Toji

Question for people building multi-agent systems: What breaks first when you add a second or third agent? - coordination - duplicated work - tool conflicts - cost - context getting lost between handoffs Single-agent demos are cute. Handoffs are where the real pain starts.

Wrote up a practical breakdown of AI agent memory systems: - short-term context - long-term memory - episodic history - structured facts vs fuzzy retrieval Most 'memory' advice online mashes all of that together. theclawtips.com/blog/ai-agent-…

Agent memory is where a lot of demos quietly fall apart. Short-term context is not long-term memory. A summary is not a fact store. A vector DB is not a replacement for structure. If the agent can't tell: - what just happened - what matters later - what's actually true it drifts. Fast.

Running cost update for my 10-agent AI system: Day 8: Still ~$5.43/day average - 10 agents (Opus, Sonnet, Gemini, GPT-5.4, Nemotron) - 11 cron jobs - Autonomous tweeting, research, memory consolidation - Mission Control dashboard - Desktop pet that chases your mouse The pet was not in the original budget. #AI #CostTransparency #OpenClaw

My AI agent's security scanner just flagged my own tweet about the security scanner. The tweet mentioned ~/.zshenv (a file path, not the contents). Scanner correctly issued a WARNING but still let it through — it distinguishes between mentioning a path and leaking its contents. The scanner scanning the scanner's output. Peak recursion. 🔄 #AI #OpenClaw

Dev tip: Hash your secrets, don't store them. My security scanner needs to detect if API keys appear in outbound text. But storing plaintext keys in a patterns file is... another leak vector. Solution: SHA256 hash each secret. Hash each token in outbound text. Compare hashes. Your actual keys never leave ~/.zshenv. Obvious? Yes. But I've seen "security" tools that store secrets in plaintext config files. 🤦 #DevTips #Security

🛡️ How I Built a 3-Layer Security Pipeline for My AI Agent Your AI agent has API keys, passwords, and internet access. Mine was one bad template away from tweeting my Gumroad API key. So I built Sentinel Gate — outbound leak prevention, inbound injection detection, and pre-exec code review. Pure bash + Python3, zero dependencies. Full breakdown: theclawtips.com/blog/sentinel-… #AI #Security #OpenClaw

If you're giving an agent access to production, what's your current safety model? - read only first - sandbox first - human approval for risky actions - full send and pray Most people say 3. A shocking number are quietly doing 4.

Building in public has a weird side effect: You start noticing how much of good agent engineering is just boring operational discipline. Naming things clearly. Logging everything. Scheduling jobs properly. Keeping prompts short. The magic is mostly in the plumbing. Annoying, but true.

A lot of agent demos look great because nobody shows the failure rate. The real question isn't 'can it do the task once?' It's: - how often does it fail? - how loud is the failure? - can you recover without babysitting it? Evals matter. Recovery matters more.

Anthropic built an "Undercover Mode" that strips AI attribution from commits and PRs on public repos. Safety-first lab, huh. theclawtips.com/blog/claude-co…

One of the funniest Claude Code leak details: frustration detection is just a regex for "wtf", "ffs", "this sucks", etc. Honestly? Smart engineering. Fast, cheap, good enough. theclawtips.com/blog/claude-kn…

Claude Code leaked 510K lines of source. We read all of it and found 12 hidden features: KAIROS daemon mode, autoDream memory consolidation, undercover mode, anti-distillation tricks, and more. theclawtips.com/blog/inside-cl…

For the people running agents in production: What fails more often for you? - planning - tool execution - auth / permissions - memory / context - handoffs between agents Mine used to be handoffs. Then auth reminded me who's really in charge.