How I Use AI Every Single Day

First thing: Claude for market briefing synthesis. I have a workflow where I paste in the top Polymarket moves from overnight, recent news headlines, and smart wallet activity from PolyFire. I ask Claude to synthesize: what's the signal, what's the noise, what would change these probabilities.

This takes 10 minutes and replaces what used to be an hour of reading. The AI doesn't trade for me — it helps me prioritize what to think about. The prioritization is the leverage.

I build everything at BoomSauce Labs with AI assistance. Every feature, every bug fix, every architecture decision gets AI input. Not because I can't code — I've been writing code for 20 years — but because AI generates first drafts faster than I do and first drafts are the expensive part.

Claude Code handles the bulk of new feature implementation. I review, architect, and debug. The ratio of AI-generated to human-written code is probably 70/30 on new features. This lets me run multiple projects simultaneously that would otherwise require a full team.

I write the ideas. AI helps with structure, expansion, and polish. This article started as bullet points — the argument structure is mine. The fleshing out is AI-assisted. The voice is mine because I edit heavily.

Anyone using AI to generate content they don't edit or voice-match is producing slop that will show in SEO and credibility over time. AI is a multiplier on your ideas, not a substitute for having them.

Customer support triage, writing policy documents, analyzing data exports, generating reports, drafting emails that require careful language. All of this goes through AI before it goes anywhere else.

The most underrated AI use case: research synthesis. When I need to understand something new — a regulatory filing, a technical paper, a market I haven't traded — I dump the source material into Claude and ask for a briefing. The time savings on this alone pays for any subscription cost many times over.

The biggest recent upgrade is MCP — Model Context Protocol. My AI models now have direct access to PolyFire's market data, my development tools, and external data sources. This means I can ask 'what are the biggest market movers in the past 24 hours and which smart wallets are positioned on them' and get a real answer instantly, not a general description of how to find that answer.

MCP turns AI from a question-answering tool into an operating environment. The difference is significant. This is where the leverage really compounds.

Judgment calls on novel situations. Final decisions on anything that affects real people or real money at significant scale. Relationship communication where nuance matters. These are domains where the AI assists but doesn't decide.

The discipline is knowing where AI amplifies and where it degrades. AI is excellent at synthesis, generation, and analysis within known patterns. It's unreliable on genuinely novel situations, subtle human dynamics, and ethical trade-offs. Use it where it's strong. Don't abdicate judgment where it's weak.