🎙️ How I AI: How this PM uses Claude to handle 70% to 80% of his workday

How I turned Claude into a self-improving PM assistant | Daniel Blum (PM, Melio)

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Daniel Blum is a product manager at Melio who has built a self-improving AI system that now handles 70% to 80% of his workday. In this episode, he breaks down how Claude and Cowork manage his Notion board, prepare him for the week, scan Slack and email for important context, and learn from his edits without waiting for explicit feedback. He explains how he turned the system into a 15-minute onboarding experience for other Melio employees, why the first few weeks of building with AI can feel painfully slow, and how the payoff eventually helped him accomplish a week’s worth of PM work in a single day.

Biggest takeaways:

1. The architecture matters more than the AI tool itself. Daniel believes a system becomes genuinely powerful when it can update its own core files and connect to the tools someone already uses. Once those pieces are in place, the system can improve and become more useful over time, whether it is built in Cowork, Codex, ChatGPT, or something else. He created a transformative setup using the tools Melio had already licensed, proving that the underlying architecture matters more than choosing the perfect platform.
2. Context isn’t something you set up once; it requires an ongoing system. Daniel spent months giving Claude voice memos, links, decks, and verbal brain dumps to build detailed context files for every area of his work. He then created recurring updates that refresh those files every few weeks. This keeps the gap between what Claude knows and what is actually happening inside the company as small as possible.
3. The most impressive part of Daniel’s morning brief is that it identifies what it does not know. Each day, Claude reviews his Slack, email, and notes for unfamiliar terms, projects, or goals that do not appear in its context files. It then asks Daniel targeted questions to fill those gaps. When it encountered the phrase “settlement cap,” for example, it had already read the relevant thread and understood the general idea. It only needed Daniel to confirm the meaning before saving it.
4. The value of a personalized AI system builds slowly, then becomes enormous. Daniel is candid about how frustrating the first few weeks can feel. The system does not know enough yet, its work is slightly off, and nearly everything requires a second look. But once someone pushes through the work of centralizing information and building context, the payoff can be difficult to overstate. He can now accomplish in one focused day what previously took him an entire week.
5. Self-improvement loops learn from the difference between what the AI drafted and what the person actually sent. Daniel built a weekly skill that compares Claude’s original drafts with his final versions, then uses those differences to improve future work. It resembles Alex Lieberman’s “write like me” loop, but it relies less on explicit feedback. Instead, it observes Daniel’s actual behavior and learns from the small edits he makes instinctively.
6. Feedback telemetry turns personal AI workflows into products that can improve themselves. Every one of Daniel’s skills captures moments of friction. If he says something is not working or requests a correction during a session, the system logs that signal. Once a week, his improvement loop identifies the most common problems and recommends updates. It is essentially analytics for his internal tools, and it gives him a structured way to refine the system based on how it performs in real life.
7. The Workstation plugin addresses one of the biggest barriers to adopting AI inside a company: personalization. Daniel watched several product managers struggle with Spectacular, his spec-writing gem, because it had been designed entirely around his own working style. He responded by building an onboarding flow that connects each employee’s tools, maps their colleagues, and learns their voice in about 15 minutes. Instead of starting from scratch with a generic system, every Melio employee now begins with a strong shared foundation personalized to their needs.
8. The biggest remaining limitation of today’s AI systems is persistence, not intelligence. Daniel estimates that Claude already handles 70% to 80% of his workday. What it still cannot reliably do is continue working in the cloud while his computer is off. He is already preparing for that future by teaching Claude how to recognize the “closed state” of different tasks. If a drafted Slack message is no longer saved, for example, the system can infer that it was probably sent. When truly autonomous operation becomes available, Daniel’s system will already understand what completion looks like.

Blog and detailed workflow walkthroughs from this episode:

Claude Cowork for PMs: My Self-Improving Productivity System: https://www.chatprd.ai/how-i-ai/claude-cowork-for-pms-my-self-improving-productivity-system

↳ Create a Meta-Workflow to Continuously Improve Your AI Assistant’s Performance: https://www.chatprd.ai/how-i-ai/workflows/create-a-meta-workflow-to-continuously-improve-your-ai-assistant-s-performance

↳ Build a Self-Improving AI Morning Brief to Capture Action Items and Learn Company Jargon: https://www.chatprd.ai/how-i-ai/workflows/build-a-self-improving-ai-morning-brief-to-capture-action-items-and-learn-company-jargon

↳ Automate Your Weekly Planning with an AI-Powered PM Assistant: https://www.chatprd.ai/how-i-ai/workflows/automate-your-weekly-planning-with-an-ai-powered-pm-assistant

If you’re enjoying these episodes, reply and let me know what you’d love to learn more about: AI workflows, hiring, growth, product strategy—anything.

Catch you next week,
Lenny

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