
Gaston Krasny
3.7K posts

Gaston Krasny
@gukras
✏️https://t.co/aNgDn1iaDT 🤖Snack AI newsletter (https://t.co/AN6bXbgVxK) 📱https://t.co/4BoNQ7NTcR (acquired by https://t.co/e3iF1rZ5tL)


For the past few weeks I’ve been teaching 50+ kids how to create their own games with AI. The results have been incredible 🤯 I started with my 5 y/o nephew because I was worried about him spending hours doomscrolling and consuming brainrot content. He built his first game using @v0, though I had to walk him through each step. Watching kids interact with these tools taught me so much about how they think, where they get stuck, and what keeps them motivated. The moment that really struck me came right after he finished that first game. Every time I saw him afterward, he’d run up and ask, “Did you bring your laptop so I can keep playing?”. For him, “playing” meant creating. That kind of excitement is organic pull. Don’t get me wrong, I love @v0 and @Lovable. They’re insanely powerful. Their UIs just aren’t designed for kids, and that’s totally fine. Kids still need guidance, structure, and a bit of tutoring to turn ideas into something playable. I’m seeing tech leaders push in this direction too, and I think there’s a real opportunity here (@woloski @Thom_Wolf). Kids are the future, and I don’t want to live in a world where they spend precious time consuming instead of creating. That’s why I love tools like v0, Lovable, @wabi , @cursor_ai . They’re empowering more people to become creators. I want that same feeling for kids. What Guillermo is saying is real. Kids need to be AI-literate from the outset. It honestly makes me sad to think about my nephew growing up and falling behind on AI and everything happening around him because he’s stuck in Roblox or TikTok. That’s why I built Rabbit 🐰 for him. It started as a simple prototype, but somehow I ended up talking with 200+ parents and kids, tech leaders, investors, and people I never imagined meeting around the world. Rabbit now has 100+ people on the waitlist, and 10 kids are using it two to three times per week on average to create their own games 🫡 I’m not sure yet if this will become my full-time project, but I deeply care about it. If you’re building in this space, I’d love to connect and share ideas. And if you’re a parent interested in Rabbit, I’d love your feedback. Sharing a short video of kids using Rabbit below:







Announcing a new Claude Code feature: Remote Control. It's rolling out now to Max users in research preview. Try it with /remote-control Start local sessions from the terminal, then continue them from your phone. Take a walk, see the sun, walk your dog without losing your flow.

TOON (Token-Oriented Object Notation) is out for some days now and it aims to make communication with LLMs more accurate and token-efficient. The TOON topic is now one of the hottest news on the LLM market and it might actually matter. 𝗪𝗵𝘆 𝗜 𝘁𝗵𝗶𝗻𝗸 𝘀𝗼: I was initially hesitant to cover this, potentially being another hype to quickly fade, but: ✅ The format has been shown to increase the accuracy of models while decreasing the token count. I was not sure if there were any accuracy retention studies made, it seems there were. ✅ Token efficiency is extremely important when working with Agentic Systems that require a lot of structured context inside of their reasoning chains. And we are moving towards a post-PoC world where there is a lot of emphasis placed on optimisation of the workflows. 𝗔 𝘀𝗵𝗼𝗿𝘁 𝘀𝘂𝗺𝗺𝗮𝗿𝘆: - Token-efficient: typically 30-60% fewer tokens on large uniform arrays vs formatted JSON. - LLM-friendly guardrails: explicit lengths and fields enable validation. - Minimal syntax: removes redundant punctuation (braces, brackets, most quotes). - Indentation-based structure: like YAML, uses whitespace instead of braces. - Tabular arrays: declare keys once, stream data as rows. An example: 𝘑𝘚𝘖𝘕 𝘧𝘰𝘳𝘮𝘢𝘵: "shopping_cart": [ { "id": "GDKVEG984", "name": "iPhone 15 Pro Max", "quantity": 2, "price": 1499.99, "category": "Electronics" }, { "id": "GDKVEG985", "name": "Samsung Galaxy S24 Ultra", "quantity": 1, "price": 1299.99, "category": "Electronics" }, { "id": "GDKVEG986", "name": "Apple Watch Series 9", "quantity": 1, "price": 199.99, "category": "Electronics" }, { "id": "GDKVEG987", "name": "MacBook Pro 16-inch", "quantity": 1, "price": 2499.99, "category": "Electronics" } ] } 𝘞𝘩𝘦𝘯 𝘦𝘯𝘤𝘰𝘥𝘦𝘥 𝘪𝘯𝘵𝘰 𝘛𝘖𝘖𝘕 𝘧𝘰𝘳𝘮𝘢𝘵: shopping_cart: items[4]{id,name,quantity,price,category}: GDKVEG984,iPhone 15 Pro Max,2,1499.99,Electronics GDKVEG985,Samsung Galaxy S24 Ultra,1,1299.99,Electronics GDKVEG986,Apple Watch Series 9,1,199.99,Electronics GDKVEG987,MacBook Pro 16-inch,1,2499.99,Electronics 𝗥𝗲𝘀𝘂𝗹𝘁: ✅ 43% savings in token amount. ✅ Directly translates to 43% savings in token cost for this LLM input. ❗️ Be sure to know when NOT to use the format (and always test it for your application specifically): - Deeply nested or non-uniform structures. - Semi-uniform arrays. - Pure tabular data. ℹ️ I will be testing it in the upcoming weeks. Let me know if you have already tested TOON and what are your takeaways! 👇 #LLM #AI #MachineLearning







@marcoporracin No lo entienden. Posta. Todos mis amigos me discuten que es una locura lo que hago, mi familia lo mismo. Nadie lo comprende. No pueden comprender que te quedes post 18hs laburando. No comprenden que labures los fines de semana. Simplemente no lo entienden. Es imposible.











