340 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
Adding TILs, releases, museums, tools and research to my blog
I’ve been wanting to add indications of my various other online activities to my blog for a while now. I just turned on a new feature I’m calling “beats” (after story beats, naming this was hard!) which adds five new types of content to my site, all corresponding to activity elsewhere.
[... 614 words]25+ years into my career as a programmer I think I may finally be coming around to preferring type hints or even strong typing. I resisted those in the past because they slowed down the rate at which I could iterate on code, especially in the REPL environments that were key to my productivity. But if a coding agent is doing all that typing for me, the benefits of explicitly defining all of those types are suddenly much more attractive.
The A.I. Disruption We’ve Been Waiting for Has Arrived. New opinion piece from Paul Ford in the New York Times. Unsurprisingly for a piece by Paul it's packed with quoteworthy snippets, but a few stood out for me in particular.
Paul describes the November moment that so many other programmers have observed, and highlights Claude Code's ability to revive old side projects:
[Claude Code] was always a helpful coding assistant, but in November it suddenly got much better, and ever since I’ve been knocking off side projects that had sat in folders for a decade or longer. It’s fun to see old ideas come to life, so I keep a steady flow. Maybe it adds up to a half-hour a day of my time, and an hour of Claude’s.
November was, for me and many others in tech, a great surprise. Before, A.I. coding tools were often useful, but halting and clumsy. Now, the bot can run for a full hour and make whole, designed websites and apps that may be flawed, but credible. I spent an entire session of therapy talking about it.
And as the former CEO of a respected consultancy firm (Postlight) he's well positioned to evaluate the potential impact:
When you watch a large language model slice through some horrible, expensive problem — like migrating data from an old platform to a modern one — you feel the earth shifting. I was the chief executive of a software services firm, which made me a professional software cost estimator. When I rebooted my messy personal website a few weeks ago, I realized: I would have paid $25,000 for someone else to do this. When a friend asked me to convert a large, thorny data set, I downloaded it, cleaned it up and made it pretty and easy to explore. In the past I would have charged $350,000.
That last price is full 2021 retail — it implies a product manager, a designer, two engineers (one senior) and four to six months of design, coding and testing. Plus maintenance. Bespoke software is joltingly expensive. Today, though, when the stars align and my prompts work out, I can do hundreds of thousands of dollars worth of work for fun (fun for me) over weekends and evenings, for the price of the Claude $200-a-month plan.
He also neatly captures the inherent community tension involved in exploring this technology:
All of the people I love hate this stuff, and all the people I hate love it. And yet, likely because of the same personality flaws that drew me to technology in the first place, I am annoyingly excited.
LLMs are eating specialty skills. There will be less use of specialist front-end and back-end developers as the LLM-driving skills become more important than the details of platform usage. Will this lead to a greater recognition of the role of Expert Generalists? Or will the ability of LLMs to write lots of code mean they code around the silos rather than eliminating them?
— Martin Fowler, tidbits from the Thoughtworks Future of Software Development Retreat, via HN)
Given the threat of cognitive debt brought on by AI-accelerated software development leading to more projects and less deep understanding of how they work and what they actually do, it's interesting to consider artifacts that might be able to help.
Nathan Baschez on Twitter:
my current favorite trick for reducing "cognitive debt" (h/t @simonw ) is to ask the LLM to write two versions of the plan:
- The version for it (highly technical and detailed)
- The version for me (an entertaining essay designed to build my intuition)
Works great
This inspired me to try something new. I generated the diff between v0.5.0 and v0.6.0 of my Showboat project - which introduced the remote publishing feature - and dumped that into Nano Banana Pro with the prompt:
Create a webcomic that explains the new feature as clearly and entertainingly as possible
Here's what it produced:
Good enough to publish with the release notes? I don't think so. I'm sharing it here purely to demonstrate the idea. Creating assets like this as a personal tool for thinking about novel ways to explain a feature feels worth exploring further.
Two new Showboat tools: Chartroom and datasette-showboat
I introduced Showboat a week ago—my CLI tool that helps coding agents create Markdown documents that demonstrate the code that they have created. I’ve been finding new ways to use it on a daily basis, and I’ve just released two new tools to help get the best out of the Showboat pattern. Chartroom is a CLI charting tool that works well with Showboat, and datasette-showboat lets Showboat’s new remote publishing feature incrementally push documents to a Datasette instance.
[... 1,756 words]I'm a very heavy user of Claude Code on the web, Anthropic's excellent but poorly named cloud version of Claude Code where everything runs in a container environment managed by them, greatly reducing the risk of anything bad happening to a computer I care about.
I don't use the web interface at all (hence my dislike of the name) - I access it exclusively through their native iPhone and Mac desktop apps.
Something I particularly appreciate about the desktop app is that it lets you see images that Claude is "viewing" via its Read /path/to/image tool. Here's what that looks like:
This means you can get a visual preview of what it's working on while it's working, without waiting for it to push code to GitHub for you to try out yourself later on.
The prompt I used to trigger the above screenshot was:
Run "uvx rodney --help" and then use Rodney to manually test the new pages and menu - look at screenshots from it and check you think they look OK
I designed Rodney to have --help output that provides everything a coding agent needs to know in order to use the tool.
The Claude iPhone app doesn't display opened images yet, so I requested it as a feature just now in a thread on Twitter.
The AI Vampire (via) Steve Yegge's take on agent fatigue, and its relationship to burnout.
Let's pretend you're the only person at your company using AI.
In Scenario A, you decide you're going to impress your employer, and work for 8 hours a day at 10x productivity. You knock it out of the park and make everyone else look terrible by comparison.
In that scenario, your employer captures 100% of the value from you adopting AI. You get nothing, or at any rate, it ain't gonna be 9x your salary. And everyone hates you now.
And you're exhausted. You're tired, Boss. You got nothing for it.
Congrats, you were just drained by a company. I've been drained to the point of burnout several times in my career, even at Google once or twice. But now with AI, it's oh, so much easier.
Steve reports needing more sleep due to the cognitive burden involved in agentic engineering, and notes that four hours of agent work a day is a more realistic pace:
I’ve argued that AI has turned us all into Jeff Bezos, by automating the easy work, and leaving us with all the difficult decisions, summaries, and problem-solving. I find that I am only really comfortable working at that pace for short bursts of a few hours once or occasionally twice a day, even with lots of practice.
Deep Blue
We coined a new term on the Oxide and Friends podcast last month (primary credit to Adam Leventhal) covering the sense of psychological ennui leading into existential dread that many software developers are feeling thanks to the encroachment of generative AI into their field of work.
[... 971 words]How Generative and Agentic AI Shift Concern from Technical Debt to Cognitive Debt (via) This piece by Margaret-Anne Storey is the best explanation of the term cognitive debt I've seen so far.
Cognitive debt, a term gaining traction recently, instead communicates the notion that the debt compounded from going fast lives in the brains of the developers and affects their lived experiences and abilities to “go fast” or to make changes. Even if AI agents produce code that could be easy to understand, the humans involved may have simply lost the plot and may not understand what the program is supposed to do, how their intentions were implemented, or how to possibly change it.
Margaret-Anne expands on this further with an anecdote about a student team she coached:
But by weeks 7 or 8, one team hit a wall. They could no longer make even simple changes without breaking something unexpected. When I met with them, the team initially blamed technical debt: messy code, poor architecture, hurried implementations. But as we dug deeper, the real problem emerged: no one on the team could explain why certain design decisions had been made or how different parts of the system were supposed to work together. The code might have been messy, but the bigger issue was that the theory of the system, their shared understanding, had fragmented or disappeared entirely. They had accumulated cognitive debt faster than technical debt, and it paralyzed them.
I've experienced this myself on some of my more ambitious vibe-code-adjacent projects. I've been experimenting with prompting entire new features into existence without reviewing their implementations and, while it works surprisingly well, I've found myself getting lost in my own projects.
I no longer have a firm mental model of what they can do and how they work, which means each additional feature becomes harder to reason about, eventually leading me to lose the ability to make confident decisions about where to go next.
Someone has to prompt the Claudes, talk to customers, coordinate with other teams, decide what to build next. Engineering is changing and great engineers are more important than ever.
— Boris Cherny, Claude Code creator, on why Anthropic are still hiring developers
The retreat challenged the narrative that AI eliminates the need for junior developers. Juniors are more profitable than they have ever been. AI tools get them past the awkward initial net-negative phase faster. They serve as a call option on future productivity. And they are better at AI tools than senior engineers, having never developed the habits and assumptions that slow adoption.
The real concern is mid-level engineers who came up during the decade-long hiring boom and may not have developed the fundamentals needed to thrive in the new environment. This population represents the bulk of the industry by volume, and retraining them is genuinely difficult. The retreat discussed whether apprenticeship models, rotation programs and lifelong learning structures could address this gap, but acknowledged that no organization has solved it yet.
— Thoughtworks, findings from a retreat concerning "the future of software engineering", conducted under Chatham House rules
Skills in OpenAI API. OpenAI's adoption of Skills continues to gain ground. You can now use Skills directly in the OpenAI API with their shell tool. You can zip skills up and upload them first, but I think an even neater interface is the ability to send skills with the JSON request as inline base64-encoded zip data, as seen in this script:
r = OpenAI().responses.create( model="gpt-5.2", tools=[ { "type": "shell", "environment": { "type": "container_auto", "skills": [ { "type": "inline", "name": "wc", "description": "Count words in a file.", "source": { "type": "base64", "media_type": "application/zip", "data": b64_encoded_zip_file, }, } ], }, } ], input="Use the wc skill to count words in its own SKILL.md file.", ) print(r.output_text)
I built that example script after first having Claude Code for web use Showboat to explore the API for me and create this report. My opening prompt for the research project was:
Run uvx showboat --help - you will use this tool later
Fetch https://developers.openai.com/cookbook/examples/skills_in_api.md to /tmp with curl, then read it
Use the OpenAI API key you have in your environment variables
Use showboat to build up a detailed demo of this, replaying the examples from the documents and then trying some experiments of your own
GLM-5: From Vibe Coding to Agentic Engineering (via) This is a huge new MIT-licensed model: 754B parameters and 1.51TB on Hugging Face twice the size of GLM-4.7 which was 368B and 717GB (4.5 and 4.6 were around that size too).
It's interesting to see Z.ai take a position on what we should call professional software engineers building with LLMs - I've seen Agentic Engineering show up in a few other places recently. most notable from Andrej Karpathy and Addy Osmani.
I ran my "Generate an SVG of a pelican riding a bicycle" prompt through GLM-5 via OpenRouter and got back a very good pelican on a disappointing bicycle frame:
cysqlite—a new sqlite driver
(via)
Charles Leifer has been maintaining pysqlite3 - a fork of the Python standard library's sqlite3 module that makes it much easier to run upgraded SQLite versions - since 2018.
He's been working on a ground-up Cython rewrite called cysqlite for almost as long, but it's finally at a stage where it's ready for people to try out.
The biggest change from the sqlite3 module involves transactions. Charles explains his discomfort with the sqlite3 implementation at length - that library provides two different variants neither of which exactly match the autocommit mechanism in SQLite itself.
I'm particularly excited about the support for custom virtual tables, a feature I'd love to see in sqlite3 itself.
cysqlite provides a Python extension compiled from C, which means it normally wouldn't be available in Pyodide. I set Claude Code on it (here's the prompt) and it built me cysqlite-0.1.4-cp311-cp311-emscripten_3_1_46_wasm32.whl, a 688KB wheel file with a WASM build of the library that can be loaded into Pyodide like this:
import micropip await micropip.install( "https://simonw.github.io/research/cysqlite-wasm-wheel/cysqlite-0.1.4-cp311-cp311-emscripten_3_1_46_wasm32.whl" ) import cysqlite print(cysqlite.connect(":memory:").execute( "select sqlite_version()" ).fetchone())
(I also learned that wheels like this have to be built for the emscripten version used by that edition of Pyodide - my experimental wheel loads in Pyodide 0.25.1 but fails in 0.27.5 with a Wheel was built with Emscripten v3.1.46 but Pyodide was built with Emscripten v3.1.58 error.)
You can try my wheel in this new Pyodide REPL i had Claude build as a mobile-friendly alternative to Pyodide's own hosted console.
I also had Claude build this demo page that executes the original test suite in the browser and displays the results:
Introducing Showboat and Rodney, so agents can demo what they’ve built
A key challenge working with coding agents is having them both test what they’ve built and demonstrate that software to you, their supervisor. This goes beyond automated tests—we need artifacts that show their progress and help us see exactly what the agent-produced software is able to do. I’ve just released two new tools aimed at this problem: Showboat and Rodney.
[... 2,023 words]AI Doesn’t Reduce Work—It Intensifies It (via) Aruna Ranganathan and Xingqi Maggie Ye from Berkeley Haas School of Business report initial findings in the HBR from their April to December 2025 study of 200 employees at a "U.S.-based technology company".
This captures an effect I've been observing in my own work with LLMs: the productivity boost these things can provide is exhausting.
AI introduced a new rhythm in which workers managed several active threads at once: manually writing code while AI generated an alternative version, running multiple agents in parallel, or reviving long-deferred tasks because AI could “handle them” in the background. They did this, in part, because they felt they had a “partner” that could help them move through their workload.
While this sense of having a “partner” enabled a feeling of momentum, the reality was a continual switching of attention, frequent checking of AI outputs, and a growing number of open tasks. This created cognitive load and a sense of always juggling, even as the work felt productive.
I'm frequently finding myself with work on two or three projects running parallel. I can get so much done, but after just an hour or two my mental energy for the day feels almost entirely depleted.
I've had conversations with people recently who are losing sleep because they're finding building yet another feature with "just one more prompt" irresistible.
The HBR piece calls for organizations to build an "AI practice" that structures how AI is used to help avoid burnout and counter effects that "make it harder for organizations to distinguish genuine productivity gains from unsustainable intensity".
I think we've just disrupted decades of existing intuition about sustainable working practices. It's going to take a while and some discipline to find a good new balance.
I am having more fun programming than I ever have, because so many more of the programs I wish I could find the time to write actually exist. I wish I could share this joy with the people who are fearful about the changes agents are bringing. The fear itself I understand, I have fear more broadly about what the end-game is for intelligence on tap in our society. But in the limited domain of writing computer programs these tools have brought so much exploration and joy to my work.
— David Crawshaw, Eight more months of agents
How StrongDM’s AI team build serious software without even looking at the code
Last week I hinted at a demo I had seen from a team implementing what Dan Shapiro called the Dark Factory level of AI adoption, where no human even looks at the code the coding agents are producing. That team was part of StrongDM, and they’ve just shared the first public description of how they are working in Software Factories and the Agentic Moment:
[... 1,664 words]Running Pydantic’s Monty Rust sandboxed Python subset in WebAssembly
There’s a jargon-filled headline for you! Everyone’s building sandboxes for running untrusted code right now, and Pydantic’s latest attempt, Monty, provides a custom Python-like language (a subset of Python) in Rust and makes it available as both a Rust library and a Python package. I got it working in WebAssembly, providing a sandbox-in-a-sandbox.
[... 854 words]When I want to quickly implement a one-off experiment in a part of the codebase I am unfamiliar with, I get codex to do extensive due diligence. Codex explores relevant slack channels, reads related discussions, fetches experimental branches from those discussions, and cherry picks useful changes for my experiment. All of this gets summarized in an extensive set of notes, with links back to where each piece of information was found. Using these notes, codex wires the experiment and makes a bunch of hyperparameter decisions I couldn’t possibly make without much more effort.
— Karel D'Oosterlinck, I spent $10,000 to automate my research at OpenAI with Codex
Mitchell Hashimoto: My AI Adoption Journey (via) Some really good and unconventional tips in here for getting to a place with coding agents where they demonstrably improve your workflow and productivity. I particularly liked:
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Reproduce your own work - when learning to use coding agents Mitchell went through a period of doing the work manually, then recreating the same solution using agents as an exercise:
I literally did the work twice. I'd do the work manually, and then I'd fight an agent to produce identical results in terms of quality and function (without it being able to see my manual solution, of course).
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End-of-day agents - letting agents step in when your energy runs out:
To try to find some efficiency, I next started up a new pattern: block out the last 30 minutes of every day to kick off one or more agents. My hypothesis was that perhaps I could gain some efficiency if the agent can make some positive progress in the times I can't work anyways.
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Outsource the Slam Dunks - once you know an agent can likely handle a task, have it do that task while you work on something more interesting yourself.
Distributing Go binaries like sqlite-scanner through PyPI using go-to-wheel
I’ve been exploring Go for building small, fast and self-contained binary applications recently. I’m enjoying how there’s generally one obvious way to do things and the resulting code is boring and readable—and something that LLMs are very competent at writing. The one catch is distribution, but it turns out publishing Go binaries to PyPI means any Go binary can be just a uvx package-name call away.
We gotta talk about AI as a programming tool for the arts. Chris Ashworth is the creator and CEO of QLab, a macOS software package for “cue-based, multimedia playback” which is designed to automate lighting and audio for live theater productions.
I recently started following him on TikTok where he posts about his business and theater automation in general - Chris founded the Voxel theater in Baltimore which QLab use as a combined performance venue, teaching hub and research lab (here's a profile of the theater), and the resulting videos offer a fascinating glimpse into a world I know virtually nothing about.
This latest TikTok describes his Claude Opus moment, after he used Claude Code to build a custom lighting design application for a very niche project and put together a useful application in just a few days that he would never have been able to spare the time for otherwise.
Chris works full time in the arts and comes at generative AI from a position of rational distrust. It's interesting to see him working through that tension to acknowledge that there are valuable applications here to build tools for the community he serves.
I have been at least gently skeptical about all this stuff for the last two years. Every time I checked in on it, I thought it was garbage, wasn't interested in it, wasn't useful. [...] But as a programmer, if you hear something like, this is changing programming, it's important to go check it out once in a while. So I went and checked it out a few weeks ago. And it's different. It's astonishing. [...]
One thing I learned in this exercise is that it can't make you a fundamentally better programmer than you already are. It can take a person who is a bad programmer and make them faster at making bad programs. And I think it can take a person who is a good programmer and, from what I've tested so far, make them faster at making good programs. [...] You see programmers out there saying, "I'm shipping code I haven't looked at and don't understand." I'm terrified by that. I think that's awful. But if you're capable of understanding the code that it's writing, and directing, designing, editing, deleting, being quality control on it, it's kind of astonishing. [...]
The positive thing I see here, and I think is worth coming to terms with, is this is an application that I would never have had time to write as a professional programmer. Because the audience is three people. [...] There's no way it was worth it to me to spend my energy of 20 years designing and implementing software for artists to build an app for three people that is this level of polish. And it took me a few days. [...]
I know there are a lot of people who really hate this technology, and in some ways I'm among them. But I think we've got to come to terms with this is a career-changing moment. And I really hate that I'm saying that because I didn't believe it for the last two years. [...] It's like having a room full of power tools. I wouldn't want to send an untrained person into a room full of power tools because they might chop off their fingers. But if someone who knows how to use tools has the option to have both hand tools and a power saw and a power drill and a lathe, there's a lot of work they can do with those tools at a lot faster speed.
Adding dynamic features to an aggressively cached website
My blog uses aggressive caching: it sits behind Cloudflare with a 15 minute cache header, which guarantees it can survive even the largest traffic spike to any given page. I’ve recently added a couple of dynamic features that work in spite of that full-page caching. Here’s how those work.
[... 1,145 words]The Five Levels: from Spicy Autocomplete to the Dark Factory. Dan Shapiro proposes a five level model of AI-assisted programming, inspired by the five (or rather six, it's zero-indexed) levels of driving automation.
- Spicy autocomplete, aka original GitHub Copilot or copying and pasting snippets from ChatGPT.
- The coding intern, writing unimportant snippets and boilerplate with full human review.
- The junior developer, pair programming with the model but still reviewing every line.
- The developer. Most code is generated by AI, and you take on the role of full-time code reviewer.
- The engineering team. You're more of an engineering manager or product/program/project manager. You collaborate on specs and plans, the agents do the work.
- The dark software factory, like a factory run by robots where the lights are out because robots don't need to see.
Dan says about that last category:
At level 5, it's not really a car any more. You're not really running anybody else's software any more. And your software process isn't really a software process any more. It's a black box that turns specs into software.
Why Dark? Maybe you've heard of the Fanuc Dark Factory, the robot factory staffed by robots. It's dark, because it's a place where humans are neither needed nor welcome.
I know a handful of people who are doing this. They're small teams, less than five people. And what they're doing is nearly unbelievable -- and it will likely be our future.
I've talked to one team that's doing the pattern hinted at here. It was fascinating. The key characteristics:
- Nobody reviews AI-produced code, ever. They don't even look at it.
- The goal of the system is to prove that the system works. A huge amount of the coding agent work goes into testing and tooling and simulating related systems and running demos.
- The role of the humans is to design that system - to find new patterns that can help the agents work more effectively and demonstrate that the software they are building is robust and effective.
It was a tiny team and they stuff they had built in just a few months looked very convincing to me. Some of them had 20+ years of experience as software developers working on systems with high reliability requirements, so they were not approaching this from a naive perspective.
I'm hoping they come out of stealth soon because I can't really share more details than this.
Update 7th February 2026: The demo was by StrongDM's AI team, and they've now gone public with details of how they work.
One Human + One Agent = One Browser From Scratch (via) embedding-shapes was so infuriated by the hype around Cursor's FastRender browser project - thousands of parallel agents producing ~1.6 million lines of Rust - that they were inspired to take a go at building a web browser using coding agents themselves.
The result is one-agent-one-browser and it's really impressive. Over three days they drove a single Codex CLI agent to build 20,000 lines of Rust that successfully renders HTML+CSS with no Rust crate dependencies at all - though it does (reasonably) use Windows, macOS and Linux system frameworks for image and text rendering.
I installed the 1MB macOS binary release and ran it against my blog:
chmod 755 ~/Downloads/one-agent-one-browser-macOS-ARM64
~/Downloads/one-agent-one-browser-macOS-ARM64 https://simonwillison.net/
Here's the result:
It even rendered my SVG feed subscription icon! A PNG image is missing from the page, which looks like an intermittent bug (there's code to render PNGs).
The code is pretty readable too - here's the flexbox implementation.
I had thought that "build a web browser" was the ideal prompt to really stretch the capabilities of coding agents - and that it would take sophisticated multi-agent harnesses (as seen in the Cursor project) and millions of lines of code to achieve.
Turns out one agent driven by a talented engineer, three days and 20,000 lines of Rust is enough to get a very solid basic renderer working!
I'm going to upgrade my prediction for 2029: I think we're going to get a production-grade web browser built by a small team using AI assistance by then.
ChatGPT Containers can now run bash, pip/npm install packages, and download files
One of my favourite features of ChatGPT is its ability to write and execute code in a container. This feature launched as ChatGPT Code Interpreter nearly three years ago, was half-heartedly rebranded to “Advanced Data Analysis” at some point and is generally really difficult to find detailed documentation about. Case in point: it appears to have had a massive upgrade at some point in the past few months, and I can’t find documentation about the new capabilities anywhere!
[... 3,019 words]Don’t “Trust the Process” (via) Jenny Wen, Design Lead at Anthropic (and previously Director of Design at Figma) gave a provocative keynote at Hatch Conference in Berlin last September.
Jenny argues that the Design Process - user research leading to personas leading to user journeys leading to wireframes... all before anything gets built - may be outdated for today's world.
Hypothesis: In a world where anyone can make anything — what matters is your ability to choose and curate what you make.
In place of the Process, designers should lean into prototypes. AI makes these much more accessible and less time-consuming than they used to be.
Watching this talk made me think about how AI-assisted programming significantly reduces the cost of building the wrong thing. Previously if the design wasn't right you could waste months of development time building in the wrong direction, which was a very expensive mistake. If a wrong direction wastes just a few days instead we can take more risks and be much more proactive in exploring the problem space.
I've always been a compulsive prototyper though, so this is very much playing into my own existing biases!
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]