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We pulled up first… @ziskvm tops @eth_proofs day, First one to arrive

There’s no single “winner” for privacy it’ll be layered and context-specific. Different stacks solve different things : 1. Payments: Privacy coins like @Zcash and @monero are great at default secrecy but bad at composability and compliance. The real future imo here is stablecoins on public L2s/L1s [] with app-level privacy (stealth addresses, note commitments, and selective proofs). 2. DeFi & trading: Programmable ZK infra (@aztecnetwork , @0xMiden ,@AleoHQ ) this would be enabling targeted privacy like sealed intents, private state, and verifiable settlement and to top it of if all are composable with existing EVM infrastructure. This might also be a mix of applications working on specific privacy computes [ FHE MPC, TEEs based etc ] 3. Institutions: Permissioned or hybrid networks (like @CantonNetwork or @zksync Prividium )might fit the institutional needs for selective visibility, policy controls, and auditability. They care about regulatory assurance first, anonymity second. [Interesting would be if @Plasma @stable join this space too] 4. Private data & computation: Encrypted compute networks (FHE, MPC, TEEs like Arcium and @zama ) are powerful for analytics and private matching or hybrid models: encrypted compute for secrecy, ZK for correctness. 5. ZK compute infrastructure: Engines like @ziskvm and @RiscZero with distributed proving, quantum-resistant security. The faster and cheaper proof generation becomes, the easier it is for the rest of the privacy stack to go mainstream. I think what we could see coming up would be: 1. Selective disclosure > absolute secrecy. 2. Verifiability > trust. 3. Composability drives adoption. 4. Policy-aware privacy is mandatory. Whoever makes “private by default” fast and cheap wins.

Control Plane for Distributed Proving This part (1/5) isn’t about scheduling or marketplace logic yet (I'll be trying to extend this though) it’s about building visibility first. I tried to built a distributed job coordination dashboard @ziskvm , helps to observe and manage distributed proof jobs. It gives visibility into what the coordinator and workers are doing i.e. which jobs are running, how much capacity is available, and how things change over time. Every data point in the UI is reconstructed directly from live polling cycles against the coordinator through a REST→gRPC gateway. More in the thread below:

Grateful to the @__zkhack__ team for featuring my recent write up on distributed proof generation. It’s been exciting to experiment with ZisK’s approach to scaling proofs across distributed environments. Read it here: teapawt.net/0xAbix/status/19817529… Link to ZK MESH: zkmesh.substack.com

1.7x faster, Distributed proof generation with @ziskvm Setup: Coordinator on my Mac, worker on remote server via SSH tunnel Key learning: The speedup [ 193s → 115s ] came from better remote h/w, BUT the distributed architecture lets you mix hardware types and actually scale proof generation. More updates incoming, Shhh.... *if you know fuckall about coordinators and workers and stuff, just say hi"

Link to the blog : teapawt.net/0xAbix/status/19817529…

Distributed Proof Generation! gm everyone, Spent this week deep-diving into @ziskvm’s distributed proving architecture and built a complete walkthrough from setup to final proof. I've tried to summarise my learnings to help you understand the following : - Full breakdown of the 3-phase proving workflow. - How the coordinator & worker model actually work. - Step-by-step manual deployment and logs If you’ve ever wondered what's there beside the door for distributed proof generation and what actually happens in practice this is a full, reproducible walkthrough. Link to the blog : teapawt.net/0xAbix/status/19817529…

Spent yesterday (thanks AWS outage) diving deep into distributed proof generation vs proof marketplaces. After 6 hours analyzing @ziskvm , @SuccinctLabs Network, and @boundlesshq Here's my thesis: For high-frequency proving (rollups, coprocessors), distributed systems are architecturally superior. Why? Parallelism (multiple workers vs one prover = 3-5x speedup potential), partial failure recovery (lose 1/N of work vs 100%), better cost structure at scale (fixed costs amortize over volume). Marketplaces are perfect for occasional proofs. But for continuous, latency-sensitive proving? Distributed wins on multiple dimensions. Details with Amdahl's Law, ZisK's three-phase architecture, and honest caveats here in the blog:

Just got ZisK's distributed proof generation running! Will be sharing a detail writeup to how you can get started with setting up the environment and start building. @ziskvm sets a new standard in the zk proving space with: 1. 128 bit security 2. Distributed proof generation 3. Fully Open source Let's connect in the DMs if you want to talk about building with ZisK

Getting started with ZisK - Part 1 This piece walks through what actually happens when you write and prove a ZisK program. It also breaks down how ZisK internally works for generating and working with @ziskvm's proof system. By the end, you’ll have an understanding of how execution and proof generation fit together in ZisK, and how you can start building private, verifiable programs. Part 2 will focus on distributed proof generation with @ziskvm

Link to register for the community call with @ziskvm team. Reserve your slot here [ luma.com/4bhxbhft ]

This is a big moment for zkVMs ! The @ziskvm team has been shipping at an incredible pace, and these latest developments are serious, Here’s a quick recap of what’s new: 1. Distributed proof generation is now live. 2. You can prove Ethereum blocks in real time. 3. Everything runs with native 128-bit, quantum-resistant security. We’re hosting a Community Call tomorrow to dive deeper. would love to have you join us, Link to register in the below chat !

GM frens, Privacy. Privacy. Privacy. Been diving into @ziskvm lately and wanted to share some learnings around I/O with ZisK.

Built a ZisK CLI tool to speed up my testing/experimentation with @ziskvm The standard ZisK workflow is modular and configurable: you set up the project → prepare inputs → compile to RISC-V 64 ELF → execute in the ZisK emulator (with metrics and stats) → verify constraints → generate proofs (CPU/GPU, single or multi-process) → verify proofs. But I am lazy 🦫 so What I tried to build : 1. zisk-dev init - Auto-detect existing projects or setup a new zisk project. 2. zisk-dev run - Complete pipeline in one command [ convert inputs → build → execute → generate proofs ] 3. zisk-dev stats - Detailed performance analysis with Air instances & memory usage. 4. zisk-dev prove - Generate proofs with comprehensive logging TL;DR: Configuration-driven with .zisk-env file Auto-detects ZisK installations and project structures > Works with existing ZisK projects without modification. > This is a personal dev. acceleration tool for testing/experimentation. ⚠️Please note I build this to speeds up my experimentation cycle with working with ZisK. Official ZisK documentation are recommended for development [ 0xpolygonhermez.github.io/zi… ] Try it here : npm install -g @abix/zisk-dev-cli@latest Repo: github.com/amiabix/ZisK-CLI Try and let me know your thoughts, Since it's a WIP It would be really helpful.