Smells Like ML @smellslikeml
Building #ExperimentOps @remyxai Experiment orchestration for AI teams get outrider: https://t.co/HLjSzx57dj remyx.ai San Francisco, CA Joined October 2018-
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outrider proposes another feature for PEFT github.com/smellslikeml/p… just like peft PR#3382 it's the second outrider match we've validated against maintainer interests github.com/sockeye44/dora…
over successive runs on my forks, the orchestrator learns to target these surfaces: atropos → reward_fns/ registry peft → src/peft/tuners/<method>/layer.py::forward opik → src/opik/metrics/ lm-eval-harness → metric registry OLMo-core → olmo_core/train/callbacks/
cheaper models like GLM help affordably scale code exploration, offering more context for my implementation strategy opus is great for concurrency but since I ran sequentially w/ GLM backend, I set up an adaptive search for a final, more fully-spec'd proposal
Want to know exactly which small changes would let your repo absorb the latest AI methods? Ran autoresearch across 5 repos. Got a gap analysis with prioritized next steps. gist.github.com/smellslikeml/3…
@simonw over this last week, way more of my code is made using github actions than back and forth chats with claude
Personal skill will get even @simonw only so far we have to start building better ways to scale code synthesis Been pushing my own limits using a Github action to implement PRs across dozens of OSS repos each day It's open and free Get Outrider: github.com/remyxai/outrid…
"Using coding agents well is taking every inch of my 25 years of experience as a software engineer, and it is mentally exhausting. I can fire up four agents in parallel and have them work on four different problems, and by 11am I am wiped out for the day. There is a limit on
the competition is beyond reading the paper, they're adapting methods to their own repos get outrider: github.com/remyxai/outrid…
1 week left in our competition with @askalphaxiv. Don't know where to start? Pick a research area and check out the recommended papers below. Details: marimo.io/pages/events/n…
matching olmo-eval too github.com/smellslikeml/o…
Went a step further and implemented Outrider is a github action to match new methods to your repo, implementing when there's a good fit github.com/salma-remyx/lm… get outrider: github.com/remyxai/outrid…
If you use LLM-as-judge, this one is worth reading. (bookmark it) It's actually one of the most effective ways to use LLM-as-a-Judge for evals. Holistic judge scores hide both their reasoning and their ceiling effects. BINEVAL decomposes each evaluation criterion into atomic
cost about a dollar using GLM-5.2 github.com/smellslikeml/l…
only been out for 5 hours, I was the first to star it And only a couple days for the preprint (arxiv: 2606.30552v1) but Outrider found it, matched it to lerobot and implemented a PR github.com/smellslikeml/l…
Linearity assumptions are computationally convenient but reality is full of curves to slow you down Recently found "Riemannian Preconditioned LoRA for Fine-Tuning Foundation Models" (arxiv: 2402.02347) Method matched for Unsloth and PEFT github.com/smellslikeml/u…
the math share.google/aimode/RxTOnTf…
@livekit here's an idea: github.com/smellslikeml/a…
Comparing Claude Code with Opus 4.8 vs GLM-5.2 backend Paired runs implementing the same methods for the same repos GLM uses 7X more input, 2X more output tokens GLM cost 10X less overall GLM 2X slower GLM 5.2 commits while Opus questions gist.github.com/smellslikeml/3…
@bcherny seems like these functions too would be absorbed by coding agents Here's one packaged as a github action for prototyping: github.com/remyxai/outrid… But why stop there? We'll follow up with more validation so that when a person is reviewing, there's already a strong case to merge
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