🔥 on GitHub Databend 为AI代理设计的企业数据仓库，支持向量搜索和Python沙箱 ⭐ 9.3k stars on GitHub 🤖 Claude Code, Cursor · 🧠 Claude 3.5, GPT-4 vibeshit.org/product/databen…

贵司的 databend 刚好适合做海量trace存储和分析

这是周六（2026.06.06）下午在 PingCAP 办公室参加 Databend 组织的 Data & AI Meetup 我的分享实录：我是如何从一个古法编程手艺人进化到 AI 软件工厂“厂长”的。 当天下午 PingCAP 放出一个企业 Agent 协作的产品： LOOP。 也有朋友说，这类产品现在不下 100 个了。 表面看起来好像是这类产品有同质化的感觉，但实际上，这是时代的氛围到这了。 并不是大家跟风 Vibe，而是有真实需求在这里，算是形成了某种共识，只不过具体的产品理念有所不同。 我的这次分享，是从近三年我的 AI Coding 进化过程，来回顾一下 AI Coding 是如何一步步进化到现在这种氛围的。 希望大家看完之后背脊不会发凉。

How does Databend Cloud scale on 1TB of TPC-H data? • Small: 1173s • Medium: 538s (2.18x faster) • Large: 286s (4.10x faster) 6 billion rows, 22 analytical queries, predictable performance scaling docs.databend.com/guides/ben…

We just shipped llms.txt for Databend — a progressive skill system for your AI agent. Fetch docs.databend.com/llms.txt for Databend context One line. Your agent gets a structured index of everything Databend — SQL, data loading, tuning, troubleshooting. From there, it progressively fetches deeper docs (sql.txt, guides.txt, tutorials.txt) only when needed. No token waste. Pair it with our MCP server and your agent goes from knowing Databend to acting on your data — query, explore, build — all safely sandboxed. → docs.databend.com/llms.txt → databend.com/mcp/

New in #Databend: Geospatial goes deeper 🌍 - Geometry aggregate funcs in SQL - Refreshable Spatial Indexes - ST_DWITHIN-powered index pruning - Geo values encoded properly in Arrow results Less full-scan geometry. More pruning. Faster spatial queries. Built in Rust, running on your object storage. github.com/databendlabs/data…

Cyberspace gif 2014? illustration, notepad , databend

Model Databend [TIF] 2014 notepad

"Identity Databend Lost" Available as an open edition on @TransientLabs for only 3 DAYS, 0.001 ETH Link below.

We just shipped .md support across all Databend Docs ⚡️ Every guide is now available as clean, raw Markdown — perfect for AI agents, RAG pipelines, and LLMs. No more HTML parsing. Just add .md and go. Try it: → docs.databend.com/guides/ai-… → docs.databend.com/guides/ai-… AI devs building on Databend, this one’s for you.

> Rimio is a lightweight write-back cache that accelerates object-native systems (SlateDB, ZeroFS, JuiceFS, NeonDB, Greptime, Databend, WarpStream, Thanos, etc.) for edge and on-prem clusters with minimal operational overhead. 🤩 github.com/flaneur2020/rimio

Rachael 24x36" databend pixelart notepad photoshop

Músicas: Cunk; Dead Weight; Coconut ranger; Leopard; John & Nancy; Beird; It's alright; Small world; Pad Thai; Gettin' my mom on; Databend; O.U.R; Pizza boy; Moonstone (bônus track, versão de vinil)

Excited to explore the future of Agent-Ready Databases—empowering AI agents in enterprise data ops with seamless, secure workflows! Our vision at Databend includes: - Unified semantic storage: Seamless SQL access to structured, unstructured, and vector data—with rich metadata for instant context - Instant schema evolution: Zero-cost changes like adding columns, no data rewrites - Git-like branching: Safe, zero-overhead snapshots for isolated testing and risk-free merges - Robust ACID transactions: Atomic multi-step operations with easy rollbacks for self-correcting agents - UDF Sandbox (coming soon): Isolated Python execution for AI logic, minimizing data risks - Extreme stability: Auto-scaling under high loads for 24/7 agent reliability We're advancing with native Parquet support, metadata versioning, zero-copy snapshots, multi-statement ACID, and more—shaping the next-gen warehouse. Dive in: databend.com/blog/category-p…

The AI-agent-ready enterprise database needs many things. The ideal includes: 1. SQL as the first-class citizen 2. Schema: automatic evolution with full transaction support 3. Data: branching Git-like versioning — agents safely operate on production snapshots 4. Sandboxed UDFs SQL orchestration — build and run agents on your enterprise data Databend has implemented most of these. The goal is an enterprise data warehouse for AI agents.

🚀 We just shipped #Databend MCP: Your safeguard for AI-driven data ops in production. Read-only access meets isolated sandboxes—zero risks, all the power. Key features: • Prod read-only: SELECT/SHOW on live data, no writes • Per-session sandbox: Safe writes in unique namespaces • Verification magic: Sample, simulate, validate queries Integrate with Codex CLI, Claude, Cursor via DSN. Dive in: databend.com/blog/category-p… Repo: github.com/databendlabs/mcp-… Docs: databend.com/mcp/