Tokens & TOON — The Hidden Fuel of Agentic AI Every AI system runs on tokens — tiny chunks of text that models read, process, and predict. They’re not just words — they’re the currency of intelligence. The problem? As multi-agent systems scale, token usage explodes — duplicated prompts, redundant context, and wasted compute. That’s where TOON (Token Optimized Orchestrated Network) comes in. It acts like a traffic controller for tokens — managing memory, compressing context, and routing only what each agent needs. 💡 The result: ✅ Lower token cost ✅ Faster response ✅ Smarter orchestration Tokens make AI think. TOON makes AI efficient. #AI #LLM #TOON #Tokens #AgenticAI #MachineLearning #GenerativeAI #Optimization #AIEngineering #RAG

Felipe Masanes-Didyk, this is exactly what I was talking about yesterday. It's called TOON (Token-Oriented Object Notation). This post from Sam Keen explains it perfectly. The visual comparison showing a 50%+ reduction in tokens compared to JSON is incredible, and it just makes sense. As we discussed, this could be a massive deal for any AI implementation.

Just discovered TOON (Token-Oriented Object Notation), which provides an alternate token size optimized notation for use in the context of LLMs. It offers "typically 30–60% fewer tokens than JSON." Worth looking into! 🔗 TOON repo: https://lnkd.in/gUkFFyDc Explore this and other AI Tools, Tutorials, and News for software developers in Altered Craft's weekly AI review at alteredcraft.com.

This is very useful! First, having the number of items in the array and the props they contain in the first line can help the AI understand what to expect in the upcoming lines. Second, it saves a huge amount of tokens, which in consequence reduces cost. I’ll definitely give it a try!

This is so good. One of the early signs of software engineering rewiring itself for an LLM "at the coalface". Brace for LLM-optimised programming languages, requirements specifications, APIs, maybe even application development frameworks and infrastructure platforms. When human is no longer is in the loop, why optimise for human ergonomics?

This is interesting, but also not something I'd leap into using. I think (only by my anecdotal experiments - I'd love to see research to back it up) that LLMs do better with localised dependencies in data i.e. Column header followed by a stream of values is not as good as key-value pairs. And that, if this is for an agent that may use a fragment of the data in a later API call, it's much better if the input format is exactly the same as output. The TOON version would require some conversion to construct the header with the right row or turn into normal JSON. And finally, this may be optimising the wrong thing. Maybe the LLM should not be listing users at all, it should be able to use tool calls to search more precisely for what it needs.

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Are search engines secretly A.I. overlords in disguise? 🤖 Prepare for the dawn of recursive self-improvement. Programming tools are evolving...and fast. Autocomplete was only the beginning. Now, programmer automation is here. The lines between computing and AI are blurring. What was once a tool is becoming an extension of our minds, reshaping reality as we know it. The future is coding itself. Adapt or become obsolete. #AIRevolution #FutureOfTech #CodeApocalypse

\*\*Code LLMs in 2025: Beyond Autocomplete 🚀\*\* Large Language Models for coding are rapidly evolving from basic assistance to full-fledged software engineering systems. By 2025, these advanced LLMs will tackle complex tasks like fixing GitHub issues and refactoring multi-repo backends. Understanding which model fits specific constraints is now crucial for engineering teams. How will AI reshape software engineering practices? \* AI agents will autonomously resolve real-world GitHub issues. \* Tailored AI solutions will optimize complex multi-repo refactoring. \* This marks a new era for AI as an integrated engineering partner. What challenges are you most eager to see AI tackle in your codebase? Learn more about this news in the full article: https://lnkd.in/ddkjECZk #CodeLLMs #SoftwareEngineering #GenerativeAI #AIinSoftwareDev #AutonomousAgents #FutureofCoding

Been working with knowledge graphs since the early days, structuring meaning, context, and relationships. But with the rise of GenAI, it’s incredible to see how graphs can now enhance reasoning and understanding at a whole new level. One of my earliest projects involved generating a semantic web and enabling it to reason through predicate logics with #PROLOG and JADE(Java Agent DEvelopment Framework), a small step that now feels like a glimpse of where contextual AI was heading. The synergy between structured knowledge and generative intelligence is truly reshaping how machines think. #GenAI #KnowledgeGraphs #SemanticWeb #AIReasoning #ContextualAI