i've basically dropped issue tracking the best way to see what our team is working on is to look at the traces

Cue the music 😁

Never, ever throw away your coding agent traces. If it wasn’t obvious before it should be now.

agent-written PR descriptions are terrible and they silently slow down review we just added a "ELI 15" skill to make PR descriptions easier to review

fable in ultracode mode with workflows is really, really good. definitely a “switch back to Claude” moment

did this for our interns, traces has been amazing!

hearing this a lot more these days

I'll be speaking at the AFK coding workshop tomorrow talking about all the value contained in your agent traces. Come check it out! Register here 👇

A product I find using more and more. Tarun has also been great a helping out with any issues over text. Simple use case right now is sending a session to my co-founder and engineers where I plan out an idea, and then can take it form there and implement it. There are so many other ways to use it though!

we didn't have to make our TUI responsive and pretty, but we did anyways Traces v0.6.1 out now with session resuming, search via cli, and tons of performance improvements

cool to reflect on this original launch moment sometimes the progress has exceeded all my expectations and this is easily the most interesting products I’ve ever shaped over the next few weeks we’ll be rolling out one of the most consequential updates since launch. can’t wait

Judging developers based on tokens is like judging a contractor based on watts consumed by their power tools There will be loose correlation to outcomes but really it’s useful only to isolate LLM skeptics The better way is always to look at their new work product (sessions)…

announcing Falcon GX the world's most powerful brand engineering tool now in public beta

what if you could see how many people downloaded your ai prompts now available on traces.com profile pages

nice

Homecoming 🇨🇦📸

Introducing a minimal training harness built on prime-rl and verifiers, so you can now train your own RLMs without sandboxes! All available in the `training/` folder in the RLM GitHub repo! We train RLM-Qwen3-30B-A3B-v0.1, using RL on a separate split of environments (OOLONG-Spam, BC split) to greatly improve performance across the board on long-context tasks evaluated in the original RLM paper. We trained for a day on an 8xA100 using prime-rl; code and model are open-source and available on GitHub / Huggingface.