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MiniMax-AI 
posted an update 2 days ago
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4040
Huge news from MiniMax: we’ve secured a $2B funding round, paired with a formal long-term commitment from our CEO IO to allocate 1% of total company equity from his personal holdings to support the global open-source AI community over the next four years.

This capital backs our continuous open model releases, community tooling and transparent frontier AI research. We’re just getting started on our open-source roadmap toward accessible AGI.

If you build with open foundation models and want to push frontier AI together, come join us.
Intelligence with Everyone. 🚀

MiniMaxAI
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danielhanchen 
posted an update 1 day ago
NatalieY 
posted an update 1 day ago
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1503
Aiden: a physical AI agent that controls phones over USB HID

Most GUI agent work assumes the agent lives inside the device or
drives it through a debugging interface. We went the other way.

Aiden is a small board that sits outside the host. It captures the
screen over HDMI-to-CSI, runs the agent loop on-device, and sends
actions back as a standard USB HID device — the host sees a keyboard
and a mouse, nothing else. No app install, no root, no ADB, no cloud.

Runtime is Go. Frame capture, full-duplex audio with VAD, the agent
loop, and HID output all run as independent goroutines. There's no
backend — nothing leaves the device, which is the only defensible
design when the input is a live feed of someone's phone screen.

Open questions we haven't solved:
· Action verification — inferring success from a re-read of the
screen breaks when loading states lie
· Prompt injection — an agent that reads screens reads whatever an
attacker puts on them
· iOS pointer control requires AssistiveTouch

Repo, including the HID gadget config and capture pipeline:
github.com/AidenAI-IO/aiden-hardware-demo

Wrote up how this differs from cloud-based computer use agents here:
https://aidenai.io/blog/mobile-ai-agent-vs-computer-use-agent-whats-the-difference/

Note: current hardware is a dev board, not a finished product.
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Quazim0t0 
posted an update 2 days ago
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5284
Disabling Gated Access for some of my models today. I will update this post with the list as I go. I had to go back recently and make updates to a lot of models and bit off more than I could chew with managing all the releases. I didn't realize many of you asked for access and I apologize for not accepting your access to the models you were wanting to look at. I don't like to release something fully unless I feel I put what I could into it for the moment. Some models will remain on gated access, but I will now be accepting those who request to view the repo.

Disabled Gated Access:
Quazim0t0/Byrne-VLM-131M - v2 Updates + Training Instructions
Quazim0t0/Byrne-Speech - 12M Tiny Speech model
Quazim0t0/Byrne-ASR-English - 12M Tiny ASR Model
Quazim0t0/Byrne-VE - Byrne-VE — Tiny Self-Distilled Vision Encoder (39M)
Quazim0t0/Positronic-144M - Research Artifact
Quazim0t0/SpikeWhale-SNN-216M - Research Artifact
Quazim0t0/Mycel-LM-79M - Research Artifact
Quazim0t0/Chimera-64M - Research Artifact

Accepting Gated Access Requests (7/9):
Quazim0t0/Wheeler-63M

Also uploaded my Neural Photonic Project:
Three trained nets in series: light interferes through the MZI2.pt optical core (verified 256/256), is measured by the PD.pt neural photodetector (verified 1024/1024), and folded into a single OUTPUT byte by the real ADC8 neural-CPU adder. Every value below is computed end-to-end by the three loaded, verified nets — no analytic formulas.
Demo: https://quazim0t0-neural-photonic-hybrid.hf.space/
Model Weights: Quazim0t0/neural-photonic

AND!

A work in progress:
Ashen Depths
https://quazim0t0-ashendepths.static.hf.space/index.html
sergiopaniego 
posted an update 3 days ago
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7460
Frontier models use distillation as a step of their post-training pipelines.

In 2026 it has three jobs: compress a big model into a small one, merge RL experts into a single model, and let a model teach itself.

I wrote up which frontier models use each one and how: https://huggingface.co/blog/sergiopaniego/distillation-2026

It pairs with Class 2 of the Training an Agent series Ben and I are doing, where we teach these techniques hands-on with TRL!
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Quazim0t0 
posted an update about 24 hours ago
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1348
🧩 Verified neural units, now for memory & storage

Following up on my neural-aarch64-units (small MLPs that emulate CPU datapath slices, verified bit-exact over their entire finite input domain — N/N), I applied the same discipline to memory and storage. Three new repos:

🔷 neural-ddr — verified units emulating DDR5 logic: DBI (256/256, 512/512), ADDR_MAP (4096/4096), CMD_DECODE (32/32), WR_CRC (512/512), and on-die ECC ODECC (256/256, 3328/3328). Composed into a bridge that presents DDR5 behavior over real DDR3/DDR4 RAM — flip a bit in every stored byte, ECC corrects all of them.
🤗 Quazim0t0/neural-ddr · 💻 https://github.com/quzi93/neural-ddr

🗄️ neural-storage — a self-healing vault on a neural-verified GF(2⁸) core (LOG/EXP compose to a multiply verified over all 65,536 pairs). Content-addressed dedup + Reed-Solomon so any k of n shards rebuild the whole, plus a whole-drive → self-healing .pt imager.
🤗 Quazim0t0/neural-storage · 💻 https://github.com/quzi93/neural-storage

💿 neural-cd-preserve — scan a disc into a self-healing .pt that detects (per-shard SHA-256) and repairs bit-rot, restoring bit-exact even from a damaged copy. Beyond the RS limit it's flagged LOST, never silently wrong.
🤗 Quazim0t0/neural-cd-preserve · 💻 https://github.com/quzi93/neural-cd-preserve

Build your own: golden finite function → enumerate the domain (decompose big/linear ops like CRC/ECC/GF into bit/byte slices) → train a small MLP → verify must be bit-exact on 100% of inputs or it's rejected → compose. Every repo ships the training + exhaustive-verification scripts.

Honest by construction: dedup removes redundancy, erasure coding adds it, ECC corrects faults — none of it pretends to beat entropy. Runs on modest/older hardware. 🤗
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Juanxi 
posted an update about 20 hours ago
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🚀WorldFoundry | Unified World Model Inference & Evaluation Infrastructure

WorldFoundry is an open-source infrastructure that unifies inference and evaluation for generative world models. It supports video generation, interactive worlds, 3D/4D representations, and embodied models through a unified workflow with TUI, CLI, and Studio interfaces. The framework integrates a growing collection of state-of-the-art models and currently includes 58 benchmarks, including VBench, VideoScore, WorldScore, WorldModelBench, Physics-IQ, and T2V-CompBench.

We welcome the community to ⭐ star the repository, submit pull requests, open issues, and contribute new models and benchmarks.

🔗 GitHub：https://github.com/OpenEnvision/WorldFoundry
📖 Project & Docs：https://openenvision.github.io/WorldFoundry
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ProCreations 
posted an update 1 day ago
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I won 4th place on the huggingface hackathon, genuinely so happy. Congrats to everyone that won, this has been super fun
Hari5115 
posted an update 2 days ago
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🌌 Calling all space lovers — every "Astronomy Picture of the Day" from NASA since 1995 is now an open dataset. For 30+ years, NASA has shared one amazing image of space every single day, colorful galaxies, bright stars, planets, and the sun, each with a short explanation written by a real astronomer.

It's now an open dataset that anyone can use. 🚀
📦 Hari5115/nasa-apod

Honestly, the pictures amazed me — full credit to all the photographers and astronomers behind them. 🙌 Whether you love space, enjoy building things, or just like looking at amazing pictures, this one's for you. If it gives you an idea, let's build it together. 🌠

Feel free to use the dataset, a mention or credit is always appreciated. 🙏

Data from NASA · public domain · not affiliated with NASA
#space #nasa #dataset #astronomy #opensource #photographers
Bc-AI 
posted an update about 14 hours ago
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we are going to release our latest NOVEL model next! it's called Nova-1-EXP and will be launched as private preview in the smilyai Laboratories BETA TESTERS organisation.
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