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How do you actually formally verify the code underpinning Ethereum's future? In this episode (the finale of the lean Ethereum miniseries), Nico sits down with Alex Hicks (Alexander Hicks), lead of Protocol Snarkification at the Ethereum Foundation, to break down formal verification from first principles. They cover: – What...

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Zero Knowledge Podcast

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15,289 görüntüleme • 7 ay önce •via X (Twitter)

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@leanEthereum @nico_mnbl @alexanderlhicks @ethereumfndn The obvious motivation for formalizing stuff is scalability.

Benzer Videolar

Leonardo de Moura (Leonardo de Moura) is the creator of Lean and the Z3 theorem prover. I talked with him about how Lean works and why LLMs plus Lean will fundamentally change how we write software and do math. In this episode: • How formal verification and proof assistants work • How Lean will impact handwritten math & software • Lean’s role in recent math breakthroughs • When software is worth formalizing Where to watch: • YouTube - • Spotify - • Apple Podcasts - • Transcript - Thank you to the sponsor of this episode for supporting my work: • WorkOS: makes your app Enterprise Ready with easy to use APIs to add SSO, SCIM, RBAC, and more in just a few lines of code, check them out at Chapters: 00:00 Intro 00:28 How formal verification works 05:21 A new way of writing software 13:15 Proof assistants vs programming languages 21:06 How Lean has assisted in mathematical breakthroughs 32:03 When is it worth formalizing software 33:29 How Lean will impact handwritten math 38:55 The Z3 theorem prover project he started 45:44 The most technically challenging work of his career 51:10 Lean vs its competitors 01:00:37 The future of Lean 01:04:10 Technical book recommendations 01:06:15 Advice for his younger self 01:07:10 Outro
1:08:06

Leonardo de Moura (Leonardo de Moura) is the creator of Lean and the Z3 theorem prover. I talked with him about how Lean works and why LLMs plus Lean will fundamentally change how we write software and do math. In this episode: • How formal verification and proof assistants work • How Lean will impact handwritten math & software • Lean’s role in recent math breakthroughs • When software is worth formalizing Where to watch: • YouTube - • Spotify - • Apple Podcasts - • Transcript - Thank you to the sponsor of this episode for supporting my work: • WorkOS: makes your app Enterprise Ready with easy to use APIs to add SSO, SCIM, RBAC, and more in just a few lines of code, check them out at Chapters: 00:00 Intro 00:28 How formal verification works 05:21 A new way of writing software 13:15 Proof assistants vs programming languages 21:06 How Lean has assisted in mathematical breakthroughs 32:03 When is it worth formalizing software 33:29 How Lean will impact handwritten math 38:55 The Z3 theorem prover project he started 45:44 The most technically challenging work of his career 51:10 Lean vs its competitors 01:00:37 The future of Lean 01:04:10 Technical book recommendations 01:06:15 Advice for his younger self 01:07:10 Outro

Ryan Peterman

13,949 görüntüleme • 2 ay önce

“Math is code. And code is math.” Carina Hong CEO Axiom on why that idea matters for AI: “For centuries, mathematicians reasoned in natural language. But because of the Curry–Howard correspondence, a proof can also be a program.” “You can translate natural-language math into formal code using systems like Lean. Lean is both a theorem-proving language and a programming language.” “That means you can generate solutions as code and then formally verify them. You can even use the same system to verify programs themselves.” “This creates a flywheel of generation and verification, where AI can both write solutions and prove that they’re correct.”
2:25

“Math is code. And code is math.” Carina Hong CEO Axiom on why that idea matters for AI: “For centuries, mathematicians reasoned in natural language. But because of the Curry–Howard correspondence, a proof can also be a program.” “You can translate natural-language math into formal code using systems like Lean. Lean is both a theorem-proving language and a programming language.” “That means you can generate solutions as code and then formally verify them. You can even use the same system to verify programs themselves.” “This creates a flywheel of generation and verification, where AI can both write solutions and prove that they’re correct.”

Forward Future

24,729 görüntüleme • 8 ay önce

An apparently AI-generated formal proof, in Lean, purporting to be a disproof to the Collatz conjecture, was actually exploiting a bug in the Lean kernel! EXCLUSIVE from Lean Creator Leo de Moura: > "This is going to keep happening. AIs are really good at exploiting soundness bugs in the kernels." It appeared to pass both Lean's official kernel and Nanoda by hitting two separate bugs. Both have now been patched - Leonardo de Moura.
4:25

An apparently AI-generated formal proof, in Lean, purporting to be a disproof to the Collatz conjecture, was actually exploiting a bug in the Lean kernel! EXCLUSIVE from Lean Creator Leo de Moura: > "This is going to keep happening. AIs are really good at exploiting soundness bugs in the kernels." It appeared to pass both Lean's official kernel and Nanoda by hitting two separate bugs. Both have now been patched - Leonardo de Moura.

Machine Learning Street Talk

34,413 görüntüleme • 2 ay önce

Ethereum’s endgame is getting rebuilt, not upgraded. With Lean Consensus (née Beamchain), Ethereum is starting from the endgame: cleaning up technical debt, pushing toward fast finality and preparing for a post-quantum future. It’s one of the most ambitious protocol workstreams for Ethereum and for crypto infrastructure overall. I had a conversation with Justin Drake to understand what Lean Ethereum really is in terms of: real-time proving, the path to higher L1 throughput and what it unlocks for rollups. Stay tuned for the full episode of TheCoordinate podcast!
0:34

Ethereum’s endgame is getting rebuilt, not upgraded. With Lean Consensus (née Beamchain), Ethereum is starting from the endgame: cleaning up technical debt, pushing toward fast finality and preparing for a post-quantum future. It’s one of the most ambitious protocol workstreams for Ethereum and for crypto infrastructure overall. I had a conversation with Justin Drake to understand what Lean Ethereum really is in terms of: real-time proving, the path to higher L1 throughput and what it unlocks for rollups. Stay tuned for the full episode of TheCoordinate podcast!

Soubhik Deb

23,332 görüntüleme • 8 ay önce

Axiom Math's Carina Hong on why verification isn't about catching mistakes, it's how you drive the cost of a proof to zero: "Formal verification is going to make your life slightly better if you're facing a proof with one million lines. Remember the Erdős unit distance problem, the chain of thought being generated? There are actual mathematicians trying to follow it step by step and scrutinize it. That seems very difficult if you're not in that very niche domain of discrete geometry intersecting with algebraic number theory." "But if you have a Lean proof accompanying it, you can just run it. And running the Lean proof gives you that provable guarantee that this proof is sound." "I have a hot take. People think Lean is this library built on the existing Mathlib. I think it's going to grow significantly. A lot of the hurdles where Lean is difficult is that the basic definitions of some mathematical fields are just not in the library." "My hot take is the scaling law, if you go down the formal mathematics path, is going to be a lot steeper than informal mathematics. So it's not just for verification, for trust, it's also for performance, it's also for optimal generation." "Verification is not like insurance. It's not something where, oh, we want to make sure there's no flaw. That's great, but it also helps you generate mathematics, both proofs and conjectures and theories, a lot better." "So imagine the cost of proof goes to zero. Then you can massage the problem statements, and even if it's an open problem, a lot more easily, flexibly, and adaptively." Carina Hong Axiom
3:17

Axiom Math's Carina Hong on why verification isn't about catching mistakes, it's how you drive the cost of a proof to zero: "Formal verification is going to make your life slightly better if you're facing a proof with one million lines. Remember the Erdős unit distance problem, the chain of thought being generated? There are actual mathematicians trying to follow it step by step and scrutinize it. That seems very difficult if you're not in that very niche domain of discrete geometry intersecting with algebraic number theory." "But if you have a Lean proof accompanying it, you can just run it. And running the Lean proof gives you that provable guarantee that this proof is sound." "I have a hot take. People think Lean is this library built on the existing Mathlib. I think it's going to grow significantly. A lot of the hurdles where Lean is difficult is that the basic definitions of some mathematical fields are just not in the library." "My hot take is the scaling law, if you go down the formal mathematics path, is going to be a lot steeper than informal mathematics. So it's not just for verification, for trust, it's also for performance, it's also for optimal generation." "Verification is not like insurance. It's not something where, oh, we want to make sure there's no flaw. That's great, but it also helps you generate mathematics, both proofs and conjectures and theories, a lot better." "So imagine the cost of proof goes to zero. Then you can massage the problem statements, and even if it's an open problem, a lot more easily, flexibly, and adaptively." Carina Hong Axiom

MTS

13,401 görüntüleme • 2 ay önce

I used Opus 5.5 to formally verify the Claude Agent SDK using Lean. A couple short prompts = 16 PRs fixing various bugs and race conditions. Video attached. TLA+ also works well. I sometimes combine Lean and TLA+ to look for issues around data flow, concurrency, and state mgmt. I don't know either language well, but Claude is excellent at both. This approach is super useful for formally modeling your code and finding bugs that a human probably wouldn't have spotted. Is formal verification the future of coding (or at least, bug finding)?
1:40

I used Opus 5.5 to formally verify the Claude Agent SDK using Lean. A couple short prompts = 16 PRs fixing various bugs and race conditions. Video attached. TLA+ also works well. I sometimes combine Lean and TLA+ to look for issues around data flow, concurrency, and state mgmt. I don't know either language well, but Claude is excellent at both. This approach is super useful for formally modeling your code and finding bugs that a human probably wouldn't have spotted. Is formal verification the future of coding (or at least, bug finding)?

Boris Cherny

2,164,250 görüntüleme • 1 ay önce

“What if the most important AI infrastructure isn’t the biggest model — but the one you can actually trust?” Sachi Miyasaki sits down with Karan Sirdesai Karan, Founder of Mira, to explore how Mira is approaching decentralized AI from a more focused angle: making machine intelligence reliable enough for real-world use. They discuss why Mira is not trying to become a full-stack AI protocol, how its approach differs from networks like Bittensor, Ritual, and Sahara, and why focusing on trust and verification could make Mira a stronger fit inside the wider AI infrastructure stack. The episode also breaks down Mira’s reliability architecture, from its core verification layer to node-level consensus, and why the next evolution of AI APIs may not be about simply accessing more models, but about knowing whether the answers you receive can actually be trusted.
2:22

“What if the most important AI infrastructure isn’t the biggest model — but the one you can actually trust?” Sachi Miyasaki sits down with Karan Sirdesai Karan, Founder of Mira, to explore how Mira is approaching decentralized AI from a more focused angle: making machine intelligence reliable enough for real-world use. They discuss why Mira is not trying to become a full-stack AI protocol, how its approach differs from networks like Bittensor, Ritual, and Sahara, and why focusing on trust and verification could make Mira a stronger fit inside the wider AI infrastructure stack. The episode also breaks down Mira’s reliability architecture, from its core verification layer to node-level consensus, and why the next evolution of AI APIs may not be about simply accessing more models, but about knowing whether the answers you receive can actually be trusted.

BeaconLayer

79,171 görüntüleme • 3 ay önce

upping my (Lean)DOOM i got curious about math proofs and Lean formalization, found out you can do general programming in lean, and i wanted to learn more so i did the obvious thing. (Lean)DOOM is entirely written in lean, and has presently a couple proofs where they make sense
1:15

upping my (Lean)DOOM i got curious about math proofs and Lean formalization, found out you can do general programming in lean, and i wanted to learn more so i did the obvious thing. (Lean)DOOM is entirely written in lean, and has presently a couple proofs where they make sense

alaska

30,390 görüntüleme • 22 gün önce

Checking that a major mathematical proof is correct can take years. Formalization—converting the mathematical reasoning into a form computer proof assistants like Lean can verify—can help. Last month, Claude completed the first formalized proof of Fermat’s Last Theorem, one of the most famous theorems of all time. This was a project experts thought would take many years. It is the largest Lean proof ever written. Fermat’s Last Theorem was first proven in 1995 by Sir Andrew Wiles, more than 350 years after it was conjectured. Our proof, which totals over 13 million lines of code, provides machine verification. More importantly, it proves over 29,000 other theorems that the proof requires, across many areas of math which had never before been formalized. We see this as a major step in the long process of firming up the core of mathematical knowledge, building on work from three centuries of mathematicians and hundreds of contributors to Lean and Mathlib. We are optimistic that AI-assisted verification of mathematical proofs will help reduce the burden of refereeing mathematics in an era where more proofs are being produced than ever before. You can read about the process on our Science Blog: And see the complete proof on GitHub:
0:34

Checking that a major mathematical proof is correct can take years. Formalization—converting the mathematical reasoning into a form computer proof assistants like Lean can verify—can help. Last month, Claude completed the first formalized proof of Fermat’s Last Theorem, one of the most famous theorems of all time. This was a project experts thought would take many years. It is the largest Lean proof ever written. Fermat’s Last Theorem was first proven in 1995 by Sir Andrew Wiles, more than 350 years after it was conjectured. Our proof, which totals over 13 million lines of code, provides machine verification. More importantly, it proves over 29,000 other theorems that the proof requires, across many areas of math which had never before been formalized. We see this as a major step in the long process of firming up the core of mathematical knowledge, building on work from three centuries of mathematicians and hundreds of contributors to Lean and Mathlib. We are optimistic that AI-assisted verification of mathematical proofs will help reduce the burden of refereeing mathematics in an era where more proofs are being produced than ever before. You can read about the process on our Science Blog: And see the complete proof on GitHub:

Anthropic

4,764,068 görüntüleme • 1 ay önce

Terence Tao: Formal Verification Breaks the Trust Barrier in Mathematics Formal verification is transforming mathematical collaborations — enabling anonymous contributions, machine-checked proofs, and radically more precise scientific discussion.
1:48

Terence Tao: Formal Verification Breaks the Trust Barrier in Mathematics Formal verification is transforming mathematical collaborations — enabling anonymous contributions, machine-checked proofs, and radically more precise scientific discussion.

SAIR

79,701 görüntüleme • 7 ay önce

Ethereum is starting from the endgame. Episode 4 of TheCoordinate is a deep dive into Lean Ethereum: a clean-slate rethink of consensus, execution, and data availability. I sat down with Justin Drake from Ethereum Foundation to unpack: > need for the rewrite, > rewrite items: post-quantum security + fast finality, > endgame finality (3-slot -> 2-slot -> maybe 1-slot), > slot anatomy, networking constraints, and the "SOL slots" meme, > real-time ZK proving changing the execution roadmap, > censorship resistance with FOSSIL, > role of L2s in the world of Lean Ethereum, > incentives across proposer, builder, prover, includer, attester. If you’re building on Ethereum or trying to understand where the base layer is headed, this one is for you. This is Episode 4 of TheCoordinate. Hope you enjoy it! ------------------------------- Timestamps: 0:00 Intro: digital intelligence needs digital institutions 0:30 The big questions: Lean Ethereum, consensus/execution, post-quantum 1:25 Why Ethereum needs an endgame mindset (and a clean-slate approach) 3:30 The two “rewrite-class” items: post-quantum security + fast finality 5:52 Beamchain → Lean Consensus → Lean Ethereum (expands beyond consensus) 6:34 ZK EVM + real-time proving within a slot → “10,000 TPS” target 10:10 “SOL slots”: pushing slot duration toward speed-of-light constraints 11:09 3-slot finality (3SF) → endgame finality (2-slot / 1-slot paths) 18:19 eFP2P: erasure-coded gossip, bandwidth efficiency, scaling blobs 26:21 FOSSIL today: inclusion lists + opening includers beyond validators 39:09 Lean VM: minimal ZKVM 51:04 XMSS explained: Merkle signatures, 2^32 leaves, statefulness tradeoff 1:00:36 Rollups: 99.9% throughput on L2s + “native rollups” 1:06:53 Economics: roles (builder/prover/includer/attester), proving costs, stake capping
1:16:31

Ethereum is starting from the endgame. Episode 4 of TheCoordinate is a deep dive into Lean Ethereum: a clean-slate rethink of consensus, execution, and data availability. I sat down with Justin Drake from Ethereum Foundation to unpack: > need for the rewrite, > rewrite items: post-quantum security + fast finality, > endgame finality (3-slot -> 2-slot -> maybe 1-slot), > slot anatomy, networking constraints, and the "SOL slots" meme, > real-time ZK proving changing the execution roadmap, > censorship resistance with FOSSIL, > role of L2s in the world of Lean Ethereum, > incentives across proposer, builder, prover, includer, attester. If you’re building on Ethereum or trying to understand where the base layer is headed, this one is for you. This is Episode 4 of TheCoordinate. Hope you enjoy it! ------------------------------- Timestamps: 0:00 Intro: digital intelligence needs digital institutions 0:30 The big questions: Lean Ethereum, consensus/execution, post-quantum 1:25 Why Ethereum needs an endgame mindset (and a clean-slate approach) 3:30 The two “rewrite-class” items: post-quantum security + fast finality 5:52 Beamchain → Lean Consensus → Lean Ethereum (expands beyond consensus) 6:34 ZK EVM + real-time proving within a slot → “10,000 TPS” target 10:10 “SOL slots”: pushing slot duration toward speed-of-light constraints 11:09 3-slot finality (3SF) → endgame finality (2-slot / 1-slot paths) 18:19 eFP2P: erasure-coded gossip, bandwidth efficiency, scaling blobs 26:21 FOSSIL today: inclusion lists + opening includers beyond validators 39:09 Lean VM: minimal ZKVM 51:04 XMSS explained: Merkle signatures, 2^32 leaves, statefulness tradeoff 1:00:36 Rollups: 99.9% throughput on L2s + “native rollups” 1:06:53 Economics: roles (builder/prover/includer/attester), proving costs, stake capping

Soubhik Deb

86,471 görüntüleme • 8 ay önce

AI is increasingly discovering new ways to attack cryptographic algorithms today, I spoke with Ethereum researcher Justin Drake on what this means for the future 0:35 HAWK signature scheme flaw 2:08 security risks 4:13 Ethereum's post-quantum roadmap 7:29 why verification is expensive 11:12 formal verification 11:59 Ethereum in a post-AI world 14:03 attackers vs. defenders 16:32 inside the Ethereum Foundation 18:15 what AI labs should prioritize
20:26

AI is increasingly discovering new ways to attack cryptographic algorithms today, I spoke with Ethereum researcher Justin Drake on what this means for the future 0:35 HAWK signature scheme flaw 2:08 security risks 4:13 Ethereum's post-quantum roadmap 7:29 why verification is expensive 11:12 formal verification 11:59 Ethereum in a post-AI world 14:03 attackers vs. defenders 16:32 inside the Ethereum Foundation 18:15 what AI labs should prioritize

sophia dew

22,954 görüntüleme • 2 ay önce

New project: a coding and formal verification agent for computational physics and applied mathematics. Auto-generate type-correct DSL code for equations and numerical schemes, autoformalize correctness properties in Lean/Isabelle/Rocq, then compile down to provably-correct C code
0:46

New project: a coding and formal verification agent for computational physics and applied mathematics. Auto-generate type-correct DSL code for equations and numerical schemes, autoformalize correctness properties in Lean/Isabelle/Rocq, then compile down to provably-correct C code

Jonathan Gorard

52,681 görüntüleme • 5 ay önce

AI agents are writing more code. How can developers ensure that code is correct? Microsoft Research’s Proof-Driven Development (PDD) Kit explores a more rigorous approach: it translates a developer’s intent into a formal specification, then uses formal verification to prove that generated code meets it. PDD Kit works with Verus for Rust and is integrated with GitHub Next’s Chopin, bringing formal methods into an AI-assisted development workflow for teams. See the work in action at GitHub Universe, Oct. 28–29, 2026.
0:34

AI agents are writing more code. How can developers ensure that code is correct? Microsoft Research’s Proof-Driven Development (PDD) Kit explores a more rigorous approach: it translates a developer’s intent into a formal specification, then uses formal verification to prove that generated code meets it. PDD Kit works with Verus for Rust and is integrated with GitHub Next’s Chopin, bringing formal methods into an AI-assisted development workflow for teams. See the work in action at GitHub Universe, Oct. 28–29, 2026.

Microsoft Research

64,030 görüntüleme • 4 gün önce

Writing formal specifications has always been the bottleneck in formal verification. Today we're launching AutoProver, an agentic formal verification system that reads your code, generates specifications, and verifies them. 🧵
0:27

Writing formal specifications has always been the bottleneck in formal verification. Today we're launching AutoProver, an agentic formal verification system that reads your code, generates specifications, and verifies them. 🧵

Certora

45,511 görüntüleme • 3 ay önce

Why did Royco get multiple audits and formal verification? We sat down with Armen from Certora to break it down. Certora completed a comprehensive audit and formal verification engagement on Royco Dawn, further fortifying its security stack. Watch it now.
31:11

Why did Royco get multiple audits and formal verification? We sat down with Armen from Certora to break it down. Certora completed a comprehensive audit and formal verification engagement on Royco Dawn, further fortifying its security stack. Watch it now.

Royco

10,756 görüntüleme • 5 ay önce

Xavier Leroy (creator of OCaml) is an expert in compilers, formal verification of software and functional programming. This interview should be an approachable resource if you're curious about formal verification of software since I was learning that on the fly during it. In this episode: • OCaml compared with Rust and JavaScript • What is formal verification and how does it work • How languages call each other across boundaries • How to address "almost-correct" LLM code • How type inference works in programming languages Where to watch: • YouTube - • Spotify - • Apple Podcasts - • Transcript - Thank you to the sponsor of this episode for supporting my work: • WorkOS: makes your app Enterprise Ready with easy to use APIs to add SSO, SCIM, RBAC, and more in just a few lines of code, check them out at Chapters: 00:00 - Intro 00:43 - What sets OCaml apart 04:39 - OCaml vs Rust 07:57 - Why is manual memory management more performant 11:21 - Javascript vs OCaml 14:00 - Famous Rob Pike quote 16:05 - Type inference and how it works 22:12 - What is formal verification and how does it work 40:07 - What made multicore support difficult for OCaml 50:17 - How programming languages interface and call each other 57:41 - The danger of almost-correct LLM code 01:05:39 - How LLMs will change programming languages 01:10:26 - Industry vs academia 01:15:05 - Most interesting unsolved problems 01:18:30 - Top book recommendations for engineers 01:21:17 - Advice for his younger self 01:23:31 - Outro
1:24:27

Xavier Leroy (creator of OCaml) is an expert in compilers, formal verification of software and functional programming. This interview should be an approachable resource if you're curious about formal verification of software since I was learning that on the fly during it. In this episode: • OCaml compared with Rust and JavaScript • What is formal verification and how does it work • How languages call each other across boundaries • How to address "almost-correct" LLM code • How type inference works in programming languages Where to watch: • YouTube - • Spotify - • Apple Podcasts - • Transcript - Thank you to the sponsor of this episode for supporting my work: • WorkOS: makes your app Enterprise Ready with easy to use APIs to add SSO, SCIM, RBAC, and more in just a few lines of code, check them out at Chapters: 00:00 - Intro 00:43 - What sets OCaml apart 04:39 - OCaml vs Rust 07:57 - Why is manual memory management more performant 11:21 - Javascript vs OCaml 14:00 - Famous Rob Pike quote 16:05 - Type inference and how it works 22:12 - What is formal verification and how does it work 40:07 - What made multicore support difficult for OCaml 50:17 - How programming languages interface and call each other 57:41 - The danger of almost-correct LLM code 01:05:39 - How LLMs will change programming languages 01:10:26 - Industry vs academia 01:15:05 - Most interesting unsolved problems 01:18:30 - Top book recommendations for engineers 01:21:17 - Advice for his younger self 01:23:31 - Outro

Ryan Peterman

24,256 görüntüleme • 3 ay önce

“In the past you would need a big GPU cluster for what you can do today with a single GPU.” Anthony Rose breaking down the incredible performance of Airbender, the world's fastest open-source RISC-V proof system, and a major breakthrough that paves the path for home-proving Ethereum.
1:04

“In the past you would need a big GPU cluster for what you can do today with a single GPU.” Anthony Rose breaking down the incredible performance of Airbender, the world's fastest open-source RISC-V proof system, and a major breakthrough that paves the path for home-proving Ethereum.

ZKsync

34,877 görüntüleme • 1 yıl önce

a16z's Ben Horowitz and Erik Torenberg speak with Alex Blania, co-founder and CEO of Tools for Humanity, to cover deepfakes, proof of human, what AI means for identity, and more. They cover the technical challenge of proving human uniqueness at scale using iris biometrics, the privacy architecture behind World ID, why platforms from social networks to dating apps to video conferencing will soon require proof of human verification, and more. Introduction Three big ideas people were interested in The Orb verification piece Social media bots: Psyops and propaganda We had proof of personhood for the longest time US go-to-market Levels of verification Alex Blania Tools for Humanity benahorowitz.eth Erik Torenberg
42:11

a16z's Ben Horowitz and Erik Torenberg speak with Alex Blania, co-founder and CEO of Tools for Humanity, to cover deepfakes, proof of human, what AI means for identity, and more. They cover the technical challenge of proving human uniqueness at scale using iris biometrics, the privacy architecture behind World ID, why platforms from social networks to dating apps to video conferencing will soon require proof of human verification, and more. Introduction Three big ideas people were interested in The Orb verification piece Social media bots: Psyops and propaganda We had proof of personhood for the longest time US go-to-market Levels of verification Alex Blania Tools for Humanity benahorowitz.eth Erik Torenberg

a16z

80,707 görüntüleme • 6 ay önce

There’s a popular theory that AI will finally make formal verification mainstream because mathematical proof of correctness will be needed when machines write most or all of the code. But will this happen? Hillel Wayne is one of the best people to answer. Timestamps: 00:00 Intro 04:32 The Crossover Project 11:37 What software engineering does better 15:30 What traditional engineering does better 18:17 Formal methods 29:32 TLA+: what it is and demo 36:58 TLA+ at Amazon 38:10 Ways distributed systems break 41:03 Formal methods and systems thinking 46:20 The value of learning math 50:23 What TLA+ is good for and isn’t 52:50 Alloy: a declarative language for software modeling 58:53 Other formal methods tools 1:01:24 Property-based testing 1:05:31 AI and the need for formal verification 1:12:29 Logic for programmers 1:14:35 Hillel’s 2025 prediction on AI’s impact 1:21:30 Book recommendation Brought to you by: • Antithesis – verify your system’s correctness without human review or traditional integration tests – and avoid bugs or outages. • turbopuffer – a vector and full-text search engine built on object storage. It’s fast, cheap, and extremely scalable. • WorkOS – everything you need to make your app enterprise ready. Two things I found especially interesting, talking with Hillel: 1. Amazon used TLA+ to find a bug almost impossible to locate without formal methods. In the paper How AWS uses formal methods, the AWS team shared that they’d found a complicated bug for which the shortest error trace to exhibit was 35 steps (!!). The bug passed unnoticed through extensive design review, code reviews, and testing. AWS concluded they wouldn’t have uncovered it if they’d stuck to conventional testing approaches. 2. Why not use formal verification for everything, then? It’s because specs in the real world are a nightmare to write. Even a simple problem like “find the file in a directory that has the most lines” gets complicated when modeled with formal methods. We would have to answer questions like: ‘do we look at ASCII or UTF-8 new line characters, what about unreadable files, and Symlinks?’ Without formal methods, we can write a simple verification that is right in 99%+ of cases. Formal methods require a lot of extra effort for the less than 1% of exotic use cases!
1:24:52

There’s a popular theory that AI will finally make formal verification mainstream because mathematical proof of correctness will be needed when machines write most or all of the code. But will this happen? Hillel Wayne is one of the best people to answer. Timestamps: 00:00 Intro 04:32 The Crossover Project 11:37 What software engineering does better 15:30 What traditional engineering does better 18:17 Formal methods 29:32 TLA+: what it is and demo 36:58 TLA+ at Amazon 38:10 Ways distributed systems break 41:03 Formal methods and systems thinking 46:20 The value of learning math 50:23 What TLA+ is good for and isn’t 52:50 Alloy: a declarative language for software modeling 58:53 Other formal methods tools 1:01:24 Property-based testing 1:05:31 AI and the need for formal verification 1:12:29 Logic for programmers 1:14:35 Hillel’s 2025 prediction on AI’s impact 1:21:30 Book recommendation Brought to you by: • Antithesis – verify your system’s correctness without human review or traditional integration tests – and avoid bugs or outages. • turbopuffer – a vector and full-text search engine built on object storage. It’s fast, cheap, and extremely scalable. • WorkOS – everything you need to make your app enterprise ready. Two things I found especially interesting, talking with Hillel: 1. Amazon used TLA+ to find a bug almost impossible to locate without formal methods. In the paper How AWS uses formal methods, the AWS team shared that they’d found a complicated bug for which the shortest error trace to exhibit was 35 steps (!!). The bug passed unnoticed through extensive design review, code reviews, and testing. AWS concluded they wouldn’t have uncovered it if they’d stuck to conventional testing approaches. 2. Why not use formal verification for everything, then? It’s because specs in the real world are a nightmare to write. Even a simple problem like “find the file in a directory that has the most lines” gets complicated when modeled with formal methods. We would have to answer questions like: ‘do we look at ASCII or UTF-8 new line characters, what about unreadable files, and Symlinks?’ Without formal methods, we can write a simple verification that is right in 99%+ of cases. Formal methods require a lot of extra effort for the less than 1% of exotic use cases!

Gergely Orosz

34,975 görüntüleme • 2 ay önce