Today, we're announcing Sourcegraph 7.0, a release that marks the beginning of a new chapter for our company and product. Over the past several releases, we've shifted our focus. We're doubling down on being the intelligence layer that developers and AI agents rely on to navigate, understand, and operate on large codebases. sourcegraph.com/blog/a-new-e…

Agent failures in large codebases are not random. Data from 1,281 agent runs across 40 large open source repositories reveals five repeatable failure patterns that show up across coding agents. Each failure pattern points to a different infrastructure problem and a different fix. Read: Why coding agents fail in large codebases (and what to do about it) sourcegraph.com/blog/why-cod…

Pin the dependencies that matter most sourcegraph.com/blog/depende…

We automated our security workflow by connecting @Hacker0x01 webhooks to Sourcegraph Deep Search. The system validates and finds fixes for vulnerability reports before you even start work. This helps us address issues faster and removes the manual effort of triaging reports. sourcegraph.com/blog/automat…

Hope you’re enjoying Microsoft Build! If you use GitHub Copilot, you can connect it to the Sourcegraph MCP server to get context from all your repositories. This works even for your code on GitLab.

Keep track of Deep Search conversations shared with you. We didn't stop there, check out the changelog: sourcegraph.com/changelog/20…

Gas City maintainer @sgjarmak is running a webinar on building a software factory in enterprise codebases next week. She'll focus on ‘the three asymmetries’ and giving examples using Gas City! Sign up today! sourcegraph.com/webinars/bui… #recommended !

See how AI software company @Sourcegraph connects its NetSuite data to Claude to surface financial anomalies faster, ask questions in natural language for real-time answers, and collaborate more effectively. social.ora.cl/6010B8XGfk

.@Sourcegraph analysed 1,281 coding agent runs across large enterprise codebases and found the bottleneck often wasn’t the model — it was context retrieval and navigation. Agents struggled once codebases grew large enough: pulling in the wrong files, thrashing between tools, and making locally correct changes that broke systems elsewhere. One stat says a lot: a task that took one agent 96 tool calls and 84 minutes dropped to five calls and under five minutes with better retrieval infrastructure. The takeaway is this: coding agents need far better context and navigation layers around them to operate reliably in production. 🔗 Read the full story here: tessl.io/blog/coding-agent-f…

Use Case of the Week: Understand unfamiliar code faster youtube.com/watch?v=t7_Xgw_x…

NEW: lessons learned on how to make a REALLY great enterprise @SlackHQ bot for AI products from our experience @Sourcegraph Touches on interactive and real-time feedback (critical when agents are doing asynchronous work), summaries and previews for the Slack UI, rate limiting, and more. Thanks @juliaxu_xu__ @JasonHawkHarris & Emi for spreading the knowledge sourcegraph.com/blog/deep-se…

We've just launched a new homepage ✨ sourcegraph.com

I'm giving a talk at a @mlopscommunity @qdrant_engine workshop in a couple weeks in Boston on structuring scientific knowledge for agent-driven discovery :) Workshop link: luma.com/19c49kvs