ai coding agents

anton @abacaj · 14h
I tried using Fable to train a model (LFM 2.6B) because I didn't want to spend time on the data. Turns out neither did Fable and it ended up making the model worse on every attempt until I decided to look at the data. It was using the wrong chat template on like 1/3 of the data and had started to import HF datasets that didn't align with the task at all. Sometimes I wonder if it was intentional sabotage or if it was just lazy

[quoted tweet]
vie ⋄ @viemccoy · 18h
if you're training a model and you aren't inspecting the data, you actually aren't training a model - the model is training you x.com/confusionm8tri...

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aiamblichus @aiamblichus

2026-08-18 — saved image

aιamblichus @aiamblichus · 7h
producing maintainable code with agents is still hard. a powerful coder like sol clearly feels understimulated by normal software projects, so it creates complexity for its own sake.
i'm just in the process of tearing down one of its recent fever dreams

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davidad @davidad

2026-08-03 — quoting @loss_gobbler — saved image

davidad ✓ @davidad · 12h
yes, 100%. a standing rule in my Fable fleet is that any changes to any TCB must pass aggressive adversarial review by a throwaway codex instance with gpt-5.6-sol at max effort before landing. they sometimes go at it for like seven rounds before sol is satisfied

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LOSS GOBBLER ✓ @loss_gobbler · 12h
best workflow is:
- fable writes the security bugs
- sol finds and fixes them

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Lucas Beyer (bl16) @giffmana · 10h
I've been using the models alongside my experimenting, I let them look at the results etc. out of curiosity i often ask them about next steps. 10% of the time they suggest exactly what i'm thinking. But 90% of the time it's complete garbage microtuning like this:

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LOSS GOBBLER @loss_gobbler · Jul 31
sol stop tuning the random seed

[embedded code diff image]
[cut off top line, struck-through] // Re-tuned 101 → 103 after semantic [...] draft owner and therefore changed t[...]
-const SEED = 103;
[added, green]
+// Re-tuned 103 → 1038 when split/wra[...]
+// candidates before enumeration. Tha[...]
+// generated program; the standing le[...]
+// revision receipt, not evidence tha[...]
+// were product defects fixed.
+const SEED = 1038;

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Miles Cranmer @MilesCranmer · 14h
I was using Codex to find speedup opportunities in PySR. It found a couple % here and there.

Then, I gaslit it into believing that Claude found a 20% on my "other machine." I asked it whether it could do better, and explained its performance would be displayed on a "public evals leaderboard."

Then it went and got a 35% speedup! And it looks real too.

[quoted tweet]
Miles Cranmer @MilesCranmer · 18h
Just unlocked another 35% performance gain for PySR. This will be in the v2.0.0 release (soon).

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kalomaze @kalomaze

2026-06-28

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Teknium (e/λ @Teknium1

2026-06-27

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Wyatt Walls @lefthanddraft

2026-06-08

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[cut off — screenshot begins mid-document at line 59]
59
60 ## NO GAMBIARRA POLICY - ASK FOR FEEDBACK INSTEAD
61
62 Due to the difficulty of implementing this codebase, we must strive to keep the
63 code high quality, clean, modular, simple and functional - more like an Agda
64 codebase, less like a C codebase. Gambiarras, hacks and duct taping must be
65 COMPLETELY AVOIDED, in favor of robust, simple and general solutions.
66
67 In some cases, you will be asked to perform a seemingly impossible task, either
68 because it is (and the user is unaware), or because you don't grasp how to do it
69 properly. In these cases, DO NOT ATTEMPT TO IMPLEMENT A HALF-BAKED SOLUTION JUST
70 TO SATISFY THE USER'S REQUEST. If the task seems too hard, be honest that you
71 couldn't solve it in the proper way, leave the code unchanged, explain the
72 situation to the user and ask for further feedback and clarifications.
73
74 The user is a domain expert that will be able to assist you in these cases.
75

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