Ilarie Letca

Anthropic just committed $100M to launch the Claude Partner Network. The capital will fund training, certification, and joint GTM for consulting firms and system integrators. They are moving beyond direct API sales to activate a network of software vendors to deploy Claude at scale. Ecosystem building is the new moat in enterprise #AI. The AI war has shifted from training the smartest models to owning the distribution channels.

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Schema data is your cheat code for LLMs. It allows you to present information in a structured, easily digestible format, enabling the LLM to read and extrapolate effectively. Embracing schema is the optimal path forward for data representation and comprehension. Structured data is the way. #SchemaData #LLM #DataStructure #AI #TechInsights

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So if semantic objects aren't schema objects like tables and columns, what are they? In short, they are units that link a concept in the real world to data in the database when those concepts need to be modeled as logic rather than as a column. That's right -- semantic layers are only required because database schemas are rigid, slow-moving, unversioned and unversionable objects. That's why people who only work on toy datasets like taxis and iris can't understand why a semantic layer would be necessary. IT ISN'T...if your data model is perfect. But in the real world, the best planned data model gets out of data almost immediately. Even if one source system manages to reflect reality perfectly, introducing a new data source by definition introduces a conflicting data model. If you want to query across them you either have to create a third harmonized data model and perfectly maintain it...or create a semantic layer that abstracts the objects they share into a parallel plane. And in that plane, we can be flexible, move fast, use versioning...all without weeks of ELT writing. So, finally, what is in this plane? We haven't normed on this as an industry, but I have an opinionated view, which is the view we've built http://schemantic.io around. The core objects are: - Entities - Events - Attributes ## Entities This is the foundation of the semantic layer. Each one of these represents a distinct noun in reality -- people, places, things, ideas. Fundamentally, these are ID columns. Everything else "hangs" on these objects. ## Events If entities roughly represent space, events represent time. They define how and when entities interact. They are fundamentally dates, and their names explain what the interaction is. Because of this principle, events "link" entities together. ## Attributes Virtually everything else in a database is an attribute. These contain the characteristics of an entity. Fundamentally, these values can be integers, strings, booleans. They can be categories, scalars, anything at all. They just need to be "bound" to an entity. Each of these objects has its own logic, written in SQL, so that they can composed by humans and machines into the infinitely complex queries we all need to write. And like all objects (both schema and semantic), they have context. But that is a topic for my next thread. If you want to see how a semantic layer can accelerate your analytics and AI readiness, request a demo here: https://lnkd.in/gXyjiq3e

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Oracle plans to borrow about $25B/yr to fund OpenAI cloud infra—$100B in four years!—raising questions on feasibility. With a $455B RPO backlog and flat cash flow, ROI must align with unit economics, not hype. A sharp read for product leaders shaping AI. Explore: https://buff.ly/bXmpPJN #AI

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By benchmarking real-world image transformation tasks, I found that Google Axion’s generous cache, offering larger L2 cache per core provides a clear architectural advantage. Key takeaways: * Performance: C4A (Axion CPU) was roughly 1.8x faster than a comparable Intel Emerald Rapids CPU for high-resolution image resizing. * Cache Efficiency: Axion maintained a 99% L2 cache hit ratio, significantly reducing CPU stalls compared to x86 counterparts. Read the full technical deep dive here: https://lnkd.in/g4S9c_3m

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🛡️ The agent control plane is, in some way, the same category as firewall and antivirus. Just for AI agents. JFrog AgentSecOps launched September 2, Boomi's Agent Control Plane with 1,000+ governed MCP tools launched September 2, and Solo.io shipped agentdesktop on September 3, an open-source desktop-local version that runs the same allow/deny envelope on the developer's laptop where Claude Code, Cursor, and Codex actually run. 🌐 Solo.io shipped the first open-source, desktop-local version this week: agentdesktop reads the same policy and identity primitives Solo already runs in kgateway and AgentGateway, but as a local daemon on the workstation, with Apache 2.0 and an MDM-deployable fleet mode for centralized IT rollouts. 🧩 The cloud-only guard was the open gap: agents already run on developer laptops outside the firewall and connect to production systems, and most enterprise governance stacks stop at the cloud edge. 👉 https://lnkd.in/dGkUiQX2

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Break down how TiDB handles 1M rows/sec in production — the complexity of multi-tenant SaaS architectures actually feasible by #TiDB

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Nvidia GTC presentation about structured & unstructured data being foundation for AI/LLMs used for business needs. We at OLake strongly believe the same and developing features around Iceberg. Apache Iceberg as per Feb 2026 release will soon be supporting unstructured data along side with structured and semi. https://lnkd.in/geYYF--6

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