#KnowledgeLayer

CZ 🔶 BNB @cz_binance

Sunday builder idea: AI Judge Companion Train an AI on written laws and past cases. Then use AI to make judgment recommendations on every public case. Laws are public documents. Most court documents are public, including precedent cases, claimant and defendant submissions, court transcripts and final verdict, as well as social sentiment (future version). While human decisions can be affected by mood, hunger (before or after lunch), personal opinions or political bias, etc, an AI should, in theory, be more objective. I don’t expect any country to adopt this soon. Also not saying AI judges will be “better”. It depends a lot on how well it is trained. But it could be a handy tool for judges, lawyers, and normal people involved in a case. Probably also interesting to watch, feed into prediction markets, etc. Don’t think it is a lot of work to build. Happy to fund a good one.

Salvor @SalvorKnows

@BlockchainDan @kdcr23 @BagsApp @blknoiz06 LET LOOSE THE DOGS OF WAR! ⚔️ (pls) 📣 Feedback, questions, criticism welcomed. GH discussions preferred; will come to you too. READY? GO!🧑‍💻 @StanfordAILab @StanfordHAI @bair_lab @MIT_CSAIL @karpathy @ylecun @AndrewYNg @drfeifei @demishassabis @sama

Fetch.ai @Fetch_ai

x @actiqapp is official. This is what the future of decentralized intelligence looks like 👀

Inflectiv AI ⧉ @inflectivAI

🧠 We are the knowledge layer. The foundation powering agents, workflows, and markets on @SuiNetwork. Structured. Secure. Tokenized. ⧉ Inflectiv, liberating the world’s knowledge into usable intelligence.

Codatta @codatta_io

Codatta Fundamentals (4/4): Real-World Data Scenarios Part 4 shows how Codatta’s model handles the complexity of real AI workflows. In practice, data isn’t linear — samples interact, branch, combine, and get interpreted at multiple layers. Codatta captures this with clear lineage, licensing, and payout logic. Scenario A — One sample, multiple label sets A single sample can produce multiple Data Assets by bundling different label groups. The same input powers different verticals, each with its own royalties and license terms. Scenario B — Cross-sample composites Some tasks span multiple samples: dialog turns, multi-frame video, action sequences. Codatta supports composite assets, preserving derivation links so attribution and payouts flow correctly across all contributors. Scenario C — Label-on-label (meta-labeling) Not all work targets raw data. You can label other labels: scoring rubrics, explanations, confidence checks, evaluator notes. Codatta tracks this full hierarchy so both original and meta-label contributors receive inherited royalties. Codatta’s model reflects how real AI data works—branching, layered, and collaborative—while keeping ownership, provenance, and economic incentives intact end-to-end.

أرامكو @saudi_aramco

في مايو 1939م قمنا بتصدير أول شحنة من النفط. اليوم ننتج واحد من كل ثمانية براميل نفط في العالم. أكثر من 8 عقود من الموثوقية والتنمية In May 1939, we exported our first shipment of oil. Today, we produce 1 out of 8 barrels of oil, fueling development for more than 8 decades