Ben Miller

🚀 New in Docling: Structured Data Extraction from Documents! 🚀 We’ve just added a brand-new functionality: extraction of structured data from complex documents using free-form schemas 🤩 What does that mean? 🔹 You can now skip the conversion step — instead of turning documents into text or JSON first, Docling directly extracts the structured fields you care about. 🔹 The requested fields are defined in a free-form schema, so you can instantly align the extraction with the schemas of your own databases. 🔹 This makes it ideal for data-pipelines that don’t need full document conversion, but do need to populate structured databases from messy, unstructured documents. ✨ It’s: 1️⃣ Super simple to use (check out the code snippet 👉 https://lnkd.in/eK6vaMEe ) 2️⃣ 100% open-source and runs fully local – no API calls needed 🙌 3️⃣ Powered by cutting-edge models from NuMind (YC S22) 4️⃣ Perfect for data-pipelines where you need to populate structured databases from documents (think invoices, Curriculum Vitae, contracts, product datasheets, etc) 5️⃣ Currently focused on PDFs and images (PNG) — support for pure text coming soon! The example below shows how easy it is to define a schema and extract structured fields directly from an invoice. 👉 Try it out, break it, send us feedback, and if you like what we’re building, don’t forget to ⭐ the repo (https://lnkd.in/d4UT-6_2)! After a long and fruitful summer, the Docling team has been cooking up many new features — and this is just the beginning! 🌟 #opensource #AI #documentAI #docling #IBM #IBMResearch

1,382

83 Comments

Excited to see OpenAI's new gpt-oss. Enterprises don’t want to send sensitive data to a closed API/black-box model. The real value here is that you can bring open-source models to your data — not the other way around. With Databricks and OSS, you keep control: ✅ Your data stays in your environment ✅ You choose the model ✅ You maintain governance and security It’s a smarter way to build AI. Give it a try on Databricks: https://lnkd.in/gDsrdBhY #GenAI #OSS #EnterpriseAI #Databricks #CIO #CDO #AIstrategy

62

2 Comments

What I love about #ApacheIceberg is how many different workloads folks are running on the same copy of data. Graph queries are a really cool example. Instead of copying data into specialized graph databases, PuppyGraph lets you run graph traversals directly on your existing Iceberg tables. Pretty cool stuff. But here's the thing - to get truly great performance, you need to ensure your Iceberg tables are well maintained and optimized. That's where Ryft comes in. We make sure your Iceberg tables are automatically maintained and optimized, no matter what engine you are using. It was terrific to work with Zhenni Wu and Weimo Liu, to create this blog post on how you can use Ryft with PuppyGraph to run graph queries on a fully optimized Iceberg lake! https://lnkd.in/giwnwMuc #ApacheIceberg #GraphQueries

99

2 Comments

#OpenSource is just like open science: shared, collaborative, and the foundation of progress. 🚀 At PyData Paris 2025, I had the privilege of moderating the panel “Open-source Business” with Yann Lechelle (CEO, :probabl., former CEO of Scaleway) and Sylvain Corlay (CEO, QuantStack) at the Cité des sciences et de l'industrie. Both leaders are deeply embedded in projects like scikit-learn and Project Jupyter—the invisible infrastructure used daily by millions from scientists to business analysts. Our conversation unpacked what it really takes to grow sustainable businesses on top of open source.: • No one-size-fits-all: Every project needs its own model; hybrids are the norm. • Community = momentum. Every user is an advocate. Marketing spend shrinks when you invest in shared value • Safeguard the team: Visibility comes with risks; support and protection are non-negotiable. • Diversify funding. From VC to contracts to public investment (like France’s support of scikit-learn), sustainability depends on many levers. • Play the long game. The most critical projects are often the least glamorous. Openness as strategy. Beyond code: open science, data, source, weights, standards, hardware. A European playbook for digital sovereignty—with France and Germany catalyzing all 27 EU countries 🇪🇺 🗽. Sustainable open-source businesses balance community values with commercial viability—and “openness” is not ideology; it’s an industrial strategy that lets society understand and control its destiny. Thanks to Yann and Sylvain for a candid, deeply practical discussion. Looking forward to Yann's book "ouvertarisme.fr" that talks about how openness (from open science to open source to open models) can become an industrial policy. In fact, it has become one. We need to seize the occasion without falling into cynicism nor "open washing”.

77

3 Comments

🚀 Building apps on Databricks just got a lot easier. This blog shows how Lakebase can serve as the transactional data layer for Databricks Apps. 🗄️ Unified stack: Build apps on top of data in your #Lakehouse with Databricks Apps + Lakebase 🔐 Secure & serverless: Native auth, governance, and access controls included 🛠️ Practical guide: Walkthrough the setup of building an App 👉 Read more! https://lnkd.in/gJDYZ9MZ PS: Had the greatest honour catching our Co-Founder and Chief Architect Reynold at our HQ during #DataAISummit earlier this summer 🤯 He's the brain behind #Lakebase and now leading the #Neon team under his portfolio ⚡ Very humbling, exciting, inspiring experience altogether (More on this meeting in my future posts.. just maybe 👀)

123

1 Comment

Databricks is reportedly raising a fresh $1B at a $100B valuation — not because it needs the cash, but because it’s making a deliberate bet on the future of AI agents. This is a big signal as here, Databricks is doubling down on infrastructure for AI agents: • Scaling Lakebase, its database for AI agents, launched in June • Building out its AI agent platform The distinction matters. We’re entering a phase where AI isn’t just powering applications — it’s orchestrating entire workflows. That requires new data architectures purpose-built for agents, not just humans! 💡 My takeaway: The companies that win the AI era won’t just be the ones building the smartest models. They’ll be the ones building the plumbing that makes those models usable, scalable, and reliable. Databricks clearly wants to be that layer!

61

Good news for Snowflake shareholders! Snowflake had delivered some impressive Q2 FY26 results with some standout metrics: 📈 Financial Performance: • Product revenue: $1.09B (+32% YoY) • Net revenue retention: 125% • Total customers: 12,062 (+19% YoY) 🏢 Enterprise Adoption: • 751 Forbes Global 2000 customers (+5% YoY) • 654 customers with $1M+ annual revenue (+30% YoY) • 3,400+ Snowflake Marketplace listings (+21% YoY) 🤖 AI Momentum: • 6,100+ accounts actively using Snowflake AI features • Unified platform spanning Data Engineering, Analytics, AI, and Applications & Collaboration • Fully managed, cross-cloud, interoperable, secure, and governed What stands out to me is the 125% net revenue retention rate - this signals that existing customers are not just staying but significantly expanding their usage. The 30% growth in $1M+ customers also demonstrates Snowflake's success in landing and expanding, adding value within enterprise accounts. The AI adoption numbers are particularly compelling as organizations increasingly recognize the need for unified data and AI platforms to drive business value at scale. #DataCloud #AI #Snowflake #DataEngineering #CloudComputing

55

1 Comment