@Teknium A tiny local model can achieve similar results and could also be useful for the smart approvals mode. It's still early, but have you seen these posts:
https://t.co/ENd0afVW7c
https://t.co/MqndIZsMyc
https://t.co/7A7Ir9IQ2i
They were building in stealth for 2 years, I was building in stealth for 2 hours…
Happy to open source Qwen-2.5-1B-RLCD, 5x faster on-device inference for JSON workloads that need to be type-safe.
⚡️Demo below on a M4 MacBook⚡️
every LLM has the ability to efficiently batch inference every key of a JSON at the same time and generate probabilities from a set of possible categories. No new training required, but it’s easy to optimize if you need!
On hugging face now!
Anyone else seeing high usage with Codex desktop when idle?
My M4 mac gets hot with no tasks running. After ~4.5 hours open, Codex Renderer was using ~5.5 GB memory. During a quick diagnostic chat, Renderer showed ~27–31% CPU and Codex Service another ~30–35%. @thsottiaux
@mattyp Loving the Grok Bot Galaxy stream so far.
One thing though - people keep having to re-explain context that another team already has.
Any plans for a way to share a team so bots can actually collaborate across accounts?
@kelvinbuildss Not without distribution, for instance, a personal brand. Without an existing audience, you'd likely need some budget to gain initial traction and make sales.
@simonecanciello hmm, what about instead of measuring weight, you measure consistency? Seems like the retention would be a lot better and would build a stronger habit 🤔