Show Notes
Punchy takeaway: This daily breaks down the vibe coders roadmap—the AI-enabled software development life cycle—and shows how to apply it in real projects, starting with problem definition and ending in a deployable, maintainable system.
Roadmap at a glance
- AI SDLC is the backbone for building high-impact AI-powered software.
- Focus on crisp product requirements, strong problem definitions, and a solid design before coding.
- Three audience buckets to tailor your learning: business owners new to AI, tech-adjacent builders, and solo founders.
Core stages and mindset
- Discovery to maintenance: Discovery, design, development, testing/QA, release, maintenance.
- Emphasize PRDs and well-scoped prompts to avoid “garbage in, garbage out.”
- Never underestimate planning: systems thinking, architecture, pattern matching, creative problem solving, debugging, ideation stay premium.
Where the tooling fits
- Cursor: official docs, architecture diagrams, and web dev setup guides; frames for diagrams and implementation within projects.
- Frame Link: used alongside Cursor to wire up architectural visuals into the workflow.
- Notebook LM: prompts and mind-maps for practical use cases (e.g., analytics API with FastAPI + TimescaleDB).
- Background agents: experimenting with Cursor background agents and Augment; plan to publish a dedicated video and open-source repo.
The Vibe with AI SDLC (the blueprint)
- High-level flow: task generation at the base, then deeper layers for data flow, process flows, and system design.
- Data flow vs. process flow: business goals drive the data and process flows; you can’t design the product without knowing the business requirements.
- Architecture decisions, database design, API and backend/frontend development, security, QA, DevOps, CI/CD, and more are all part of the loop.
- The cadence: learn a bit every day (just-in-time learning) and immediately build something with it.
Learning approach you can steal
- 80/20: pare down to the critical path for what you’re building.
- Use a study guide and break topics into actionable skills; lean on tools like Eraser.io and Excaliraw to map and plan.
- Self-grade 1–10 on each topic; if you’re weak, allocate 20 minutes daily and push into real projects.
- Do real-user problem discovery (Mom Test) to ground your product in actual needs.
Current work and implementation notes
- Echo project: automating grunt work around YouTube workflows; testing a pipeline from data gathering to posting.
- Vibe with AI product: new co-pilot features and a stronger integration with community tools.
- Architecture-forward thinking: mapping business goals to data and process flows; planning for scalable, maintainable systems.
Community, product updates, and what’s coming
- Discord-focused updates with Val (S.Val) co-pilot: proactive summaries, channel indexing, and selective notifications.
- TLDDR and Birdie co-pilots: quick video summaries and news takes behind the scenes.
- Builder network approach: tighter ICP, collaboration potential, and portable co-pilots for other communities.
- Collaboration opportunities: working with members like Michael to prototype an application shell; potential SaaS paths for the co-pilot and member-management portal.
Actionable takeaways
- Start with problem definition and clear PRDs for any new AI project.
- Build a simple architecture sketch (data flow + process flow) before coding.
- Do 20 minutes of focused study daily, then ship something small to reinforce learning.
- Use the three learning cohorts to prioritize what to learn first.
- If you want the roadmap in PDF, there’s a ready-to-share version; check the video description for access.
Links
- Cursor - Docs and guides including architectural diagrams
- NotebookLM - Prompts and mind-map approaches
- Eraser.io - Diagramming and planning tool
- Excalidraw - Learning path prompts and topic breakdown
- The Mom Test - Customer discovery and interviews book