2026-02-19 13:44:29
I've long been resistant to the idea of accepting sponsorship for my blog. I value my credibility as an independent voice, and I don't want to risk compromising that reputation.
Then I learned about Troy Hunt's approach to sponsorship, which he first wrote about in 2016. Troy runs with a simple text row in the page banner - no JavaScript, no cookies, unobtrusive while providing value to the sponsor. I can live with that!
Accepting sponsorship in this way helps me maintain my independence while offsetting the opportunity cost of not taking a full-time job.
To start with I'm selling sponsorship by the week. Sponsors get that unobtrusive banner across my blog and also their sponsored message at the top of my newsletter.
I will not write content in exchange for sponsorship. I hope the sponsors I work with understand that my credibility as an independent voice is a key reason I have an audience, and compromising that trust would be bad for everyone.
Freeman & Forrest helped me set up and sell my first slots. Thanks also to Theo Browne for helping me think through my approach.
Tags: newsletter, blogging, troy-hunt
2026-02-19 12:48:47
SWE-bench February 2026 leaderboard update
SWE-bench is one of the benchmarks that the labs love to list in their model releases. The official leaderboard is infrequently updated but they just did a full run of it against the current generation of models, which is notable because it's always good to see benchmark results like this that weren't self-reported by the labs.The fresh results are for their "Bash Only" benchmark, which runs their mini-swe-bench agent (~9,000 lines of Python, here are the prompts they use) against the SWE-bench dataset of coding problems - 2,294 real-world examples pulled from 12 open source repos: django/django (850), sympy/sympy (386), scikit-learn/scikit-learn (229), sphinx-doc/sphinx (187), matplotlib/matplotlib (184), pytest-dev/pytest (119), pydata/xarray (110), astropy/astropy (95), pylint-dev/pylint (57), psf/requests (44), mwaskom/seaborn (22), pallets/flask (11).
Here's how the top ten models performed:
It's interesting to see Claude Opus 4.5 beat Opus 4.6, though only by about a percentage point. 4.5 Opus is top, then Gemini 3 Flash, then MiniMax M2.5 - a 229B model released last week by Chinese lab MiniMax. GLM-5, Kimi K2.5 and DeepSeek V3.2 are three more Chinese models that make the top ten as well.
OpenAI's GPT-5.2 is their highest performing model at position 6, but it's worth noting that their best coding model, GPT-5.3-Codex, is not represented - maybe because it's not yet available in the OpenAI API.
This benchmark uses the same system prompt for every model, which is important for a fair comparison but does mean that the quality of the different harnesses or optimized prompts is not being measured here.
The chart above is a screenshot from the SWE-bench website, but their charts don't include the actual percentage values visible on the bars. I successfully used Claude for Chrome to add these - transcript here. My prompt sequence included:
Use claude in chrome to open https://www.swebench.com/
Click on "Compare results" and then select "Select top 10"
See those bar charts? I want them to display the percentage on each bar so I can take a better screenshot, modify the page like that
I'm impressed at how well this worked - Claude injected custom JavaScript into the page to draw additional labels on top of the existing chart.
![Screenshot of a Claude AI conversation showing browser automation. A thinking step reads "Pivoted strategy to avoid recursion issues with chart labeling >" followed by the message "Good, the chart is back. Now let me carefully add the labels using an inline plugin on the chart instance to avoid the recursion issue." A collapsed "Browser_evaluate" section shows a browser_evaluate tool call with JavaScript code using Chart.js canvas context to draw percentage labels on bars: meta.data.forEach((bar, index) => { const value = dataset.data[index]; if (value !== undefined && value !== null) { ctx.save(); ctx.textAlign = 'center'; ctx.textBaseline = 'bottom'; ctx.fillStyle = '#333'; ctx.font = 'bold 12px sans-serif'; ctx.fillText(value.toFixed(1) + '%', bar.x, bar.y - 5); A pending step reads "Let me take a screenshot to see if it worked." followed by a completed "Done" step, and the message "Let me take a screenshot to check the result."](https://static.simonwillison.net/static/2026/claude-chrome-draw-on-chart.jpg)
Via @KLieret
Tags: benchmarks, django, ai, openai, generative-ai, llms, anthropic, claude, coding-agents, ai-in-china, minimax
2026-02-19 09:25:33
LadybirdBrowser/ladybird: Abandon Swift adoption
Back in August 2024 the Ladybird browser project announced an intention to adopt Swift as their memory-safe language of choice.As of this commit it looks like they've changed their mind:
Everywhere: Abandon Swift adoption
After making no progress on this for a very long time, let's acknowledge it's not going anywhere and remove it from the codebase.
Via Hacker News
2026-02-19 02:56:56
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.
Tags: ai-assisted-programming, programming, programming-languages, static-typing
2026-02-19 01:07:31
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.
Tags: new-york-times, paul-ford, careers, ai, generative-ai, llms, ai-assisted-programming, ai-ethics, coding-agents, claude-code
2026-02-19 00:50:07
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)
Tags: martin-fowler, careers, generative-ai, ai, llms, ai-assisted-programming
