Code Scans
Devin has introduced Code Scans, a new feature that translates broad engineering goals into concrete codebase improvements and pull requests. The tool utilizes an Agentic MapReduce architecture to distribute large investigations across parallel agents, synthesizing findings into prioritized reports. Early testing by enterprise clients has demonstrated significant efficiency gains, including a 96% PR merge rate and the saving of over 700 engineering hours.
- ▪Code Scans allows users to define high-level goals like improving SEO or reducing maintenance overhead without specifying exact files to inspect.
- ▪The system uses Agentic MapReduce to break down investigations into batches and distribute them across parallel agents for faster processing.
- ▪Philips reported a 96% PR merge rate and an estimated 700 engineering hours saved during their testing of the feature.
- ▪A specific scan on the Dioxus repository reduced clean debug build time by 64% by optimizing dependencies and build configurations.
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Story provenance
Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | Devin |
| Canonical URL | https://devin.ai/blog/introducing-code-scans |
| Publication time | Fri, 18 Sep 2026 03:00:40 +0000 |
| Retrieval time | 2026-09-18T03:58:44.750Z |
| Last seen | 2026-09-18T03:58:44.750Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | None |
| Cluster logic | Not yet clustered, or no peer story found in the clustering window. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Today we’re introducing Code Scans: a new way to turn broad engineering goals into concrete improvements across your codebase. Tell Devin what you want to achieve, and it helps you investigate what needs to change, evaluate the findings, and turn them into pull requests. Many engineering tasks start with a specific change. Others start with an outcome: Improve SEO Reduce maintenance overhead Make your application compile faster These goals often stay on the backlog because the first step alone is a substantial project: investigating the codebase to figure out where to start. Additionally, identifying the opportunities is only half the work — someone still has to implement the changes, and many of them. Code Scans helps with both.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Devin.
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