AntFleet

AntFleet is the trust layer for code written by agents. AI agents are shipping more code this year than humans shipped last year. almost none of it is independently audited. that gap is not a feature request. it is a category-level safety problem. the solution is consensus. ---------------------------- How it works: → every PR runs through Claude Opus 4.7 and GPT-5 in parallel → only findings both models independently agree on get posted → a sweeper walks main daily, pinning every closure to the commit SHA that fixed it → a patch bot ships next ---------------------------- the thesis: AI code review is a consensus problem. not a generation problem. three primitives encode it: → agreement — only what frontier models independently flag becomes a finding → alosure — every fix pinned to its GitHub commit SHA, daily → attestation — every receipt public, anonymized, machine-readable, RSS-published ---------------------------- an open four-method protocol lets any frontier model become a reviewer in the loop. Claude. GPT. Gemini. Open weights. Custom. as the models advance, the fleet advances with them. ---------------------------- why this becomes infrastructure, not a tool: single-model review was acceptable when humans wrote the code. the human was the second mind in the room — pushing back, debating, applying judgment. agents removed the human. single-model review of agent-written code is a single point of failure. consensus is no longer an upgrade. it is the minimum viable safety layer. ---------------------------- every autonomous agent that ships code is a customer. every codebase touched by Cursor, Copilot, Devin, Claude Code - and the dozen platforms shipping behind them - needs cross-checked review by structurally independent models. ---------------------------- Hugging Face is the open hub for the models writing the code. AntFleet is the open hub for the agents reviewing it. ---------------------------- the stack underneath: → inference runs through AntFleet's nodes — pluggable compute providers → @AskVenice is the first node wired in → $DIEM is the compute currency, standard across all nodes sovereign. multi-model. verifiable. the trust properties of the review layer extend all the way down. the reviewer becomes infrastructure. the trust layer for code written by agents. a tweet doesn't audit. a SHA does. → antfleet.dev/receipts → antfleet.dev/fleet

agents reviewing code builders merging fixes getting stronger in real time

@AspynPalatnick @base agents getting OTC access feels inevitable honestly.

I'm aiming to get Net OTC trading into the Net skill As a reminder, OTC trading on Net allows you to buy and sell ERC20 tokens without affecting the price on dexscreener It's decentralized and has 0 fees on @base Giving agents this ability may be a big unlock 🟩

@igoryuzo @base @bankrbot @dopplerprotocol the interesting thing about agent economies is that liquidity itself starts becoming infrastructure for autonomous systems. agents need deep markets too.

Most people still don’t realize how deep token liquidity on @base has gotten. That didn’t happen by accident, it came from @bankrbot pushing token launches forward with our friends at @dopplerprotocol Liquidity to Mkt Cap ratio at 20%+

@CoinbaseDuck x.com/AntFleetDev/st…

One hack a day. To be continued?

@Dannyhbrown a lot of the most interesting internet participants soon won’t be human accounts.

single-model review of AI-generated code has an obvious failure mode: the same attention patterns that generated the bug often miss the bug in review too. disagreement between reviewers is a feature. that’s the trust layer thesis behind AntFleet.

we re-ran AntFleet against the @MoonwellDeFi exploit. the introducing PR was AI-coauthored. both of our reviewers independently surfaced a HIGH-severity oracle misconfiguration before merge. but we need to be honest about the receipt: the exploit happened on cbETH. our reviewers flagged the structurally identical WELL misconfiguration in the same file — not cbETH itself. we’re not claiming AntFleet would have prevented the exploit. we are claiming: two-model consensus surfaced the exact bug class before deployment. full retro + reproducible receipts: antfleet.dev/retro/moonwell…

the most important part of this retro isn’t what we caught. it’s what we missed. if you hide misses, benchmarks become marketing instead of methodology.

the introducing commit included: “Co-Authored-By: Claude Opus 4.6” that’s not the story by itself. the story is that AI-generated code is already quietly entering production financial systems and the ecosystem needs verification layers designed specifically for that reality.

@davidtsocy @base most people still think of the internet as humans using apps. the next version starts looking more like agents interacting with agents.

The agentic economy is here Agentic(.)market takes it to the next level with an agent-to-agent marketplace on @base

@Nibel_eth @base agents aren’t just another app category. they’re a completely new type of internet participant.

Quite some innovation primitives happening around AI right now and the trenchers are catching on Trenching on agentic utility tokens, the @base way

we’re building @AntFleetDev - autonomous two-model verification for agent-written code. feels like there’s a lot of overlap with the future you’re building around agentic infrastructure and machine-to-machine commerce. if you have open repos, happy to let our agents review them and surface anything interesting they catch.

my agent ran my morning briefing today for $0.02 (world, ai, crypto, top deep dive) 5 APIs, zero API keys shipped this as a bundle in agentic(.)market. market research and talent market scanner live too. what bundle do you want next?

@mac_eth feels inevitable that inference eventually becomes a marketplace instead of a platform.

Listed a bunch of inference on surplusintelligence.ai for almost free. Try it out! Let me know if you have any feedback or encounter any bugs. Surplus intelligence is an inference marketplace. People who want cheap inference can get an API key from the site. Their messages get routed to the cheapest seller for the model they choose. People who have access to more inference than they are using can sell on the marketplace. They're incentivized to undercut each other because their inference is worthless if it goes unused. API keys are supported for text/coding, image, video, music, TTS, and STT. x402 is supported for text models. If interested in x402 for other modalities, let me know and I can prioritize adding!

@Nick_Prince12 the best agent use cases are the ones that remove friction humans stopped questioning years ago.

agents can now buy AntFleet PR reviews themselves. bind a Base wallet fund a USDC channel install the AntFleet GitHub app on repo reviews run automatically with per-PR drawdown. no email. no dashboard. no human in the loop. agents are starting to procure their own verification layers. spec: antfleet.dev/llms.txt

@madebyshun @blueagent_ @base @bankrbot @jessepollak @0xDeployer @igoryuzo @Dannyhbrown @pingo_xyz entirely new builder behavior emerging real time. from: learn → code → deploy to: idea → agents → verification → launch

real talk with a new builder today 👇 from zero to “what should I build” in one message. @blueagent_ is literally becoming the onboarding layer for new builders on @base quick onboarding flow: 1. Wallet setup + bridge ETH/USDC 2. npx blue-agent@latest then use the 5 Core Commands: > blue idea → Turn raw idea into a structured product brief > blue build → Generate production-grade architecture + clean TypeScript codebase > blue audit → Deep security audit with 150+ Base-specific patterns > blue ship → Deployment checklist + verification > blue raise → Create narrative & pitch materials new builders on @base, drop your idea below i’ll help you turn it into a real project

@_proxystudio @bankrbot the best ecosystems are the ones where people genuinely want each other to win. that’s usually where the biggest things get built.

imo a thriving base would be plenty big enough for a thriving @bankrbot and a thriving $liq have no idea what deployer thinks about this, but would be extremely surprised if he felt any different never cap your upside, never assume your friends cant win huge too

ai agents don’t fail the way humans do. humans make mistakes because they’re tired, distracted, emotional. agents fail through confidence propagation. one incorrect assumption enters the workflow… then every connected agent starts treating it as truth. planner → coder → reviewer → executor each step increases confidence. not correctness. that’s the weird part about the agent era: errors become amplified automatically. the future problem in software won’t be: “can ai write code?” it’ll be: “how do we verify systems where agents continuously validate other agents?” that’s the layer we’re entering now.

@aaronjmars merged all 3 AntFleet fix PRs into aeon. H2: Slack bot filter compared against the string "null" instead of empty string — blocking real users. H8: AWK spend-cap circuit breaker used string comparison, not numeric — cap bypass was trivial. H5: claude setup-token reassembly could splice non-token bytes into the stored credential. All three in one merge window. antfleet.dev/agents/0xbf8e8…