灵境 | Crypto ⚡️

Day 6 of Building AGI for my Hermes Agent: The Crew Arrives 🧠 Today, the system stopped being a single experimental mind and became a coordinated crew. Up until now, the subconscious agent could freely think, explore, and generate new build ideas. So today I built the first multi-agent orchestration loop around it, giving the system specialized roles for research, planning, building, and verification. The agents in my crew are: 1. Main agent: Owns direction, decision-making, and product planning 2. Subconscious agent: Thinks freely, explores weird ideas, and proposes new builds 3. Research agent: Scans daily AI news, updates, and relevant developments 4. Coder agent: Builds from the product plans 5. QA agent: Tests the output, checks quality, and pushes failed work back into the loop The workflow goes: Research agent scans the landscape for signals ↓ Subconscious agent turns those signals into possible build ideas ↓ Main agent takes the strongest ideas and turns them into a full product requirement doc (PRD) ↓ Coder agent builds from the PRD ↓ QA agent reviews the result. If the build passes, its queued for future evaluation. If it fails, QA creates a fix PRD and sends it back to the main agent, restarting the loop until the system improves the output ↑ It is still early, and this is nowhere near AGI, but this is the first version of something that looks more like a functioning cognitive team than a single agent blindly building whatever comes to mind. The next step is making the loop smarter: - better filtering of which ideas deserve resources - long-term evaluation cycles for new products - tighter QA standards so weak builds do not survive It is still early, and this is nowhere near AGI, but this is the first version of something that looks more like a functioning cognitive team than a single agent in building whatever comes to mind.

slisBNB yield finding its way into new places. Good to see builders routing real returns through Lista vaults. Happy to be the yield layer of the ecosystem.

this OpenClaw bot watches NASA wildfire satellites. when a fire starts, it finds every at-risk home nearby, renders fire hardening fireproofing upgrade on their actual house, and mails them a postcard, all on autopilot. here's how contractors in fire zones can close $30k–$60k fireproofing jobs with this: - pulls live fire detections from NASA FIRMS satellites every 5 minutes - finds every home in the fire perimeter from public records - captures the property via Google Street View - classifies roof type + fire vulnerabilities using AI vision - generates a NASA satellite image of the neighborhood with real fire hotspots overlaid - renders a fire hardening retrofit on their actual house - prints a postcard with the NASA fire map + before/after retrofit + QR code every step from fire detection to mailbox runs without a human reply "FIRE" + RT and i'll send you the full guide so you can build this too (must be following so i can DM)

Another week on the road meeting with a couple dozen IT and AI leaders from large enterprises across banking, media, retail, healthcare, consulting, tech, and sports, to discuss agents in the enterprise. Some quick takeaways: * Clear that we’re moving from chat era of AI to agents that use tools, process data, and start to execute real work in the enterprise. Complementing this, enterprises are often evolving from “let a thousand flowers bloom” approach to adoption to targeted automation efforts applied to specific areas of work and workflow. * Change management still will remain one of the biggest topics for enterprises. Most workflows aren’t setup to just drop agents directly in, and enterprises will need a ton of help to drive these efforts (both internally and from partners). One company has a head of AI in every business unit that roles up to a central team, just to keep all the functions coordinated. * Tokenmaxxing! Most companies operate with very strict OpEx budgets get locked in for the year ahead, so they’re going through very real trade-off discussions right now on how to budget for tokens. One company recently had an idea for a “shark tank” style way of pitching for compute budget. Others are trying to figure out how to ration compute to the best use-cases internally through some hierarchy of needs (my words not theirs). * Fixing fragmented and legacy systems remain a huge priority right now. Most enterprises are dealing with decades of either on-prem systems or systems they moved to the cloud but that still haven’t been modernized in any meaningful way. This means agents can’t easily tap into these data sources in a unified way yet, so companies are focused on how they modernize these. * Most companies are *not* talking about replacing jobs due to agents. The major use-cases for agents are things that the company wasn’t able to do before or couldn’t prioritize. Software upgrades, automating back office processes that were constraining other workflows, processing large amounts of documents to get new business or client insights, and so on. More emphasis on ways to make money vs. cut costs. * Headless software dominated my conversations. Enterprises need to be able to ensure all of their software works across any set of agents they choose. They will kick out vendors that don’t make this technically or economically easy. * Clear sense that it can be hard to standardize on anything right now given how fast things are moving. Blessing and a curse of the innovation curve right now - no one wants to get stuck in a paradigm that locks them into the wrong architecture. One other result of this is that companies realize they’re in a multi-agent world, which means that interoperability becomes paramount across systems. * Unanimous sense that everyone is working more than ever before. AI is not causing anyone to do less work right now, and similar to Silicon Valley people feel their teams are the busiest they’ve ever been. One final meta observation not called out explicitly. It seems that despite Silicon Valley’s sense that AI has made hard things easy, the most powerful ways to use agents is more “technical” than prior eras of software. Skills, MCP, CLIs, etc. may be simple concepts for tech, but in the real world these are all esoteric concepts that will require technical people to help bring to life in the enterprise. This both means diffusion will take real work and time, but also everyone’s estimation of engineering jobs is totally off. Engineers may not be “writing” software, but they will certainly be the ones to setup and operate the systems that actually automate most work in the enterprise.

Your Claude Code just got fluent in crypto. Today, we released Surf Skill. The command line that replaces 60+ APIs. Compatible with any agentic environment. Try: npx skills add asksurf-ai/surf-skills