We’re improving Batch Changes to better support large-scale code transformations - through a renewed focus on security, lovability, and usability.

Sourcegraph is now ISO 27001:2022 certified, strengthening our commitment to enterprise-grade security.

How we've adapted the classic BM25 algorithm to improve code search by 20%

How our security team built Sherlock, an AI-powered tool that automates security code reviews to cut through false positives and surface real risks faster.

We dug into cAdvisor memory metrics and resolved some common confusion to figure out what they really mean.

The latest installment from Steve Yegge on viiiiibe coding and what that means for developer jobs.

The Software Development Life Cycle (SDLC) is the structured foundation for shipping reliable features with speed and confidence. This complete guide explores the essential phases, high-impact best practices, and the modern toolchain you need to optimize your team's development workflow from planning to production.

Every engineering leader eventually hits the same wall of unknown bottlenecks; this essential guide provides the data to break through, covering DORA metrics, productivity, and quality signals. Learn how to implement the right metrics to illuminate your delivery process, avoid toxic 'surveillance culture,' and turn measurement into meaningful, systemic improvement.

A deep dive into context retrieval - the crucial search component that helps our AI coding assistant understand your codebase.

Today we’re announcing Sourcegraph Enterprise Starter for growing teams of up to 50 devs, a new self-serve plan allowing even more developers to leverage our advanced code search and AI capabilities.

With the release of Sourcegraph 6.0, we are introducing a new unified UI for searching, understanding, and creating code

Cycle time is the most actionable metric for software delivery, revealing every bottleneck from first commit to production. Learn how to measure it, what good looks like (including DORA benchmarks), and proven strategies to eliminate friction in code review and CI/CD pipelines for faster, more efficient development.

We believe AI coding agents are best suited to automate the repetitive, mind-numbing parts of enterprise software development, not to try (and fail) to replace humans.

Agentic chat makes coding with AI better by autonomously gathering and refining the most relevant context from your codebase, terminal, and other tools.

Auto-edit is the first integration that goes beyond autocomplete to suggest context-aware edits natively in Visual Studio Code.

With the help of agentic frameworks and Sourcegraph’s APIs, Priceline was able to demonstrate that AI agents operating within the proper context and with the right tools can act as a force multiplier for a developer.

AI coding agents dominated discussions at the Virtual Code AI Summit, where industry leaders shared how AI tools are revolutionizing software development through automation, workflow acceleration, and developer empowerment.

We're partnering with DX, the engineering intelligence platform, to help teams better measure the impact of code AI.

How stubborn developers are getting left behind by refusing to adopt chat-oriented programming (CHOP) as their primary development approach.

Today, we're introducing Prompt Library to tackle this problem. It will speed up AI adoption in your engineering organization