What’s new with PostgreSQL at Microsoft in 2026? From developer tools, open-source contributions, Azure HorizonDB, Azure Database for PostgreSQL, and community initiatives, catch up on the latest innovations shaping the future of Postgres. Read the blog.

AI is driving new PostgreSQL adoption. See how organizations are moving to Azure to modernize faster, simplify operations, and build AI ready apps. Learn what customers are unlocking:

With Lakehouse federation for AlloyDB, you can query data in Iceberg and BigQuery from the PostgreSQL plane. Get historical insights in BigQuery or Iceberg without any data movement →

In this episode of #AzureFriday#, @shanselman and Charles Feddersen explore Azure HorizonDB, covering performance and scale, AI Pipelines, and AI-powered PostgreSQL developer productivity using VS Code. Watch now:

# Claude Code Features and Practical Usage 🔌 Still copy-pasting schemas and logs into chat? Connect Sentry, PostgreSQL, or GitHub via MCP and Claude reads and writes those systems directly. "Investigate the last 30 days of this table" just works. 📌 Title and Feature URL Title: MCP URL: 📝 Overview MCP (Model Context Protocol) is an open standard for AI tool integration that connects Claude Code to hundreds of external tools and data sources. MCP servers give Claude Code access to tools, databases, and APIs, so it can read and operate those systems directly instead of working from what you paste. 🔧 How It Works - Multiple transports exist: HTTP (recommended) for remote connections, WebSocket for bidirectional push, stdio for local processes, and the deprecated SSE. - Three scopes are available: local (default, private to you, stored in ~/.claude.json), project (shared via .mcp.json), and user (all projects, private to you). - Many cloud servers require authentication and support OAuth 2.0; on a 401/403 response you can complete the flow from /mcp. - Tool search is on by default, lazily loading MCP tools on demand to keep context usage low. 🛠 Practical Usage - Add with claude mcp add --transport http , or for stdio claude mcp add [options] -- [args...]. Options go before the server name. - Manage with claude mcp list / claude mcp get / claude mcp remove , and use /mcp inside a session to check status and authenticate. - Set scope with flags like --scope project; for project sharing, the .mcp.json file is version-controlled. - Reference MCP resources with @/server:protocol://resource/path, e.g. analyze @/github:issue://123. - Run MCP prompts as commands in the form /mcp__servername__promptname. 🎯 Use Cases - Connect Sentry and debug production issues: "What are the most common errors in the last 24 hours?" - Connect PostgreSQL and query without pasting the schema: "Find customers who haven't purchased in the last 90 days." - Connect GitHub's remote MCP and ask "Review PR #456# and suggest improvements." - Commit .mcp.json so the whole team shares the same set of MCP tools. ⚠️ Caveats - Confirm you trust each server before connecting; servers that fetch external content can be exposed to prompt-injection risks. - Project-scoped servers require approval before use for safety (reset choices with claude mcp reset-project-choices). - A warning appears when tool output exceeds 10,000 tokens; raise the cap with MAX_MCP_OUTPUT_TOKENS (default max is 25,000). - SSE is deprecated; use HTTP where possible. #ClaudeCode# #MCP#

🚨 SlowMist TI Alert 🚨 MistEye has detected a large-scale npm supply chain compromise impacting the Keyv/Cacheable ecosystem. Attackers published over 2,000 malicious package versions across the affected ecosystem, including keyv@6.0.0. Keyv, a widely used key-value storage abstraction with adapters for Redis, SQLite, PostgreSQL, MongoDB, and other backends, has roughly 127 million weekly downloads, leading to significant downstream supply chain exposure. The attackers' tradecraft closely mirrors techniques previously observed in the Shai-Hulud npm worm campaign, pointing to a highly automated and scalable supply chain attack. Potential attacker actions include credential theft, environment variable exfiltration, CI/CD secret compromise, remote payload delivery, and lateral propagation through compromised development environments. Security teams should immediately identify and remove affected package versions, upgrade to verified safe releases, review dependency lockfiles and build logs, monitor for suspicious outbound connections, rotate exposed credentials, and rebuild impacted environments from trusted sources if compromise is suspected. You can also visit to check for free whether the npm packages, pip packages, domains, or IPs you use are safe. As always, stay vigilant!

TL;DR Reliable agentic AI comes not from "a better model or prompt" but from explicitly engineering context and the orchestration harness—lessons from PRINCE, a Bayer × Thoughtworks pharma system. 🧪 Title: Building Reliable Agentic AI Systems URL: Highlights 🧭 Sequential agents with pause points: clarify intent → Think & Plan → Researcher → Reflection → Writer, verifying step by step 🔁 Three reflection loops: process (trajectory), data (evidence sufficiency), draft (output completeness) catch distinct failures 🔎 Hybrid retrieval: query expansion n=5, weighting 0.7 semantic + 0.3 keyword, bge-reranker cutting ~20 → 7 chunks 🗃️ Structured data via Text-to-SQL: SELECT-only, up to 3 self-corrections, ≤50 rows per query 🛟 Harness engineering: state persisted in PostgreSQL/DynamoDB, resume from failure point, automatic provider fallback 📌 Sentence-level citations plus RAGAS and Langfuse evals; daily batch checks on live traffic catch hallucinations 🏷️ NER extracts entities from study PDFs; high-confidence fields auto-update, low-confidence quarantined for human review The "even with big context windows, selectivity still matters" stance rings true to anyone shipping this stuff. #AIAgents# #LLMOps#

🗄️PostgreSQL just landed in CloudBase With CloudBase, your Vibe Coding product can spin up its own database with a single prompt In practice: - Tables, permissions, and auth rules are declared in SQL, the same language your Agent already reads - Row-level security means author_id = auth.uid() does the heavy lifting, no custom middleware - Each user environment is isolated by default: spun up, hibernated, or deleted via API (or MCP) Go build→：

Postgres 19 is one of those releases you feel more than you notice. Built-in repack, better replication, and steady improvements across the board. Dive into this breakdown from @craigkerstiens on what actually matters: