jooman ➡️ working on AI inference

🇸🇬 SINGAPORE | 11th June 🇸🇬 We hosted one of the most hardcore Whitepaper Reading Club sessions in Singapore last Thursday, where we were deciphering: 🧠 Cryptography’s XLA Moment: Why ZK may need a compiler, not another proving system 🧠 This session was led by @jumanzi_dev from Fractalyze, the team building “the PyTorch for ZK” — a Python-first compiler stack for Zero-Knowledge. Big shoutout to our: - Discussion: Jooman & Batzorig from Fractalyze for working on this amazing project. They are deeply passionate about what they do and I feel I've learned so much. - Thank you to my partner in crime @BrianSeong who made this session possible. - Great to see @ulandaoon @kowei1995 @9oelM and welcome @tomespel (welcome back to ZK) 🙏 Appreciate @kimmi from @SuperteamSG, for being there for us :) 📝 For more information: - Fractalyze: fractalyze.io - Event summary: docs.google.com/document/d/1… 📆 Upcoming events: luma.com/whitepaper ‼️ THE EVENT NOTES > TL;DR ‼️ (1) ZK’s Problem: zkVM/proving teams like @SuccinctLabs, @RiscZero, @ziskvm, OpenVM, Plonky3 (@0xPolygon), Jolt from @a16zcrypto, and Binius from @IrreducibleHW are all fighting the same hard problem: turning Rust/CUDA, memory layout, GPU execution, and field arithmetic into production-level proving performance. (2) Fractalyze's Solution: Build ZK proving systems in high-level Python, then let a cryptography-native compiler handle optimization underneath. (3) Zorch: Fractalyze’s stack aims to become the “PyTorch for ZK” — making proving systems easier to write, test, optimize, package, and deploy. (4) The XLA Analogy: ML scaled as researchers stopped hand-writing kernels. @OpenXLA / MLIR helped high-level models compile into efficient hardware execution. (5) Cryptography Needs the Same Layer: ZK, FHE, MPC, post-quantum crypto, and formal verification all need better compiler infrastructure if they are going to become real products they are going to become real products — see @zama, @TACEO_IO, @rv_inc, and @NethermindSec. (6) Not Just Developer UX: The goal is not only abstraction. The deeper claim is that compiler automation can beat hand-written optimization by reasoning across the whole computation graph. (7) Hardware Portability: Today’s ZK systems are often tied to specific @nvidia GPU assumptions. A compiler layer could make cryptographic workloads easier to move across CPU, GPU, FPGA, and future crypto-specific hardware — a direction also explored by teams like @Ingo_zk / ICICLE and @cysic_xyz. (8) Ethereum: This matters for real-time block proving, lighter validation, larger block capacity, and the long-term vision of @ethereumfndn becoming easier to verify. (9) Bigger Than ZK: The real question is whether advanced cryptography can move from research papers and hand-built systems into reusable infrastructure — the same way AI did. 🧠QUESTIONS: (i) Is the next major ZK breakthrough a new proving system — or the compiler layer underneath all of them? (ii) If Ethereum itself becomes ZK-powered, what is the future role of ZK rollups? (iii) Can cryptography become a profitable product category, or is it closer to public-good infrastructure? (iv) Will AI-generated code make formal verification and verifiable compute much more important? (v) What happens when cryptography gets 100x or 1000x cheaper to run? #ZK #Cryptography #Ethereum #MLIR #XLA #VerifiableCompute #Fractalyze #WhitepaperReadingClub