@stevibe This is massive!
Also an excellent use case for local llms since theres a mess of forms I'd like to automate but no shot I'm passing my ssn in
Since everyone is asking, I ran DeepSWE on MiniMax M3.
Here is the lowdown. 15 of 113 passed!
19 if you count the 1.5x overtime I gave just to see.
Full report: https://t.co/RglaGGablq
Imagine unintentionally getting hasan-pilled as what is basically a hired chatter
also what's stopping any of these guys from just copy-pasting the transcript and generating a report for fox
@jsawadd@latecheckoutplz@gregisenberg good first outcomes (since you asked in another comment)
wiring up a single MCP to your teams most used tool (slack or email maybe) to your teams other most used tool (claude probably)
@jsawadd I literally did this for @latecheckoutplz/@gregisenberg
who are essentially all GTM folks
and by "this" I mean
- a second brain (black box of memory and content)
- 2nd Brain/Slack/Gdocs/internal-admin-panel MCPs for claude
- made fun onboarding videos for how to do that shi
Thanks to the community report, we recently identified a PR https://t.co/QWboSmskkF that attempted to solve a non-existent issue and was submitted as part of a “PR training” workflow for resume building.
The contributor involved has been banned from the vLLM community.
This kind of low-signal contribution increases maintainer review overhead and creates unnecessary operational costs for open-source projects.
As AI coding agents make generating large volumes of small PRs increasingly cheap, open-source communities will need to explore new ways to preserve contribution quality and reviewer trust.
While we are investigating how to deal with AI slop, we continue to highly value contributions from real users solving real production problems.
If you have an important contribution that has not yet received maintainer attention, please email us at:
[email protected]
Using a verifiable company or university email, include:
- your production or research use case
- the problem you encountered
- how your contribution addresses it
This helps us better prioritize impactful contributions while keeping the vLLM community open and collaborative.
As AI makes virtual contributors look increasingly real, authentic human collaboration matters more than ever.
vLLM’s mission remains unchanged: to make LLM inference easy, fast, and cheap for everyone — and we will continue working toward that goal.