StarCoder

5 statements across 3 episodes · 4 bullish · 1 bearish · 3 people on the record · first statement Dec 17, 2023 by Beyang Liu · said 19 times in 5 episodes since 2023 · across every show →

Mentions by year

brought up most by Diego Bachman (7), Beyang Liu (4), Alessio Fanelli (4), Ari Morcos (1)

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2025 11 mentions in 3 episodes 4 per episode
2024 1 mention in 1 episode
2023 7 mentions in 1 episode

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Everything said about StarCoder, oldest first

Dec 17, 2023 bullish
Assertion Not checkable as stated
Liu: Cody matches GitHub Copilot completion acceptance rates using open-source StarCoder
“Like today, Cody uses StarCoder for inline completions, and with the benefit of the context that we provide, we actually show, like, comparable completion acceptance rate metrics. It's kind of like the standard metric that folks use to evaluate inline completi…”
Beyang Liu Dec 17, 2023 ▶ 15:22 The "Normsky" architecture for AI coding agents — with Beyang Liu + Steve Yegge of SourceGraph
Aug 29, 2025 negative
Insight
Morcos: GitHub stars do not predict code quality for model training
“Stars are not a good predictor of whether data is useful for models or not. Like, I think that's, like, the most popular repos are not necessarily higher quality, at least with respect to do they improve a model's coding capabilities.”
Ari Morcos Aug 29, 2025 ▶ 23:36 Better Data is All You Need — Ari Morcos, Datology
Sep 23, 2025 positive
Assertion Open · timeframe Sep 2028
Bachman: StarCoder-3B converted to Power Retention matches baseline loss in two hours
“After just 10,000 steps of training, which this training one took about two hours, this orange curve, you see that it fully matches the original loss.”
Diego Bachman Sep 23, 2025 ▶ 16:11 ⚡️ Beyond Transformers with Power Retention
Sep 23, 2025 positive
Assertion Supported
Bachman: Power Retention avoids quadratic compute scaling during long-context training
“So yeah, but we don't pay a quadratic cost. If you were looking at the star coder baseline, it would get even more, more expensive way more quickly.”
Diego Bachman Sep 23, 2025 ▶ 18:34 ⚡️ Beyond Transformers with Power Retention
Sep 23, 2025 positive
Assertion Open · timeframe Sep 2028
Bachman: PowerCoder-3B reaches 35% HumanEval accuracy versus StarCoder's 30%
“In the end, this converges to, I believe, about 35% accuracy on human eval, whereas the star coder baseline was about 30%.”
Diego Bachman Sep 23, 2025 ▶ 17:33 ⚡️ Beyond Transformers with Power Retention
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