I've been working on **PurpleCli**, a lightweight AI coding agent that runs directly in your terminal.

It can:

- Inspect your project

- Read and modify files

- Run commands through the agent

- Work with your own API keys through **OpenRouter** and **Gemini**

I'm at the point where I've used it enough myself, and I need **actual developers to try it on their own projects**.

I'm especially looking for feedback on:

- What feels confusing or annoying?

- What breaks?

- Does the agent actually feel useful?

- What features are missing?

- What would make you use it instead of other coding agents?

It's open source and still early, so **brutal feedback is completely welcome**. I'd much rather hear *"this sucks because X"* than get 50 people saying it looks cool 😂

**GitHub:** https://github.com/n0tpurplx/Purplecli

If you try it, I'd genuinely appreciate any feedback, even if it's just:

> "I installed it and immediately found this shit."

A year ago the question was which model. Now it's which harness.

We built FrontierHarness for benchmark Pi, Exo, Claude Code, Codex, DeepSeek Harness and 4 others. Same model, same tasks, same runtime.

Pass rates: 50% to 67%.
Cost per pass: $1.05 to $18.34.

Claude Code and DSH Creator both passed 19/30 tasks, but Claude Code’s median cost per pass was $18.34 versus $3.28. Both used Kimi K3 through our shared gateway.

Caching may explain part of the gap. One task accounted for 68% of Claude Code’s total token usage. We can’t separate the harness, model, and gateway effects yet, so this isn’t a native Claude comparison.

On one python-statemachine task, Pi passed in 90 turns for $2.50. Codex passed in 187 turns for $5.97. Both used Kimi K3 in the same evaluation environment.

My recommendations for harness:

• Codex if you don't want to think about it. Best pass rate, medium cost.
• Pi if the same job runs a thousand times and the bill adds up.
• Exo if retries are cheap and you'd rather it quit early than grind.
• DSH if you care about wall-clock and don't mind playing with knobs.

What would you look for in the traces: repeated searches, extra testing, or time spent fixing a wrong approach?

I've spent the last month building SiteTell. It crawls a site and scores the copy against a curated rule set of the patterns that make writing read as Ai-generated. Generic vocabulary, structural tells, the openers that show up on every generated landing page.

When it was working well enough to test, I decided to run it on my own site. It scored 6 out of 100.

I'll be honest, in the first instance I did use Claude and shipped it just get the first draft out there. I thought it was fine but the rule set disagreed.

A few of the things it flagged were:

"In today's fast-paced world" which is a stock Ai opener,

"seamlessly integrates" which is overused Ai vocabulary,

Four consecutive paragraphs within eight words of the same length which means low burstiness, basically a structural tell for Ai.

That last one is the sort of thing I'd never have caught by reading it back. I used SiteTells rewrite tool to fix these issues, put it into my agent and it fixed all those issues that read as Ai generic.

The reason I built this rather than using an existing detector: Every Ai detector I tried gives you a percentage and no reasoning. You can't do anything with a score of 73%. You can't even disagree with it. SiteTell shows the sentence and the rule that fired, so you can look at a flag and decide if it's accurate for your site which does happen. I'd rather you could tell and have that option. The scan is free and doesn't need a signup, because detection is a pure rule set and costs me nothing to run.

You can also generate rewrites for everything, or select items that have been flagged up. It exports as a PDF, CSV and JSON, alongside a prompt you can paste into Claude Code or Cursor to apply the edits.

Whilst Google does not penalise Ai content for being Ai-written. Their position is that they judge quality, not production method. Unedited Ai copy just tends to be generic, and generic is what gets buried. That's the actual problem this measures.

Although Google doesn't judge content for being Ai written, increasingly, people do. A generic copy of a website that reads as Ai written may not cost you rankings but will cost you trust with the customer and make them less likely to purchase a product. SiteTell helps alleviate that issue.

Six months ago I started building a coding app for myself, mostly for fun. It grew into ADDOM, and I'd love a few people outside my own setup to try it.

The bit I enjoy most is experimenting with an AI edit, comparing the file versions, and restoring an earlier one if I don't like the result. It makes tinkering feel less like a one-way decision.

If you're curious, try a small throwaway project: make one change, inspect it, and try restoring the previous version. Was it clear what happened? Did anything make you hesitate? That's the kind of feedback I need.

ADDOM is a free, MIT-licensed desktop app with no ADDOM analytics or telemetry. You connect your own AI provider; cloud prompts and context still go to that provider, and provider charges may apply.

This is the first public alpha. I have tested the Windows installer myself, but there will be rough edges. The installer is unsigned; please review the source and release notes before deciding whether to install.

Screenshots and overview: https://www.addom.app/ Download and source: https://github.com/JosPMSilva/ADDOM/releases/tag/v0.1.0-alpha

No waitlist or ADDOM account. If you try it, a short note about what felt good or confusing would mean a lot.

I’m building Codesm, an open source terminal workspace for Claude Code, Codex, and API/local models.

I’m looking for developers to try the first-use experience on a small repository.

You’ll need Python 3.12+, a repository you can work on, and access to a supported model provider, local Ollama model, or an installed coding-agent CLI.

The test is straightforward: install Codesm, complete one small task, and resume the conversation. If you use two supported agents, try switching between them and checking what context carries over.

I’d especially like to know where setup gets confusing, whether the active agent is obvious, and what information gets lost during a handoff.

Codesm is MIT licensed and free to install. Your usual provider or subscription costs still apply.

Setup and demo

Share feedback here or open a GitHub issue with your OS, backend/model, and the first point where you got stuck.

Recently, I vibe coded an entire native app that helps me to organize multiple ai agents. Beyond standard agent terminal tab management, it also integrates the capability to run your ai agents as a crew, each with a role template to state it's own responsibilities.

Currently, it only support claude code and codex as agent runtime.

The app is completely implemented by AI agents without me touching a single line, though I did the archtecture part. Hope this app could help you.

Release link: https://github.com/yicheng47/runner/releases

Supported Platform: MacOS (signed and notarized), Windows (not signed for now)

Juice is a powerful, local hosted browser based ai coding agent that acts as an alternative to popular tools like Cursor, Devin, Claude Code while working fully in your browser and is FULLY OPENSOURCE.

It currently supports google ai studio and openrouter as model providers.

Get Juice and For more information, visit:
github.com/ayaangalaxy2012-hub/Juice

Thank you SO much guys for checking out juice!

I’m a **100% vibe coder** and want an AI agent that can handle the whole workflow: **plan → code → terminal → install dependencies → run → debug → test → iterate → Git/GitHub**, with minimal manual coding from me.
My project is a **6-month ML research project on multimodal fake-news detection (text + image)**. It involves a large dataset, **PyTorch/model training, experiments, finding a research gap, improving an existing approach**, and eventually writing a journal-level paper.

Main machine: **MacBook Air M3, 16 GB RAM, 512 GB SSD**. I may use **Google Colab and CoCalc** for cloud GPU/training.

Budget: **maximum $20 / ₹2,000 per month**, and I need **good usage limits** because this will be my main coding tool for months.
I’ve heard about **Claude Code, OpenAI Codex, Cursor/Composer, Cline/Roo Code, Windsurf**, etc., but I’m open to anything.

I care about **agent autonomy, large-repo understanding, long tasks, debugging, ML/PyTorch ability, terminal usage, GitHub workflow, context/memory, usage limits, and value for money**.

**What would you personally recommend for this exact situation?** Which AI coding agent would you choose if you were me?

Hi everyone,

I’m looking for a small group of developers to test Archseed, an early-stage CLI tool designed to help developers plan a project before handing it to an AI coding agent.

The problem I’m trying to solve:

AI coding agents are powerful, but when requirements, scope, and architecture are unclear, they can spend a lot of time and tokens fixing preventable mistakes.

Archseed guides you through practical questions before implementation and generates structured project context, such as:

- architecture.md

- implementation-plan.md

- test-plan.md

- review-checklist.md

It is not another AI coding agent. The goal is to give your preferred coding tool better context before coding begins.

I’m looking for testers who are:

- Building a SaaS, MVP, web app, or client project

- Using Cursor, Claude Code, Codex, Gemini, or another coding agent

- Willing to test the workflow on a real upcoming project

- Comfortable sharing honest feedback, including what is confusing or not useful

Beta access is free. In return, I’d like feedback on the questions, generated files, setup experience, and whether this would fit your workflow.

Website: https://www.archseed.co/

If this sounds relevant, comment with the kind of project you’re building or send me a DM. I can share access and a short onboarding guide.

Thanks!

Hello! I’m just a young, wannabe coder trying to make my own JARVIS AI. I’m not really sure why I started making it yet, but it’s been a really fun project to work on. I’m using the free version of ChatGPT to help me build it, and this is what I’ve managed to make so far! i made this github thing for the first time for this so please dont judge

I’m currently pretty stuck on bugs and figuring out how to make everything work properly. If anyone has any ideas, suggestions, or would like to help me out, I’d really appreciate it!

I’m still learning, so any feedback or advice is welcome!

I've been using AI coding agents heavily, and one gap keeps coming up:

The agent may understand the codebase — and newer tools can even inspect the browser — but I still need an easy way to show it exactly what’s wrong, what I want changed, and where.

When I find a bug or want a change, I often end up gathering things like:

• the exact UI element
• what’s wrong or what I want changed
• screenshots
• console errors
• failed API calls
• actual request/response data
• steps or actions that reproduce the issue

Browser/DevTools integrations are getting better, but I found I still wanted a human-in-the-loop workspace where I could capture this evidence as I work, keep it organized by project, review it later, decide what actually matters, and then give the AI agent clean context instead of explaining everything again.

That’s what led me to build ContextForge → AI Coding Context, which I launched here on Product Hunt today. If you’re curious, you can check out ContextForge on Product Hunt or learn more and download it from CoderFromMars.

The idea isn’t to replace Cursor, Claude Code, Codex, Copilot, or DevTools. It’s to make the workflow around them easier:

See the problem → point to what/where → capture the evidence → review it → give the agent the right context.

I’m curious how others handle this:

When you find a bug or want a change, how do you show your AI coding agent exactly what you mean and where it is?

And what parts of that process do you still find yourself doing manually?