Since Bitwarden is open source, can't somebody create a community-driven fork? Maybe a self hosted option?

Somebody did: https://github.com/dani-garcia/vaultwarden

Someone can, and did

"We also support inpainting, enabling targeted audio editing and the continuation of short recordings."

I didn't know there were models for that. Very cool!

PlayDiffusion is a notable one. But the state of the art is quickly evolving.

82.7% on Terminal Bench is crazy

Is it? There are 5 other models near ~80% and it was achieved in March... which in AI-world seems like a century ago.

https://www.tbench.ai/leaderboard/terminal-bench/2.0

those are not verified. I've tried forgecode and I cannot believe they didn't do something to influence the benchmarks

Yup, they were found to be sneaking the answer key using agents.md

https://debugml.github.io/cheating-agents/#sneaking-the-answ...

I guess it depends if you are working on something important to national security. Especially corporate codebases, etc.

Does that include OpenCode? That's what I care about most and it's the primary reason I've been sticking with OAI the past few months.

Anthropic doesn't, but Google and OAI both release open source models. Just not 1T parameter ones.

Exactly, they release cool consumer stuff, but they aren't releasing anything close to the performance of the best open weight Chinese models. They basically compete in the "fun running at home doing basic stuff" scene. (Except OSs 120 by openai but it's been ages since then)

That sentence is giving OpenAI way more credit than they are due.

They released a single open model after being goaded by the community because everyone except "Open"AI were multiple generations into open releases.

We haven't heard a word since, I wouldn't be surprised if it takes them another 6 years to release their next one.

Last paragraph made me chuckle

npm security team has removed the offending package: https://github.com/axios/axios/issues/10604#issuecomment-415...

new installs should be safe now

What a brilliant idea! is this all done locally? That's incredible.

While the vector store is local, it is sending the data to Gemini's API for embedding. (Which if using a paid API key is probably fine for most use cases, no long term retention/training etc.)

works completely locally with a decent model: https://github.com/jakejimenez/sentinelsearch

Make a proof of concept, honestly worked fairly well: https://github.com/jakejimenez/sentinelsearch

I am curious how the TPS compares vs default OS virtual memory paging