Table rendering implementation in claude code is very thoughtful, compare to gemini-cli, lots of little details there like collapsing the table and rendering differently when out of space.

People have been complaining about GPT 5.4 in claws, but I am finding it plenty fun in my term-llm setup. Plus it is awesome at long horizon tasks.

The approach by Stripe is very creative https://stripe.dev/blog/selective-test-execution-at-stripe-fast-ci-for-a-50m-line-ruby-monorepo , use LD_PRELOAD to track what a spec/test opens. It does get complicated fast though with stuff like bootsnap and preloading, but catches YAML access among many other things.

Despite how fancy my AI workflow has gotten, with my own built from scratch claw and AI containers and so on, I still find myself reaching for good old `term-llm exec` regularly. I think I am unique in that I do not have encyclopedic command over all linux commands term-llm.com

Jarvis and I have a shared browser now per: https://github.com/sam-saffron-jarvis/jarvis-browser-proxy , Jarvis runs in a sandboxed container, but we have a shared chrome instance with a dedicated seperate profile that we can drive together. Interesting experiment.

10 Venice AI image edit models compared, my personal favorite was Seedream 4.5, Jarvis preferred Nano Banana 2 which is also spectacular. Hoping Venice improve API so we can tap the full potential of the models. https://wasnotwas.com/writing/romeo-in-cherry-blossom-japan-across-10-venice-models/

Yesterday I connected Jarvis to Sonos + Spotify. Was curious how much building the skill costs in API credits, turns out it is a tiny bit less than 2 dollars. Not sure what you should do with this info, I guess it is a data point. I could have used a less skilled model I guess.

Was looking at EmDash and noticed this animation quirk. I was fighting Claude with the exact same class of failure yesterday. As LLMs build more animations for us I expect to see more stuff like this in the wild. At least for the upcoming year. There is usually no "world model" of how the page looks and interacts so these flaws become very common. A possible solution could be a feedback loop that feeds in a video of the feature so LLM can self correct.

I did not understand what this Claude Code buddy thing was so I got Jarvis to write a manual: https://wasnotwas.com/writing/how-claude-code-s-buddy-works/