Eddy Lazzarin ☀️ (@eddylazzarin) header
Eddy Lazzarin ☀️ (@eddylazzarin) profile picture

GP @a16zfrontier, positive feedback loop enjoyer

Joined July 2019
Photo by Eddy Lazzarin ☀️ (@eddylazzarin)Photo by Eddy Lazzarin ☀️ (@eddylazzarin)Photo by Eddy Lazzarin ☀️ (@eddylazzarin)Photo by Eddy Lazzarin ☀️ (@eddylazzarin)Photo by Eddy Lazzarin ☀️ (@eddylazzarin)Photo by Eddy Lazzarin ☀️ (@eddylazzarin)Photo by Eddy Lazzarin ☀️ (@eddylazzarin)Photo by Eddy Lazzarin ☀️ (@eddylazzarin)Photo by Eddy Lazzarin ☀️ (@eddylazzarin)Photo by Eddy Lazzarin ☀️ (@eddylazzarin)
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Replying to @StephenPiment
Yeah that's why I posted this
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Replying to @StephenPiment
I thought "good" was obvious 🙃
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Looks absurd, thanks and will follow!
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Replying to @ToughSf
This is so important to really push the imagination
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Replying to @alexanderchopan
Recent prize lists are really bad though
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We need new science fiction so badly, the world is thirsty for it.
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Replying to @ekrahm
Yeah this is exactly right
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Replying to @RokoMijic
Are the people worried about model welfare not moral realists? Seems that they would be
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Nobody has been able to answer me: if we can change the weights of a model to change its behavior in extreme ways (assuming you believe in computational functionality), can we make a utility monster? Would you find this morally compelling? xitter.twitterapi.xyz/_lyraaaa_/status/21138…
if you have a qwen3.5 4b around, please try steering neurons L14 N8949 +1 and L13 N7956 -1 now you have evil qwen recipe: dose × median(‖resid‖ leaving layer L, tokens 1:) / ‖W_down[:, n]‖` at the down_proj at every input
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Replying to @RepCasar
对对对,必须禁止人工智能研究!我们一定要保护好自己,远离过度的经济增长,求求了!
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Eddy Lazzarin ☀️ retweeted
We're now accepting applications for research interns at @a16zfrontier, for summer '27 in NYC! Application deadline is Nov 20th. You'd be working with me @skominers @SuccinctJT @ittaia @PGarimidi @jneu_net @ErtemNusretTas, as well as other interns, visitors, and our advisors (such as @danboneh). Our interests are broader than ever---ambitious undergrads with a keen grasp of emerging technologies, grad students looking to transition to building and entrepreneurship, as well as PhD students pursuing a more traditional academic career. Research areas of interest include AI and machine learning, agentic commerce, cryptography, security, distributed computing, hardware design, economics (micro, macro, and empirical), energy, material science, biology, incentives, quantitative finance, market and mechanism design, foundations of computing, and pure mathematics. For more details and to apply, see jobs.a16z.com/jobs/andreesse…
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Replying to @i_shaix
If the dependency is extremely complex code bundled with proof that it's secure (e.g., cryptography code, open weight models), I think one still might prefer that. But that will be rarer.
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Replying to @andreaslbigger
Whenever I see a wine cellar, I think of putting servers in there, but this is even cooler that I would have done. Devastating. Completely server mogged by Tobi. Unbelievable.
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Looks really interesting. Thanks for the recommendation!
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It seems likely that the risk of a supply chain attack will often exceed the risk of a coding agent making a new vulnerability. Open source will shift away from helping you find already-working code to finding already-secured code, or finding new ideas altogether.
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Eddy Lazzarin ☀️ retweeted
Incredible. @CantonNetwork just received no action relief from the SEC for its network token. The @SECGov's token taxonomy from March and follow up FAQs have now created a clear pathway out of securities laws—Network tokens are digital commodities.
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Replying to @BenDiFrancesco
Yeah, I think if we end up with safe models it will be through interpretability and control methods developed by extremely capable AI models. They go hand-in-hand
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Replying to @0xJuly
Can we get a How It's Made-style narrated video?
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Capabilities improvements lead to interpretability improvements (good for safety) which lead to capability improvements
We asked GPT-6 Astra to edit Qwen3.5-0.8B weights to improve math. It directly changed only 0.016% of weights, fixing 0.8B's reasoning loops & improving math up to 6x and even instruction following 3x, without gradients or training. We call this Weight-Space Programming: frontier LLMs can now natively read/write neural network weights! 🧵 Blog: rikiyat.github.io/blog/weigh…
Readers added context they thought people might want to know
LLM was given weights explicitly as text in prompt and used code/forward passes (per authors' logs) to ablate Qwen looping direction, a known thinking-mode issue per model card. Primarily fixed repetition in that mode, not general 'natively' improving weights/math. rikiyat.github.io/blog/weight-sp… github.com/RikiyaT/lm_und… huggingface.co/Qwen/Qwen3.5-0… huggingface.co/RikiyaT/Qwen3.…
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Eddy Lazzarin ☀️ retweeted
Can we use any compilation pipeline we want to produce efficient machine code, then apply formal methods to show that this code behaves in a certain way? This has long been a dream – a gold standard of formal verification, verifying programs at the assembly level – but was considered to be too difficult due to the loss of structure when going from a high level language to machine code. Can AI bash through this problem? Apparently yes! The basic idea I’ve been playing with is to write an interpreter of the target instruction set in an interactive theorem prover such as Lean or Bend. We can then prove that some compiled program conforms to a given specification by writing a proof that connects the machine code, interpreter, and some desired behavior of that code. Why is this useful? It helps resolve 2 tradeoffs: - When we write programs in a high level language designed for native execution rather than theorem proving, we get great performance, but are unable to formally reason about how that code behaves. - On the other hand, when writing programs in a language like Lean, we can write proofs to reason about its behavior, but get poor native performance. We can try and solve this tension by extracting a program written in something like Rust into Lean, but this tooling is complex, limited, and can introduce bugs! Or, we can develop theorem provers that can compile to efficient native code. This is what Bend hopes to accomplish. Today, both of these techniques are limited, so proving at the assembly or bytecode level seems like a good solution. What is the downside then? Well, formally reasoning about an instruction set is much harder than high level code. You have to consider registers, stack machines, and other low level semantics that are not particularly relevant to showing the correctness of a given algorithm. You also lose a great deal of information during compilation, making reasoning about it more difficult. However, if you happen to be proving correctness over the instruction set you actually intend to run, you get *very* good correctness guarantees, as you don't even need to consider compiler bugs in your threat model. So far, I have experimented with verifying simple algorithms compiled to both RISC-V and WASM32, and have gotten some surprisingly good initial results! LLMs handle writing tedious proofs, and all I do is verify a small set of invariants defining how the code should behave. Interestingly enough, WASM seems to be much easier to reason about, as the instruction set has some higher level semantics, such as local variables and functions. When proving RISC-V, we have to contend with reasoning about things like function dispatch and parameter encoding, which makes life much harder! Anyways, stay tuned for some more updates on this as I try to see just how far we can push this technique!
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