Show Notes
Today’s daily update blends a no-fluff take on a Microsoft productivity lesson with real-world moves in the AI tooling space, then lays out practical paths for learning deeply and practically as a developer.
Key takeaway from the Microsoft advice
- Go one layer deeper in whatever you build with AI. If you’re a frontend, dive into the underlying concepts; if backend, push into the next layer of understanding. Ask AI to “dig deeper” and explain what’s not immediately known.
- Keep learning actively and consistently. Curiosity plus a structured path beats passive vibing through your day.
OpenAI deal and ecosystem shifts
- OpenAI bought Wind Surf for about $3B; Cursor raised $900M at a $9B valuation in the same window.
- Likely impact: developers get better access or discounts on tools as the ecosystem consolidates; potential changes in model usage terms to protect market share.
- Other ecosystem signals: Anthropic + Apple collaboration and a new toolbar concept, plus model explorer ideas. These point to faster, more integrated tooling for developers.
Learning by digging: monorepos, polyglots, and pragmatic exploration
- History and forms: Google and Meta pioneered large monorepos; Piper-like systems spread to Microsoft, Uber, Airbnb, Twitter; TurboRepo, NX, PNPM workspaces are popular tools to manage multi-app codebases.
- A practical learning method:
- Define your context (e.g., a TypeScript frontend with Next.js, a thin backend, and an interest in FastAPI for agents).
- Use independent research tools (examples: Grok, Gemini Deep Research) to learn history, benefits, and trade-offs.
- Find real-world codebases to study (starter projects or portfolios that use monorepos with multiple apps and services).
- Consume creators who break down concepts clearly (e.g., TanStack Router vs Next.js patterns) to build intuition.
- Embrace “learning in public”: follow peers who share their learning journey (e.g., someone iterating on LeetCode or a learning path) to stay motivated and get feedback.
- GitHub and starter projects are your labs. Pay the idiot tax—tackle friction and gaps head-on to move faster.
The self-healing AI-assisted development concept (practical brainstorming)
- A concrete, agent-driven loop:
- Bug finder → Trace reader → Fix agent → Test runner → Deploy agent
- Tech stack in view: Deno for backend, Python agents, Prometheus for telemetry, Grafana for observability, TanStack Router and Vit for frontend.
- Outcome: a self-healing, AI-assisted scheduling app with rich telemetry and an agent-driven backend that can detect, diagnose, and patch issues during development.
Learning in public and practical examples
- Learning in public works: peers sharing the process helps others ramp up faster; examples include constant practice like daily coding/LeetCode videos.
- Use real codebases and GitHub projects to practice and reveal gaps. The faster you confront gaps, the quicker you grow.
Quick prompts and community notes
- If you want prompts for product management or a tailored prompt set, ask me—I can generate them.
- The community offer: half off now until the new platform launch—worth noting if you want to dive in.
Actionable takeaways
- Today: pick a single layer you work in and commit to going one layer deeper with AI help. Ask for explanations, edge cases, and deeper diagrams.
- Start a learning log: document what you learn, what questions you still have, and what you’ll explore next.
- Explore monorepos with a real-world lens: pick a project, map its repo structure, and compare tools (TurboRepo, NX, PNPM workspaces) to see what fits your stack.
- Sketch a small, auto-debug loop concept for your stack using Deno/Python, Prometheus, and Grafana to practice observability and incremental improvement.
Links
- OpenAI Wind Surf acquisition and the Cursor funding round
- Anthropic + Apple collaboration (tooling signals and model explorer ideas)
- Monorepo history and tooling references: Google Piper, TurboRepo, NX, PNPM workspaces
- Independent research tools (examples referenced): Grok, Gemini Deep Research
- Deno (backend), TanStack Router, Vite (frontend tooling)
- Prometheus and Grafana (telemetry/observability)
If you want prompts tailored to product management or a concrete learning plan based on your stack, tell me your stack and goals and I’ll generate a focused prompt list and a 2-week learning path.