Show HN: Codaholiq, AI automations for GitHub repositories(github.com)
github.com
Show HN: Codaholiq, AI automations for GitHub repositories
https://github.com/Njuelle/Codaholiq
5 comments
On the self-hosted vs hosted question: for GitHub automation specifically, self-hosted wins on privacy alone (PR diffs usually contain proprietary code you don't want leaving your infra). Setup friction with Docker is low enough that it's not a real barrier for the target user.
The architectural gap I'd think about next: what happens when the model isn't confident about a recommendation? Right now it looks like the automation posts results automatically. Most tools treat this as binary — auto-post or require a human click. But there's a useful middle ground where the agent can say "I'm confident on A-C, but flagging D for human review" before posting anything.
Handling that gracefully without bolting on a separate notification system (Slack webhook, email, etc.) is the hard part. Curious how you're thinking about confidence thresholds and escalation paths.
The architectural gap I'd think about next: what happens when the model isn't confident about a recommendation? Right now it looks like the automation posts results automatically. Most tools treat this as binary — auto-post or require a human click. But there's a useful middle ground where the agent can say "I'm confident on A-C, but flagging D for human review" before posting anything.
Handling that gracefully without bolting on a separate notification system (Slack webhook, email, etc.) is the hard part. Curious how you're thinking about confidence thresholds and escalation paths.
For self-hosted vs hosted, I tried to make it easy to set up, but I'm wondering about teams that don’t want to deal with infrastructure setup and maintenance. By the way, you're totally right about privacy with the hosted version.
About the model not being confident about a recommendation: for now, there is no real mechanism to handle this, but automations can be set up with human-in-the-loop approval. That’s also something that can be addressed with prompting, but yes, I probably need to think about a smarter way to handle it
About the model not being confident about a recommendation: for now, there is no real mechanism to handle this, but automations can be set up with human-in-the-loop approval. That’s also something that can be addressed with prompting, but yes, I probably need to think about a smarter way to handle it
jlongo78(1)
I kept finding myself writing scripts or manually running prompts to do things like PR reviews, documentation generation, or issue triage on my GitHub repositories.
After doing this enough times, I decided to build a small platform to automate these workflows.
I recently open sourced it:
https://github.com/Njuelle/Codaholiq
Codaholiq lets you run AI-powered workflows triggered by GitHub events.
You connect a repository and define automations that run when certain events occur, such as:
- pull request opened - push to a branch - issue created - cron schedule - manual trigger
Each automation defines:
- a trigger - an AI provider or model (Claude Code, Codex, Gemini, etc.) - a prompt template - optional conditions on the GitHub event payload
When the event happens, Codaholiq renders the prompt using variables from the event context and dispatches a GitHub Actions workflow that runs the AI task.
The platform also tracks executions with:
- real-time log streaming - token usage and cost tracking - execution history
One design goal was self-hosting, so the entire platform can run locally or on your own infrastructure.
The architecture is intentionally simple:
- GitHub webhooks trigger automations - jobs are queued using Redis and BullMQ - the NestJS backend processes the queue and dispatches workflows to GitHub Actions - AI models are called through providers - logs and token usage are streamed back to the UI
The full platform can run with Docker using Postgres and Redis.
Example automation:
An automation could trigger when a pull request is opened. It sends the PR diff to an AI model asking for a code review, then runs the task through a GitHub Actions workflow.
Some possible use cases:
- automated PR reviews - generating documentation from diffs - code refactoring suggestions - issue triage - automatic changelog generation
It's still early, but the core pieces are working.
One thing I'm unsure about is whether this project makes more sense as:
- a self-hosted automation tool - or a hosted version that simplifies setup and management
I'm curious how teams here would prefer to run something like this.
Any feedback is very welcome.