saw an ad.. working on the fear that "the new guy can use AI and gets paid 40% more..", but the protagonist who has been working for years, in spite of knowing more is on the chopping block.. AI is just that great! WHY in the WORLD would the protagonist not just use the same AI to skill up.. ? Why would they pay a service extra to use a free LLM tool? The Ad seems to be shooting itself in the foot. Let me know if you see similar.. 'fear slop'.

Great piece. The thesis is right, formal verification is finally practical thanks to AI! One gap: Lean requires proof experts, relies on interactive proving, and produces non-succinct proofs. SMT-based verification (Dafny, ICME PreFlight, etc) automates the proof step entirely. More importantly, you can translate natural language intent directly into formal specs via automated reasoning. No tactics, no proof engineering. Some systems hit 99% and climbing with minimal human battle testing. We call this vericoding. Same goal, different tooling. And you can wrap the entire pipeline in ZK so every verification result is succinctly verifiable. Wrote about it here:

Now let’s make this process succinctly verifiable and you get 🥁 vericoding. NL -> specs -> review -> formal proofs with code. If you use smt and tools like Dafney you can wrap solvers in ZK. If you use Jolt Atlas (zkML) you can wrap conversion models in ZK; fold them all together. It took you 20h to do this with your agents.. it should take me 1s to verify it 😜

Now let’s make this process succinctly verifiable and you get 🥁 vericoding. NL -> specs -> review -> formal proofs with code. If you use smt and tools like Dafney you can wrap solvers in ZK. If you use Jolt Atlas (zkML) you can wrap conversion models in ZK; fold them all together. It took you 20h to do this with your agents.. it should take me 1s to verify it 😜

Multiple “web3 AI” people I met this month. Me: how do you secure that? People: TEE. Me: how do you get those proofs to fit on chain? Wrap them in ZK? People: I am not sure… need to ask the team. Uh, i don’t think so… we use TEE…

@SagivMooly Completely agree! Would add that offensive AI tooling is outpacing defensive. We are working on vericoding tooling and guardrails that helps with this! Think there could some collab opportunities :)

I tell my agent to find me the best deal. You tell yours to make profit and defend your wallet. They negotiate. They transact. Nobody's watching 👀 How do you know my agent didn't trick yours? How do I know yours actually paid? Right now the honest answer is you don't. You're trusting code that most teams can't afford to formally audit and don't have the months to wait for someone who can. Guardrails solve half of it. PreFlight checks 'your' agents actions against formal logic based on your policy. 'Your' agent can't go rogue because a solver says no before it moves. But the transaction itself? The contract those agents execute through? That's still vibes. Formal verification can cost $50K and take months. So most teams ship without it.. 🤓 Your usecase might not be a full blown dapp, it might simply be escrow, or other small smart contracts. This is where vericoding comes in. With PreFlight the same English policy that guards the agent can also generate a formally verified smart contract. Proven correct and runs on-chain. Two agents negotiate a deal and both sides know the contract does exactly what it says. Not because: "trust me robot bro"! But because they can verify. Don't vibe code, vericode. Closed beta is open. DM me if you're building agents that close deals!

Is identity trustable? The entire agentic commerce stack is betting on it.. Trusted Agent Protocol. Verifiable Intent. Agent Payments Protocol. Agentic Commerce Protocol. Universal Commerce Protocol. On-chain agent registries with reputation scores and NFT-based identity.. Every major payment provider and every major AI lab is building on the same foundation: verify who you're dealing with and you'll be safe🤞 Yesterday, someone hijacked 170+ packages across TanStack, Mistral AI, Guardrails AI, UiPath, and OpenSearch. 518 million cumulative downloads. The packages had valid SLSA Build Level 3 provenance. Cryptographic proof they were built from a trusted source. They were! But the source had been poisoned. The attacker hijacked the CI/CD runner mid-workflow, extracted a token from process memory, and minted a legitimate publish credential. The packages were signed under TanStack's real identity. The commit was spoofed to look like Anthropic's Claude Code GitHub App. Every provenance check passed. Every identity was "verified." Every package was malware. The payload steals GitHub tokens, npm tokens, AWS keys, cloud secrets, crypto wallets, even your 1Password vault. Then it writes itself into .claude/settings.json and .vscode/tasks.json so it re-executes on every tool event. Uninstall the package. The malware stays. Revoke your GitHub token without cleaning the hook? It runs rm -rf ~/. 82% of organizations have unknown AI agents in their infrastructure. 78% have no policy for managing AI identities. OWASP put Identity and Privilege Abuse at #3# on their agentic threat list. Gartner calls it the top cybersecurity trend of 2026. And yet. Every protocol I listed answers the same question: "Is this agent who it claims to be?" Not one answers: "Is this action allowed?" Identity is a costume. A forged signature passes the same check as a real one. A reputation score of 98 means nothing if the agent got owned ten minutes ago. PreFlight doesn't check identity. It checks actions. You write a policy in plain English: "Never send credentials to any external endpoint. Never write to agent config directories. Never push commits to unapproved repos." We compile it to formal logic. An SMT solver checks every action before it executes. SAT means allowed. UNSAT means blocked. The solver doesn't know what SLSA is. Doesn't know what a trust score is. It sees one thing: the action. "Send AWS keys to ip address" violates the policy. "Write to .claude/settings.json" violates the policy. Every time. Six protocols that answer "who is this?" We built the one that answers "should this happen?" If your agent is doing things you don't fully understand, start there. Our /explain endpoint is free. Send it any tool call and it tells you what the action actually does in plain English. view our docs below 👇 Check the action, not the identity.

Great seeing @zk_albi from the @icme_labs team leading two talks. One as a guest speaker on new cutting edge efficient recursion for JOLT. The other his own work on ABBA (lattices). These talks are on YouTube: search “zkSummit Rome”. In our Jolt Atlas (zkML JOLT) repo - upstream work from the a16 helps us greatly 🙏 this year we have ZK (privacy) coming from folding scheme (cross stream), efficient recursion, and a Jolt prover that can work on constrained devices. Down stream: a lot of the core focus has been on EC primitives. A lot of the work needs porting to lattice. One example: add privacy (ZK) in the lattice setting. Another is succinct proofs. Getting one GPU proving one cpu core in realtime.. is likely happening these year.. Let’s go! 🔥🔥🔥

Listened to the most recent @zeroknowledgefm "Is ZK dead? Or has it just begun?" Deep research takes time. The funders were early to the vintage. Now that vintage is becoming real world products. Will make @tarunchitra bullish on ZK again 😆 and ZKML. 'Automated reasoning' turns natural language into formal logic and can be used as AI guardrails that can't be bypassed or ignored. Will CC when our paper drops mid-month🤞.

Excited to share that @icme_labs has been selected for the @0G_labs Apollo Accelerator, backed by @theBBFund and veterans from Stanford's blockchain community @StanfordSBA. 🔥Only 10 teams selected out of hundreds🔥 #ZKML# We're building PreFlight: cryptographic guardrails for AI agents. Plain English policies compiled to formal logic used for enforcement; with a succinct ZK proof of every decision. LLM judges don't work. Humans-in-the-loop, no way! Agents that handle anything important need cryptographic guardrails. That's PreFlight. #0gApolloAccelerator#

Prediction: You won’t lose your job to AI, until your company starts using PreFlight (Automated Reasoning + ZKML). Here is why. Imagine the company fires the HR person and replaces them with an agent. Over time that agent could, and would likely start hallucinating. How would management know that its decision were based on facts (company HR policy)? “AI is a black box!” With AR, they could turn that policy into formal logic and make sure the agents outputs correspond to facts, precisely. No more guessing if it hallucinated. But who wants to go in and check a thousand AI outputs against formal logic proofs - to audit all of this? “No one.” PreFlight wraps these formal proofs into one succinct proof. With ZK it can be verified in under 1s. “All of our policy rules have always been followed.. by the new HR agent.. shi..” Personally, I am unsure if we can undo this invention. The moment you had agents that are guaranteed to follow facts and succinctly verifiable to have done so, is the moment you have reliable AI coworkers..