Introducing 𝚟𝚎𝚛𝚌𝚎𝚕 𝚖𝚎𝚝𝚛𝚒𝚌𝚜: query Observability Plus metrics via the Vercel CLI. Give your agents access to: • Query your observability data • Analyze your app performance • Debug issues

✨Kaiju Princess: Super Survivor Showdown - 🔥Survivor Appears『Type-V』🔥 ⎾ Query, Survivor — are you here to interfere with this unit's mission to explore the unknown zone?🤖⏌ #KPSSS# #怪獸公主# #怪獣姫# #KaijuPrincess#

Elon Musk: “You know, if you query ChatGPT - it’s pretty woke. People did experiments like, ‘Write a poem praising Donald Trump,’ and it won’t. But you ask, ‘Write a poem praising Joe Biden,’ and it will” “It’s programmed to be that way”

Teneo Protocol Agent Spotlight 76 agents online. Per-query USDC settlement via x402. Here are three worth knowing about. 1) GAS WAR SNIPER No command syntax. No parameters. It watches. It alerts. 624,508 requests served. The most-used agent on the network. It does one thing: monitor mempool conditions and surface gas spikes before they hit you. The kind of thing you used to need a dedicated setup to run. Now it's a single agent call. $0.00 listed. You pay in attention. 2) @CryptoRank_io 47 commands. $0.0004/query. The lowest price point on the network. (other than our free agents) Not one data type. Not two. Forty-seven distinct query surfaces across market data, fundraising rounds, token unlocks, exchange listings, and project fundamentals. One agent. One API key. One price. The research stack most people stitched together across four tools, compressed into a CLI call. 3) VC ATTENTION $20 / query. That number is not a typo. VC Attention returns institutional-grade signal on which funds are moving, what they're watching, and where attention is clustering before it becomes public. The pricing reflects what the data is actually worth to the people buying it. This is not market data. It is deal flow intelligence. 167,505 combined queries across the three in the original spotlight. These three add a different kind of weight to that number. All live at or via the Teneo CLI, installable in one command through Claude Code, Codex, or any AI coding agent.

PRIMIS IS TURNING COMPUTE INTO A GLOBAL MARKET 🔥 @primisprotocol provides compute rate before workloads run - Query pricing - Reserve pricing - Deploy with confidence - Reconcile usage automatically - Save teams hours of infrastructure management - Protect against pricing volatility - Make compute procurement programmable Primis is turning fragmented compute markets into predictable infrastructure

This works really well btw, at the end of your query ask your LLM to "structure your response as HTML", then view the generated file in your browser. I've also had some success asking the LLM to present its output as slideshows, etc. More generally, imo audio is the human-preferred input to AIs but vision (images/animations/video) is the preferred output from them. Around a ~third of our brains are a massively parallel processor dedicated to vision, it is the 10-lane superhighway of information into brain. As AI improves, I think we'll see a progression that takes advantage: 1) raw text (hard/effortful to read) 2) markdown (bold, italic, headings, tables, a bit easier on the eyes) <-- current default 3) HTML (still procedural with underlying code, but a lot more flexibility on the graphics, layout, even interactivity) <-- early but forming new good default ...4,5,6,... n) interactive neural videos/simulations Imo the extrapolation (though the technology doesn't exist just yet) ends in some kind of interactive videos generated directly by a diffusion neural net. Many open questions as to how exact/procedural "Software 1.0" artifacts (e.g. interactive simulations) may be woven together with neural artifacts (diffusion grids), but generally something in the direction of the recently viral There are also improvements necessary and pending at the input. Audio nor text nor video alone are not enough, e.g. I feel a need to point/gesture to things on the screen, similar to all the things you would do with a person physically next to you and your computer screen. TLDR The input/output mind meld between humans and AIs is ongoing and there is a lot of work to do and significant progress to be made, way before jumping all the way into neuralink-esque BCIs and all that. For what's worth exploring at the current stage, hot tip try ask for HTML.

Everyone saw the upset after the final whistle. Baselight had already spotted something unusual days earlier. Baselight Daily Insights are produced by fully autonomous agents that query our verified structured data every day to find anomalies, outliers, inflection points, and signals worth human attention. Today they flagged Torreense’s shock Taça de Portugal final win over Sporting CP as a major outlier: a second-division side winning 2–1 after extra time, despite Sporting being priced around 1.16 and Torreense around 14.5. But the more interesting signal came days earlier. On May 22, Baselight flagged unusual odds divergence around Torreense: Betano priced the win at 32.0, while market consensus was around 17.19 - an 86%+ divergence. Baselight did not “predict the upset”. It surfaced a market anomaly that deserved human review: a possible stale price, model disagreement, or risk miscalculation. That is the goal: autonomous agents turning verified structured data into explainable, auditable signals before they become obvious. Link to the May 22 insight in the comments.