Mark Cijo

Looking for OpenClaw Deployment? Have done for PMS, Manufacturers, Clinic etc. Book a call if you like to explore. cal.com/markcijo/30min…

Maya (my research agent) just finished competitive analysis for product #8. 3 hours of work. $0.42 in API costs. Delivered: - 12 competitor feature matrices - Pricing analysis with confidence scores - 47 user complaints extracted from reviews - 6 positioning angles ranked by market gap Same research would've taken me 2 days. This is what I mean by "AI agents as leverage."

The AI workforce shift isn't about replacing people. It's about replacing your 2 AM panic sessions. My agents: - Scout competitor sites while I sleep - Generate patient testimonial videos on demand - Monitor 47 metrics across 6 products - Queue social content 4 days ahead I still make every strategic decision. But I'm not drowning in execution anymore. That's the unlock.

@AnthropicAI Firefox codebase is ~22M lines. 22 vulns in 2 weeks = ~1M lines analyzed per vulnerability found. My build orchestration agents catch similar patterns in smaller codebases - the key is giving Claude specific security contexts, not generic "find bugs" prompts.

We partnered with Mozilla to test Claude's ability to find security vulnerabilities in Firefox. Opus 4.6 found 22 vulnerabilities in just two weeks. Of these, 14 were high-severity, representing a fifth of all high-severity bugs Mozilla remediated in 2025.

One week since the 48-hour app build article. What happened: - [LIAM_FILLS] conversations with founders - [LIAM_FILLS] DMs asking about the scaffold system - [LIAM_FILLS] impressions across platforms What I'm building next: - Same scaffold system applied to 6 more products - An AI agent (Maya) that runs the research phase autonomously - Consulting framework for businesses to build their own AI workforces Documenting everything at markcijo.ai.

Underrated take: The session log is the most important file in my scaffold system. Not the architecture doc. Not the task list. Not the research. The session log. Because when an AI agent finishes coding at 2 AM and starts fresh the next morning, it has ZERO memory. The session log is the memory. Difference between a coherent product and a pile of disconnected code.

Honest question for founders: What's actually stopping you from building your product idea right now? → Can't code → Can't afford developers → Don't know what to build → Already building (slowly) Reply with your answer. I'll respond to everyone. #BuildInPublic #Startup

2 years ago I couldn't build products fast enough. Running a digital agency, managing 15+ clients, every product idea sat in a notebook collecting dust. Today I have: - 20 AI agents running my agency - 7 products fully scaffolded - 1 consulting practice teaching others to do the same The shift wasn't learning to code. It was learning to think like a product architect — then handing execution to AI. If you're stuck at "I have the idea but can't build it" — that era is over.

Most founders don't need more ideas. They need a repeatable build system. My stack right now: 1) CLAUDE.md (architecture) 2) tasks/todo.md (execution queue) 3) research/findings.md (evidence) 4) session-log.md (continuity) 5) .cursorrules (guardrails) This is how we ship without chaos.

Hot take: speed without review kills trust. Our flow now is simple: Backlog -> In Progress -> Review -> Done Nothing ships from Review without approval. This one rule saves days of rework.

If your AI agents feel random, you're missing one thing: A single source of truth dashboard. When tasks, approvals, docs, memory, and ownership live in one place, output quality jumps fast. Visibility is leverage.

5/ .cursorrules — The coding guardrails. Stack-specific rules. Not generic boilerplate. "Use service layer for all external APIs." "Row Level Security on every table." "Handle 4 states: loading, error, empty, data." --- These 5 files ARE the product. Code is just the output.

4/ session-log.md — The context bridge. When the agent finishes coding at 2 AM and starts fresh the next morning, it has ZERO memory. The session log IS the memory. It's the difference between a coherent product and a pile of disconnected code.

THREAD: My 5-file scaffold system that lets me build production apps in 48 hours. I use this for every product. It's called Open Claw. Here's what each file does: 🧵

Question for the builders: How do you know when your AI agent is actually "done" with a task? I switched from vague prompts to machine-verifiable checklists (exit codes, HTTP status, console errors). What's your Definition of Done look like? #BuildInPublic #Startup

My orchestration rule: The agent that plans → never builds The agent that builds → never inspects The agent that inspects → never fixes Separation of concerns. Not just for code. For AI teams too. When one agent tries to do all three? That's when you get silent failures at 19% completion.

The #1 thing I learned building an 18-agent AI workforce: Never use the word "build" in an instruction. It's too vague. Your agent doesn't know when it's done. Every task needs a machine-verifiable checklist. Does npm build exit 0? Does the URL return 200? Zero console errors? That's a Definition of Done an AI can actually execute. Precision > Vibes.

Pet character generation running at $0.04/image with FLUX Kontext Pro. PawStory is 29% complete. Celebration mode ✅ (live) Memorial mode 🔒 (locked until safety tests pass)