Simon Willison

So I see pretty modest traffic to my site. Discounting bots/scrapers, it's about a thousand visits a month, so it's not a lot. I also don't have a newsletter, which definitely doesn't help.

Honestly, you really should focus on a niche. For example, Simon Willison [1] keeps his blog super consistent, he's the AI journalist. Josh Comeau [2] is another good example from Hacker News: probably 99% of his content is front-end design and development. Becoming (or establishing oneself) as an expert in some space goes a long way toward increasing your subscriber count.

My articles are mostly satirical in nature and have all determinism of a literary brownian motion machine (writing on algorithms, thought experiments, fiction, etc.) so there’s not a particularly cohesive identity to the site.

Another thing that is often mentioned is that you need to publish regularly and often. I tend to be a perfectionist, and spend a lot of time crafting each article with custom illustrations, animations, and interactive elements. This works for people like Bartosz Ciechanowski [3], but I'm not sure it helps drive traffic for me personally.

[1] - https://simonwillison.net

[2] - https://www.joshwcomeau.com/svg/interactive-guide-to-paths

[3] - https://ciechanow.ski/mechanical-watch

SimonW used to have more articles/guides on local LLM setup, at least until he got the big toys to play with, but well worth looking through his site. Although if you are in parts of Europe, the site is blocked at weekends, something to do with the great-firewall of streamed sports.

https://simonwillison.net/

Indeed, his self hosting inspired me to get Qwen3:32B ollama working locally. Fits nicely on my M1 pro 32GB (running Asahi). Output is a nice read-along speed and I havent felt the need for anything more powerful.

I'd be more tempted with a maxed out M2 Ultra as an upgrade, vs tower with dedicated GPU cards. The unified memory just feels right for this task. Although I noticed the 2nd hand value of those machine jumped massively in the last few months.

I know that people turn their noses up at local LLM's, but it more than does the job for me. Plus I decided a New Years Resolution of no more subscriptions / Big-AdTech freebies.

There's many examples of exactly what you're asking for, such as Kenton Varda's Cloudlfare oauth provider [1] and Simon Willison's tools [2]. I see a new blog post like this with detailed explanations of what they did pretty frequently, like Steve Klabnik's recent post [3], which while it isn't as detailed has a lot of very concrete facts. There's even more blog posts from prominent devs like antirez who talk about other things they're doing with AI like rubber ducking [4], if you're curious about how some people who say "I used Sonnet last week and it was great" are working, because not everyone uses it to write code - I personally don't because I care a lot about code style.

[1]: https://github.com/cloudflare/workers-oauth-provider/

[2]: https://tools.simonwillison.net/

[3]: https://steveklabnik.com/writing/a-tale-of-two-claudes/

[4]: https://antirez.com/news/153

Thanks for the benefit of the doubt. I typed that in a hurry, and it didn’t come out the way I intended.

We had the idea that there’s a class of apps [1] that could really benefit from our tooling - mainly Fireproof, our local-first database, along with embedded LLM calling and image generation support. The app itself is open source, and the hosted version is free.

Initially, there was no login or signup - you could just generate an app right away. We knew that came with risks, but we wanted to explore what a truly frictionless experience could look like. Unfortunately, it didn’t take long for our LLM keys to start getting scraped, so the next best step was to implement rate limiting in the hosted version.

[1] https://tools.simonwillison.net/

- everything that simon writes (https://simonwillison.net/)

- anything that goes deep into issues (I seldom read “i love llms” type posts like this is great: https://blog.nilenso.com/blog/2025/09/15/ai-unit-of-work/)

- lots of experimentation - specifically I have spent hours and hours doing the exact same feature (my record is 23 times).

- if something “doesn’t work” I create a task immediately to investigate and understand it. even the smallest thing that bother me I will spend hours to figure out why it might have happened (this is sometimes frustrating) and how to prevent it from happening again (this is fun)

My collegue describes the process as Javascript developer trying to learn Rust while tripping on mushrooms :)

Theo video based on data from Tencent's Autocodebench: https://youtu.be/iV1EcfZSdCM

Conclusion: Elixir was the best (had the highest problem solve rate).

Reasons (Theo's interpretation):

- code collocation, where documentation is integrated directly within the source code

- design philosophy of a language (readability, clear idioms, and strict expectation management)

---

Sources from video:

- https://github.com/Tencent-Hunyuan/AutoCodeBenchmark/tree/main

- https://martinalderson.com/posts/which-programming-languages-are-most-token-efficient/

- https://simonwillison.net/2026/Jan/19/nanolang/#atom-everything