Justin Hamilton

dbt Labs announcement of #dbt (con)Fusion has a lot of things that we as a data community need to discuss. dbt Fusion is a complete rewrite of dbt Core in Rust. Unlike dbt Core, which is completely free and open source under Apache 2.0, dbt Fusion is not open source as it is under the more restrictive Elastic 2.0 license. Although Fusion is free to use, it restricts its usage in a hosted or managed offering to 3rd parties. You may think this is fine, but there are far-reaching implications. Open source is amazing because it incentivizes individuals and companies to invest in it without risk. A company can go all in on open source because no matter what the direction of the core maintainers is, it can be forked and used for whatever reason you want. A non-permissive license like Elastic will disincentivize companies from investing. Don't get me wrong, there's nothing ethically wrong about dbt Labs' decision. It may even be in their best financial interest to do so. However, I want to analyze what led to this situation and what it might mean for the future of dbt Core. What I believe this means is that dbt's strategy is to put dbt Core in "maintenance mode" to focus on Fusion and their other proprietary offerings. The wording of the announcement was very carefully selected to be vague. In particular, when referring to dbt Core support, it was only highlighted that bug fixes, security patches, and compatibility would be ongoing. According to their dbt Core roadmap, they've separated out the dbt language from the runtime. There's a specific callout that Fusion and Core will inevitably diverge because Fusion has additional capabilities that cannot be added to Core. To me, it makes sense that this is the chance for dbt Labs to invest in more restrictive and profitable software while slowly deprecating what not only made them great but is also their biggest challenge to financial growth. Ultimately, resources are finite, and companies must prioritize what makes sense for the business. Given dbt Core's foundational importance to modern data infrastructure, Analytics Engineers deserve a free, open, and continually evolving transformation platform. Otherwise, your career will be dangerously dependent on the decisions of a single company. To safeguard the continuing innovation and development of the transformation space, it may be time to start a discussion about an open standard for defining data transformations.

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Producer Claude just got an upgrade! Anthropic AI just dropped Claude Sonnet 4.5, their cutting-edge new model. But what makes the new Claude better than the old Claude? 💻 Faster code refactoring + debugging ⚡ Better workflows for messy, real-world tasks 🛠️ Easily processes incomplete reqs + legacy code These companies are making their models 2X better every year, but can they keep up this relentless pace? Alex Wilhelm and I consider the possibilities in this exclusive TWiST clip

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📣 Introducing RRE-1: A New Model for Retrieval Evaluation & Reranking for RAG based applications. Retrieval-Augmented Generation (RAG) has massively helped transform LLM-powered applications, but let’s face it—retrieval quality is still the weakest link in many pipelines. Poor retrieval is one of the major contributors of lower accuracy of your LLM applications. 🧐 That’s why we at AIMon built RRE-1, a powerful retrieval evaluation and reranker model designed to optimize ranking quality at scale. 🎯 👉 Why does this matter? Traditional retrieval methods like vector search are powerful, but they have limitations in effectiveness, especially when handling complex queries and domain specific knowledge. RRE-1 addresses this by offering: ✅ Offline evaluation of retrieval effectiveness, helping teams benchmark and refine their pipelines. ✅ Real-time low-latency reranking to ensure the most relevant context reaches the LLM. 👉 Who is this for? If you’re working on LLM agents, enterprise RAG systems, or AI copilots, this is for you. Our model ensures your retrieval pipeline is as optimized for your generative model—because great responses start with great retrieval. 🔎 Want to see it in action? Read more about AIMon RRE-1 in our blog post (link in comments). If you’re tackling retrieval challenges in your AI stack, we would love to chat! #AI #RAG #Retrieval #LLMs #MachineLearning

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Nuanced is a structured context layer for AI coding tools: call graphs, symbol maps, and static analysis that make LLMs reliable. We just launched our explainer video: 📽️ https://lnkd.in/eA8wmYeA If you’ve ever banged your head against Claude Code or Cursor, you know the pain: LLMs can write code, but they don’t understand it. They hallucinate helpers, invent imports, and break prod, because they can’t trace real structure across your repo. Nuanced gives them that structure. No more guesswork. Just deterministic edits, grounded in your codebase.

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Wow nice Microsoft AI introduced a new baseline benchmark called SD bench (Sequential Diagnosis Benchmark) to reflect more realistic "medical" problems for a large language model. Also they tested it on this benchmark with their model called Microsoft AI Diagnostic Orchestrator (MAI-DxO), kind of an "ensemble" model of large language models reaching pretty high diagnostic accuracy. Interesting idea, you kind of build another "voting" layer on top of mixture-of-expert 😅 Smart but not energy efficient! 👉 Read more here: https://lnkd.in/gyWsEyMa

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Can clinical AI summaries be not just useful... but fun? We built something unusual at Abstractive Health: a free simulation game called Clinical Time Machine. Try for free here -> https://lnkd.in/epeuph9m Why a game? Because less than 1% of doctors have ever seen an actual AI summary of a real patient. Not because they don’t want to - it’s an access problem. Our summaries are really good on real patients - but it takes time to verify new users are doctors. And synthetic data doesn’t cut it. So we thought: what if we used famous historical patients instead? The first case we built is King George III - whose condition was long labeled as "madness." No spoilers if you know what it really was... We tracked down his original handwritten records from 200+ years ago, ran OCR on them, and fed them through our AI summary engine. Then we layered in RAG and agent-based reasoning to build a playable simulation. Doctors can now interact with the case like it’s their own: ask clinical questions, submit a differential, choose treatments, and watch the outcome. It’s built on top of our real product, and early testers said: “This made the board exams fun.” Hence Abstractive decided to build the first clinical AI game with chart summaries 🙂  Shout out to Caroline Reiner for the original idea! #HealthcareAI #ClinicalSimulation #FutureOfMedicine Doctors can diagnose King George III with our AI:

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