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Sprime
Sprime

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Build log: Making product onboarding actually engaging (codename: Path)

#buildinpublic #ai #python #learning

Three experiences, one pattern:

1. Path of Exile - Complex game, overwhelming onboarding despite extensive wikis

2. Delta Force - Coming from COD's DMZ (tutorials everywhere) to this (figure it out yourself)

3. Aden - Got invited to contribute to their AI agent platform (github.com/adenhq/hive). Message said: "Star, Fork, Watch the repo, create issues, submit PRs. Join Discord for questions."

Good docs. Still had to piece together my own path through Discord → GitHub → issues → PRs → contribution patterns.

My background:

  • Researched learning extensively
  • Built my own learning framework
  • Tried building 2 learning startups
  • Managed open-source maintainer communities

Even with that, I found these onboarding processes challenging.

The moment it clicked:

Found a YouTube tutorial on Path of Exile (https://youtu.be/ZWLpP-Z6sCM?si=0L3KN0ntxYUqmdOq) that simplified it so well, I was eager to try the game again.

Realisation: Good learning doesn't just inform. It makes you want to engage.

The pattern:

Bad onboarding experiences + learning research background + startup attempts + proof that good learning works = something worth building.

What I'm building:

An AI agent (codename: Path, from Path of Exile - fit well looking backwards) that:

  1. Listens to knowledge sources (GitHub, Discord, Twitter, YouTube, wikis)
  2. Processes through AI
  3. Generates structured learning content that drives engagement
  4. Delivers where people are (Discord bots, Telegram, learning hubs)

Architecture: 

Knowledge Sources → Adapters → AI Processing → Formatters → Distribution
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Layer 1 - The Ear:

  • Adapters for each source (GitHub, Twitter, etc.)
  • Converters that standardise the data
  • Philosophy: Keep only what helps create engaging learning content

Layer 2 - The Brain:

  • AI processing (Gemini free tier initially)
  • Extract learning-relevant info
  • Remove jargon, add context
  • Structure for engagement, not just information

Layer 3 - The Voice:

  • Formatters for each channel
  • Discord bots, Telegram messages, learning hub content
  • Optimised for how people actually learn

MVP scope: 

# Input: GitHub webhooks only
# Processing: Gemini 1.5 Flash (free tier)
# Output: Discord bot only
# Stack: Python, Flask, discord.py, Supabase

# All free tier
# All documented
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Why I'm building this publicly:

Looking for work. My career is product management + community management + product engineering + learning research. Varied companies, varied contexts.

Hard to explain on a CV. Easier to build something: "Here's the code. Here's what I made. Here's how I think."

Contributing:

Not recruiting, but if you want to build your portfolio publicly too, you can contribute.

I have a registered company (Nigeria), so it's verifiable work experience.

If it makes money eventually, everyone shares revenue. But the goal is portfolios and getting hired.

What I'm documenting:

  • Setup and architecture decisions
  • Code walkthroughs
  • Weekly progress
  • Problems I hit
  • What's working, what isn't

Just building and sharing the process.

Current status:

  • Setting up repo structure
  • Writing contributing docs
  • Building first GitHub webhook listener
  • Basic Gemini integration

Next:

  • Complete MVP (GitHub → AI → Discord)
  • Expand sources (Twitter, YouTube, Slack)
  • Expand channels (Telegram, learning hubs)

Links:

GitHub: [will update when ready]

Discord: [will update when ready]

If you've worked on similar problems or have thoughts on learning systems, would be interested to hear.

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