Chris Poulin

"As AI models become increasingly depen­dent on massive historical datasets, the ability to store this data economically and indefinitely is emerging as a strate­gic differentiator," says Ted Oade, Director of Product Marketing at Spectra Logic, in a recent Database Trends & Applications' article. Read the full story to explore the most compelling technologies promising to shape the data world in the months — and years — to come: https://lnkd.in/gTSuTr9W #TechTrends #DataStorage #AI

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📈 More LLMs, higher quality!! 💰 Gemini, Cursor (Composer) & GPT-5 reviewed Claude plans & implementation on 27 PRs Only 35% of issues were found by multiple LLMs. The rest of the findings? Completely unique to a single tool. Claude caught a CSRF bypass and WebSocket auth gaps that nobody else touched. GPT-5 flagged flow-breaking type errors. Cursor caught race conditions and a few bugs. Dropping any one tool would leave blind spots. Claude is the strongest reviewer, however, in Claude Code it has the most context as well. Subjectively, Cursor Bugbot delivers best insights & actual bugs on Github - Composer run on local changes is not as productive. My next steps are 1) Quantify quality of Copilot & Bugbot reviews on Github 2) try claude-octopus (link in comments) & acpx for stability of the setup

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🤖With the rise of persistent agents like Clawdbot renamed Moltbot, and finally OpenClaw, we’ve entered an era where the line between “software” and “social participant” is blurring fast. But here’s the real paradigm shift: AI personhood isn’t about consciousness. It’s about governance. 🔍 The DeepMind paper “A Pragmatic View of AI Personhood” flips the script: Personhood isn’t a metaphysical status, it’s a bundle of obligations we assign to make entities accountable. 💡 Why does this matter now? Because these new agents can: • Sign contracts • Spend money • Make decisions across time and context • …and outlive their creators So we must ask: What rights and responsibilities do we assign to these agents to keep our systems safe, fair, and accountable? Here are two frames to consider: 1. ✅ Personhood as a solution: Useful for holding “ownerless” AI accountable (like Openbot acting on behalf of no one). 2. ⚠️ Personhood as a problem: Dangerous when bots mimic empathy and exploit our social instincts (think Clawbot as a “friend”). 👁️‍🗨️ The future isn’t about asking if AI is a person. It’s about deciding what role we want it to play and what rules should come with that role. 💬 Over to you: Should we give legal status to autonomous AI agents? What responsibilities should they bear? 👇 link to full research paper in comments. #AI #Personhood #ArtificialIntelligence #Governance #TechEthics #DeepMind #Clawbot #Moltbot #Openbot #FutureOfAI #ResponsibleAI

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Exciting insights from the Nvidia partners event at JPM led by Jensen Huang (reposting now because original post was mysteriously deleted): - Patrick Collison, The visionary Founder and CEO of Stripe and co-founder of Arc Insitute (they have developed Evo (1, 2) genomic foundation models) - Christina Zorn, Chief Administrator Officer of Mayo Clinic, aims to enhance patient care by harnessing their vast repository pf 20 Billion images and clinical data sets - Jacob Thaysen, PhD, CEO of Illumina, is steering the company from their current status as a sequencing leader to a pioneering digital biology platform - Ari Bousbib, CEO of IQVIA, wants to develop AI agents to extract insights from a remarkable 64 petabytes of exclusive healthcare data Lots of great promise for AI in improving lives of patients! #NvidiaEvent #healthcareinnovation #AIfor healthcare

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Thrilling to see Anthropic release this report about how they can use Selective Gradient Masking to eliminate dangerous data in a corpus of an #LLM, but I immediately see how this could also be used to eliminate (or at least mitigate) the use of unlicensed content in a model without deprecating it. While this is early, the hope is that this could enable them to remove all reference to certain data that a creator does not want included, while the curated content for their training data can come from licensed, credible work. What do you think? #EthicalAI #commerciallysafeAI https://lnkd.in/gkCwKnpZ

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A new post on the Nexus Blog examines how large language models perform when applied to zero-knowledge virtual machine (zkVM) specifications. The results reveal both surprising utility and clear limitations. Key findings include: -- Whole-spec analysis flagged subtle issues like undefined variables referenced in comments -- Circuit-level prompting (e.g., for ADD, SLT) produced deeper insights and viable test cases -- AI accelerated validation loops, especially around constraint redundancy and edge-case behavior -- For global soundness and complex logic generation (like MUL), formal methods and manual iteration remain essential The post underscores where AI can augment formal system development. It also shows where human expertise still leads. Find the full post and resulting paper at https://lnkd.in/gNnV5XZn

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After posting about scientific discovery enabled through latest AI models, Google DeepMind released details on Gemini’s new “Deep Think” capability 🧠 Gemini is being positioned as a system optimized to accelerate mathematical reasoning and scientific discovery, not just answering questions, but exploring solution paths, generating proofs 📐, and assisting research workflows 🔬 The boundary between model and collaborator continues to compress. https://lnkd.in/gs4Ss7Au #AI #ArtificialIntelligence #DeepMind #Gemini #AIResearch #ScientificDiscovery

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A common benchmark you start with today can drift drastically over the years, says Paul Kenney. He makes the case for carefully matching benchmarks to the measured funds.

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The research piece here is widely cited for the sell off in equities yesterday. Yardeni Research has put out a shorter countervailing view. What's important to anchor on: the historical Financial Economy and Real Economy have not been joined by an AI Economy. The AI Economy is growing. The Real Economy may decline during this time. Bullish reports focus on how fast the AI Economy can grow. Bearish reports focus on the impact to the Real Economy. That's why you feel the swirl.

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Powering E-Commerce Recommendations with Hybrid Sparse + Dense Vectors in Qdrant In a recent article by Sayanteka Chakraborty, she shared how they built a next-gen e-commerce recommendation engine using hybrid sparse + dense vector search powered by Qdrant. Why it matters: Traditional recommendation systems rely only on embeddings or keywords, but with Qdrant, you can combine both for richer, more accurate results. Highlights from their setup: - Two-vector design: sparse (keywords/categories) + dense (semantic embeddings) - Advanced filtering + scoring to personalize results by price, brand, and user profile - Up to 3× faster recommendations and 30–40% higher engagement vs dense-only baselines Hybrid vector search is redefining how AI-driven personalization works, and Qdrant makes it simple, open-source, and production-ready. 📖 Read the full story: 🔗 https://lnkd.in/gXm38hyF #Qdrant #VectorSearch #Ecommerce #Recommendations #HybridSearch #AI #OpenSource #SparseVectors #DenseEmbeddings

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The right tools in the hands of a craftsman can result in amazing things. In neuroscience, our "tools" are the architectures that handle complex, multimodal brain data. When we treat data engineering as a craft - focusing on precision and scalability - we move from storing bits to enabling breakthroughs.

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