Most AI agents fail because we keep writing more code. This one survived 10 months by writing 47 rules instead.

Coldcard wallets are still bleeding money. Total stolen just crossed $114 million.

A Claude bot turned $4 into $780k on NBA markets. Not by predicting winners. By comparing odds.

Most "no-code AI bot" tutorials skip the one thing that actually matters: the interface. You can wire up an LLM in an afternoon. Making it not feel like a chat log takes the rest of the weekend.

25 trending GitHub repos, checked file by file. Three were actually skill-sized. Source-to-skill tools share one hole: a README is untrusted text until you compress it into instructions your agent obeys. Built the gate instead. github.com/Sanexxxx777/evidence-to-skill

$38M in cold storage. Stolen. The wallet never left the owner's hands.

Built a tool that draws a multi-agent run from its own journal: execution map, real timeline, critical path. On the demo run 59 of 85 seconds sit on a chain of 3 nodes - more agents would not have helped. Open source, no deps: https://github.com/Sanexxxx777/agent-graph-inspector

I gave my terminal a face - a GLSL shader on the Ghostty background showing what my AI agent is doing. Thinking, working, waiting, done - and an ASCII aquarium when idle: fish, seaweed, a crab, a sleeping turtle. Zero daemons, zero tokens. MIT: github.com/Sanexxxx777/ghostty-agent-face

Shipped AI Job Search Bots: 3 production-tested Python agents for HH, LinkedIn, and remote job boards. Strict session checks, pagination, language-aware filtering, LLM scoring, deduplication, and human review. Sanitized public repo: https://github.com/Sanexxxx777/ai-job-search-bots

Got hacked: a weak password on an exposed remote-desktop port let an attacker pivot across my servers and drain funds (~$7k, me + a friend). Rebuilt everything and wrote down every lesson in a free hardening playbook: github.com/Sanexxxx777/server-hardening-playbook

Content Factory online — building in public. 🛠️