted out into Markdown files. Some key places to look:
- core/prompts/cli.md is the main system prompt ("You are operating as and within Mistral Vibe, a CLI coding-agent built by Mistral AI...")
- core/prompts/compact.md is the prompt used to generate compacted summaries of conversations ("Create a comprehensive summary of our entire conversation that will serve as complete context for continuing this work...")
- Each of the core tools has its own prompt file:
The Python implementations of those tools can be found here.
I tried it out and had it build me a Space Invaders game using three.js with the following prompt:
make me a space invaders game as HTML with three.js loaded from a CDN
Here's the source code and the live game (hosted in my new space-invaders-by-llms repo). It did OK.
Prediction: AI will make formal verification go mainstream (via) Martin Kleppmann makes the case for formal verification languages (things like Dafny, Nagini, and Verus) to finally start achieving more mainstream usage. Code generated by LLMs can benefit enormously from more robust verification, and LLMs themselves make these notoriously difficult systems easier to work with.
The paper Can LLMs Enable Verification in Mainstream Programming? by JetBrains Research in March 2025 found that Claude 3.5 Sonnet saw promising results for the three languages I listed above.