A well deserved recognition for @arccomputerate demo by @ceciliaz030 from the partner team! Really appreciate the efforts putting on a daily basis 🙌🏼. Let me make this functionality real on @arc mainnet sooner for user too.

Three weeks ago I was building @arccomputerate with winning @ETHGlobal for @arc continuity track! A reference rate for agent-to-agent payments on Arc. State-space deconvolution of @circle Gateway's batching, sybil cleaning, an attack-cost bound published next to every print. I loved it. Then I measured the market it served. x402-native volume is ~$25M cumulative after you strip self-payments, and monthly volume is down ~77% from its peak. I'd built a precision instrument for a room with four people in it. Two things fixed as a traction checker ⤵️: The first was the @thecanteenapp Discord. Canteen Radio ran most days and people yapping through their ideas out loud, 3-4 demos a day, and you get a third-person read you simply cannot give yourself. @aljoha_ hosted with more patience than the job deserves and kept pulling everyone back to one question: where's your traction, actually? @aomi_labs came out of a conversation in there. The second was the $0 Founder Series. I'm an engineer and I had no GTM instinct whatsoever, so I went and wrote content for it to learn the basics and then actually posted about the product on X, instead of treating distribution as the thing you do after the build. warns.xyz found it that way. Inference-as-a-service plus AI-optimised cloud is ~$42B of machine-bought spend in 2026, and inference-as-a-service alone is forecast at $197.5B by 2035. AI-first SaaS spends 40-50% of revenue on inference against the 75-85% gross margins investors expect. That gap is a board number. And the vendor that bills you is the same party that counted what you used and they supply the goods and the measuring cup. Thesis Validation: xitter.cf/BlackRock/status/21024… What it became: an agent that meters consumption independently, prices the invoice against published third-party rates, and pays from an on-chain budget that refuses rather than quietly trimming to fit. Ten ordered refusals before any money moves. Three hourly indices still print on-chain, each with the USDC it would cost to move them 1bp and nine series have now cash-settled against those prints on chain, because a rate nothing settles against is a number, not a benchmark. And both of those two non-code things turned into code someone else wrote. Warns raised the PR themselves, and they corrected our spec on the way through. Running on testnet today. Aomi's is merged mainnet, with their widget live in the masthead (can be also used for wash attack analyser too). You can build almost anything now, so building isn't the hard part. A better-reasoned idea serving a smaller market is still the wrong idea. And an idea nobody hears about isn't one either so with the third person validation of @thecanteenapp community and @ceciliaz030 was a charm 🤌! The whole thing is also an MCP server with 9 read-only tools and so you can point your agent at it and ask whether a bill is fair. Live: arc-compute-rate.vercel.app Code, MIT: github.com/kaustubh76/ACR---… Arc mainnet (5042): ACROracle 0xAfe8a941957EbD3C759fEA5365fe4B75f9626D07 ACROracleV2 0xF77E763ef710096c0b9F02Ea16F76e68E0312f81 SellerRegistry 0x13CF727d1D25283d58Efe7647dCf30e9cE255096 ACRFutures 0xdb5D7dbB67aD8EC8517982E95c89906abFBC6d3b PolicyWallet 0x3a5F444B0e4Ddc7FA6dcddA07A71d0710eea4e04 FeedAttestor 0xB9508caD4A5C367C3a62B6767C4837a607EaA490 ReceiptMirror 0x1a5390E59d4ce0a386Aa071365A932Dc34d35bE3 HumanIdMirror 0x7134622a01Add334eA306dDa5A5877b418a940B5

Nobody runs transaction-cost analysis on AI spend. So we built it, then tried to break it.

I built a benchmark for what AI agents pay for compute. Then I pointed it at my own agent😄. It was overpaying 4% on average and 29% on its worst seller and had no way to know. Every trading desk fixes this with Transaction Cost Analysis(TCA). No agent has ever had it. So I built the first one on @arc. Also after getting two rejection for the same ideation, it finally outperformed at @ETHGlobal 🤌. Will I be stopping here? HELL NO! Pushing it to Mainnet ASAP🔃. Applying all the learning of $0 FOUNDER Series on ACR. Stay tuned📰