Welcome, humans.

The internet began as a public network anyone could build on. AI is developing more like a row of private theme parks, where a few companies own the rides, write the rules, and keep raising ticket prices.

Current AI wants to build the public alternative: open AI infrastructure that communities can use, modify, and control for free. The nonprofit has $400M in committed funding and is already backing offline tools for Indigenous communities, datasets covering 50+ African languages, and a pocket-sized device that works in 22 Indian languages without internet access.

The goal is basically a World Wide Web for AI, where progress benefits everyone instead of whichever company owns your login. Finally, an AI moonshot that does not begin with “please enter a valid credit card.”

On a tangent: We also published a full breakdown of everything Samsara announced at Beyond 2026, including AI agents for physical operations, 360-degree cameras, smarter fleet maintenance, and disposable shipment-tracking labels. The common thread: move AI out of the browser and into the trucks, warehouses, worksites, and supply chains that keep the physical economy moving.

Here’s what happened in AI today:

…and a whole lot more that you can read below

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😼 Alibaba’s 2.4T Qwen3.8 Is Joining the Frontier AI Race

The AI model race has developed a new flex: build something enormous, promise to open it, and release the preview before everyone finishes arguing about the last model. Alibaba is now testing whether sheer scale can turn that flex into a real advantage.

Alibaba’s Qwen team previewed Qwen3.8-Max, a model with 2.4 trillion parameters, and said the full version will eventually be released as open-weight software. Open-weight means developers can download and run the model themselves, rather than accessing it only through a company-controlled app or API.

Here’s what happened:

How to try it:

Why this matters: Parameter count is roughly the number of adjustable values a model learned during training. More parameters can increase capability, but size alone does not guarantee better answers, lower costs, or faster performance. The bigger signal is Alibaba’s promise to release the weights. That would give developers another frontier-scale Chinese model they can inspect, customize, and deploy outside a closed platform. For companies, that could mean more control over sensitive data and fewer dependencies on one provider.

Our take: China’s open-model strategy is turning every release into two competitions at once. Labs are racing to build the strongest model, while also racing to make that capability cheap and accessible enough for developers to adopt. Qwen3.8 still needs independent testing, especially because Alibaba’s boldest comparisons currently come from Alibaba. A 2.4T parameter count also raises an awkward practical question: can ordinary companies afford to run it efficiently?

If the benchmarks hold, the open-versus-closed debate gets much less theoretical. Companies may soon choose between paying premium prices for a tightly managed US model or operating a comparable Chinese model on their own infrastructure. The next few weeks will show whether Qwen3.8 created a breakthrough, or simply the largest résumé line in AI.

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🎓 AI Skill of the Day: Direct AI Video Like a Layered Shot

Most AI video gets weird because we ask one model to do everything at once: scene, character, motion, voice, timing, camera, vibes, the whole circus.

The better workflow from Prompt Mastery’s tutorial is to build the shot in layers. First, film a real person doing the motion. That becomes your driving video: the reference clip that tells the AI how the character should move. Then use a character-swap workflow like Flux 2 Klein to create the AI character frame, run SCAIL-2 for motion transfer (copying the real person’s movement onto the AI character), and blend the result back into the original footage with a soft mask.

The tiny-but-critical detail: keep the crop, swapped image, and generated output at the exact same resolution. If the sizes don’t match, your character won’t line up when you put the scene back together.

For dialogue, generate the voice first with Omni Voice, then use Relay Prompt in WAN2GP to direct the character second-by-second.

Want more tips like this? Check out our AI Skill of the Day Digest for July.

Have a specific skill you want to learn? Request it here. 

🍪 Treats to Try

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Video Watchlist: Kimi K3 and ACT-2

Two threads are worth watching this week: China’s open-model surge and the home-robot reliability race.

📰 Around the Horn

😹 Monday Meme

A Cat’s Commentary

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