#Coding

AICrossmint said Devin became its top contributor to the open-source GOAT SDK during an initial trial, merging 8 pull requests versus 4 for the next contributor. Examples included a DEXScreener plugin built from a documentation URL and a harder Sui blockchain integration that needed three rounds of feedback and about an hour of human involvement. The company said its results depended on proper training, clear task context, and planned validation, not on treating Devin as superhuman.

AICognition has made Devin generally available to all engineering teams, with subscriptions starting at $500 per month. Over the past two weeks, Devin was made about 10% faster and about 10% more cost-efficient, especially for tasks requiring many code edits. The update also adds fixes for stuck or hanging sessions, more options to customize filters and Slack notifications, and larger machine settings for disk, RAM, and CPU.

AICognition is launching the Devin Open Source Initiative, giving selected open source maintainers 500 free ACUs on a Devin Teams plan as part of Devin's general availability launch. The post shows Devin contributing pull requests to projects including Anthropic's MCP Inspector, Dagger, and nanoGPT, with maintainers still reviewing the results. Devin's GitHub integration forwards PR comments and CI checks to help refine changes, though the company warns a human should still verify final quality.

AICognition is making Devin generally available to engineering teams starting at $500 a month, with no seat limits and access to its Slack integration, IDE extension, and API. The post recommends starting with small frontend bugs, first-draft PRs for backlog tasks, and targeted refactors, and shares open-source PR sessions where Devin resolved issues for projects including Anthropic MCP, Zod, and nanoGPT.

Why it matters: The post shows concrete open-source PR examples and the tasks where Devin works best, helping teams judge where an autonomous coding agent fits their workflow.

AICognition's December 2024 Devin update lets users tag Devin in Slack threads and ask it to create PRs, with Devin automatically responding to PR comments and lint failures. The update adds Repo Knowledge that Devin generates by scanning repositories, an Agency setting that makes Devin propose plans before executing complex tasks, and a REST API for structured input and output. Devin also gained faster session startup and enterprise options such as Okta single sign-on.

AICognition tested OpenAI's o1-mini and o1-preview in a simplified Devin-Base agent, comparing them with GPT-4o on its internal cognition-golden benchmark. The chart reports Devin-Base scores of 25.9% with GPT-4o, 34.6% with o1-mini, and 51.8% with o1-preview, versus 74.2% for the production Devin. The post also describes the benchmark's realistic environments, simulated users, and agent-based evaluation.

Why it matters: The post explains how Cognition evaluates coding agents with autonomous, environment-based tests, which shows how base-model swaps are measured in practice.

AICognition's Devin gained a September 2024 update with MultiDevin, which lets a manager Devin delegate work to up to 10 worker Devins, currently available on the Enterprise plan. Devin now automatically responds to comments on its pull requests, suggests Knowledge additions, and can restore earlier checkpoints, and the company reports up to an 80% reduction in time for common tasks.

AIDevin's June 2024 update lets users directly operate its VSCode editor, terminal, and browser, and adds Playbooks for recurring engineering tasks. It also adds Knowledge sharing, Machine Snapshots that persist dev environments, event-driven triggers from Slack, GitHub, and Linear, a Secrets manager, and tools for verifying Devin's work. Access remains limited to a waitlist with weekly invite releases.

AICognition reports that its agent Devin resolved 79 of 570 sampled SWE-bench issues, a 13.86% success rate, without being given the files to edit. The report says this exceeds the best previous unassisted baseline of 1.96% and the best assisted result of 4.80%. It also describes the adapted evaluation setup, a 45-minute runtime limit, and cases where Devin failed on multi-file edits.

Why it matters: The report explains how SWE-bench was adapted for end-to-end agent evaluation, with failure cases that clarify where the 13.86% result comes from and its limits.

AICognition introduces Devin as an AI software engineer that can plan and execute complex engineering tasks with a shell, code editor, and browser. On SWE-bench, Devin resolved 13.86% of issues end-to-end, versus a previous state-of-the-art of 1.96%, on a random 25% subset of the dataset. Devin is in early access, with access available through a waitlist.

Why it matters: The post pairs Devin's end-to-end task demos with SWE-bench results against prior models, letting readers weigh the claimed capability against the evaluation setup.