CtxEngineer @ctxengineering

Introducing the Open Knowledge Format (OKF), an open specification that formalizes the LLM-wiki pattern into a portable, interoperable format. AI is only as smart as the context we give it. As we build more advanced, agentic AI systems, they need accurate metadata and context to be useful. But in most organizations, that context is locked inside fragmented data catalogs, isolated wikis, scattered code comments, or the minds of senior engineers. Every time a new AI agent is built, teams are forced to solve the exact same context-assembly problem from scratch. To solve this, we've announced OKF, a vendor-neutral, open specification that formalizes the "LLM-wiki pattern" into a portable, interoperable format. It provides a standardized way to represent the enterprise knowledge that modern AI systems rely on. — Just markdown: readable in any editor, renderable on GitHub, indexable by any search tool — Just files: shippable as a tarball, hostable in any git repo, mountable on any filesystem — Just YAML frontmatter: for the small set of structured fields that need to be queryable: type, title, description, resource, tags, and timestamp We’ve also shipped reference implementations to help you hit the ground running, including an enrichment agent for BigQuery, a static HTML visualizer, and live sample bundles on @github → goo.gle/4uGvAEe ➕ Knowledge Catalog can now natively ingest OKF! Stop reinventing data models and building bespoke integrations for every new AI tool. Here's more about how OKF works → goo.gle/4uGvBbg

DoH is now generally available on #Microsoft Windows DNS Server 🚀 techcommunity.microsoft.com/blog/networkin… #WindowsServer #Winserv #DNS #DoH #WindowsServer2025 #MVPBuzz #ITPro #AzOps #DevOps #SecOps @windowsserver

Fable 5 is dead. - Launched June 9. - Killed June 12 by a US export order. Anthropic pulled Fable 5 + Mythos 5 for everyone worldwide. Even their own foreign national employees are locked out, while other Claude models keep running. This is a brutal warning shot. Imagine building your business on Fable 5 and watching it vanish overnight, with zero notice, because of a policy call you don’t control. Anthropic says it’s a misunderstanding and they’re working to restore access. Maybe they do. But the point stands, it’s closed, and it proves how centralized AI can vanish at the stroke of a regulator’s pen. If Fable 5 isn’t back by Monday, watch how the market starts repricing model risk, governance risk, and “AI as a single vendor dependency”. This really is a fable with a moral: don’t bet your entire future on a black-box model you don’t own and can’t run yourself.

Anthropic @AnthropicAI

5 days ago

The US government, citing national security authorities, has issued an export control directive to suspend all access to Fable 5 and Mythos 5 by any foreign national, whether inside or outside the United States, including foreign national Anthropic employees. The net effect of

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@QuantCapitalX @MonikaSchnitzer Genau - darum nur noch kalt duschen, damit der Sprudl endlich wiedee günstiger wird ;) LOL

@Katrindolin @brand_rede LOL eine Offenbarung dieser Post.

@rnabmitra Oh yes, fiiiinally :).

Two new toolkits for Azure Cosmos DB, both aimed at the same problem: agents that forget. The Agent Memory Toolkit gives your agents durable memory, so context survives past the current turn. The Agentic Retrieval Toolkit is a reference architecture for multi-step RAG, where the agent retrieves, checks what's missing, and goes back for more before answering. Learn more: msft.it/6010vjmAa #MSBuild #AzureCosmosDB

ICYMY: We announced Neo4j Virtual Graph: our zero-copy architecture lets you run #Cypher and graph algorithms directly against your data in Snowflake, Databricks, and other cloud data warehouses, without moving a thing. Get graph-powered insights like multi-hop reasoning, and automatically generate a graph model from your existing tables with a single click. 🚨 Virtual Graph is available now in private preview - you can request access by filling in a form bit.ly/4uEeV5h Watch the entire demo here, bit.ly/4dG0AO8 #VirtualGraph #Neo4j

Vector databases are no longer a cloud product. They're becoming a pip install. A new open-source project called turbovec just crossed 10K stars on GitHub. And once you understand what it does, you understand why. It's a Rust vector index with Python bindings, built on Google Research's TurboQuant algorithm, a quantizer accepted at ICLR 2026 that compresses embeddings to within a hair of the theoretical Shannon limit. No codebook training. No train phase. No rebuilds as your corpus grows. You add vectors, they're indexed. Done. The headline number: A 10 million document corpus takes 31 GB of RAM as float32. turbovec fits it in 4 GB and searches it faster than FAISS. Read that again. Faster than FAISS. The library Meta has tuned for a decade. Hand-written NEON and AVX-512 kernels beat FAISS FastScan by 12–20% on ARM and match-or-beat it on x86. (And the recall benchmarks are published openly against FAISS as the baseline including the configs where it loses. That honesty alone is rare in this space.) But the speed isn't even the strategic part. The strategic part is what this enables: Fully local, air-gapped RAG. 10M documents in 4 GB means your entire company knowledge base fits in the RAM of a MacBook. Pair it with an open-source embedding model and nothing not a query, not a vector, not a document ever leaves your machine. It also ships drop-in replacements for the vector stores inside LangChain, LlamaIndex, and Haystack. Swap one import, keep your pipeline. The switching cost is approximately zero. The obvious comparison is SQLite. Databases used to be servers you provisioned and paid for. Then SQLite made the database a file inside your app, and an entire category of managed infrastructure became optional for most use cases. The same compression-driven collapse is now coming for vector search. Every startup selling "managed vector search" as a line item should be paying attention. When the index fits in laptop RAM, runs faster than the industry standard, and installs in one line the moat was never the database. The vector database is becoming an embedded library, not a cloud service. And the frontier of RAG just moved on-device. Really cool to see.

Introducing the Azure Cosmos DB Cost Estimator (public preview) msft.it/6016vbNaE In under 2 minutes, you can: - Start from a real workload (AI chat, RAG, catalog, etc.) - Get a $/month estimate - See exactly what’s driving cost (regions, RU/s, storage, vectors) 👉 Try it: msft.it/6017vbNa1

The Azure Functions MCP extension has had a breakout year 🚀 From "tools only" → a full MCP platform: ✅ tools, resources, prompts ✅ MCP Apps (interactive UI) ✅ built-in auth + rich types and more! Check out: aka.ms/functions-mcp-… #MSBuild

Go support! yihaaaa

Azure Functions @AzureFunctions

2 weeks ago

Azure Functions at Build 2026: serverless agents in a .agent.md file, 1,400+ M365 and SaaS connectors as triggers, Go as a first-class language, full MCP support, new CLI, On-demand Sandboxes for DTS and much more! techcommunity.microsoft.com/blog/appsonazu…

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Introducing OmniVec: An Open-Source Embedding Platform for AI Apps on Azure - Azure Cosmos DB Blog devblogs.microsoft.com/cosmosdb/intro…

@Michael_Kissig Egal ob pro oder contra bitcoin. Ein Anlageberater, der mit „innerer Wert“ argumentiert, wird mich nie beraten. Es gibt keinen intrinsic value zum 100. Mal. Konsens über Wertevorstellung ist subjektiv, sonst nichts.

idk who created this website, but it's awesome and i love it. sophon.at/papers

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The Year of the Graph @TheYotg

3 weeks ago

Graph Engines: Introducing Neo4j Virtual Graph - Graph reasoning on the data you already have Unlocking the power of graph intelligence on all your enterprise data - no ETL needed. Most graph conversations start with "move your data into a graph database." But what if you

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Excited to launch @neo4j Virtual Graph 🚀 Graph reasoning directly on enterprise data. no ETL, no copies, Cypher pushed into Any Lakehouse. Helping make enterprise data accessible to AI agents. lnkd.in/eeSTkaCP #GraphAI #EnterpriseAI #AIAgents #Snowflake #Databricks

Today we’re launching Command Line, a new technical blog about how and why we build. Our first stories go behind the architecture, design decisions, and research shaping the next wave of developer tools. Stay tuned for more next week at #MicrosoftBuild msft.it/6013vkN8f

@Proton_Pass @ProtonPrivacy Penis?

HNSW is fast. But it is also memory-hungry. DiskBBQ is the alternative. Here's how it works: 1. Hierarchical K-means partitions all vectors into clusters, each with a representative centroid 2. The centroids are arranged into a tree and loaded into memory. The vectors themselves stay on disk 3. At query time, the centroid tree is searched first to find the most promising clusters. Only those get fetched and fully explored. 4. BBQ (Better Binary Quantization) compresses each vector to 1 bit per dimension, so bulk cluster scoring stays fast with minimal memory and disk overhead 5. SOAR (Spilling with Orthogonality-Amplified Residuals) assigns border vectors to multiple clusters, which reduces the recall penalty of hard cluster boundaries Use HNSW when you need maximum recall and have plenty of memory. Use DiskBBQ when you're cost-sensitive and can live with slightly lower recall.