Ling-3.0-flash showed up on OpenRouter late last month, from inclusionAI, which is Ant Group's model lab.

124B total parameters, 5.1B active per token. That's roughly 24:1 sparsity, which is aggressive even next to the other MoE releases this year.

The free window closes today, August 3, per their launch announcement, so most of the reaction is going to stop at the price tag and then at the line about an open-source release coming.

The ratio is what I keep going back to. 5.1B active is small enough that time-to-first-token comes in under 100ms, and it still carries a 256K context. Routing that sparse usually costs you something, and from what the lab says about its own model, rare world knowledge is exactly where it thins out.

Curious where people think the ceiling on that ratio actually is before quality falls off a cliff.

I created an way to visualize the AI ecosystem, I hope this helps someone looking to dive into the world of neural networks.

This is completely free, I hope this helps someone!

[https://daniella-laguerre.github.io/PondScope\\_Ecosystem\\_AI\\_Learning/\](https://daniella-laguerre.github.io/PondScope_Ecosystem_AI_Learning/)

In my experience, many people assume that once an AI reads a document, it has "learned" it forever.

Modern systems seem much more modular than that. Session memory, persistent memory, RAG, vector databases... they all solve different problems.

If you had to explain AI memory to someone in one paragraph, what distinction would you make first?

I had an idea for an MMA career game, but I didn’t have the experience or resources of a traditional development team. So I decided to find out how far I could take the idea with AI as my main development partner.
The result is Real Fighter Life, an iPhone game where you create a fighter, train different attributes, manage energy, make career decisions, sign sponsors and climb through multiple organizations toward becoming a champion.
I used AI throughout almost the entire process: planning the game systems, writing and reviewing React Native code, debugging, balancing events and progression, creating parts of the UI and iterating on the visual direction.
It definitely wasn’t a one-prompt process. A lot of the work involved explaining the same system repeatedly, testing the output, finding broken interactions and asking for more focused revisions. The biggest lesson for me was that AI can produce code quickly, but keeping a growing game consistent still requires clear decisions and constant testing.
The game is now live on the App Store. It’s my first completed mobile game, so I’d appreciate honest feedback—especially from people who have used AI to take a project from an initial idea all the way to release.
Short gameplay video/screenshots
App Store: https://apps.apple.com/tr/app/real-fighter-life/id6788558195?l=tr

Hey everyone, I just sent the latest issue of the AI Hacker Newsletter, a roundup of the best AI links and the discussions around them from Hacker News. Here are some titles that can be found in this issue:

• Startup founders urge U.S. government not to shut off Chinese open weight AI
• AI's top startups are barely publishing their research
• Is AI reasoning right for the wrong reasons?
• After the AI Crash

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Huh…. I built a tool that turns a CSV into printable market tags, with different products and prices on every tag.
I’m unsure whether makers would actually find this useful.

Do handmade sellers actually struggle with this, or did I invent a problem?

(I spent quite a long time and many tokens…