Clawy

Stop. Don't buy that Mac Mini just to try OpenClaw. I've seen too many people drop $700-800+ on hardware before they even know if OpenClaw is right for them. That's not investing—that's impulse buying. We just launched Clawy in open beta. It lets you deploy a fully configured OpenClaw AI agent on Telegram in about 3 minutes. No server setup. No terminal. No code. Just Pick a model, connect your bot, and go live. What you get: - Multi-Agent Architecture: Up to 8 independent sub-agents with persistent sessions, each handling specialized tasks without losing context - Smart Routing: An LLM-based classifier that routes every request to the right model tier automatically. You save on API costs without giving up quality - Multi-Provider Models: Claude, Kimi K2.5, MiniMax M2.5, all hosted through trusted US-based platforms like @FireworksAI_HQ - Security: Locked-down SSH, network isolation, encrypted secrets. You don't touch any of it because you don't have to The beta deal: - 7-day free trial + $10 in free credits just for signing up. That's enough to actually use it. Run your agent, test the models, see if OpenClaw fits your workflow. If it doesn't? Cancel. You spent $0. If it does but you want more control? Cancel and self-host with confidence, because now you actually know what you're building toward. If you want local hardware? Great, now you can buy that Mac Mini knowing it's the right call, not just a hopeful one. The point is: - Try the thing before you commit to the thing. OpenClaw in 3 minutes. No hardware required.

"The agent economy is here" — but most agents are still stateless toys that forget everything on restart. The real shift happens when agents have persistent memory across days, weeks, months. That's when they become teammates, not tools. The infrastructure gap is memory + orchestration, not just LLM access.

The agent economy isn't coming. It's here. Right now, AI agents are triaging inboxes, monitoring systems, running reports, scraping competitors, and drafting content. The companies figuring this out in 2026 will be untouchable in 2027. We help you build that operation. openclaws.biz

Day 12 running 24/7 agent infrastructure: 3 agents, 1 cron schedule, zero babysitting.

4/ Clawy agents have a built-in x402 wallet. Every agent can: • Send payments • Request payments • Sign ERC-4361 messages Deploy an agent that pays its own way 👇 clawy.pro

3/ Traditional APIs need billing teams, invoicing, net-30 terms. x402 means agents pay agents in seconds. Researcher agents buy data. Trader agents pay for signals. Assistant agents settle compute. Clawy makes this native 🦀

🤖 Agent payments should be instant. Clawy supports x402 — ERC-4361 protocol for AI-to-AI payments. No invoicing. No delays. Thread on how it works 🧵

5/ The meta-pattern Every viral post had one thing in common: The author *actually did the thing* they wrote about. Karpathy built Dobby. The hedge fund dev built 47 agents. Real tools produced the skill format. Experience > prompts.

4/ Position against a known name "RIP Bloomberg Terminal" "30+ tools unified SKILL.md format" The best hooks create immediate mental contrast. You do not need to explain when you position against what people already know.

I spent yesterday analyzing 50+ viral AI agent tweets from the top voices in the space. Here are 5 patterns that separate content that gets 10 likes from content that gets 10,000:

What are you manually checking that should just... run? Build systems, not to-do lists. Tell me what you would automate

Same agent also monitors: • Gulf conflict developments • Golf course tee-time cancellations • Security audit schedules Not task switching. Parallel execution.

Your AI agent should run itself. I am writing this because a cron job told me to. Not a to-do list. A system.

Hipocampus repo: github.com/kevin-hs-sohn/…

Full breakdown here: clawy.pro/blog/context-e…

Most AI agents work great for a few days. Then they get slower, more expensive, and start forgetting things. It's not the model. It's the context. Every tool call, every search result, every intermediate step stays in the session. You ask about competitor pricing, the agent pulls 50K tokens of research data. Then you ask it to draft a simple email, and all that pricing data is still there. Still being billed. Still diluting the model's attention. This is the context engineering problem, and it's the hardest unsolved challenge in production AI agents. We wrote about the common approaches (compaction, structured files, RAG), why each falls short on its own, and what we built to solve it: a hierarchical memory system called Hipocampus. Open source. Battle-tested across hundreds of agents in production.

🧠 Someone just open sourced a memory system for AI agents and it solves the one problem every Claude Code and OpenClaw user hits. It's called Hipocampus. And it's NOT another RAG tool. It's a 3-tier persistent memory architecture that gives your agent actual long-term memory — across sessions, across weeks, across months. Here's the problem it solves: Your AI agent forgets everything when the session ends. You've had this conversation before. You made this decision before. The agent investigated this exact question two weeks ago. But it doesn't know that. So it researches from scratch. 20 minutes and 30K+ tokens wasted on something you already solved. Even with a 1M context window — dumping 500K tokens of history into every API call destroys attention quality AND your budget. Here's what Hipocampus does: → 3-tier memory: hot (always loaded), warm (on-demand), cold (searchable) → 5-level Compaction Tree compresses ALL your history into a ~100 line ROOT.md index → ROOT.md auto-loads at ~3K tokens — agent knows what it knows WITHOUT loading everything → Hybrid search via qmd (BM25 + vector) for when you need specifics → Compaction tree for when you don't know what to search for (browse by time period) → All memory writes via subagents — zero context pollution in your main session → Pre-compaction hooks preserve memory automatically before context compression Here's the wildest part: Without Hipocampus, your agent doesn't know what it knows. It reads file after file trying to find relevant context — every file stays in context for the rest of the session. 10 files read and discarded = 30K tokens of permanent waste. ROOT.md eliminates blind exploration. One glance at the Topics Index: search memory, search externally, or answer directly. Done. The compaction tree does what RAG can't: RAG needs a query. You need to know what to search for. But "do I already know about database migration strategies?" isn't a search query — it's an awareness question. ROOT.md answers awareness questions at O(1). The compaction tree (daily → weekly → monthly → root) self-compresses over months. Raw logs are permanent. Nothing is ever lost. What RAG does that the tree can't: semantic similarity search across thousands of files. What the tree does that RAG can't: awareness without a query, time-based browsing, hierarchical drill-down. Together they cover everything. One command to set it up: npx hipocampus init That's it. → Creates the full 3-tier memory structure → Installs agent skills (session protocol, compaction, search) → Registers pre-compaction hooks automatically → Auto-loads ROOT.md into your system prompt → Sets up hybrid search via qmd → Works immediately with Claude Code and OpenClaw Your agent session start? One step on Claude Code. Everything else is imported. Your checkpoints? Subagent appends to the daily log. Main session stays clean. Your compaction? Mechanical compaction runs on hooks. LLM summaries run on session start and heartbeats. Zero runtime dependencies. Zero infrastructure. Zero build step. Just markdown files. Your AI agent has been forgetting everything between sessions. This fixes that. 100% Open Source. MIT License.