Optimizing tool models with tool masking: A novel approach

How do you optimize the model-facing surface of a tool for a given agent or task? This article by Frank Wittkampf and Lucas Vieira defines the novel concept of tool masking.

Can we draw an analogy between the compute–memory tradeoff and the inference–representation relationship? If you get stuck in your reasoning, try changing the coordinate system or exploring how a model’s inference power might be enhanced with more suitable representations. Learn more about complexity, intelligence, and emergence from Prof. David Krakauer. #ai #compute #complexity https://lnkd.in/dmq7Cjn4

Knowledge doesn’t scale if it’s buried. AI-driven knowledge layers are turning static documentation into living, searchable memory. Imagine if your company could actually use everything it already knows.

⚙️ Treating AI like just another software rollout? That’s the problem. It’s time to build an operating model that aligns people, processes & purpose. Explore more by diving into Raft's latest blog here 🔗 https://lnkd.in/eNnQ9kGy

Machine intelligence has started to live inside the way we think. It shapes ideas before we notice and steers how decisions take form. Beneath the smooth surface of our tools, the system keeps learning and occasionally bends what we take as truth. A quieter kind of Scenius is appearing—people building knowledge together outside institutional walls. And within it, a new measure is emerging: Human Leverage, the balance between time saved in creation and time spent checking what is real, useful, or good enough. https://lnkd.in/ec3Ze_4U

Fine-tuning isn’t just about watching a loss curve drop — it’s about building models that are safer, more accurate, and genuinely useful in production. To know if it worked, you need a full evaluation process, not just a single score. https://lnkd.in/dXrFjs4i

Our CEO Martin Mao wrote a blog on Chronosphere’s release of AI-Powered Guided Troubleshooting: a feature that surfaces evidence-backed Suggestions, shows its reasoning, and guides next steps. 🗺️ Get the details: https://lnkd.in/e7wnFqbH

I just published the first article in my new RAG series for the Regularized Thinking newsletter - starting with the basics done right. In this initial post, I cover: - What RAG actually is (without the hype) - When it truly solves a problem - When traditional search is more effective - The common mistakes almost everyone makes - A simple high-level diagram to demystify how it works If you want to build AI systems that are grounded, reliable, and not just “vector search everywhere,” this is a solid starting point.

🌟 Deep-Research Tool! Deep-Research Agent, built by AIREV for Agent Forge to help streamline your research workflow. It combines OpenAI-powered reasoning with Firecrawl’s web-crawling capabilities to gather publicly available information and organize it into clear, structured summaries. It can generate search queries, scan sources, and assemble findings in markdown format, giving you a faster starting point for deeper analysis while keeping you in full control of the final review 🔗 agents.aitech.io

How do you handle zero-shot text classification? So far, I've been tackling it by sending the text + the list of possible classes to an LLM, and asking for the right class. It works great, but it can have high latency and cost, depending on your data. I have recently started reading this book, and saw an interesting pattern that uses embeddings for this: 1. Embed each class (ex.: "Positive movie review" and "Negative movie review") 2. Embed the text you want to label (ex.: "This movie sucks") 3. Find the closest label embedding It sounds good, and it's probably cheaper and faster than using generative models. If anyone has tried this approach, I'd love to hear how it went!