Posts
Independent essays on AI-native work patterns, agent infrastructure, and what actually works when running agents as real tools.
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Two AI interfaces. Same desktop. Completely different jobs.
Running two AI tools built on the same model — one for co-piloting, one for delegating. A non-developer's frame for splitting knowledge work by workflow mode.
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AI Collapsed the Startup Advantage. Enterprises Just Haven't Noticed Yet.
The startup playbook depended on code being expensive. AI made code cheap. What's expensive now — data, distribution, domain expertise — is what enterprises already own.
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AI Didn't Reduce My Work. It Expanded What Work Means.
Everyone I talk to who works natively with AI says the same thing: more hours, more exhausted, more engaged than ever. The paradox is real — and there's a specific mechanism driving it that most productivity research misses.
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Build vs. Buy for AI Knowledge Infrastructure: Capability First, Cost Second
SaaS platforms auto-generate MCP servers. But auto-generated MCP does text search. If your use case requires metadata-filtered retrieval, you're comparing different architectures — not different feature sets. Capability first, cost second.
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Four Things That Can't Be Delegated
After weeks of delegating nearly everything to AI agents, I found exactly four things that genuinely can't be handed off — not shouldn't, can't.
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ACP Is the Bridge Between Knowledge Work and Build Work
Knowledge agents and coding agents are powerful in isolation but can't talk to each other. The Agent Client Protocol (ACP) is the open standard that connects them — turning your orchestrator into a team lead that delegates build tasks without breaking flow.
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Methodology Is Infrastructure
Every AI session starts fresh. If your methodology lives only in your head, it resets too. The fix is treating it like code.
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Cloud Sync Is the Wrong Storage Layer for AI Agents
Cloud-synced filesystems (OneDrive, Dropbox, Google Drive) are eventually consistent layers masquerading as transactional storage. AI agents write at machine speed and expect durable writes. The architectural mismatch causes silent data loss. The collaboration model is wrong too.