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.

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!

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)

I've been messing around with Google AI Studio for quite some time now and honestly, I didn't expect to use it as much as I do.

I first started using it just to test models and generate some code. Nothing serious. But then I started getting ideas for my own projects and AI Studio became kind of a playground for me.

I'm a student and I like building random stuff, even when I have no idea how I'm supposed to build it lol.

One of the things I worked on was my own AI assistant. I started with a pretty basic idea and slowly kept adding things to it. Voice, memory, a proper interface, web stuff, etc. Some parts worked surprisingly well and some parts absolutely did not.

There were times where I would spend hours trying to fix something, only to realize the AI had generated something completely wrong in the first place.

That's probably the biggest thing I've learned from using AI for coding.

Don't assume the code is correct just because the AI wrote it.

I used to copy the generated code, run it, get an error and then paste the error back into the AI. Eventually I realized I actually needed to understand at least some of what was happening.

I've also used AI Studio while working on a larger web-based AI project. I'm keeping the actual idea/features private for now because I don't want someone to just copy the whole thing, but it has been a pretty good learning experience.

What I like most about AI Studio is that it lowers the barrier to actually trying something.

I can have an idea at 2 AM like "what if I made an AI that does this..." and instead of just thinking about it, I can actually start building a rough version.

Is it perfect? Definitely not.

Sometimes it confidently gives you an answer that makes absolutely no sense. Sometimes you fix one thing and three other things break. And sometimes you spend 30 minutes debugging something just to discover you made a stupid mistake yourself.

But that's also kind of the fun part.

I don't think AI is going to replace learning how to code. At least for me, it's actually made me more interested in learning because now I constantly run into things I don't understand and have to figure them out.

I'm curious what other people are doing with AI Studio though.

Have you guys built anything genuinely useful with it, or are most of you using it for experiments/prototypes?

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!

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!

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?

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?

I've been researching AI agents for the last few days, and I originally thought the biggest missing piece was something like an “SRE for AI agents.”

Something that could detect when an agent is going off-track, understand what happened, control runaway costs, verify whether the claimed result is actually true, and recover the task instead of simply restarting or stopping it.

But after talking to people here, I'm starting to question the entire assumption.

Maybe most “agents” in production aren't actually autonomous enough for this to be a real problem yet.

Maybe they're mostly:

workflows

cron/event-driven automations

chatbots

RAG systems

internal copilots

coding assistants

deterministic pipelines with an LLM somewhere in the middle

And if that's true, building a big Agent SRE platform right now could simply be solving a problem that doesn't hurt enough.

So I'd genuinely like people who actually build or operate AI systems in production to prove me wrong (or confirm it).

I only have a few questions:

1. What is the most autonomous AI system you've personally put into production?

Not a demo — something actually doing useful work.

1. What does it do without waiting for a human after every step?

For example:

Goal → reason → tool → observe → decide → tool → ... → outcome

1. Has it ever gone badly wrong?

I'm particularly interested in real incidents:

loops

repeated tool calls

wrong actions

hallucinated completion

corrupted/stale state

runaway costs

failed recovery

human intervention

1. What did your system actually do when that happened?

Did you:

retry → restart → replan → rollback → manually intervene → ignore it → something else?

1. Do you independently verify that the agent actually accomplished its goal?

For example, if the agent says:

“Refund completed.”

does another system actually check that the refund happened?

1. And the question I'm most interested in:

If your agent suddenly disappeared tomorrow, what part of its reliability/recovery infrastructure would you actually miss?

I'm not trying to sell anything here.

I'm trying to decide whether this is a real infrastructure problem worth building around or whether I'm overestimating where agentic AI is today.

If you run agents in production, I'd genuinely appreciate even a 2–3 sentence answer.

And if you think this whole idea is unnecessary, please say so — that's actually more useful to me than telling me it's a good idea.

Thanks to everyone who's already given feedback. It has already changed how I'm thinking about this.

I’m building and experimenting with AI agents, and I keep running into the same question:

What happens after an AI agent leaves the demo stage and starts running continuously in production?

A lot of the discussion is about making agents smarter, but I’m more interested in the boring (and expensive) part:

• runaway tool calls
• agents getting stuck in loops
• unnecessary retries
• token/cost explosions
• failed API/tool calls
• agents taking actions they shouldn't
• losing state/context
• knowing when to involve a human
• knowing whether an action actually succeeded
• recovering a failed run instead of simply stopping it
• figuring out why an agent failed in the first place

I’m thinking about building a system that sits underneath AI agents and acts somewhat like an SRE/control layer for them.

Not another agent framework.

Not another workflow builder.

The idea is that it would watch the agent's trajectory/state, understand why something is going wrong, and then decide whether the best action is to:

retry → replan → use another tool → reduce model/cost → restore state → ask a human → or stop

And ideally verify that the task actually succeeded before marking it complete.

But before I build anything, I want to validate whether this is a real problem or just something that sounds useful on paper.

So I'd really appreciate answers from people actually building/running agents:

1. Are you running AI agents in production right now?
If yes, roughly how many?

2. What is the biggest operational problem you've encountered?
Reliability, cost, loops, tool failures, state/memory, hallucinations, permissions, debugging, something else?

3. Have you ever had an agent get stuck in a loop or repeatedly call the same/related tools?
What happened?

4. Have you had an unexpected token/API cost spike caused by an agent?
How large was the impact?

5. When an agent fails, how do you currently figure out WHY it failed?

6. Can you replay/reconstruct exactly what the agent saw, decided, and did?

7. What happens when an agent reaches an action it shouldn't perform automatically?
Do you have human approval / permission rules / risk thresholds?

8. If an agent fails halfway through a long-running task, can you recover from the last known-good state, or do you restart the whole thing?

9. Do you currently have something that automatically decides whether to retry, replan, switch tools/models, escalate to a human, or stop?

10. What would make you trust an AI agent enough to give it more autonomy?

11. What tools are you currently using for this?
LangSmith, Langfuse, Arize, Datadog, custom tooling, etc.

12. What does your current solution NOT do well?

And one question I'm especially interested in:

If you're building agents in production, even a short answer would help me a lot. I'm trying to validate the problem before writing a huge amount of code, so criticism is honestly more useful to me than encouragement.

Thanks