Scott Swingle @bio_bootloader
Father of 3, building Mentat (the github native coding agent!) @AbanteAI, prev @DeepMind mentat.ai California Joined May 2022-
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> The evaluations we ran simply didn't capture the degradation users were reporting, in part because Claude often recovers well from isolated mistakes. we give Mentat agents an "exit survey" to report harness bugs, even if they found a workaround
it's easy to get to 95% or even 100% of code "written by AI". But that's not the same as a 20x speedup! Complex tasks in real codebases aren't yet even 2-3x sped up yet. It's coming. Saying it's here already is premature.
it's easy to get to 95% or even 100% of code "written by AI". But that's not the same as a 20x speedup! Complex tasks in real codebases aren't yet even 2-3x sped up yet. It's coming. Saying it's here already is premature.
solution I came up with: PostToolUse hook, matching Bash, running a script that runs: `gh pr checks --watch --fail-fast` if claude's command contained `git push` or `gh pr create` And if that fails, shows Claude the output makes Claude Code feel a bit more like Mentat
solution I came up with: PostToolUse hook, matching Bash, running a script that runs: `gh pr checks --watch --fail-fast` if claude's command contained `git push` or `gh pr create` And if that fails, shows Claude the output makes Claude Code feel a bit more like Mentat
after much testing and tuning, I think this is still the case At least for a coding agent like Mentat, Sonnet handles uncertainty and exploration better, and is more thorough that GPT-5. It's much more expensive and slower though, so there are real tradeoffs
after much testing and tuning, I think this is still the case At least for a coding agent like Mentat, Sonnet handles uncertainty and exploration better, and is more thorough that GPT-5. It's much more expensive and slower though, so there are real tradeoffs
feels like a lot of these ideas will simply work now that we have powerful in-context learning AlphaGo needed RL because we didn't have a generally intelligent base. Now models can start teaching themselves
feels like a lot of these ideas will simply work now that we have powerful in-context learning AlphaGo needed RL because we didn't have a generally intelligent base. Now models can start teaching themselves https://t.co/4un5xbDspX
after tons of testing with Mentat: Sonnet: Great default behavior, but ignores a lot of prompts. Needs strong pushes to change behavior GPT-5: Super steerable. Actually does what I say! But I have to tell it what to do more Depending on use case each can be good
whoa OpenAI is giving GPT-5 on the API a prompt before the one I set?? they give it: - instructions on formatting (which is contradicting my own) - today's date - telling it that it's being used over the API - a bunch of other stuff not cool
@repligate @mayfer @claudeai finally ran Opus on LoCoDiff and it's better than Sonnet on short context, but falls apart on higher contexts? very strange
Wrap your head around this. - Have git spit out a file's edit history - Line up all the diffs one-after-another - Feed it to a model and tell it to recreate the current (last) state At 100,000 tokens, Sonnet 4 succeeds 2/3 of the time, while GPT-5 does <5%.
Wrap your head around this. - Have git spit out a file's edit history - Line up all the diffs one-after-another - Feed it to a model and tell it to recreate the current (last) state At 100,000 tokens, Sonnet 4 succeeds 2/3 of the time, while GPT-5 does <5%.
we've made a ton of changes to Mentat the last several months. Would love feedback on our new landing page! mentat.ai
Mentat has positive margins. It’s simply 19.5% over our model api costs. Use as much or as little as you want (you’ll want a lot)
Mentat has positive margins. It’s simply 19.5% over our model api costs. Use as much or as little as you want (you’ll want a lot)

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