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Londonmaxxing

London, England
Joined October 2019
Photo by codingsh | GOAT (@codingsh)Photo by codingsh | GOAT (@codingsh)Photo by codingsh | GOAT (@codingsh)Photo by codingsh | GOAT (@codingsh)Photo by codingsh | GOAT (@codingsh)Photo by codingsh | GOAT (@codingsh)Photo by codingsh | GOAT (@codingsh)Photo by codingsh | GOAT (@codingsh)Photo by codingsh | GOAT (@codingsh)Photo by codingsh | GOAT (@codingsh)
codingsh | GOAT retweeted
O cartaz oficial da 2a temporada de Pablo e Luisão! Estreia dia 3 de dezembro.
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codingsh | GOAT retweeted
Isso é a coisa mais legal de ter um programa na televisão
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codingsh | GOAT retweeted
Signed!
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codingsh | GOAT retweeted
“A new Go API for JSON” by Michael Pratt, Joe Tsai, Daniel Martí, Johan Brandhorst-Satzkorn, Roger Peppe, Chris Hines, and Damien Neil — go.dev/blog/jsonv2 #golang
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codingsh | GOAT retweeted
Curiosity about formal verification: Both of the major languages, Lean and Bend, were created by Brazilians 🇧🇷
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codingsh | GOAT retweeted
Last orders for licensing delays. New powers mean City Hall can step in on the biggest licensing decisions, helping London's venues, events and night-time economy flourish.
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codingsh | GOAT retweeted
too obsessed to remain ordinary
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codingsh | GOAT retweeted
Ferrari 308 GTB & Jaeger-LeCoultre Reverso Hybris Mechanica Calibre 185
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codingsh | GOAT retweeted
So my friends I do not believe the MIT paper about LLMs understanding / writing weights was bad science. It is impressive and it is something you may want to explore. However the first set of tests, the game recognition of the tests, it looked like too straightforward... 1/N
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codingsh | GOAT retweeted
Chrome Hearts fire extinguisher
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codingsh | GOAT retweeted
Omarchy 4.5 will ship with full compatibility for M1, M2, M3, and Neo! Incredible achievement from the Omarchy M team. The installer makes this so smooth, the dual boot makes it a no brainer. Apple hardware liberated. M4-6 on the way. WE CAN FIX EVERYTHING!
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codingsh | GOAT retweeted
I am happy to share FlashProve: our performant open-source prototype for provable inference. We believe that this has significant implications for security, pacing AI development, integrity guarantees, and more.  FlashProve allows a provider to commit to model weights, and then prove that replies originate from the committed model.  The proof leaks no information about the weights, and the verifier can efficiently verify it on consumer hardware.  Our library is performant, proving a 400B parameter MoE model at 100 tok/s. We have open-sourced the associated code and Lean formalization. We hope that it catalyzes further progress in verification of inference, and look forward to seeing it.
New Anthropic research: progress on provable inference for large language models. Here, we introduce FlashProve. Building on established cryptographic techniques, FlashProve allows developers to verify whether a given response came from a given AI model. It’s substantially more efficient than prior open-source methods for doing so. These proofs have a range of applications. They can, for instance, make it harder for attackers to hijack a chat, or enable safety evaluators to verify that the model they’re testing is the same one being served to customers. For our full write-up, see here: anthropic.com/research/prova… Further progress in provable inference will likely be an important part of improving trust in AI deployment. To that end, we're open-sourcing FlashProve’s code, here: github.com/anthropics/FlashP… We’re grateful to our collaborators: Alessandro Chiesa, @GiacomoFenzi, @gwberg95, @miranda__christ, Rahul Dalal (rdalal.github.io) and Ben Mayer (@transmissions11).
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codingsh | GOAT retweeted
Cartel mc’s
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codingsh | GOAT retweeted
num mundo cheio de websites vibe coded e tudo a tentar chamar à atenção, apresento o meu novo website pessoal
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codingsh | GOAT retweeted
NEW: @VitalikButerin OPENS DEVCON IN A SMILEY FACE MASK
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zk proofs for LLM inference 👀 The same cryptographic techniques that help secure millions on Ethereum L2s could soon prove which AI model generated your answer. Happy to see our data finding its way into @AnthropicAI's latest research! anthropic.com/research/prova…
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Anthropic @AnthropicAI ❤️ zkSTARKs: In collaboration with @StarkWareLtd co-founder Prof. Alessandro Chiesa and other brilliant researchers, Anthropic just open sourced a zk STARK system for proving the integrity of LLM inferencing, bringing us closer to verifiable AI. As we said long ago (since 2018), zkSTARKs are the end-game: highest scale, best security (post-quantum). Now, also AI agile and supporting AI integrity. anthropic.com/research/prova…
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codingsh | GOAT retweeted
I'm building a new programming language, Zed (once it's released I'll be posting about it at @ZedLang). I have been working on this language for over 20 years, but never got it built, because the scope of it is so ambitious. It's an entirely new type of language based on category theory, with core design objectives of precise automatic parallelization up to the Amdahl limit; automatic memory management, with no GC and no need for manual malloc/free; and pervasive use of partial evaluation to increase the power of the language, among other very unique features, such as units (`accel = 5 with units("m s^-2")`) as part of the type system. I gave Opus 200 pages of design notes that I have been compiling over the last 20 years, and the code I had already written, and asked Opus to compile everything I have created so far into a single cohesive and detailed plan. Then I went through many rounds of cross-review between Claude, ChatGPT and Gemini, having them all do critical reviews, then meta-critique each other's critiques, then collaborate on combining their critiques into an improvement plan, until convergence. I also had the agents cross-check every part of the spec with every other part of the spec (all-pairs comparison) to check for inconsistencies, gaps in the spec, etc. Then I had the agents turn the spec into a long series of tiny incremental steps (a topological ordering of what has to be built before what), as well as the DAG-structuring of dependencies underlying the topological ordering. I then had Opus build a visualizer that shows me all the tasks in the DAG as my coding agents are working on each task, with realtime update as tasks change status (in progress / completed), which I highly recommend when you ask Opus to do some mammoth complex task, because you can see exactly what it's doing and when. It is absolutely magic to see this coming together before my eyes, and to see how Opus is handling this massive DAG of complex tasks and their interdependencies. It is crazy to me that Opus is now simply building what I have dreamed of and wished would exist for over 20 years, but never had enough waking hours to implement! I now have an army working for me, completing what I couldn't due to the bandwidth limitations of having just one brain, and 24 hours in a day. What a day of miracles we live in.
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codingsh | GOAT retweeted
Anthropic is releasing an open source library, FlashProve, for provable inference. It was measured on Llama 4 Maverick, a 400B-parameter mixture of experts model. Code: github.com/anthropics/FlashP… Blog post: anthropic.com/research/prova…
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codingsh | GOAT retweeted
i've reproduced the results from the MIT blog post today on weight-space programming. but one additional result i have to add: this is a new, step-change emerging capability that's appeared in just the latest set of models.
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