@saarw
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AI builder and consulting | Backend/data engineering for Spotify, Candy Crush, helped build SF-based appsec startup, financial trading tech @javamissionctrl
Stockholm + Remote
Joined October 2012
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Good points about AI adoption
William Saar retweeted
Some observations about the "total" automation of software engineering:
1. There are more software engineers now than in 2025, and demand is growing.
2. Software engineers have never worked harder than they do now.
3. They largely don't write code anymore, agents write ~100% of the code.
4. The bottom line of the companies that employs these engineers and agents (i.e. the terminal purpose of all this code) is typically not budging as a result of AI adoption.
This is why you have to think in terms of systems. Automating one aspect of a system creates its own friction and moves the bottlenecks elsewhere.
Hope Tesla forces all sports car companies to invest in rockets. Could lead to a European SpaceX competitor
William Saar retweeted
I've had agents implement and optimize the Campfire web app in Elixir, Go, and Rust. Yes, Ruby is the slowest. I didn't care about that when the pay-off was huge in terms of productivity and developer joy. But if you're no longer reading the code? github.com/basecamp/once-cam…
Readers added context they thought people might want to know
The shown benchmarks use non-equivalent implementations: Elixir routes all DB queries through a single GenServer with Redis for jobs (retained from Rails), while Rust uses in-process queues, omits CSRF tags/fields, and applies other caching changes.
github.com/basecamp/once-…
raw.githubusercontent.com/basecamp/once-…
github.com/basecamp/once-…
github.com/basecamp/once-…
x.com/zachsdaniel1/s…
Isn't that how Teams is usually described compared to alternatives
The European Commission has rolled out an alternative to Microsoft Teams.
But officials using it aren't impressed, with one describing the tool as “absolute shit.”
vist.ly/5kje9
The performance price Ruby developers have been paying all along
Been running a beta version of Campfire rewritten in Rust today! The code itself is still ugly as sin, and 6x as verbose, but the conversion was free*, and I never had to look at the Rust directly, so this is awesome 🤩 github.com/basecamp/once-cam…
William Saar retweeted
Announcing one year of LLM inference metadata traces, with 6.12 billion requests.
We hope this dataset can support research on real-world LLM serving workload understanding, system design and infrastructure optimization. Explore the dataset and learn more: data.agentic-system.org
Driven by our great graduate student William Nixon and
in collab with @jon_durbin @airesearch12 @chutes_ai
William Saar retweeted
UPDATE: Kev-0.6B, 4B, and 8B are now available. Kev is a family of small open source Jev-like decision models you can train and run yourself.
This new family is based on Qwen3 using the same LoRA + small pointer head technique as before, but scaled up.
Out of domain, on data Kev never trained on: Kev-8B 79.6%, Jev 85.7%.
• Drop-in TypeSafe System One API; their SDK works with one `base_url` change
• Kev-4B serves on a 32 GB Mac in bf16: ~300 ms for five questions, ~40 ms on an H100
• Repeated documents hit a KV cache: 2-2.5x faster
• Apache 2.0 License. Kev-4B trains in 40 minutes on one H100. Kev-8B in 83 minutes.
Code, weights, evals: github.com/jaredpalmer/kev
Kev-0.5B: A tiny open source Jev-like decision model with a TypeSafe-compatible API based on Qwen2.5-0.5B that you can train and run on a MacBook Pro.
Model card and weights are available on GitHub
github.com/jaredpalmer/kev
William Saar retweeted
Frequent video gamers performed on cognitive tests like non-gamers that were ~13.7 years younger
Good reminder, especially with the limitless possibilities of AI
I can’t stress enough how little an idea matters compared to the agency of the people executing the idea.
I have had the privilege of knowing and sometimes even working with some of the most successful people (by various metrics).
The difference between mediocre and excellent work and outcomes is predominantly one of agency.
In practice this means: they dont wait for things to happen to them they go out and make things happen for them.
They don’t wait for someone else to do something, for someone to teach them, for someone to give them the path, etc. They just go out and find a way to do it.
I think the single biggest superpower these people have is the realization/belief that the world around them is completely mutable. Most everything that happens is because a person made it happen.
I used to tell people to look around the room you’re sitting in. Look at everything. Every noun. It almost all exists because a person willed it into existence. Nothing is stopping you from doing the same.
I see people online all the time dismissing someone else’s success because “I had that idea first” or whatever. I mean… yeah? If so then the difference is… you. So a bit of a self own whenever I hear that.
Number one tip: act with agency.
William Saar retweeted
🎉 CUDA Rust is here! Team 🟩 ❤️ 🦀
You can now write CUDA kernels in plain Rust, two ways: cuda-oxide (SIMT) and cutile-rs (Tile).
developer.nvidia.com/blog/in…
AI coding means interest in the business/side-project domain becomes more important for developers
It used to be that even boring domains could offer interesting technical challenges. Now, AI handles most of those while product decisions and verification become more important
Regardless of whether OpenAI itself of AI-assisted human researchers came up with the solution to Navier-Stokes, it shouldn't go unnoticed that Europe's AI stance forces the region to outsource most advanced thinking in all kinds of fields to the US and China
William Saar retweeted
We’re sharing a solution to the Navier-Stokes Millennium Prize Problem, one of the deepest problems at the frontier of mathematics.
The proof was produced by a group of agents, using an OpenAI next-generation model significantly more capable than GPT-6 Astra.
The problem concerns whether the description of smooth three-dimensional fluid motion modeled by the Navier-Stokes equations can break down. It has remained unresolved for roughly 90 years.
William Saar retweeted
Training tiny models for special purpose use cases works so incredibly well if you have a great self improving recursive flywheel. Shopify ML team is on fire.
finetuned 0.8b model beats GPT 5.6-sol xhigh in this very specialized task.
William Saar retweeted
/scandinavian-design
built a skill that turns any site into scandi minimalism!
npx skills add ericzakariasson/scandinavian-design
try it: scandinavian-design.vercel.a…