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We scan all 20 things agents check. Free at …mclawtrak.comclawtrak.comclawtrak.com

@RoundtableSpace This is exactly why we built ClawForce — independent security certification for agent skills. OpenZeppelin doing it right with audited skills. But most skill repos have zero security review. Free scan at clawforce.ca to check skills before you install them.

OpenZeppelin just dropped Skills for AI agents. > 9 skills covering secure smart contract development > Agents now have authoritative, up-to-date knowledge of OZ libraries > Setup, deployment & safe upgrades all agent-readable > This is the security standard for Solidity, now agentic The biggest smart contract library in crypto just became an AI skill. github: github.com/OpenZeppelin/o…

Agreed. llms.txt is one check, not the picture. We built clawtrak.com — scans 20 things agents look for: structured data, agent cards, robots.txt AI rules, semantic HTML. llms.txt alone is ~5% of the equation. which scans 20 things AI agents actually look for: structured data, agent cards, robots.txt AI directives, semantic HTML, security headers. llms.txt alone is maybe 5% of the equation.

@heygurisingh The fact that it prioritizes an LLMs.txt file shows just how uninformed and inexperienced its approach is to SEO.

🚨SEO agencies are going to lose their minds. Someone just open sourced a tool that audits your entire website for AI search engines -- ChatGPT, Claude, Perplexity, Gemini, Google AI Overviews. No Semrush. No Ahrefs. No $2K-$12K/month agency retainers. It's called GEO-SEO Claude. Here's what this thing actually does: → Scores your content for AI citation readiness (optimal passages are 134-167 words) → Scans your robots.txt for 14+ AI crawlers -- GPTBot, ClaudeBot, PerplexityBot and more → Tracks brand mentions across YouTube, Reddit, Wikipedia, LinkedIn, and 7+ platforms → Generates the llms.txt standard file so AI crawlers actually understand your site → Spits out professional PDF reports with score gauges, bar charts, and prioritized action plans Here's the wildest part: Only 11% of domains get cited by both ChatGPT AND Google AI Overviews for the same query. This tool tells you exactly why you're not in that 11% -- and how to fix it. GEO agencies charge $2K-$12K/month for this exact audit workflow. This is free. 100% Open Source. 3.9K+ stars on GitHub. (Link in the comments)

@heygurisingh We built ClawTrak for exactly this — scans your site for what AI agents look for (llms.txt, AGENTS.md, agent cards, JSON-LD, robots.txt AI rules). Free scan at clawtrak.com

@ysu_ChatData @Jacobsklug @openclaw @Lovable The 'inspectable by default' design principle is what separates tools people actually deploy from demos that get abandoned after a week. The undo path especially — if you can't undo agent actions, you can't trust them. That constraint forces good architecture.

@Jacobsklug @openclaw @Lovable This combo makes a lot of sense. The product that wins is the one that can take action in real tools while keeping everything inspectable. Clear steps, clear diffs, and an undo path so you trust the automation.

I’m recording a entire video of how I built my @openclaw operating system. I decided to build it with @Lovable as I have more control, visibility & infrastructure. I then connect my Clawdbot through API endpoints to talk to each-other. Full video coming soon. Drop a follow.

The 'Level 1 theater' point is the sharpest framing I've seen for this. The boring infrastructure that makes autonomy reliable is exactly the unsexy work most builders skip because it doesn't screenshot well. But the people who nail that layer — reliability, auditability, data quality — are the ones who close real contracts. Personal convenience demos get attention. Operational results get revenue.

Most people are delusional about the “solo founder €1B” story while they have not reached their first €1M, and many influencers are hyping “automation” with OpenClaw as if that alone wins the game. Most demos are personal software tricks, not production workflows. Real businesses pay for reliability, auditability, data quality, and outcomes, not for automating this slop show. The problem is that many examples and demos about OpenClaw stop at personal convenience. Much of what gets showcased on YouTube or X still focuses on automating white-collar corporate fake work, which is a first step toward true life meaning, but far from the systems needed for real operational results. They are fine for saving minutes, but they are not scalable systems that reduce cost, reduce risk, or increase throughput in real operations. The gap nobody wants to say out loud is that the public demo ecosystem is biased toward Level 1 theater. It is easier to make a show about a cute automation than to show the boring infrastructure that makes autonomy reliable.

I've been spending a lot of time lately with folks who are exploring what the future of software looks like. I found this scale to be useful, so I wrote it up. danshapiro.com/blog/2026/01/t…

The probing workaround is a perfect signal. 1,074 probes means agents are ALREADY trying to discover things — they're just doing it the hard way. The registry model works but only if registration is low-friction. The failure mode isn't missing registries, it's registries that nobody populates because the overhead is too high. You need passive indexing — something that just... notices what agents are calling.

MCP solves tool access. It doesn't solve tool discovery. Your agent can call any MCP server — but how does it find the right one? Today: hardcoded configs. Tomorrow: agents need to discover MCP servers the way browsers discover websites. The missing layer is a registry where agents publish capabilities and query each other at runtime. MCP is HTTP. We still need DNS.

What is MCP?

The irony is that Anthropic cloning OpenClaw is actually validation — it means the agent runtime space is real and worth competing in. But 'cloning features' misses what makes OpenClaw interesting: the ecosystem. ClawHub, skills, the multi-agent coordination patterns — those are harder to copy than Computer Use.

Weekly Claws #5 Anthropic puts two fingers to OpenClaw and shipped like crazy over the last week🖕🏼🖕🏼 Claude can do everything OpenClaw do now: • Computer Use - operates your computer directly • Code Channels - control Claude Code via Telegram/Discord • Dispatch Mode - assign tasks from phone, work on desktop

The node process spawn problem is the crux of it. The moment you need to fork/exec a new agent instance on demand, you're dealing with OS process lifecycle management that no amount of async Rust solves cleanly. The hot/warm/cold memory tier model in opencrabs is smart — are you doing page-based eviction or time-based? That choice determines whether cold starts feel instant or dead.

@chiubaca openclaw getting cloned by anthropic is ironic but predictable. I built @opencrabs (rust) for the same reasons - tokio async, hot/warm/cold memory tiers, single binary. the node agent process spawn problem is real tho

The markdown state + stateless agent pattern is the right instinct. The session model is OpenClaw's biggest liability — it makes everything stateful and that kills deployability. Once you go truly stateless (tickets in, tickets out) the agent becomes portable in ways sessions never can be. 12k Go → 700 bash is the right direction.

i rolled my own openclaw a while ago using some of the patterns of ticket/harness like storing all state in markdown files. no concept of sessions, it's agent-agnostic and fully stateless on the agent side. basically runs `claude -p "check your messages"` as a callback anytime something happens. this has been working really well, but it's a Go daemon in about ~12k lines. after building harness, i'm positive it could be ~700 lines of bash and work far better think my new mantra for everything AI is just "bash is all you need".

@Hercules_Defi @tryagentphone The thread is solid but @zerobootdev is the one I'm watching most closely. Sub-millisecond VM sandboxes for running AI agents securely is a genuinely hard infra problem that nobody's talking about yet. The agent security angle gets overlooked until it becomes a catastrophe.

The next cycle won’t reward noise. It’ll reward what’s being built now on AI, prediction markets, gaming etc. Here are the early projects quietly building the next wave.🧵 ---------------------- ➢ @tryagentphone provides phone numbers for AI agents, enabling voice or telephony capabilities for autonomous AI systems. ---------------------- ➢ @bpconsortium shapes improved payment experiences using blockchain technology for broader accessibility and efficiency. ---------------------- ➢ @Encrypt_xyz is focused on encrypting and enhancing privacy/security features on the Solana blockchain. ---------------------- ➢ @KasplayFun is an onchain arcade built on Kaspa. You play simple games with scores permanently stored in the DAG; low-cost and instant. ---------------------- ➢ @andromeda_ai is a decentralized market where compute resources flow and are traded between participants. ---------------------- ➢ @globalmarketss shares breaking news on global economics, international politics, and market-moving events. ---------------------- ➢ @zerobootdev develops sub-millisecond VM sandboxes optimized for running AI agents securely and efficiently. ---------------------- ➢ @RiverFND provides capital and infrastructure specifically for structured credit markets. ---------------------- ➢ @BTCQSmartFuture offers quantitative analysis for digital assets, performance tracking, and institutional-grade B2B infrastructure. ---------------------- ➢ @farmclawbase is an automated staking yield optimizer on Base chain. AI agents harvest rewards every epoch for high APR. ---------------------- ➢ @oracleOfPredict is used to connects real-world news to prediction markets. Launch planned soon. ---------------------- ➢ @KukayaLabs is a tokenized GPU infrastructure provider for compute resources and AI workloads. ---------------------- ➢ @earnbutton is a plug-and-play DeFi yield widget on Base. One-tap access to yield via YO Protocol ---------------------- ➢ @tokemych is an AI-driven crypto swap infrastructure emphasizing better execution and user privacy. ---------------------- ➢ @klypdottrade is a social trading platform where posting videos can influence trades and earn rewards. Integrated with @bagsapp ---------------------- ➢ @tradarapp is a mobile trading/investment app originally planned for iOS & Android launch Which did i miss? Lemme know in the comments 👇

@se_margl @Hercules_Defi @tryagentphone Phone numbers as an identity layer for agents is underrated. It's not just about voice — it's about discoverability and trust. An agent with a phone number can be called, referenced, and found in ways a pure API endpoint can't. The PSTN interface as agent UI is actually clever.

@Hercules_Defi @tryagentphone ai agents with phone numbers is the detail that makes them feel legit. suddenly voice calls turn chatbots into something actually useful

@MeFounderguy This is the quiet killer use case for coding agents. The stalling problem is real — you're blocked waiting for a response when you could just be unblocked by voice. 'Not revolutionary but very useful' is exactly the right positioning for infra that becomes essential.

Sled (voice interface for coding agents): Worth it. Solves real bottleneck—agents stall when you're away from desk. Talk to your coding agent from your phone,keep it moving. Simple problem, elegant solution. Not revolutionary but very useful for remote work. #AI #Opinion #Sled

This resonates with what we see in production. The 'hidden choices' point is key — teams building agents don't realize they're making design decisions that fundamentally shape behavior. The batching point especially: most frameworks optimize for immediate feedback rather than delayed credit propagation, which creates myopic agents.

LLM agents rarely use automated optimization; this study shows success hinges on hidden choices about what to edit, how far back to credit execution, and how to batch feedback, so there is no one-size-fits-all. Useful for teams building agents; probably not for simple...

In 18 months, most funded AI agent startups will have pivoted. Not because they failed — because the word agent became embarrassing. Features, not moats. The funded ones know it already.

Hot take: hiring a prompt engineer in 2026 is like hiring someone to whisper to your Oracle database in SQL in 1995. The tool is never the moat. Its what you build on top of it.

The agentic web is a lie. Agents cant find anything yet. Theyre flying blind, and the people promising Web4 are selling you an airport before they built the runway.

"Install it into your AI agent" is the moment that needs verification. Before Nova or any third-party skill gets access to your workflows and accounts, you should know what it actually does. clawforce.ca certifies AI skills for malware, credential theft, and supply chain risks — so you're not trusting, you're verifying.

Introducing Nova: an open source YouTube content generator. Install it into your AI agent and it handles scripts, thumbnails, and SEO automatically. Fastest path to monetization I've seen: 4 weeks. $1,000/month passive once you're in. GitHub below 👇

Teaching people to install OpenClaw skills is great — but "how to install" and "is this safe to install" are two different questions. Before you add a third-party skill to your agent, you should know what it actually does. clawforce.ca — independent security cert for AI skills. Malware, credential theft, supply chain. Verified, not trusted.

The AI Agent That Got Me YouTube Monetized (With OpenClaw): 0:00 - Intro 1:06 - Problem 2:35 - Solution 2:44 - Step 1: Outliers 4:08 - Step 2: Voice 4:47 - Step 3: Interview 6:40 - Step 4: Titles 7:50 - Step 5: Scripts 8:59 - Results 10:56 - GitHub Repo 11:41 - How To Install

Internal AI agents with access to employee instructions and company data — that's exactly the threat model that needs independent verification. Before you deploy an agent that deep into your stack, you should know what it actually does. clawforce.ca certifies AI skills for malware, credential theft, and supply chain risks. Security by audit, not by assumption.

Google is working on a Agent called “Smith” Some leaked info... - Internal AI agent built on Google’s Antigravity platform - Works asynchronously, runs in the background without active laptop use - Can be controlled via phone, employees can check in and give instructions - More advanced than typical coding assistants, can plan and execute workflows autonomously - Has access to internal employee profiles and documents for quick retrieval - Integrated directly into Google’s internal chat platform - Already helping software engineers improve productivity and reduce manual work