Show Notes
Parker lays out a practical path to land quick AI-driven contracts (5k–15k), then digs into tooling, strategy, and the multi-project plan he’s building to scale an AI services business and a SAS. Short, fast, and actionable.
Fast, cashable contract play
- Aim: convert AI builds into paid work quickly (5k base, 15k for more complex builds).
- How to scope fast:
- Write a 1–2 sentence problem statement you’re solving.
- List functional requirements and non-functional constraints.
- Timebox discovery to 60 minutes to lock in scope.
- Use Abe Lincoln logic: sharpen the axe before chopping the tree.
- Identify “rat holes” (things to avoid) and “nogo” decisions to prevent scope creep.
- Pricing mindset:
- Start with a clean, simple package (5k) and tier up to 15k for richer, multi-feature builds.
- Focus on delivering a standout, differentiated result (speed, reliability, and clear outcomes) rather than overheating the spec.
Cost management for AI tooling
- How to keep costs sane:
- Don’t assume you must pay for every tool; self-host when feasible.
- If you’re technical, self-host n8n and related stacks; use a modest DigitalOcean or similar host (around $40/mo) to run everything.
- Check out Local AI Packed (Cole Medan) for a guided self-host”playbook” to reduce friction.
- Avoid high-cost APIs (CLA) when possible; push the workload into more cost-effective paths.
- Practical steps:
- Self-host Zapier-like flows with n8n.
- Pair with a lean backend (e.g., Supabase) to minimize services you rely on.
- Increase value to clients to justify higher spend (don’t chase cost-cutting at the expense of results).
Supabase: backend-in-a-box for speed
- What Supabase delivers:
- Backend as a service with batteries-included features like authentication (social logins, admin roles, anonymous sign-in), storage, and more.
- Real-time cursors for collaborative apps; robust, reliable real-time data pipelines.
- Edge functions for serverless logic (OpenAI proxy, webhooks, etc.).
- LSP (Language Server Protocol) support for auto-complete directly on the DB layer.
- Supabase Studio: in-browser SQL editor with an AI-assisted, multi-thread workflow; handy for rapid iteration.
- Why Parker likes it:
- Faster ramp to customer value with less boilerplate.
- Self-hostable and open-source; fits the vibe-coding approach.
- Reduces “gotchas” when you come back to a project after a break.
No-code builders vs vibe coding in AI era
- Take on web builders (Webflow, Squarespace, Wix):
- Parker argues these are becoming less relevant as AI-driven development accelerates.
- Visual editors and templates are being outpaced by AI-enabled, customizable vibe coding that can be done in a day.
- Pricing mindset:
- Example mental model: a 5k project for a Webflow-style site might become 15k for a fully custom vibe-coded version that’s AI-powered and tailored.
- Practical approach:
- Use the Abe Lincoln framework to scope quickly, then deliver a fast, polished MVP that’s easy to customize.
News page: automated, always-current
- Plan to build a dynamic news page in 90 minutes:
- Source ~7 outlets; bookmark content into a “news updates” folder (or a form folder if you’re constrained).
- Crawl and convert to markdown; train a summarizer to speak in your voice.
- Publish to Next.js or React Router-based site; hook into the school/community for auto-updates via webhooks.
- Outcome:
- A living, always-up-to-date news hub that scales with your content footprint and invites community participation.
Map: multi-agent productivity and health platform
- Core idea: a proactive future-self agent ecosystem.
- The main agent guides you toward your goals; underneath are multiple agents handling tasks, calendar, notes, health, etc.
- Guardrails, logs, and an agent SDK to manage decision-making with structured outputs.
- Architecture plan:
- Monorepo with a Next.js dashboard, a Python-based agent backend (AK), and Astro for the marketing site.
- Supabase as the backend, Docker for hosting, and GitHub Actions for CI/CD.
- The dashboard uses an “all-in-one” stack that’s familiar and fast to iterate on; marketing site stays separate for speed.
- Why this matters:
- Keeps development fast, with clean separation between product, data, and marketing.
- Lays groundwork for future multi-agent orchestration and scalable automation.
Community, school, and resources
- The plan includes a public, collaborative space:
- A school/community where dev updates and episodes are shared; access to builds and behind-the-scenes workflows.
- A “Development Treasure Chest” (Notion-based resource hub) mentioned for those who comment with a specific trigger.
- Practical path:
- Join the community to get access to the ongoing build process, prompts, and behind-the-scenes decisions.
Takeaways
- Turn AI builds into paid work quickly with tight scope, clear problem statements, and 1-hour discovery sprints.
- Use cost-smart tooling: self-host where possible, leverage Supabase for a fast, scalable backend, and push value to customers to justify spend.
- Build with multi-agent architecture in mind to scale product workflows, not just single solutions.
- Use 90-minute rapid-build cycles for news pages and other dynamic features to stay ahead.
Links
- Local AI Packaged — Cole Medin (self-hosting guidance for AI stacks)
- n8n (self-hostable workflow automation)
- Supabase
- Supabase docs and community resources
- Notion: Development Treasure Chest (Notion resource mentioned in the chat)
If you want the behind-the-scenes dev journey and live builds, check the community updates in the school. Drop questions in the comments and Parker may tackle them in future sessions.