
Reelevant
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Introducing Cowork: Claude Code for the rest of your work. Cowork lets you complete non-technical tasks much like how developers use Claude Code.



Ukrainian forces just hit one of Moscow’s largest power stations, the Shatura TPP. Seen here, a massive explosion on the northern side of the Russian power plant.







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







