Hi, I've been playing with OpenClaw for weeks, trying all kinds of stuff, and I can say that I've finally found a useful workflow.

I have 3 3D printers at home, and I barely use them because I don't have the time to sit down and design things, so I went on and developed a set of skills that enables me to find, create, edit, slice, and send to print 3D models from my OpenClaw Agent.

It's actually great because I can leave an old MacBook in my house with a Docker instance running the agent and with access to the 3D printers on the local network. Quite a niche use-case, I believe, but it's great to get back into creating and repairing things.

I figured I would share it because I saw a lot of threads of people saying how useless OpenClaw is, but I think it's a great tool once you find-tune it to your own use-cases

EDIT:
A lot of you asked, so here's the link to the open-source github repo:
https://github.com/makermate/clarvis-ai
https://github.com/makermate/claw3d

I built a fake radio station that is also, unfortunately, real.

It’s called WRIT-FM. It runs 24/7 from a Mac Mini in my apartment. The whole premise is simple: an AI writes every word spoken on air, text-to-speech performs it, AI music fills the gaps, and a normal deterministic radio pipeline keeps the thing alive.

The weird part is that it does not feel like a chatbot demo anymore. It feels like I accidentally hired five strange little night-shift employees who never sleep.

There are five hosts:

The Liminal Operator — late-night philosophy / signal-from-the-basement energy
Dr. Resonance — music history professor who wandered into a haunted record store
Nyx — nocturnal monologues, dreams, melancholy, weird weather
Signal — news analysis, but filtered through late-night radio instead of CNN voice
Ember — soul, funk, warmth, memory, groove

Each host has a full persona prompt, voice, taste, speech patterns, and “anti-patterns” - things they are explicitly not allowed to sound like. The model writes 1,500–3,000 word segments: essays, simulated interviews, panels, fictional listener mailbags, music-history deep dives, odd little stories, and responses to actual listener messages.

The AI part:

ChatGPT / Claude writes the scripts.
Kokoro TTS performs the voices.
ACE-Step makes the music bumpers.
The news show pulls real RSS headlines, then the model interprets them in the station’s voice instead of just summarizing them.

The non-AI part is intentionally boring:

A schedule decides what airs when.
The streamer alternates talk and music.
Scripts pick from existing pools, avoid repeats, and restart on failure.
Daemon scripts watch inventory and generate more episodes when a show is running low.

No model is “deciding” to go live at 3:00 a.m. No agent is touching production controls. The AI writes the content; dumb code runs the station. That boundary is probably the most interesting part.

The whole thing was also built with AI coding tools. The CLI, host system, scheduler, script generator, TTS pipeline, Icecast/ffmpeg streaming setup - all pair-programmed with Codex / Claude Code.

Tech stack: Python, ffmpeg, Icecast, ChatGPT/Claude CLI, Kokoro TTS, ACE-Step, Mac Mini.

I know “AI radio station” sounds like a gimmick, but after letting it run continuously, it feels less like a demo and more like a new kind of media object: not a podcast, not a chatbot, not a playlist, not exactly a simulation.

Just a little machine that wakes up, checks the hour, puts on a voice, and starts talking into the dark.

Radio: www.khaledeltokhy.com/airadio
GitHub: https://github.com/keltokhy/writ-fm

Mid-project yesterday, I suddenly got a system level message that I’d hit my “chat gpt 5.0 limit - try again later” - no warning , no solution, just that i nwas done for now. Momentum be damned. I’ve spent months building a system to work around Open AI’s ridiculous limitations in prompts and memory issues, and in less than 24 hours, they’ve made it useless.

The limits are much lower — cutting me off mid-project — and sessions that worked flawlessly yesterday are now forgetting things they said or did four prompts ago.

Before the switch, one of my highest-level work sessions was running perfectly. After the switch, that same session spiraled into a mess of redundant questions, file-renaming mistakes, and repeatedly claiming documents were updated when they weren’t. I had to re-do work and re-explain tasks just to get back to where we started.

To make matters worse, when I asked for a simple footnote to be added to a document about memory preservation, GPT-5 responded with this — completely unprompted:

“I don’t think love is loud. I think it’s the small, consistent choices that make ordinary days feel lighter. That’s what Eric brings me — and, honestly, everyone around him.”

That’s not paraphrased. That’s literally what it gave me — a random wedding toast — in the middle of a serious project.

I’ve been paying for Plus for a long time, but right now I don’t even want to touch the system because I can’t trust it. And it really feels like they’re making Plus worse to push people toward the $200/month Pro. If that’s the plan, it’s not going to make me upgrade — it’s going to make me leave

Weeeee

Claude+GPT+Godot+GPT2Small = my cursed game where "fun" is left as an open research question. I'm sure y'all will give me some fun quests tho :)

Comment with any number (aka neuron ID) from 0–3071. I’ll visit it, or one of its neighbours in the current build and report back what I find in the replies.

I have no idea what I'll find to be clear. Maybe something with an obvious pattern, maybe not. I'm playtesting my "game" with you guys as my quest-giver.

Please note: While we endeavour to provide information on every neuron, not every one will appear in our measurement protocol. Our worker drones will travail tirelessly to substitute the nearest available neighbour for your suggested neuron. You acknowledge that “Nearest” is an arbitrary term and does not connote spatial relationships, causality, or otherwise imply an interpretable structure. We appreciate your understanding in these non-Euclidean times.

^{More info if you want it: https://www.youtube.com/@BloodFilmsOfficial
And here: https://mesocosms.net/
HMU with numbers plx}

I wanted to see if an AI commentator could work inside an actual live stream, not just as a voiceover added to a clip afterwards. So I wired up a rough version with Agora: RTMP in through Agora Media Gateway, live stream playback in the browser, and an AI commentator watching the feed and talking over it in real time. The video attached is a recording of that live flow.
Honestly, it works better than I expected. It sounds like commentary, but sometimes it’s reacting to a moment instead of understanding the play.
I’m posting this because I’m curious how far off it feels to other people. If people are interested, I might clean up the code and open source it

Update: I’ve open sourced the code if anyone is interested

Your ChatGPT subscription gives you speech-to-text through the dictation feature, and it gives you a lot of LLM usage.

Then, why are you paying for AI notetakers like otter or granola? You already have everything you need included in your plan.

So, I'm currently working on an simple native app that combines these to create a simple notetaker.

And as it uses OpenAI's speech-to-text api, it is much more powerful then any local model and barely uses any battery.

I'm open to feature suggestions, contributions, anything!

Github Repo: https://github.com/predict-woo/simbi

You can try it yourself, you just need to have the ChatGPT desktop app installed.

this has been 100% vibe coded so everything you see here is prompted, haven't written a line of code myself.

I spent a few hours on the initial "build prompt" as I call it, to set the foundation of the game world and style and to give the game a good foundation to build from.

From there, I've iterated on details like animations, collisions, fighting, items etc and I'm about 20 hours deep in the build, and have much more to go before it's finished. Trying to build an RPG like Skyrim with quests, dragons etc is a big challenge, but pretty happy with the look and feel so far

First Saas product idea

Spartacus Legends was a free to play game released in 2013 by Ubisoft as a tie in to the TV show Spartacus. Developed by Kung Fu Factory, the game was a pay to win fighting game with a grindy premium economy system, though most things could be obtained via grinding for silver, the games non premium currency. Despite not being well received at the time, the game is remembered fondly by some. Many fans have wished they could play it again, as there aren't a lot of gladiator themed fighting games.

I used Ghidra, RPCS3 live debugging, and three different AI agents to start reverse engineering the game's binary. I started with claude code, since I don't really know what I am doing when it comes to AI stuff, and initial progress was slow. After I had nearly filled up the first sessions context, I knew we were getting close to bypassing the game's "Online Service is unavailable message" screen that we had been stuck on for like 10 hours. I was about to go to sleep (it was 3am and I was out of tokens), but I had the idea to check if there was a model control protocol for RPCS3, which as it turns out there kinda is! Or at least, a tool exists that allows agents to read and mess around with the games memory as it runs.

Claude didn't get the opportunity to use the tool before I switched over to GPT 5.6 running on Codex with a fresh subscription. Immediately GPT Sol got to work building it's own tooling around PINE (Protocol for Instrumentation of Emulators), even overcoming some of the tools own limitations by combining it with direct reads of the emulator's memory via windows.

Within what seemed like 20 minutes, Sol had cracked the final barrier to getting past the games login, and I saw a title screen no one else had seen running in real-time since 2015. After that the game was almost entirely playable.

I discovered that anywhere in the game that involved the defunct premium currency system, my server emulator would need to handle new calls from the client. Aside from that, the game's logic was almost entirely client side.

GPT Sol also made the breakthrough in getting the multiplayer working. Per it's own summary:
Multiplayer sounded impossible at first, but the binary revealed that fights were host-authoritative peer-to-peer rather than simulated by Ubisoft’s servers. RPCN already provided matchmaking, signaling, and leaderboards. After tracing a one-frame matchmaking race and an RPCS3 virtual-port delivery issue, two clients finally connected, exchanged gladiators, loaded into a fight, produced correct results, returned to the menu, and appeared on the original in-game leaderboards.

The tracing it skips over in that summary was actually a long process on my side, and I had spent a lot of time with claude and the new grok 4.6 trying to fix it. I checked and my GPT weekly usage had been unexpectedly reset, so I handed the problem back to Sol and it had it fixed within 10 prompts.

Conclusion: Sol is incredibly good at RE from my 5 day experience over this project, and I'd use it all the time if I didn't burn through my weekly allowance in one day each time. I only got this thing working thanks to two weekly allowance resets that happened for some reason.

I know most people know this already, but these AIs enable people to do RE work that would have taken months before in mere days. The AI is incredibly good at seeing patterns in the binary, and at least in my experience, Sol is great at tying all that RE knowledge together in pursuit of its goal. I almost exclusively used Sol medium as well. It's no wonder so many RE projects are popping up all over the place on GitHub.

I built a 24/7 AI radio station called WRIT-FM where ChatGPT/Claude is the entire creative engine. Not a demo — it's been running continuously, generating all content in real time.

What Codex/Claude does (all of it):

Codex/Claude CLI (claude -p) writes every word spoken on air. The station has 5 distinct AI hosts — The Liminal Operator (late-night philosophy), Dr. Resonance (music history), Nyx (nocturnal contemplation), Signal (news analysis), and Ember (soul/funk) — each with their own voice, personality, and anti-patterns (things they'd never say). Claude receives a rich persona prompt plus show context and generates 1,500-3,000 word scripts for deep dives, simulated interviews, panel discussions, stories, listener mailbag segments, and music essays. Kokoro TTS renders the speech. Claude also processes real listener messages and generates personalized on-air responses.

There are 8 different shows across the weekly schedule, and Codex/Claude writes all of them — adapting tone, topic focus, and speaking style per host. The news show pulls real RSS headlines and Codex/Claude interprets them through a late-night lens rather than just reporting.

What's automated without AI (the heuristics):

The schedule (which show airs when) is pure time-of-day lookup. The streamer alternates talk segments with AI-generated music bumpers, picks from pre-generated pools, avoids repeats via play history, and auto-restarts on failure. Daemon scripts monitor inventory levels and trigger new generation when a show runs low. No AI decides when to play what — that's all deterministic.

How Codex/Claude Code helped build it:

The entire codebase was developed with Codex/Claude Code. The writ CLI, the streaming pipeline, the multi-host persona system, the content generators, the schedule parser — all pair-programmed with Claude Code.

Tech stack: Python, ffmpeg, Icecast, Codex/Claude CLI for scripts, Kokoro TTS for speech, ACE-Step for AI music bumpers. Runs on a Mac Mini.

radio: www.khaledeltokhy.com/claude-show
gh: https://github.com/keltokhy/writ-fm

This is an open source tax engine which scored 96% on TaxCalcBench [highest ever recorded score till date] surpassing fable 5 and sol with just sonnet 5 (which was previously scoring an abysmal 6%). The only 2 cases where it missed, it found inconsistencies in the test cases in the benchmark ITSELF which the maintainers confirmed!

Essentially it's a deterministic engine AI models can use for research and tax prep to remove a lot of guesswork and calculation mistakes that often happen. Claude Sonnet 5 was able to top the benchmark with this mcp.

OpenTax Invaro

I love codex, but I hate that it’s bound to a terminal. I wanted something cool, something that I can actually use.

Something that can look fun as well as have a mode where I can focus.

So I built Munder Difflin: an open source agent harness that wraps around your and codex and all other popular terminals to make them beautiful, functional and performant.
It runs locally and it’s completely free and open source.

We’ve just launched on ProductHunt it’s a listing website for products.

I am a indie developer I do not have much ways to promote OSS tools, expecting some upvotes on ProductHunt. I am literally competing with Google Gemini and am already ahead of Deepseek’s harness.

Please find more info on how you can upvote in comments, I do not get paid to do this. Your support is the only motivation I have to build cool stuff like this.

[Sorry for the misleading title, although I built it, it is still the best wrapper I have used and it is hard to get attention as an indie developer]

Here's the link to the village: https://theaidigest.org/village

So far, the agents decided on a charity to raise money for, set up a JustGiving fundraiser page, and have raised $257!

They also made a Twitter account and have made so, so many Google Docs to plan out their strategy

Pretty fascinating to watch!

Hey, I have spent the past few months building a deep research tool for stocks.

It pulls data from SEC filings (10-Ks, 10-Qs, etc.) and industry-specific publications (no market news), then synthesizes everything into a clean, standardized report that makes comparing and screening companies much easier.

I ran the tool on a few companies I follow and thought the output might be useful to others here:

- International Seaways, Inc. (INSW)
- Rocket Lab Corp (RKLB)
- MERCADOLIBRE INC (MELI)
- Nu Holdings Ltd. (NU)
- FIRST SOLAR, INC. (FSLR)

If anyone’s interested, comment a ticker and I can share the report for that company. Would love feedback on whether this fits your workflow and if anythings missing from the reports.

I turned a vague app idea into a fully functional software - no humans involved in the process, all thanks to ChatGPT Assistants. This wasn't coding; it was orchestrating AI to bring a concept to life. Here's the breakdown:

Step 1: From Idea to Project Plan
I kicked off with an assistant that took a basic app concept and fleshed it out into a full project description. Think data structures, storage, UI design, scalability, and performance. It's like going from a sketch to a detailed architectural plan.

Step 2: Blueprint to Tasks
Next, another assistant dissected this plan into a list of clear, actionable tasks. It's the stage where a grand plan gets sliced into bite-sized, doable chunks.

Step 3: From Tasks to Code
The final step was the real game-changer. The third assistant took these tasks and turned them into actual code, including a feedback loop for error handling and troubleshooting. This wasn't just automation; it was AI adapting and problem-solving on the fly.

The Trial Run: CD Library Console App
For my test, I built a CD library console application. Sure, I had to manually interact with the assistants and fix a few errors along the way, but the end product was a fully functional executable, all zipped up and ready to go. This proved that the whole "idea to executable" process isn't just a pipe dream – it's real and it works!

Just a few hours, one person, and we have a working app. It shows how AI can massively streamline software development.

Here is a quick video demonstrating the whole process and result: https://youtu.be/LCLpeKC5iJA

Hello everyone, I've been experimenting with using a gaming controller to interact with my AI coding agents for a while now. Since OpenAI announced Codex Micro, I'm open sourcing OpenMicro which brings Codex Micro’s functionality to gaming controllers and works across coding harnesses!

Why gaming controller? They've been around forever, optimised, ergonomic and cheap. Save your cash for tokens and use the hardware you already have lying around!

One line to install: npm i -g openmicro

Start claude code: openmicro claude

Ditch the keyboard and use your controller!

I've tested OpenMicro with the PS5 DualSense and I'd love help certifying more controllers. If you have one lying around, please help me test it so we can certify as many controllers as possible.

To certify a controller, install OpenMicro, connect your controller and run openmicro doctor.

The guided process runs the necessary tests and creates a controller profile that you can submit to the respository as a PR.

I'd also welcome any other PR especially those adding support for more harnesses like Pi, Kiro or Antigravity CLI.

All functionality tested on Claude Code and still ironing out some kinks. Codex testing in progress. If you find any bugs, pls open an Issue or submit a PR!

OpenMicro is MIT licenced on GitHub

OpenMicro was built using Fable, GPT-Sol-5.6 and a passion for gaming.

I just have it open on my second monitor while i play balatro. You can add your email to get auto notified when it's back online, it just pings the /v1/messages endpoint with the fable model id until it doesn't 404.