Alex Pan ♻︎ @HelloAlexPan
deport machines they're taking our jobs goodreads.com/helloalexpan 🍞 Joined September 2018-
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Both DSPy and (especially) GEPA are currently severely under hyped in the AI context engineering world
The reason most (not all) methods don't add value (over baseline) when scaled is because they're "extra training data in disguise", so their benefit vanishes in the high data regime
The reason most (not all) methods don't add value (over baseline) when scaled is because they're "extra training data in disguise", so their benefit vanishes in the high data regime
The fact that Python is the standard for machine learning is a serious indictment of the field’s engineering standards
The fact that Python is the standard for machine learning is a serious indictment of the field’s engineering standards
Analyzing the Hierarchical Reasoning Model by @makingAGI We verified scores on hidden tasks, ran ablations, and found that performance comes from an unexpected source ARC-AGI Semi Private Scores: * ARC-AGI-1: 32% * ARC-AGI-2: 2% Our 4 findings:
AI/LLM benchmarks in 2025 be like
✌️New blog post We'll look at @DSPyOSS SIMBA optimizer and how it works. It's a great start for understanding self-introspective prompt optimization and DSPy's newest optimizer GEPA, which will come out soon. (link in first response)
DSPy's biggest strength is also the reason it can admittedly be hard to wrap your head around it. It's basically say: LLMs & their methods will continue to improve but not equally in every axis, so: - What's the smallest set of fundamental abstractions that allow you to build…
DSPy's biggest strength is also the reason it can admittedly be hard to wrap your head around it. It's basically say: LLMs & their methods will continue to improve but not equally in every axis, so: - What's the smallest set of fundamental abstractions that allow you to build…
Anyone who has built things at scale knows sometimes it is impossible to plan with certainty as to what’s next. Conspiratorial responses to a strategic product shift, a product of blood, sweat and tears is always disappointing, especially when the product was literally free
Anyone who has built things at scale knows sometimes it is impossible to plan with certainty as to what’s next. Conspiratorial responses to a strategic product shift, a product of blood, sweat and tears is always disappointing, especially when the product was literally free
the #1 predictor of high agency is how likely you are to jaywalk jaywalking is a symbol of higher risk tolerance and capacity to exercise free thinking this is not advice and i do not claim responsibility for any repercussions of this post
got my dog microchipped so if he runs away I can just press a button and he’ll explode
Man, I really got DSPy-pilled hard lately.
Towards the end of the interview, @im_roy_lee shockingly put down his guard & got vulnerable I was not expecting this. "I'm faced with a situation that almost nobody faces in their in their lives ever & it's hard. There's like no one I can turn to for advice. Who's the…
Towards the end of the interview, @im_roy_lee shockingly put down his guard & got vulnerable I was not expecting this. "I'm faced with a situation that almost nobody faces in their in their lives ever & it's hard. There's like no one I can turn to for advice. Who's the… https://t.co/hEeVX4uq0O
Ai overuse of em dashes may be related to the how em dashes can be a replacement for any pause marker (, ; / etc) leading to a probabilistic token spike
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