Ewa Dominowska

This is a great experiment by the Anthropic team, showcasing the "true autonomous" capabilities of LLMs. By "true autonomous," I mean that Anthropic (I believe) purposefully did not bombard the LLM with numerous tools in areas where LLMs are weak. For example, LLMs are not good in math, and performance would have been much better if they hd provided a tool like "calculate_profitability." This might have prevented the $100 opportunity miss. In AI agent development, tools bridge the gap where LLMs are not "deterministic." This is currently the MOAT in the GenAI app business: the better your tools, the more deterministic results you can provide. Only till LLMs can write their own tools on the fly and execute them where they know they are weak and using deterministic softwares (tools) to fill these gaps. https://lnkd.in/gMk3VRJv

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Truly transformative. Within the next 6 months coding will be fully commoditized. Only design, distribution, and hardware will be the moat. It is time to lean in #AI #FutureofWork #OhioTech

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Search relevance is the backbone of any RAG system. If the retrieval is bad, the answers are bad — no matter how good the LLM is. We just published a deep dive on how we approach this at Dropbox Dash, and I think the core idea is worth sharing because it applies well beyond our product. The challenge: Dash searches across millions (sometimes billions) of enterprise documents to find the handful that matter for a given query. A relevance model ranks those results, and that model needs training data — lots of it. Human labeling is the gold standard, but it's expensive, slow, and can't touch sensitive customer data. Pure LLM labeling is cheap and fast, but uncalibrated LLMs make systematic mistakes. Our approach: use humans to teach the LLM, then use the LLM to scale. A small set of human-labeled examples calibrates the LLM's relevance judgments. Once the LLM meets quality thresholds, it generates hundreds of thousands of labels that train the production ranking model. The result is a 100x amplification of human labeling effort. A few things that made this work in practice: We focused evaluation on the hardest cases — documents users clicked that the LLM rated low, and documents users skipped that the LLM rated high. That's where the learning happens. We gave the LLM tools to research context before judging. Enterprise search is full of internal acronyms and jargon (at Dropbox, "diet sprite" is a performance management tool, not a soft drink). Without context, even strong models get these wrong consistently. We used DSPy (Community) to programmatically optimize prompts against human judgments, which turned prompt tuning from an art into something measurable and repeatable. The pattern — small human reference set, calibrated LLM evaluation, scaled labeling, continuous monitoring — is generalizable to any domain where you need reliable judgments at scale. Full our post on the Dropbox tech blog: https://lnkd.in/gB_5TECm

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I met one AI development agency founder, and they said our 50% RAG apps failed in production last year. Because teams don’t know how to handle: chunking + embeddings + vector DB + reranking + hallucinations + evaluation + monitoring. So I'm sharing a RAG Playbook that shows exactly how to build production-ready RAG systems, with practical examples. ✅ What you’ll learn from this playbook This playbook helps you go from: “RAG demo is working” → “RAG system is reliable in production” You’ll learn how to: - reduce hallucinations using prompting techniques - use advanced chunking methods (not random splits) - select the right embedding model for your use case - choose the best vector database (based on real needs) - add reranking to improve relevance - follow clear steps to build an enterprise-grade RAG system - run evaluation before production using key scenarios - monitor and optimize performance after deployment - track RAG quality using 4 powerful metrics If you’re building a RAG-based feature for your product, this playbook will save you weeks of trial and error. ✅ Save this post (you’ll need it later) 🔁 Repost for your team/network ➕ Follow Shubham Vora to learn more on AI agents.

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Ontologies remain one of the most effective ways to ground AI memory, keeping extracted knowledge anchored to existing structures and adaptable through feedback. As memory systems evolve, their role remains essential, and a core focus at Cognee.

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🧠 While LLMs continue to grab the spotlight, Small Language Models (SLMs) are quietly reshaping how we think about efficiency in AI. I recently explored how SLMs can run entirely on CPUs, unlocking new possibilities for low-cost, privacy-friendly, and scalable inference, ideal for teams experimenting with AI or deploying it at the edge. In my latest piece for Towards AI publication, I cover: 💡 Why CPUs are back in the AI conversation: smarter models, quantization, and optimized runtimes like llama.cpp ⚙️ SLMs vs LLMs: how a hybrid strategy can balance capability and cost 🧩 CPU Inference Tech Stack: GGUF, Ollama, and other tools making CPU inference practical 🧠 Hands-on section: building a translation app using llama.cpp, Streamlit, and AWS EC2, fully CPU-based The best part? You can try it locally - no GPU required. It’s been exciting to see the response so far, the article has already crossed 200+ likes on Medium! 📖 Read my full blog post here: 👉 https://lnkd.in/gby-EBmJ #SLM #llamacpp #cpu #BeginnerFriendly #AWS

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#1 - Toss a ball to a friend that is running at an angle and have them catch it. Not that hard for humans in general. Very hard for AI. #2 - Document and explain the math behind #1, with variants for wind speed, elevation, etc. Very hard for humans, relatively trivial for AI.  There's a lot of opportunity at the jagged frontier.

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I made an explorable esoteric research environment with a Custodian named MERIDIAN that can launch its own agent swarms to solve problems. Does isopsephic arithmetic and lookup for any Neoplatonists :-) https://lnkd.in/gYzqVn7m

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People are leveraging AI to build semantic layers. People are leveraging semantic layers to use AI. That just sounds too funny to me.

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Exciting times for AI! Google's launch of Gemini 3 and the Ironwood AI chip marks a powerful comeback. With industry leaders like Marc Benioff switching from ChatGPT to Gemini 3, it's clear the innovation is making waves. Alphabet's impressive stock performance and TPU chips challenging Nvidia highlight the momentum. The integration of YouTube as a training data advantage for video and image generation is a game-changer. Google's strategic moves in AI are worth watching closely. For more details, check out this article: https://lnkd.in/gfEbwk2V

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Important thoughts on LLMs and Knowledge preservation. If we want aligned AI, we need to align it to grounded truths and curated human knowledge. That was the original promise of the web.

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As large language models (LLMs) evolve with native support for function calling, many developers ask: “Do AI agents still need to look at the reasoning text in LLM responses?” This post explores that question.

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