This is a really interesting way to bring a Vibe Coding product to life! @Pexoai_offical makes the whole video creation process feel much more intuitive, from turning an idea into a polished launch video to refining the final result. The workflow looks simple, creative, and genuinely useful for anyone building and launching products. AI-powered content creation is moving fast, and tools like this make the process even more exciting. Definitely worth checking out!

When people talk about robotics, they usually talk about models, data, or hardware. Few people talk about the infrastructure that lets you iterate on all three quickly. Today we're publishing how we trained Dyna-2 on over 1,000,000 hours of egocentric video, repeatably. At this scale, most of what worked at ten thousand hours did not hold up: • ingestion throughput was capped at 14,000 episode-hours per week — a million hours would have taken over a year • building a training manifest took 48 hours before a run could even start • reading a petabyte from cloud storage during training left GPUs exposed to latency and packet loss 🧵

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🚨 BREAKING: Claude can now BUILD a YouTube channel from scratch—and hit monetization in just 90 days. 100% free. Here are 8 prompts to make it happen:

StepAudio 3 Music adds another model to compare on the same creative brief. I’d include an unusual genre combination; familiar pop is only part of the test.

There is something remarkable about building thousands of hours per language, then doing it across dozens of languages. Monsoon is a massive data operation.

The next billion voice AI users are not all speaking American English into studio microphones. Datasets need to reflect that reality.

Architectures advance quickly. Deeply curated speech across underserved languages takes sustained work. Monsoon is built around that scarce layer.

StepAudio 3 Music has four entry points: 🎤 Songs 🎹 Instrumentals 🔁 Covers 🎙️ Vocal-to-song arrangement Useful when your starting point is more than a text idea. Find your route in: static.stepfun.com/blog/step…

StepFun and ACE Studio present StepAudio 3 Music, StepFun’s first music generation foundation model — built to turn a prompt and lyrics into a complete song. Describe the sound you want: • Genre, mood and vocal character • Instruments, key and BPM • Song structure and arrangement Four workflows in one model: 🎤 Song generation 🎹 Instrumental generation 🔁 Music cover 🎙️ Vocal-to-song arrangement Powered by ABC-COT, it plans musical structure and arrangement before synthesis. Generate a version, rewrite the prompt, edit the ABC notation, and iterate toward the sound in your head. (ABC-COT API coming soon) Try the interactive demo: › static.stepfun.com/blog/step…

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Parameter count tells you capacity. Active parameters tell you something about the cost of using that capacity.

The broader significance of this work may be methodological. If AI can meaningfully participate in the process of improving AI systems, the research workflow itself becomes part of the technology being optimized.

🚨 Powerpoint is finished! ChatGPT can now build full professional presentations in seconds. Here are the prompts to use:

The interesting number here isn’t 309B. It’s how little of it needs to be active at once.

We’re getting better at building large models without paying the full computational cost every time.**

The part I’d actually like to see more of is what the model decided to experiment with.

AI helping write the next model is interesting. AI helping decide what to try next is more interesting

A 309B model with ~16B active parameters says a lot about where model efficiency is heading

309B sounds massive, but ~16B active is the number I’d pay closer attention to.