Show Notes
Parker shares real-world progress on self-hosted AI workflows, how to learn faster with AI, and practical bets on vibe coding/marketing to stay ahead in a fast-moving space. Straight to the point, no fluff.
Self-hosting journey and learning with AI
- He’s self-hosting dozens of containers, re-learning DevOps, and recording the process for transparency.
- Key approach: use AI to learn the fundamentals without losing time to “hand-holding” tutorials.
- Practical takeaway: mix codebase, docs, and an AI companion to deepen understanding as you build.
Prompting patterns that actually teach and work
- A simple, repeatable prompt structure helps you learn faster:
- Who you are (identity and capability)
- The concept you’re learning (task)
- Context (your codebase, docs, etc.)
- Additional context (constraints, goals)
- Output format (explain, diagram, step-by-step)
- Example learning flow:
- Define concept and pull in docs
- Find an open-source project on GitHub (sorted by popularity)
- Ask the AI to explain using ASCI charts or other clear formats
- Iterate with code questions and concrete questions (e.g., what is a pooler tenant ID?)
Level-up learning flow for real projects
- Level 1: learn basics with a guided prompt, add documentation, and a small codebase.
- Level 2: pick an open-source project and study in the wild (thousands of examples exist).
- Use a GPT companion to answer questions, while you have docs open and the codebase in view.
Agents: what they are and why they matter
- An agent = an LLM with access to tools that can perform real work.
- The leverage isn’t “drags and drops”—it’s prompting the agent to do valuable, money-saving tasks.
- Actionable mindset: start with manual tasks, then hand them to agents to scale, always measuring impact (time saved, money saved, revenue gained).
The 10-man agent concept and node-based vision
- Core idea: agents are built from modular nodes (tools, data stores, APIs) that connect into workflows.
- A node can represent a codebase, a marketing process, or a automation scenario (e.g., “crawl top ads,” “train image trainer,” “run ads”).
- End goal: a node-based editor where you orchestrate multi-step processes across code, data, and marketing actions.
- Practical example: a marketing node chain that crawls top-performing ads, downloads assets, trains a fine-tuned model, and then launches a paid media workflow.
Vibe Marketing: marketing orchestration at scale
- Concept: turning a core piece of pillar content into multi-channel campaigns (email, LinkedIn, Twitter, TikTok, etc.) using automated orchestration.
- Use cases:
- Content pipeline: generate, summarize, and repurpose content across channels.
- Paid media: automate crowding ads from top-performing creatives, with a trainer to refine outputs.
- SEO and on-page work: set up pipelines for content, metadata, and knowledge graphs.
- Customer success automation: personalized follow-ups and NPS-style feedback loops.
- Strategy tip: contracts should be high-value (aim for 10K+ per deal). Qualify aggressively to maximize long-term value and leverage.
Self-hosting stack and practical setup
- Current plan: Supabase full stack with Postgres (vector capabilities via PG vector), optional external vector stores.
- Cloud options: Cloud Run on Google Cloud, with a plan to leverage Google stack for scalability and speed.
- Considerations:
- Do you want real-time capabilities? Google stack and Elixir-backed services can help.
- Start with a local/remote hybrid (self-hosted + cloud) to balance control and reliability.
Quick market signals and trends (news vibe)
- Gemini/Google edge: Google’s chip and AI R&D advantage is shaping outcomes; Grock’s API release timing may shift with Gemini’s advances.
- OpenAI image gen momentum: high-quality image generation is becoming mainstream; expect more visual content in marketing.
- Open takes on platforms: search and data ownership matter; the “blue links” revenue model isn’t going away, but the tech stack to win is evolving.
- Personal experiment note: Netcup showed the friction of hosting—Google stack is the preferred path for reliability and scale.
Practical prompts and tips you can use today
- Learning prompt pattern (template):
- You are [persona]. Your task is [learning objective]. Context: [codebase/docs]. Additional context: [constraints]. Output: [ASCI charts, bullet summary, step-by-step].
- Rubber duck prompt idea (quick boost for life and work prompts): ask for a blunt, first-principles critique of your plan and then reconcile with your context.
- Use prompts to push for context and missing edges: “What am I missing if I don’t have X in place?” Then fill the gap before proceeding.
Build queue and current setup
- Basic: Supabase full-stack with Postgres, vector capabilities, and a possible Cloud Run deployment.
- Integration ideas:
- Node-based agents to manage workflows
- A content/ad training pipeline (image trainer, ad runner)
- A cheap, scalable hosting path with Google Cloud services when ready
- Long-term: a “vibe code”/“vibe marketing” engine that orchestrates content, ads, and customer success with a human-in-the-loop safety net.
Strategy: qualification, leverage, and pricing
- Don’t chase every deal; qualify for long-term value and leverage potential growth.
- Prioritize high-ticket engagements (10K+ contracts) to maximize ROI and reduce churn.
- Build processes that scale: automated qualification, clear success metrics, and a path to higher-value work.
Links
- Gemini 2.5 Pro (Google)
- Grok API and Grok 3 timing
- OpenAI image generation
- OpenAI / Claude alternatives (context: AI agents and prompts)
- Perplexity AI agents and agent workflows
- Vertex AI / Model Garden (Google)
- Supabase (full-stack backend)
- Postgres with vector capabilities (PG Vector)
- Cloud Run (Google Cloud)
- Netcup hosting
- n8n (workflow automation)
If you found this helpful, drop your questions in the comments and I’ll tackle them in the next update.