Skip to content
YasuuiPublic

About

Config files for my GitHub profile.

Topics

Resources

Stars

1 star

Watchers

1 watching

Forks

Latest commit

 

History

12 Commits

Folders and files

NameName
Last commit message
Last commit date
 
 
 
 
 
 

Repository files navigation

Yonis Diriye

AI automation developer in Toronto. I build AI products for small businesses, and the agent workflow I use to ship them, with tests that prove they're safe to put in front of real customers.

diriye.ca · LinkedIn · Book a call

Now

  • Lintel (private): an AI front desk for GTA repair shops. It answers missed calls, quotes from the shop's own price list, books drop-offs and texts the owner. Piloting with two shops; a public case study follows once there are real numbers.
  • Barakah Rewards: co-founder of a halal loyalty platform for Muslim-owned GTA merchants, live on iOS and Android. I'm building the owner dashboard.
  • Canada Revenue Agency: AI automation developer through the Federal Student Work Experience Program.

Public work

Project What it does Status
Workflow Friction Mapper A chat agent that reads a workflow you share and returns a grounded brief: friction, bottlenecks and one first move. Live, open source
HarnessGrade Scores the instruction files around a coding agent (AGENTS.md, Cursor rules, skills) on a deterministic rubric. Scoring library; CLI and per-rule ablation in progress
Hermes Community Skills A provenance-aware registry for finding and checking community agent skills. Live
Hermes Mobile The bridge from my retired multi-agent setup. Retired; why I stopped

How I build with agents

Codex and Claude Code write most of my code. These rules are what make that shippable:

  • Cross-model review. Codex reviews Claude's pull requests and Claude reviews Codex's. Nothing merges without passing CI and an independent review.
  • Hooks, not hope. Agents can't push to main, force-push or write env files; the hooks block it and the rules are unit tested.
  • Code owns the money. A model never sets a price or computes tax. Every model output is schema-validated before use, and customer text is treated as untrusted input.
  • Tenant isolation in the database. Row-level security on every table, tested with two tenants.
  • Every model call is metered. Tokens, cost, latency and model id go to a ledger.
  • Plan, then build. One plan per slice, small PRs, and a NOW.md that separates what's proven from what isn't.

Lab

Experiments and build notes, kept separate from finished work: the lab.

About

Config files for my GitHub profile.

Topics

Resources

Stars

1 star

Watchers

1 watching

Forks

Releases

Packages

Contributors