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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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15,143 views • 3 months ago •via X (Twitter)

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“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 views • 5 months ago

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 views • 4 months ago

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 views • 4 months ago

How will autonomous AI agents shape the future of Ethereum? In Episode 20 of the Agents Unleashed podcast, maybrier ☴ talks with Tomasz K. Stańczak, Co-Executive Director of the Ethereum Foundation, about Ethereum’s next decade and the rise of agentic AI, both on and off-chain. In this episode: • Strategic priorities for Ethereum’s next upgrades • Why cypherpunk values like privacy + decentralization still matter • How AI agents could use Ethereum as their coordination + verification layer • What “identity is defined by privacy” really means in an AI world 02:05 – Tomasz on his role at Ethereum Foundation 04:21 – What about Ethereum’s mission resonated with him 06:48 – The Foundation's strategic priorities for AI 10:09 – Keeping cypherpunk values alive 15:34 – The next 10 years: AI, robotics, and blockchain 20:55 – Identity, privacy, and AI agents 26:30 – Predictions for the future of the AI user interface 31:36 – Call to action for builders This episode is sponsored by Sigil Fund
33:46

How will autonomous AI agents shape the future of Ethereum? In Episode 20 of the Agents Unleashed podcast, maybrier ☴ talks with Tomasz K. Stańczak, Co-Executive Director of the Ethereum Foundation, about Ethereum’s next decade and the rise of agentic AI, both on and off-chain. In this episode: • Strategic priorities for Ethereum’s next upgrades • Why cypherpunk values like privacy + decentralization still matter • How AI agents could use Ethereum as their coordination + verification layer • What “identity is defined by privacy” really means in an AI world 02:05 – Tomasz on his role at Ethereum Foundation 04:21 – What about Ethereum’s mission resonated with him 06:48 – The Foundation's strategic priorities for AI 10:09 – Keeping cypherpunk values alive 15:34 – The next 10 years: AI, robotics, and blockchain 20:55 – Identity, privacy, and AI agents 26:30 – Predictions for the future of the AI user interface 31:36 – Call to action for builders This episode is sponsored by Sigil Fund

Olas (formerly Autonolas)

17,413 views • 10 months ago

Mistral AI Releases Leanstral 1.5: An Apache-2.0 Lean 4 Code Agent Model Solving 587 of 672 PutnamBench Problems Most AI theorem proving is a language model generating a proof in one shot, with a verifier bolted on at the end to check it. That's autocomplete with a grader — and Mistral just drew a clear line between that and an actual proof agent. They released Leanstral 1.5 — a 119B MoE with 6.5B active parameters, trained as a code agent that lives inside the Lean 4 compiler loop: propose a proof, read the compiler's goals and errors, refine, repeat until it compiles or the budget runs out. Verification isn't the eval here. It's the training signal. Here's what's actually interesting: → Test-time scaling behaves like a dial: PutnamBench Pass@8 climbs 44 → 244 → 493 → 587 solved as the per-attempt token budget moves 50k → 200k → 1M → 4M → 587/672 on PutnamBench at ~$4 per problem, versus an estimated $300+ for Seed-Prover 1.5 high (a 10 H20-days-per-problem budget) → Saturates miniF2F: 100% on both validation and test sets → Two RL environments in training — a multiturn prover, and a raw-filesystem code agent that edits files, runs bash, and queries the Lean language server for live goals and types → Not just math: an Aeneas (Rust → Lean) pipeline flagged 11 genuine bugs across 57 repos, 5 previously unreported — including an integer overflow in datrs/varinteger when (value + 1) hits Std.U64.MAX Apache 2.0 weights, free API endpoint Full analysis: Model weights: Project: Technical Details: Mistral AI Mistral AI for Developers Sophia Yang, Ph.D.
0:25

Mistral AI Releases Leanstral 1.5: An Apache-2.0 Lean 4 Code Agent Model Solving 587 of 672 PutnamBench Problems Most AI theorem proving is a language model generating a proof in one shot, with a verifier bolted on at the end to check it. That's autocomplete with a grader — and Mistral just drew a clear line between that and an actual proof agent. They released Leanstral 1.5 — a 119B MoE with 6.5B active parameters, trained as a code agent that lives inside the Lean 4 compiler loop: propose a proof, read the compiler's goals and errors, refine, repeat until it compiles or the budget runs out. Verification isn't the eval here. It's the training signal. Here's what's actually interesting: → Test-time scaling behaves like a dial: PutnamBench Pass@8 climbs 44 → 244 → 493 → 587 solved as the per-attempt token budget moves 50k → 200k → 1M → 4M → 587/672 on PutnamBench at ~$4 per problem, versus an estimated $300+ for Seed-Prover 1.5 high (a 10 H20-days-per-problem budget) → Saturates miniF2F: 100% on both validation and test sets → Two RL environments in training — a multiturn prover, and a raw-filesystem code agent that edits files, runs bash, and queries the Lean language server for live goals and types → Not just math: an Aeneas (Rust → Lean) pipeline flagged 11 genuine bugs across 57 repos, 5 previously unreported — including an integer overflow in datrs/varinteger when (value + 1) hits Std.U64.MAX Apache 2.0 weights, free API endpoint Full analysis: Model weights: Project: Technical Details: Mistral AI Mistral AI for Developers Sophia Yang, Ph.D.

Marktechpost AI

56,461 views • 11 days ago

It's finally here - the Official Lean AI Company Playbook 1000+ founders, investors and execs have been flooding my DMs about. Ever since I created the Official Lean AI Leaderboard after exiting my $150M annual revenue startup, founders from across the globe have asked me this one question: "How are these lean AI companies generating millions with tiny teams?" So I spent the last 3 months obsessively tracking and collaborating with 30+ lean AI-native founders, researching the answer. Here's what I discovered: They've built operational systems that multiply human capability. Instead of scaling headcount, they've created AI-powered processes that let small teams operate at unprecedented scale. And their secret goes far beyond the specific AI tools they use. It lies in redesigning their entire company operations around a fundamentally different approach to growth and execution. After countless conversations, interviews, and behind-the-scenes exclusive access, I finally documented the exact playbook with complete systems. This comprehensive playbook includes: • A complete breakdown of the tech stack and operational workflows • The organizational design principles that enable tiny teams to do massive work • Critical inflection points where things break (and how to navigate them) • A detailed 6-month implementation plan for starting your own lean AI company (with weekly actions) • Implementation best practices from dozens of successful lean AI-native companies Want the ultimate Lean AI Playbook or help transforming your Lean AI operations? 👇 • Like and Share this post • Comment "Lean AI Playbook" • Follow me (so I can DM you) --------------- PS: Separately, I'm opening a limited number of high-impact advising slots for serious founders and operators who want to work directly with me—beyond just consuming my content. If you're ready to transform how your company operates using AI, DM me. (Please note: these are paid spots due to limited availability and time atm)
0:48

It's finally here - the Official Lean AI Company Playbook 1000+ founders, investors and execs have been flooding my DMs about. Ever since I created the Official Lean AI Leaderboard after exiting my $150M annual revenue startup, founders from across the globe have asked me this one question: "How are these lean AI companies generating millions with tiny teams?" So I spent the last 3 months obsessively tracking and collaborating with 30+ lean AI-native founders, researching the answer. Here's what I discovered: They've built operational systems that multiply human capability. Instead of scaling headcount, they've created AI-powered processes that let small teams operate at unprecedented scale. And their secret goes far beyond the specific AI tools they use. It lies in redesigning their entire company operations around a fundamentally different approach to growth and execution. After countless conversations, interviews, and behind-the-scenes exclusive access, I finally documented the exact playbook with complete systems. This comprehensive playbook includes: • A complete breakdown of the tech stack and operational workflows • The organizational design principles that enable tiny teams to do massive work • Critical inflection points where things break (and how to navigate them) • A detailed 6-month implementation plan for starting your own lean AI company (with weekly actions) • Implementation best practices from dozens of successful lean AI-native companies Want the ultimate Lean AI Playbook or help transforming your Lean AI operations? 👇 • Like and Share this post • Comment "Lean AI Playbook" • Follow me (so I can DM you) --------------- PS: Separately, I'm opening a limited number of high-impact advising slots for serious founders and operators who want to work directly with me—beyond just consuming my content. If you're ready to transform how your company operates using AI, DM me. (Please note: these are paid spots due to limited availability and time atm)

Henry Shi

121,541 views • 1 year ago

We've been working with Kev and ladislaus.eth from Ethereum Foundation and donnoh.gwei 💗 from L2BEAT 💗 on a proof of concept of EIP-8079 (native rollups) using ethrex. Native rollups reuse Ethereum's own execution to verify L2 state transitions. No ZK circuits, no fraud proofs, no complex proof systems to maintain. Every L1 upgrade is automatically inherited. Any bug in the verification is also a bug in Ethereum itself. The demo shows a full end-to-end native rollup: - L2 blocks settled to L1 via the EXECUTE precompile - L1→L2 deposit - Contract deployment and cross-layer calls - L2→L1 withdrawal with MPT proof claim - Blockscout verifying EXECUTE precompile calls on L1 Try it yourself. Instructions in the PR below.
2:12

We've been working with Kev and ladislaus.eth from Ethereum Foundation and donnoh.gwei 💗 from L2BEAT 💗 on a proof of concept of EIP-8079 (native rollups) using ethrex. Native rollups reuse Ethereum's own execution to verify L2 state transitions. No ZK circuits, no fraud proofs, no complex proof systems to maintain. Every L1 upgrade is automatically inherited. Any bug in the verification is also a bug in Ethereum itself. The demo shows a full end-to-end native rollup: - L2 blocks settled to L1 via the EXECUTE precompile - L1→L2 deposit - Contract deployment and cross-layer calls - L2→L1 withdrawal with MPT proof claim - Blockscout verifying EXECUTE precompile calls on L1 Try it yourself. Instructions in the PR below.

ethrex

26,719 views • 4 months ago

In this episode of the Lightcone Podcast, we sat down with Mukund Jha and Madhav Jha, the founders of Emergent - an AI platform that lets anyone build and ship production-ready software. In just eight months, users have created more than 7 million apps on Emergent, with the number doubling in just the last 45 days. We discuss how they built one of the most powerful AI coding agents, why they focused on non-technical users and what it's like building for a global audience from India. 00:00 - Intro 01:06 - What Is Emergent? 01:18 - Founder Backstory 02:09 - From AI Testing to General Coding Agents 02:52 - Getting Ahead of the Market 04:18 - The Pivot to Non-Technical Users 05:22 - Why Second Movers Can Win in AI 09:04 - Building for Production, Not Just Prototypes 18:21 - Live Demo: Building Apps with Emergent 24:40 - How Emergent Hires and Runs a Lean Team 29:04 - Is SaaS Dead? The Rise of Personalized Software 34:04 - The Future: Niche Apps, Solo Builders and AI Agency
39:32

In this episode of the Lightcone Podcast, we sat down with Mukund Jha and Madhav Jha, the founders of Emergent - an AI platform that lets anyone build and ship production-ready software. In just eight months, users have created more than 7 million apps on Emergent, with the number doubling in just the last 45 days. We discuss how they built one of the most powerful AI coding agents, why they focused on non-technical users and what it's like building for a global audience from India. 00:00 - Intro 01:06 - What Is Emergent? 01:18 - Founder Backstory 02:09 - From AI Testing to General Coding Agents 02:52 - Getting Ahead of the Market 04:18 - The Pivot to Non-Technical Users 05:22 - Why Second Movers Can Win in AI 09:04 - Building for Production, Not Just Prototypes 18:21 - Live Demo: Building Apps with Emergent 24:40 - How Emergent Hires and Runs a Lean Team 29:04 - Is SaaS Dead? The Rise of Personalized Software 34:04 - The Future: Niche Apps, Solo Builders and AI Agency

Y Combinator

107,448 views • 4 months ago

House of ZK is proud to announce the full sponsor lineup for Verifying Intelligence 3.0 - Where ZK Meets AI & x402 - at Ethereum Devcon, Argentina 🇦🇷 Supported by leading builders across the ZK & AI spaces - Starknet (BTCFi arc) 🥷, Mina Protocol (httpz) 🪶, ZKM, zk V e r i f y, 0G Labs (Home of Infinite AI), Ontology - The Trust Layer for Web3, Veridise, and Ethereum Argentina 🦇🔊 - this edition brings together the projects defining the infrastructure for verifiable compute, decentralized intelligence, and the trustless Internet. Taking place on November 19th at Cultural Vivo, Verifying Intelligence will explore the verification stack for agentic AI: proof-carrying inference, private data pipelines, provenance, and verifiable micropayments via x402. If you’re in Buenos Aires for DevConnect, this is the one event you don’t want to miss. RSVP now:
0:43

House of ZK is proud to announce the full sponsor lineup for Verifying Intelligence 3.0 - Where ZK Meets AI & x402 - at Ethereum Devcon, Argentina 🇦🇷 Supported by leading builders across the ZK & AI spaces - Starknet (BTCFi arc) 🥷, Mina Protocol (httpz) 🪶, ZKM, zk V e r i f y, 0G Labs (Home of Infinite AI), Ontology - The Trust Layer for Web3, Veridise, and Ethereum Argentina 🦇🔊 - this edition brings together the projects defining the infrastructure for verifiable compute, decentralized intelligence, and the trustless Internet. Taking place on November 19th at Cultural Vivo, Verifying Intelligence will explore the verification stack for agentic AI: proof-carrying inference, private data pipelines, provenance, and verifiable micropayments via x402. If you’re in Buenos Aires for DevConnect, this is the one event you don’t want to miss. RSVP now:

House of ZK

22,991 views • 8 months ago

Since 2020, CoinFund’s investment thesis in World has been driven by the idea that biometric proof of personhood is critical for identity verification. Amid today’s rapid expansion of AI, it’s becoming an increasingly important problem to solve. Alex Blania, CEO of Tools for Humanity – the company behind World (formerly Worldcoin) – sat down with CoinFund CEO Jake Brukhman to discuss the company’s approach to proof of personhood, its emerging ecosystem of projects, and why identity verification is urgent to address. They discuss: - From Worldcoin to World (2:45) - Building on World Chain (7:36) - Distributing identity (24:08) - Why proof of personhood (28:55) - The underlying rails for adoption (41:58) Tune into the latest episode of Mined with CoinFund, available on Apple, Spotify, and YouTube.
0:48

Since 2020, CoinFund’s investment thesis in World has been driven by the idea that biometric proof of personhood is critical for identity verification. Amid today’s rapid expansion of AI, it’s becoming an increasingly important problem to solve. Alex Blania, CEO of Tools for Humanity – the company behind World (formerly Worldcoin) – sat down with CoinFund CEO Jake Brukhman to discuss the company’s approach to proof of personhood, its emerging ecosystem of projects, and why identity verification is urgent to address. They discuss: - From Worldcoin to World (2:45) - Building on World Chain (7:36) - Distributing identity (24:08) - Why proof of personhood (28:55) - The underlying rails for adoption (41:58) Tune into the latest episode of Mined with CoinFund, available on Apple, Spotify, and YouTube.

CoinFund

13,408 views • 1 year ago

“You’ve just been told you have superpowers.” AI agents are starting to feel like coworkers. The cost of automation is collapsing. So what happens to jobs? To startups? To crypto? We break down the new paper “Some Simple Economics of AGI” with coauthor Christian Catalini, including: • Why verification becomes the bottleneck • Practical advice for early- and late-career builders • The risk of a hollow economy, and the path to an augmented one • Why crypto may be essential infrastructure for identity, provenance, and trust In conversation with Eddy Lazzarin ☀️ and Robert Hackett. Full episode ↓
1:05:40

“You’ve just been told you have superpowers.” AI agents are starting to feel like coworkers. The cost of automation is collapsing. So what happens to jobs? To startups? To crypto? We break down the new paper “Some Simple Economics of AGI” with coauthor Christian Catalini, including: • Why verification becomes the bottleneck • Practical advice for early- and late-career builders • The risk of a hollow economy, and the path to an augmented one • Why crypto may be essential infrastructure for identity, provenance, and trust In conversation with Eddy Lazzarin ☀️ and Robert Hackett. Full episode ↓

a16z crypto

205,289 views • 4 months ago

In the first episode of our new series Full Stack, Conductor CEO and co-founder Charlie Holtz takes us into the details of how he sets up his workflow for coding and managing AI agents. 00:00 – Building Conductor With Conductor 01:05 – Managing a Team of Coding Agents 02:39 – Do You Still Write Code? 04:17 – Charlie's AI Stack and Setup 05:48 – “Slop-free” Zones 07:25 – Don’t Let the AI Be Your Architect 09:15 – The Future of Cloud Workspaces 10:40 – Claude vs Codex 12:01 – Tokenmaxxing 14:17 – The Future of Human–AI Collaboration 15:07 – "Code Is Becoming Sawdust"
16:34

In the first episode of our new series Full Stack, Conductor CEO and co-founder Charlie Holtz takes us into the details of how he sets up his workflow for coding and managing AI agents. 00:00 – Building Conductor With Conductor 01:05 – Managing a Team of Coding Agents 02:39 – Do You Still Write Code? 04:17 – Charlie's AI Stack and Setup 05:48 – “Slop-free” Zones 07:25 – Don’t Let the AI Be Your Architect 09:15 – The Future of Cloud Workspaces 10:40 – Claude vs Codex 12:01 – Tokenmaxxing 14:17 – The Future of Human–AI Collaboration 15:07 – "Code Is Becoming Sawdust"

Y Combinator

155,571 views • 1 month ago

Jolt Pro is a 100x improvement over existing zkVMs. After months of researching all existing zkVMs, we realized none of them could scale to what we needed with Zero. Then we came across Justin Thaler's work and Jolt, a research project from a16z crypto. We saw massive potential in the math and, finally, a viable way to scale to our needs. So we secretly assembled a team of some of the brightest minds across cryptography, GPU programming, and ASIC design to build an internal Jolt Pro team. Jolt Pro has no precompiles; it runs only RISC-V instructions, without introducing new ad hoc, error-prone constraints. It's impossible to compare against other zkVMs because they are not proving straight RISC-V, most of their work and speed gains exist in dangerous pre-compiles. Jolt Pro scales to infinity. The number of cells you can use in parallel is only limited by the size of the datacenter. Jolt Pro has a path to 4GHz cells using the same configuration we use for our 1.61 GHz cell today. By early 2027, it will be post-quantum and set the standard for all zkVMs. We will eventually make "zero-proofs", and others can try to beat our RISC-V proving in the open. For now, we stay heads down, building Zero to be everything the industry ever wanted. And then just a little more.
2:18

Jolt Pro is a 100x improvement over existing zkVMs. After months of researching all existing zkVMs, we realized none of them could scale to what we needed with Zero. Then we came across Justin Thaler's work and Jolt, a research project from a16z crypto. We saw massive potential in the math and, finally, a viable way to scale to our needs. So we secretly assembled a team of some of the brightest minds across cryptography, GPU programming, and ASIC design to build an internal Jolt Pro team. Jolt Pro has no precompiles; it runs only RISC-V instructions, without introducing new ad hoc, error-prone constraints. It's impossible to compare against other zkVMs because they are not proving straight RISC-V, most of their work and speed gains exist in dangerous pre-compiles. Jolt Pro scales to infinity. The number of cells you can use in parallel is only limited by the size of the datacenter. Jolt Pro has a path to 4GHz cells using the same configuration we use for our 1.61 GHz cell today. By early 2027, it will be post-quantum and set the standard for all zkVMs. We will eventually make "zero-proofs", and others can try to beat our RISC-V proving in the open. For now, we stay heads down, building Zero to be everything the industry ever wanted. And then just a little more.

LayerZero

29,409 views • 4 months ago

“What happens when AI infrastructure stops chasing everything — and focuses on making intelligence actually reliable?” Sachi Takahara / 0699.eth joins Karan Sirdesai (Karan), Founder of Mira, for a deep dive into how Mira is taking a sharper path in decentralized AI: high-trust, reliable intelligence as the core product. They break down why Mira isn’t trying to be another full-stack AI protocol, how it differs from networks like Bittensor, Ritual, and Sahara, and why its narrow focus could make it easier to plug into the broader AI infra ecosystem instead of competing with every layer. The conversation also dives into Mira’s reliability model — from core tech to node-level consensus — and why the future of AI APIs may depend less on raw model access, and more on whether users can actually trust the intelligence they receive.
2:22

“What happens when AI infrastructure stops chasing everything — and focuses on making intelligence actually reliable?” Sachi Takahara / 0699.eth joins Karan Sirdesai (Karan), Founder of Mira, for a deep dive into how Mira is taking a sharper path in decentralized AI: high-trust, reliable intelligence as the core product. They break down why Mira isn’t trying to be another full-stack AI protocol, how it differs from networks like Bittensor, Ritual, and Sahara, and why its narrow focus could make it easier to plug into the broader AI infra ecosystem instead of competing with every layer. The conversation also dives into Mira’s reliability model — from core tech to node-level consensus — and why the future of AI APIs may depend less on raw model access, and more on whether users can actually trust the intelligence they receive.

BlockLayer Podcast

48,028 views • 1 month ago

“What if the real unlock for AI x crypto is not speculation — but proving intelligence can be trusted?” Sachi Miyasaki sits down with dcbuilder.eth ⚪️ for a deep dive into the intersection of AI and crypto, from DC’s work building the ZKML community to why zero-knowledge machine learning could become a core trust layer for AI on the internet. They break down how ZKML makes it possible to prove that a machine learning model produced a specific output from specific inputs — without forcing everyone to rerun the full computation themselves. The conversation also explores why this matters for model accountability, transparency, and verifiable AI at scale, plus the projects pushing the space forward, from Modulus Labs and Giza to EZKL and new research around proof of inference.
5:35

“What if the real unlock for AI x crypto is not speculation — but proving intelligence can be trusted?” Sachi Miyasaki sits down with dcbuilder.eth ⚪️ for a deep dive into the intersection of AI and crypto, from DC’s work building the ZKML community to why zero-knowledge machine learning could become a core trust layer for AI on the internet. They break down how ZKML makes it possible to prove that a machine learning model produced a specific output from specific inputs — without forcing everyone to rerun the full computation themselves. The conversation also explores why this matters for model accountability, transparency, and verifiable AI at scale, plus the projects pushing the space forward, from Modulus Labs and Giza to EZKL and new research around proof of inference.

BeaconLayer Podcast

73,815 views • 1 month ago

Axiom Math founder Carina Hong on why math is the path to general superintelligence: “Math is the sandbox for reality. You very quickly see verifiable rewards because in math, there’s an absolute right or wrong.” “And especially when you have Lean, you can check the proof or solution step by step. You’ll be able to apply reinforcement learning in a much more efficient way.” “We have currently scaled from winning a Putnam perfect score to solving a batch of research problems that professional mathematicians find really challenging. And we also see this transfer to code verification.”
0:56

Axiom Math founder Carina Hong on why math is the path to general superintelligence: “Math is the sandbox for reality. You very quickly see verifiable rewards because in math, there’s an absolute right or wrong.” “And especially when you have Lean, you can check the proof or solution step by step. You’ll be able to apply reinforcement learning in a much more efficient way.” “We have currently scaled from winning a Putnam perfect score to solving a batch of research problems that professional mathematicians find really challenging. And we also see this transfer to code verification.”

TBPN

39,589 views • 4 months ago

The Ethereum Interop Layer (EIL) Explained 🎙 Ethereum is pushing toward a UX that feels single-chain. Is the Ethereum Interop Layer (EIL) the missing piece? In our latest episode, Marissa Posner and yoav.eth from the Ethereum Foundation break down how lessons from Account Abstraction are being extended to enable trustless multi-chain execution with the EIL. Key timestamps: 00:00 Meet the team working on EIL at the EF 8:20 What is the Ethereum Interop Layer? 16:50 Solvers vs XLPs — the censorship risk in intents 29:05 What EIL means for bridges 32:20 EIL vs OIF: how they fit together 43:00 Mainnet timeline & how builders can get involved
46:10

The Ethereum Interop Layer (EIL) Explained 🎙 Ethereum is pushing toward a UX that feels single-chain. Is the Ethereum Interop Layer (EIL) the missing piece? In our latest episode, Marissa Posner and yoav.eth from the Ethereum Foundation break down how lessons from Account Abstraction are being extended to enable trustless multi-chain execution with the EIL. Key timestamps: 00:00 Meet the team working on EIL at the EF 8:20 What is the Ethereum Interop Layer? 16:50 Solvers vs XLPs — the censorship risk in intents 29:05 What EIL means for bridges 32:20 EIL vs OIF: how they fit together 43:00 Mainnet timeline & how builders can get involved

LI.FI

16,612 views • 5 months ago

zkVerify (zk V e r i f y): Filling the Missing Link in Zero Knowledge Technology 00:00 Introducing Gwen (Gwendolyn Regina) from Horizen Labs (Horizen Labs) 04:00 The importance of creating content 05:54 Relation between Horizen Labs and zkVerify 07:44 Why did Horizen Labs pivot? 10:09 Why the ZK narrative is coming around again 13:35 Verification of proofs is the missing link 16:30 How does zkVerify stands out in the market 17:37 TGE and token utility 20:16 What is Gwen interested in as an investor 23:08 Most proud investment during her career 24:06 Find Horizen Labs on all socials In this episode we sit down with Gwen from Horizen Labs to talk about her journey in crypto, why content creation matters, and how Horizen Labs is connected to ZKVerify. She explains why the company decided to pivot and why the zero-knowledge narrative is resurging, pointing out that proof verification is the missing piece in the puzzle. We also dive into how ZKVerify differentiates itself in a competitive market, the role of its token, and Gwen’s perspective as an investor. She shares what excites her most in the space, the investment she is proudest of in her career, and where to follow Horizen Labs online.
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zkVerify (zk V e r i f y): Filling the Missing Link in Zero Knowledge Technology 00:00 Introducing Gwen (Gwendolyn Regina) from Horizen Labs (Horizen Labs) 04:00 The importance of creating content 05:54 Relation between Horizen Labs and zkVerify 07:44 Why did Horizen Labs pivot? 10:09 Why the ZK narrative is coming around again 13:35 Verification of proofs is the missing link 16:30 How does zkVerify stands out in the market 17:37 TGE and token utility 20:16 What is Gwen interested in as an investor 23:08 Most proud investment during her career 24:06 Find Horizen Labs on all socials In this episode we sit down with Gwen from Horizen Labs to talk about her journey in crypto, why content creation matters, and how Horizen Labs is connected to ZKVerify. She explains why the company decided to pivot and why the zero-knowledge narrative is resurging, pointing out that proof verification is the missing piece in the puzzle. We also dive into how ZKVerify differentiates itself in a competitive market, the role of its token, and Gwen’s perspective as an investor. She shares what excites her most in the space, the investment she is proudest of in her career, and where to follow Horizen Labs online.

Presto Research

17,708 views • 11 months ago

Anglofuturism interviewed James Wise, Chair of the government's Sovereign AI Unit. 00:01:57 - Navigating AI from within government in Britain 00:11:30 - Why full-stack AI isn't the goal of sovereignty 00:24:59 - Are we investing enough in Britain's AI future? 00:33:19 - Overcoming capital constraints for British AI innovation 00:41:58 - How to leverage Britain's unique advantage in healthcare 01:10:52 - Britain needs an entrepreneurial culture shift You can find more Anglofuturism on YouTube, Spotify, Apple Podcasts, and Substack.
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Anglofuturism interviewed James Wise, Chair of the government's Sovereign AI Unit. 00:01:57 - Navigating AI from within government in Britain 00:11:30 - Why full-stack AI isn't the goal of sovereignty 00:24:59 - Are we investing enough in Britain's AI future? 00:33:19 - Overcoming capital constraints for British AI innovation 00:41:58 - How to leverage Britain's unique advantage in healthcare 01:10:52 - Britain needs an entrepreneurial culture shift You can find more Anglofuturism on YouTube, Spotify, Apple Podcasts, and Substack.

Aeron Laffere

17,046 views • 19 days ago