Daniel Isaac (@danpacary) header
Daniel Isaac (@danpacary) profile picture

idk what I’m doing half the time. but sometimes I design systems for constrained compute | local & edge ai current curiosity: designing a model

Earth
Joined September 2023
I got a 1T (trillion) parameter model running on my MacBook Pro. Kimi-K2. 1.029T params. ~1 TB raw weights. 524 GB converted. ~1.7 tok/s. Yesterday it was 671B. Today it's 1T. Same laptop. Same M4 Max. No cloud. When I say we: I mean Claude and me.
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this is the way
I think everyone should be securing their own local intelligence. It's as much of a human right as access to the internet. But more than that I think people need to be educated as to why local AI is the single most important technological development in modern times
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Need a new standard metric based on tok/correct answer Then correct answers / kWh
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All functional compute just got more valuable
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Ofc
the dem sweep is gonna cause inference benchmarks y axis to go from Token throughout per gpu to Token throughput per kW
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barely holding the frontier
Nice DeepSWE score! Does it survive a lowercase filename? Introducing MaxxBench: 89 DeepSWE tasks with small changes to the instructions or starting code. Same engineering problem, slightly different requirement. Models clustered around 74% on DeepSWE end up 23 pp apart. Good at coding, or good at DeepSWE? 🧵
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real tasks graded by fable grok 4.7 xhigh VS GLM 5.3 max
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using the api for glm 5.3 and it's surprisingly good. it catches issues that fable did not... (in max thinking)
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this is good stuff @MiaAI_lab glm 5.3 flash on 2 sparks
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Important work.
Introducing Inkling 2 Small. Scoring 31 on the AA Intelligence Index, it's a big jump over its predecessor. Inkling 2 Small is available for fine-tuning and inference on Tinker today, with full weight release planned soon. thinkingmachines.ai/news/ink…
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Qwen3.5-4B edited by Claude (fable 5.1) 9.2% less GPU energy per correct answer learns from its own shortest correct answers 600 problems x 4 tries -> 382 kept 1.7 Wh of GPU energy per kept answer
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Daniel Isaac retweeted
i built a swift package that lets my iphone run brand new swift code without compiling it, opening xcode, or updating the app to test it, i had a small llm running on my iphone generate an entire chess game in swift ~550 lines of code i've been building a swift interpreter that turns code into real, native swiftui views at runtime and apparently it can handle quite a bit (although still room for improvement)! the chess game has legal moves, checkmate, castling, pawn promotion, and even spring animations when the pieces move all running directly from a string of swift code the package boils down to just one line: KilnView(code: source) it's free, open source, and works on ios and macos but the part i'm most excited about is what this means for ai i can now ask an ai running on my iphone to build me a little app (keyword is little 😅), and actually use it right away no compiler or app updates required! link in reply :)
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we’ll be seeing more of this
Introducing Arete-27B: Qwen3.8-27B, post-trained to reinforce Western values. Chinese open models are currently the strongest available, but they ship with state-aligned behavior in the weights. We built an 803-prompt eval across seven value categories, including freedom of expression, democracy, and privacy, and measured how often a model adopts China’s party line. After fine-tuning on roughly 53 million tokens, Arete-27B's propaganda rate fell from 22% to 1%, in line with the Western models we tested. Its Hermes Bench score shows no significant performance tradeoffs. The values in an open model are one fine-tune away. Our eval, weights, and training data are available now along with the full blog post: nousresearch.com/arete
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