Timescale

We asked three experienced TigerData (creators of TimescaleDB) engineers about strategies for incorporating MCP into existing developer workflows: https://lnkd.in/gQfDkHvu ▶️ The engineers recommend hands-on learning for effective MCP integration. Anton Umnikov built MCP servers from scratch in bash to understand the underlying JSON-RPC protocol, emphasizing that doing things "the hard way" builds essential understanding. James Guthrie suggests starting with documentation servers and maintaining human-in-the-loop approaches for sensitive operations. ▶️ John Pruitt focuses on practical applications: using MCP for database context integration and automating "chore" tasks like writing tests and commit messages. All three engineers emphasize that MCP should complement existing workflows rather than replace them, with thorough understanding of fundamentals preceding any production deployment. 🔗 Learn more by watching the video (and reading the companion blog: https://lnkd.in/gUk_WnGW). #ai #mcp #engineers #developers

TigerData customer WaterBridge IPO’ed today! Here’s their post announcing this milestone: https://lnkd.in/p/gcPU-sQH Congrats to the WaterBridge team from all our team at TigerData (creators of TimescaleDB)! WaterBridge develops, owns and operates permanent, integrated water infrastructure networks to address the long-term produced water management requirements of E&P companies. Waterbridge uses TimescaleDB as a data historian, ingesting up to 10,000 data points/second and displaying them in real time for monitoring and alerting. Here’s the full case study: https://lnkd.in/gHMUXUJY

🚨Crypto Supply Chain Attack Alert 🚨 A popular npm package, error-ex (47M+ weekly downloads), was compromised last week. What happened: - A tiny, one-line utility package was silently updated (v1.3.3). - The new version contained heavily obfuscated malicious code. - The payload appeared to target Ethereum environments, attempting to exfiltrate secrets. - Because this package is a transitive dependency in thousands of projects, the blast radius was massive. Why it matters: Even if you’ve never heard of error-ex, your applications may depend on it through another library. That’s the fragility of the modern software supply chain. What you can do: - Use npm ci in CI/CD pipelines (not npm install). - Pin transitive dependencies with overrides. - Regularly audit your dependencies and lockfiles like you would production code. This incident is a stark reminder: a single dependency can compromise the entire chain. Stay vigilant. 🛡️

"You don't need to read documentation, just tell the LLM what you want to accomplish." We asked three experienced TigerData (creators of TimescaleDB) engineers how MCP will influence developer workflows, particularly in database management and integration tasks: https://lnkd.in/guChCEEK Anton Umnikov envisions "easy buttons" for common tasks like automated CSV data capture into databases. James Guthrie highlights how MCP eliminates API documentation friction - developers can simply describe what they want rather than parsing complex documentation. John Pruitt focuses on context integration, wanting MCP servers that give his development environment direct database access for smarter query development. The common theme across all three perspectives is MCP removing friction from routine tasks while remaining additive to existing workflows rather than replacing core development skills. See comment for companion deep-dive blog on this topic ⬇️. #ai #mcp #dev #thoughtleadership #engineers

After my recent piece on AI search limitations went viral (over 1M impressions!), the most common question was: "If not just vector search, what should we use instead?" The answer lies in understanding a crucial distinction: similarity isn't relevance. Vector search excels at finding similarity - when you search for getUserById, it returns getUserByEmail because they're semantically close. But what if similarity isn't what you need, and you need the EXACT function instead (like in a codebase?). Sometimes you want semantic similarity (exploring "articles about climate change"), sometimes exact relevance (finding "SKU DQ4312-101"), and often you need both. This is where hybrid search comes in. The key to a good implementation to AI search is simply knowing your use case. Use similarity when users are exploring concepts, relevance when they need specifics, and both when building systems that require both. Hybrid search combines exact matching, full-text search, and vector search, with a reranker determining what's actually relevant to user intent - not just what's mathematically similar. At TigerData (creators of TimescaleDB), we tested this on 1000 news articles. A great dataset that stress tests relevancy vs. similarity due to the nature of news articles themselves. When searching for "carbon monoxide poisoning deaths," vector search returned articles about "home safety" and "emergency procedures" (similar topics), while hybrid search returned actual carbon monoxide death incidents (relevant results). The numbers speak for themselves: vector-only achieved 73% precision with 6.2/10 user satisfaction, while hybrid hit 94% precision with 8.7/10 satisfaction. Looking ahead, I see the future in agentic search (multiple searches until finding what's relevant) combined with context engineering (asking "what context does my LLM need?" rather than "what's semantically close?"). I've shared all my learnings building hybrid search with reranking directly in PostgreSQL in the comments!