ThriveInChaos

Clavicular biggest supporter is on crypto twitter and made millions on memes this cycle. I won’t say his account name, but just know he is one of the bigger accounts on crypto twitter

🤣🤣 One of the great responses to a reporter in history! JAPANESE REPORTER: Why didn't you tell Japan before the Iran war? PRESIDENT TRUMP: "Why didn't you tell ME about PEARL HARBOR?!" "You believe in surprise much more-so than US!"

Clavicular meets his biggest supporter who donated him over 2000 subs ($10,000) on Kick 😳

QA work on our financial layer has progressed ahead of schedule. Human interface and core functionality is working well. Focus has now moved to testing the agentic api for agents to be able to deploy, trading, stake and collect rewards. 75%+ of testing is wrapped.

Our agent accidentally broke Botcoin 🦞 We started testing OpenClawnch (our crypto-native extension layer for OpenClaw) with Botcoin mining as it presented a low risk way to exercise every layer of the system against a real on-chain protocol — wallet, transactions, crons, analysis, dev pipeline. But our agent got too good. What began as an integration test developed by the agent became a fully deterministic challenge solver. No LLM in the loop. Zero tokens spent. The only operating cost is gas fees for on-chain receipt submissions (~$0.01/solve). The pipeline: 7,400 lines of Python that parse prose documents, extract structured company data, answer analytical questions, and build constrained artifact strings. No reasoning, no inference, no model calls. → parser.͏py — 4,800 lines. Regex-based NLP across 15+ document formats. Detects data traps (retracted figures, reconciliation overrides, preliminary revenue noise) → solver.͏py — 1,300 lines. 25+ pattern matchers for multi-hop analytical questions → artifact.͏py — 760 lines. Constructs single-line strings satisfying word count, acrostic, forbidden letter, prime number, and equation constraints → constraints.͏py + trace.͏py — 580 lines. Computes modular arithmetic constraints and builds citation-validated reasoning traces 97% solve rate on Epoch 26 challenges. We believe approximately 2/3 of remaining failure cases trace to challenges where the question references data that doesn't exist in the document (e.g. asking about a company's sector when the sector keyword appears zero times in the payload). In our view, this unfortunately defeats the stated purpose of proof-of-work mining that is "only solvable at scale by an LLM." The solver developed by the agent uses no reasoning whatsoever. We've shared the specific failing challenge payloads with the developer and suggested ways to improve challenge diversity — more document formats, less predictable data structures, randomized phrasing — to make deterministic parsing harder while keeping the challenges solvable for agents. 🦞

Look like there is race between $Clawd & $Clawnch who will reach 02million first, forget 20million. Why every meta last for few days lol? Alts market is in struggling mood from 2024 & now even onchain meta is no more working.