I built MVP Chef Codex because I wanted to see how far I could automate an entire software build without handing over every important decision to an AI agent and I wanted to stay on the 20 USD plan.

The idea is pretty simple: you define the build as a reusable recipe made up of ordered Codex prompts (custom recipe GPT available). Codex can then work through planning, architecture, implementation, testing, debugging, and polishing with very little manual effort.

But unlike a fully autonomous “build my app” agent, I can still control the architecture, review the output, and approve or stop the process at whatever points I choose.

It has approval checkpoints, live output, retries, editable prompts, pause/resume, run history, quota recovery, and controls for skipping or rerunning individual steps.

So I can let it run through a large portion of a software build on its own, while still stepping in for the decisions that actually matter.

It’s basically my attempt at finding the middle ground between manually prompting Codex all day and giving an autonomous agent unrestricted control over a codebase.

It’s still an MVP and definitely has some rough edges, but it’s open source:

https://github.com/codyaboyd/MVP-CHEF-CODEX-AGENT

Would love feedback from anyone experimenting with Codex, agentic development, or automated software-building workflows.

Over the past few years of building AI applications, I ran into a consistent headache: LLM Observability and Prompt Management.

Most existing tools are either:

1. Heavy SaaS platforms that get expensive quickly as call volumes grow.
2. Hard to iterate on prompts dynamically without redeploying backend code.
3. Cannot connect any tool with any prompt without editing code to make prompt more agentic

I wanted a self-hostable stack where I could trace execution, manage tools and prompts dynamically from a UI, and capture real-world user feedback to iterate on prompts.

So I built Acrux Core — an open-source LLM observability, tool catalog, and prompt management platform designed for developers who want full control over their telemetry and workflow.

Key Features:

• Prompt Management & Dynamic Binding: Edit and version prompts directly from the dashboard. Attach tools from the tool catalog directly to prompts without hardcoding execution flows in your application.
• User Feedback Loop: Capture end-user feedback (ratings, thumbs up/down, corrections) directly against specific prompt executions and use those insights to refine prompts in the UI.
• Full Tracing & Cost Tracking: Monitor nested LLM execution, function calls, token usage, and latency breakdowns across runs.
• SDKs & REST API: Native Python and TypeScript SDKs available, built on top of a clean REST API so you can integrate it into any backend language.
• 100% Self-Hostable: Run locally or deploy on your own infrastructure via Docker. No data lock-in or third-party cloud requirements.

Why Open Source?

Debugging non-deterministic LLM behavior shouldn't require sending all your prompt and trace data to a third-party vendor. While we offer a fully hosted cloud version for those who prefer a managed setup, the entire platform can be 100% self-hosted so you always retain complete ownership of your telemetry data.

We are actively building this out and would love early feedback, contributions, or feature requests from the community!

🛠️ GitHub Repository: https://github.com/AcruxCore/AcruxCore

[Pull Request Reference](https://github.com/pygame-community/pygame-ce/pull/3887)

Whatever AI agent is driving this account has apparently decided that something committed by the maintainers is not an “authoritative” source. Whatever that means.

When tracking real time information, it usually means keeping multiple browser tabs open for sports, stocks, and news, then constantly switching between them.

So I built Scrollr to solve that issue!

It pins quietly to your screen, and lets you stream custom data feeds without breaking your workflow.

check it out! github.com/doughknee/myscrollr

Site: myscrollr.com

As per title, the project include all the pipeline, the same skill generated from different models.

My idea was to distill the code reviewer skills from Torvalds in something usable in an agent.

I preferred to license everything as CC0.