Reading up on Cognition
10 deep · digging since dec 23, 25
- Autoresearch: The feedback loop behind self-improving agents
Autoresearch uses outer loops with feedback signals and human input to let agents improve and maintain systems, reducing bottlenecks while keeping humans central.
- Introducing FrontierCode
Cognition's FrontierCode benchmark measures code mergeability, finding even top models like Claude Opus 4.8 score only 13.4% on its hardest 50 tasks.
- Inside the grind: The SF startup racing to build an AI software engineer
Cognition CEO Scott Wu argues AI coding agents like Devin will expand software creation rather than eliminate engineering jobs despite industry layoffs and competition.
- Institutional AI vs Individual AI - by George Sivulka
Individual AI tools boost personal productivity but fail to improve firm-level outcomes until organizations redesign their processes and coordination structures around AI.
- How Cognition Uses Devin to Build Devin - by Nader Dabit
Cognition uses its own Devin agents across Slack, Linear, and CLI to merge 659 PRs weekly, automating code review, bug triage, and design-system audits.
- Cognition | Devin Review: AI to Stop Slop
Cognition launches Devin Review, an AI tool that organizes diffs, catches bugs, and enables live chat to solve the code-review bottleneck created by coding agents.
- ASCII characters are not pixels: a deep dive into ASCII rendering
A developer built an ASCII renderer that uses shape vectors and contrast enhancement to achieve sharp edges, overcoming blurriness from treating characters as pixels.
- Async Coding Agents "From Scratch"
A developer demonstrates how to build a custom async coding agent by combining Slack, Modal, GitHub, and Claude Code/Codex headless mode, arguing this makes cloud coding agents like Devin undifferentiated.
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