CHNAI LAB is a Cambodian student-run product studio. We build owned software products so students can earn real product responsibility, operating experience, and equity instead of only class-project practice.
We are intentionally AI-native: agents help us research, build, test, document, and review, while humans stay accountable for product judgment, security, verification, and final decisions.
Most active repositories are private because they are future startup assets. This public organization profile exists to explain how we work, what we are building, and how to read our public GitHub surface without exposing customer data, source code, credentials, or strategy.
| Track | Direction | Current boundary |
|---|---|---|
| BayonHub | Opportunity infrastructure for Cambodian tech students looking for internships, freelance work, jobs, and portfolio-building paths. | Private product repo; public case-study material only. |
| Svaeng Yul | Khmer self-study video learning for AI engineering, data science, cybersecurity, software engineering, and related tech fields. | Private learning platform repo; public positioning and selected screenshots only. |
| Chomkar | B2B agriculture coordination between buyers, farmers, and cooperatives before harvest demand is locked in. | Private product repo; collaborator access is managed through the org. |
| Sat Digital | Cyber guard tooling for Telegram communities, groups, and websites. | Private product repo; security-sensitive implementation stays private. |
| Vantrex | Trading software for signals, model-assisted tooling, and indicator workflows. | Private product repo; financial claims are kept conservative until validated. |
| PHSAROS | SME operating system for restaurants, coffee shops, marts, staff, inventory, and finance. | Private product repo; operating workflows are documented internally. |
- Build narrow, working products before scaling claims.
- Keep source code, credentials, user data, financial logic, and strategy private by default.
- Use issues, branches, pull requests, and reviewable verification for team work.
- Treat AI-generated code as draft work until a human verifies behavior, security, and product fit.
- Document what is shipped, what is simulated, and what still needs validation.
- Give students real ownership, accountability, and operating discipline.
mainstays deployable.- Every meaningful change traces to an issue and pull request.
- Pull requests record human verification, AI involvement, risk, and rollback.
- Agents receive context, never secrets.
- Security-sensitive work stays private and follows the org security policy.
AGENTS.md— default rules for AI agents in CHNAI LAB repos.docs/AI_AGENT_WORKFLOW.md— team workflow for humans and agents working together.CONTRIBUTING.md— branch, PR, review, and access rules.SECURITY.md— public/private reporting boundary.
CHNAI LAB currently has seven members across cybersecurity, infrastructure, software engineering, computer science, and business.
If a repository is public, it should be safe for a recruiter, partner, student, or automated reviewer to inspect. Public content should show our standards, direction, and proof of work without overstating traction or leaking startup assets.
