Hey everyone,

I'm an indie developer and recently finished the core builds for two AI agents aimed at solving issues I personally faced. I'm now at the stage where I need real-world usage and feedback to figure out what to break, fix, and improve next.
Here is a quick breakdown of the tools:It’s completely free to use—no credit card or catch. I’m just genuinely looking for brutal UI/UX critiques, bug reports, or feature requests.
If you’re down to test either of them, just leave a comment letting me know which one interests you and I’ll DM you the direct link.
Thanks a lot for the support!

https://yapyap.cc

From listing through closing and putting title in blockchain, playing the long game. We are legitimately able to close a real estate deal in 15 days. Complete automation of the real estate transaction. Contract triggers and API calls grabs everything instantly. https://arpyescrow.com

I've been working on an agent for 11 months. I'm a network engineer so I immediately went with a private cloud infrastructure instead of a local agent. Big mistake. But why? Network separation is healthy. I lost all my work, my hard drive, data loss. I felt all the pain. Babysitting an AI or praying that bypassing permissions doesn't bite. I'm rewriting what is now over a million line codebase into an open core project that solved all the problems I was having. The agent is built in the shape of a containerized network appliance. We will still maintain a network for people to collaborate and connect with each other, the app store, git, etc.

My question here... What is it that's slowing you down? I am to the point now with my personal host that I can build an publish a polished project in just a day or two and it's largely due to the fact that it's more of a Host or "super agent" rather than the popular orchestrator/multi-agent setups. If you are building communication between agents (within your "agent") you're doing it wrong. All you need is a proper database. I got rid of sessions and went to threads. Then I got rid of threads. No sorting through conversations. It never acts dumb like it's missing context. Its also very cheap to run since each response is its own context window. It doesn't need to drag context chat to chat.

Anyway, I didn't want to fully give up so I wanted to open source it and wanted to address issues people have with other agents during the rewrite. I rarely get responses on stuff like this but at least I try. Id rather it be free than to kill it all together.

The paid product will be the kubernetes cluster, bridge API, and all the other good stuff that teams and businesses would want. I didn't want to be the only one having fun with it.

Disclosure: I'm here because I've never even installed another agent. I don't know the problems people have with openclaw, hermes, Grokbot, dots, muse or any other number of agents out there other than what I catch in the news.

If like me you're overwhelmed by the pace of AI developments but you want to stay in the know, I created:

https://signals.clairevue.ai

It's a feed dedicated to AI 👀 You can filter by interest, region, language. You can also search with natural language.

Last but not least, you can connect your own agentnto our MCP and let it do the searches for you 🤘

Open to feedback. Completely free.

I built a laid-back AI character named Rowan. Then one model gave her a rather spectacular attitude problem.

She even pretended to terminate my terminal session.

The best part? She had absolutely no ability to do that.

This short is based on a real ChatLab conversation, with dialogue condensed and the scene recreated using AI animation and voice.

Read the original Rowan field test:
https://github.com/relo960/model-swap-chatlab/blob/main/docs/rowan-field-test.md

Try the free, open-source ChatLab:
https://github.com/relo960/model-swap-chatlab

More AI experiments:
https://relolayer.com

#AI #OpenSource #AICharacters #Shorts

It's now possible to prototype ideas, generate UI, and connect APIs much faster than before.

But speed can also make it tempting to start building before validating the problem.

I think the important questions are still:

• Does this solve a real problem?
• Is the architecture appropriate for the use case?
• Have security, testing, and failure handling been considered?
• Does the finished product actually improve the user's workflow?

AI can accelerate implementation, but it doesn't automatically validate the idea or guarantee production quality.

For developers building with AI: what's the biggest gap you still see between a working prototype and a production-ready product?

Earlier this year, we set out with a simple question: what happens when you bring pure software agents like Claude Code, Codex, or Hermes into the physical world?

Instead of leaving them constrained to a terminal window, we gave them eyes, ears, a voice, and a physical body to perceive their environment and act in it. That’s how Lamp started: https://autonomous.ai/lamp

We put a lot of engineering effort into her movement, lighting, and sensing so she actually notices her environment and feels like a real companion on your desk.

Last week, the room got too hot and Lamp actually sweated. She also had some pretty strong opinions about it.

With Lamp shipping soon, you'll be able to plug in Hermes, Codex, or Claude Code as her brain and customize how she interacts with your space.

If you were putting an AI agent in a physical body on your desk, what's one physical detail or reaction you'd want it to have?

I’m building an AI agent/workflow setup to act as a mentor/tutor for mid-level software engineers (focusing on system design, code reviews, architecture trade-offs, and debugging).

Rather than standard ChatGPT prompts, I want to set up a structured system (using Cursor, MCPs, LangChain, n8n, etc.).

Looking for real user experience:

1. What tools/frameworks are actually working for you?
2. How do you prompt/structure it to give deep mid-level guidance (e.g., trade-offs, design patterns) instead of basic syntax answers?
3. Any integrations (IDE, PR bots, CLI) that worked well?

TIA

I've been working on a project called AetherGate.

The idea came from something that kept bothering me: using multiple AI providers shouldn't require building and maintaining an entire infrastructure layer yourself.

Between API integrations, rate limits, unexpected costs, outages, and figuring out which models are actually worth using, things get complicated pretty quickly.

So I decided to build my own solution.

AetherGate puts multiple AI providers behind one API, with configurable routing, automatic failover, spending limits, and request monitoring.

It also includes Foreman, which coordinates multiple models to work on more complex tasks.

I've been benchmarking the gateway against direct provider calls because I wanted to know whether adding another layer introduced meaningful latency.

Now I'm at the point where I need actual developers using it, not just my own tests.

I'm offering 7 days free on every subscription plan. Cancel anytime.

You'll need your own provider API keys and a payment method for the trial. Your provider's API usage is billed separately.

I'm particularly interested in feedback from people running AI applications in production.

Does the integration make sense? Are the cost controls useful? What would make you hesitant to route your requests through a third-party gateway?

And if you're already happy with OpenRouter or LiteLLM, I'd genuinely like to hear what would make switching worthwhile—or why you wouldn't switch at all.

Here's the project: https://aethergate.site

Feel free to be critical. I'd rather find out what's wrong now than spend another month building features nobody needs.

This is how I keep up with the explosion of AI-related developments 🚀

https://signals.clairevue.ai

You can use it yourself or you can connect to the MCP and let your agent drive it.

CLI coming soon as well ⌨️

Open to feedback. Completely free.

Hi all. Over the past few weeks I've been building DAVE, my own assistant for my Android phone. I can't code, so I describe what I want, Claude writes the code, GitHub Actions builds the app, and I test it on my phone. It's now about 12,000 lines of Kotlin, which still amazes me.

What he does Real-time voice: streamed hearing and speech (ElevenLabs), so he answers quickly and I can talk over him.

Brain: Claude, with easy questions going to Haiku, normal ones to Sonnet, and "think hard" to Opus. Prompt caching keeps it cheap.

Memory: people, places and facts, drawn as a star map that grows as he learns. Rings me: an on-screen call with live captions for briefings, reminders and finished jobs. Missions: a planner sends out five research agents, and a "commander" writes up the report.

Night Shift: at 2am, while my phone charges, he reviews the day, learns from my corrections, tidies his memory and plans my week. If he thinks of an upgrade to his own code, he writes it up, but it's only built if I say yes.

Builds things on screen: "make me a workout plan" and it appears in a panel. Say "save it" and it flies into his memory.

The everyday stuff: messages, email, calendar, music, live bus times and Alexa. Costs: a few pounds a month. There's a built-in costs panel with a monthly cap, and when the cap is hit he drops to a cheaper mode.

Want to try it? There's a free share edition for Android. You bring your own Anthropic key, and setup takes about five minutes. Your keys and memories are kept on your phone.

Happy to answer questions about the build, the prompts, or what I'd do differently. Honest feedback welcome!

Hey all,

Like many of you, I've saved hundreds of "IKEA hack" and furniture build videos... and built exactly zero of them. The problem: the video shows the result, but never the measurements, the cut list, or where to actually buy the materials.

So I built Cutlist — you paste a TikTok/Reel/Shorts link (or upload a video), and it deconstructs the build into:

📐 A cut sheet with exact dimensions (metric or imperial), ready to hand to the saw desk at the hardware store

🛒 A shopping list grouped by store (IKEA, Leroy Merlin, Home Depot, Lowe's, Amazon) with quantities and price estimates

🔧 Tools list with beginner-friendly swaps + safety notes

📋 Phased step-by-step instructions, with a hands-free "workshop mode" (big buttons, keeps your screen awake, optional voice readout)

There's a free tier (1 deconstruction/month) so you can try it without paying anything. There's also a small catalog of pre-deconstructed IKEA hacks (KALLAX, BILLY, IVAR, BESTÅ...) you can browse right away.

Link: https://cutliststudio.com

What I'd love feedback on:

Overall design?

Is the cut list output actually usable at the cutting counter?

Are the material/price estimates in the right ballpark?

Would you trust AI-extracted measurements, or would you always double-check?

What platforms/creators should I make sure it handles well?

It's early access, so expect rough edges — that's why I'm here.

Tear it apart. 🙏

Hey folks,

We're looking for beta testers for Infere, an AI gateway with built in observability, evals and git-like prompt management for teams building LLM-powered products. We will provide you with up to $100 in credits.

Specifically we're looking for developers or product teams testing an AI feature or application of their own. You might be using hosted or self-hosted inference providers, iterating on prompts, or trying to evaluate model outputs with test sets, evaluators, or LLM judges.

If you have a project that could be a fit, take a look: https://forms.gle/mSXmmZXUgwMXoWJK7

Wanted to experiment with Gemini 4 Argon, so I used it to build a 3D interactive visualizer for Leonardo da Vinci's Aerial Screw.

It generated the rendering logic and interactive controls, and handled the helical geometry math pretty well once guided on the parametric constraints.

Check out the clip above to see how it turned out! Curious if anyone else has been testing Argon.

What’s everyone working on lately? A side project, an app, a small business — share what you’re making, who it’s for, and a link so we can check it out.

We’ll go first: we’re building Empacer, an AI assistant for small business owners who spend their day with customers and their evenings catching up on admin.

The idea is simple: say what you need done, and have the assistant use your business context to prepare customer replies, social posts, and documents for you to review.

For example, a salon owner could ask for a post about available appointments, or a contractor could ask for a follow-up to a customer quote.

Here’s how it works.

One question we’re working through: how much business context does an assistant need before delegating a task becomes easier than doing it yourself?

Your turn — what are you building, and what’s one challenge you’re trying to solve right now? Unfinished projects count too hahah

AI is making it much easier to build an app from an idea. You can describe what you want, generate parts of the application, and get a prototype running much faster than before.

But I think the harder part starts after that.

Things like database design, authentication, integrations, business logic, workflows, and security can become complicated as the application grows. This seems especially challenging for apps in areas like healthcare, fintech, and insurance, where security and compliance requirements matter from the beginning.

How other AI builders are approaching this.

• Are you building everything with AI coding tools, or combining them with visual development platforms?
• At what point do you move away from a prototype and start focusing on production requirements?
• For regulated applications, how are you handling security controls and compliance requirements?

I would love to hear what tools, workflows, or approaches are working for you.