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AIThe article argues that engineers are splitting their work between building a harness of constraints, tools, and documentation for agents and directing the agents' work. It cites OpenAI, Stripe, and Anthropic examples, including architecture guardrails, custom linter messages, AGENTS.md updates, and plan-first execution. The author notes that open problems remain around code maintainability, verification at scale, and adopting these practices in older codebases.

AINicholas Carlini of Anthropic's Safeguards team describes an agent-team setup where 16 Claude instances worked in parallel on a shared codebase without human intervention to write a Rust-based C compiler. Over nearly 2,000 Claude Code sessions costing about $20,000 in API fees, the team produced a 100,000-line compiler that can build Linux 6.9 on x86, ARM, and RISC-V. The post focuses on harness design, including high-quality tests, lock files for task claiming, GCC as a reference oracle for the kernel, and the limits the project reached.

Why it matters: The post shows concrete harness design choices for long-running agent teams, including test design, locking, and parallel work division, that readers can adapt to their own autonomous projects.

AIAnthropic's performance engineering lead Tristan Hume describes how a take-home test for hiring performance engineers was repeatedly defeated by successive Claude models. Claude Opus 4 outperformed most human applicants within the 4-hour limit, and Claude Opus 4.5 matched the best candidates in 2 hours. Anthropic is releasing the original take-home as an open challenge, with the best known Claude result at 1487 cycles.

Why it matters: The post traces how each Claude model defeated the take-home test, showing concrete redesign tradeoffs for evaluating engineers when AI assistance is available.

AITim Dettmers, a professor who has used Claude Code for eight months to automate his own work, argues that more than 90% of code and text should be written by agents. He says the coding-focused hype on Twitter overstates parallel sessions and autonomy, which translate poorly to most non-software tasks. The post offers a balanced guide to what actually works in agent-based automation.

AICognition says its autonomous coding agent Devin can complete a .NET Framework to .NET Core migration in as little as two weeks, using a Strangler Fig approach adapted from Jimmy Bogard's guide. The post says Devin handles planning via Ask Devin and DeepWiki, dependency sharing, controller and view conversion, and session state adaptation through a remote app.

AICognition rebuilt its Devin coding agent for Claude Sonnet 4.5, reporting 2x faster performance and 12% better results on its Junior Developer Evals, now available in Agent Preview. The team found the model is aware of its context window, which led to premature wrap-up behavior that they countered with repeated prompts and a 200k usage cap within a 1M token beta.

Why it matters: The post explains which agent behaviors changed under Sonnet 4.5, such as context-window awareness and note-taking, that forced a rebuild rather than a simple model swap.

AIEight Sleep integrated Cognition's Devin into its data workflows, letting staff tag Devin in Slack to query Snowflake, dbt, and Looker and check Amplitude. The company says it is now shipping 3x as many data features and investigations each week, with its ad-hoc data request queue near zero. Devin was used to trace a suspicious revenue spike to a better-than-expected email campaign.

AICognition published a guide titled "Build Your Own AI Data Analyst" on its blog, but the provided text contains only a navigation listing of unrelated posts and no guide content. No tools, models, features, or steps can be verified from the source.

AICognition says generative AI agents such as Devin can read internal docs and convert Jenkins pipelines to GitHub Actions syntax, replacing custom plugins with Actions or APIs and validating the results. The company claims enterprises can cut multi-year migration efforts to a few months, with Devin running inside the customer's secure environment so code and secrets stay internal.

AICognition Blog published "Coding Agents 101: The Art of Actually Getting Things Done" on June 26, 2025, as a guide to using coding agents effectively. The available source text contains only navigation and a list of other Cognition posts, so no specific techniques, features, or results can be verified from it.

AICognition built and open-sourced blockdiff, a file format that creates block-level diffs of VM disks using only filesystem metadata. The company reports its otterlink hypervisor cut snapshot times from 30 to 60 minutes on EC2 to about 5 to 10 seconds for a 128 GB disk with a 5 GB diff, roughly a 200x speedup. The post also covers the sparse file and copy-on-write concepts behind the approach and why OverlayFS, ZFS, and qcow2 were not chosen.

AILinktree has used Devin, Cognition's AI software engineer, to merge roughly 100 pull requests in a month, mostly fixing customer-reported bugs and implementing small features. The engineering team also used Devin to add support for new social media platforms, launching five Devins, one per repo and PR, and later used the Devin API with a Playbook script to spawn multiple Devins for multi-repo features. The team says Devin works best on tasks an engineer could finish in a couple of hours.

AICognition's Devin can be triggered through its External API by GitHub Actions to automatically review pull requests, typically within five to ten minutes. The setup involves adding a workflow file, storing a DEVIN_API_KEY secret, and customizing the review prompt to match team conventions. Cognition recommends treating Devin as an extra reviewer rather than a replacement for human oversight, since it does not catch every bug.

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 tested whether its Devin AI agent could recover Windows machines stuck in the Blue Screen of Death after the CrowdStrike outage. Following a playbook of eight steps, Devin mounted the drive, deleted the faulty CrowdStrike files, and debugged a failed volume detach. The blog says the machine was confirmed bootable, and that playbooks are most useful when the same fix must be repeated across many machines.