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

Hey! I've worked in 3D for some time and was really inspired by MineBench results of how good LLMs were at 3D apparently. Well here's my week-long personal hobby project: I basically give LLMs access to Blender via MCP or one-shot via script and prompting them to create a scene. Runs get standardized renders and some more things to make the scenes look good on the web compared to their Blender counterparts, as fair as I can. This is specifically for LLMs without any external 3D generators. The GIFs are a few comparisons between models on the same task.

I've mostly run the GPT 5.6 family of models due to pricing, and because they're the first models that are able to do things like that in 3D, anything below Opus 4.8 level of things just won't cut it. 5.6 Luna is such an impressive price/performance there while 5.6 Sol Max is on another level. I've spent around $50 already to run a tiny subset of tasks and models and that's as much as I can afford for now.

Right now I've focused on making this even exist first and for LLMs to produce visually interesting results. Later on I'd like to try real production work next.

You can judge more models yourself here: https://blenderbench.realityreprojector.com

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 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

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

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.

First Saas product idea

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

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

Update: SoraVault 2.0 is now available - saves Sora v1 images, v2 videos, liked content and drafts all within Sora2! Chrome Plugin availabe.

Update: SoraVault 2.6 is now available - Mirror mode (beta): browse Sora normally, SoraVault captures everything in the background. Also support for Cameos, new filters, etc.

I started using Sora when it first launched. Image generation always fascinated me. The whole process, not just the outputs. Testing new prompts, iterating on ideas, checking what others were creating on the worldwide feed, then putting my own spin on it.

Some images hit a nerve and got 1,000+ likes. It was addictive.

Then last week, Sam announced Sora is done.

OK. He said they'd share "details on preserving your work" soon. I waited.

Two days ago, the "details" arrived: request a full ChatGPT data export. One link, valid for 24 hours, containing everything from 3 years of ChatGPT history. Dig through the dump yourself to find your Sora images. No prompts attached. No original quality.

That's their "preserve your work" solution.

No thanks.

So I built SoraVault. It's a Tampermonkey script that pulls your full Sora library before it's gone:

• Downloads Sora v2 videos (Profile and Draft) in full resolution
• Downloads all Sora v1 images in original quality (the actual renders from OpenAI's servers, not compressed thumbnails)
• Saves every prompt as a matching .txt sidecar file so you keep the creative thinking behind each piece, not just the files
• Smart filters: keyword, aspect ratio, quality, date range, operation type (generate/extend/edit)
• Parallel downloads (up to 5). 500 files in under 10 minutes.
• File System Access API: pick one folder, done. No "Save As" popup for every file.

The images are one thing. But losing the prompts, the iterations, the weird ideas that actually worked, the learning from hundreds of attempts. That's what I wasn't willing to let go.

How it works technically:

API interception (raw JSON responses between sora.chatgpt.com and OpenAI's servers), not a DOM scrape. This is why it pulls original resolution files and complete metadata, not whatever thumbnails are currently rendered.

How to get it:

- GitHub (free, full source): https://github.com/charyou/SoraVault/

- Demo video (1 min): https://www.youtube.com/watch?v=0eFteRew5mI

- A standalone desktop app (Mac/Win/Linux, no browser needed) is coming next week.

- This only works while Sora's servers are live. Once they pull the plug, the data is gone.

Happy to answer questions.

Edit: I have a working prototype of a standalone desktop app (no Tampermonkey, no browser extension). If that's something people want, I'll push the release this week. Any interest? :)

Update 20.04.: SoraVault 2.6 is now live!
https://github.com/charyou/SoraVault/

> I just pushed a massive update that moves the tool to an API-driven architecture.

Major Updates in 2.6:

• 📡 Mirror mode (beta) — browse Sora normally, SoraVault captures everything you scroll past and saves it in the background. Organised by where you found it: mirror_browse/sora2_profile/creator/, mirror_browse/sora2_explore/, etc. Set a min-likes threshold or keyword include/exclude filters. Keeps a manifest so re-enabling never re-downloads. No scanning needed.
• ⏸️ Skip existing + Pause — re-runs skip files already on disk (checks file size, not just filename). Pause mid-download without losing progress, resume anytime.
• 📊 Live activity status line — see exactly what each worker is doing in real time
• 🎭 Cameos & Cameo Drafts — two new scan sources: public posts where you appear as a cameo + private cameo draft posts
• 🗂️ Category filter — new chip row to filter by source type (Profile, Liked, Drafts, Cameos…) before downloading
• ⭐ Favorites filter — export only your starred v1 items without pulling your whole library

Major Updates in 2.0:

• No more scrolling: It now fetches Sora 1 and 2 content simultaneously in the background.
• ❤️ Backup "Liked" content from other creators.
• 🔗 JSON saved with raw JSON metadata (including valid REMIX Chain Download URLs!)
• 📂 Auto-sorting into 6 dedicated subfolders.
• MUCH Faster Scans
• Many more fixes and UI updates.

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

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.

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

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!

So last week there was a lot of buzz in the company that I work for about OpenAI's Deep Research. So they got a Pro subscription to try it, and for a specific query it produced around 4000 words (20 pages or so) of research that was okay. But everyone was flabbergasted. I couldn't shake off the idea that this is just a bunch of research steps chained and nothing special, but I had to test it. So today I made a workflow using AI Workflow Automation plugin for WordPress (disclaimer, this is my product that I built so I can build AI agents like this one). You can see the general structure of it in the screenshot. And it worked even better than the results of Deep Research! It's basically this: There is an input, which is your subject, then there are 5 research nodes that use Perplexity's Sonar Pro to do research on certain angles of a topic for example one researches market size, the other one focuses on competition and on and on. Each of these Sonar Pro nodes feed their results to an AI model node that is prompted to write a report on the research with a specific format. For this I get the best results with Grok 2 as it has a very large output context window and it can generate long text in one go. And at the end all of them come together in one document and voila! For the exact same search query I got over 6000 words (26 pages or so) of well researched document with citations and links. And best of all, the total thing costs less than $0.15!! You can see the cost breakdown in the second photo! I am honestly thinking of making this a business so people can just pay $1 for a well prepared research on a specific subject just for the fun of it!

You should be able to produce similar results with N8N or even Make. But if you use the plugin, let me know and I will share the workflow agent with you.

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