Product updates

AICognition launched the official DeepWiki Model Context Protocol server, which is free and requires no login or authentication. It gives programmatic access to ask_question, read_wiki_contents, and read_wiki_structure for GitHub repositories indexed on DeepWiki.com. Private repositories require a Devin account with GitHub connected, and open-source maintainers can apply for $500 in Devin credits.

Why it matters: The source names the three tools and the access path, showing how indexed GitHub repositories can be queried programmatically without login.

AICognition has released Devin 2.1, which reports its confidence in completing tasks using green, yellow, and red ratings. The company says green scores led to twice the likelihood of a merged PR compared with red, and Devin now also answers codebase questions and scores Linear and Jira issues.

Why it matters: The post explains how Devin now shows confidence scores and asks clarifying questions, which changes how teams can decide which tasks to hand over.

AICognition has released Devin 2.0, a new agent-native IDE experience with a flexible plan starting at $20. The update lets users run multiple parallel Devins, each with its own cloud-based IDE, and adds Interactive Planning, Devin Search, and Devin Wiki.

Why it matters: The release adds planning, codebase search, and auto-generated wikis to Devin, showing how an agent can prepare work before executing it.

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 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.