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All digests tagged Code Review
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Building an AI code review agent requires more than a simple sandbox setup. Key fixes include model inversion, deep codebase context, autonomous testing, and cost-efficient model routing.
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Gabriel Martinez argues that AI accelerates 'slop'—work lacking judgment. He stresses that generation is cheap, but evaluation is expensive, advocating for diagrams and conventions over massive PRs.
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AI is making PRs bigger and more numerous, turning CI/Code Review into a bottleneck. Discover agentic validation: an automated system that reviews, autofixes, and auto-merges PRs, keeping your development velocity high.
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Entire is a new developer platform built to solve the 'missing middle' of the SDLC in the agent era. It captures context, decisions, and evaluations from intent to production, reimagining the PR into 'Trails' and integrating programmable 'Runners' and 'Gates.'
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Exa explains how they built search for coding and review agents, addressing the critical issue of LLM knowledge cutoffs. They provide context-rich highlights by distilling massive documents into small, highly relevant snippets for the LLM.
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Scaling development with AI requires more than just generating code. Learn how automated code review, Jev classifiers, and robust CI/CD guardrails are essential to manage the flood of AI-generated PRs and maintain system reliability.
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A quarter of PRs reviewed by Greptile are now AI-generated. The talk compares agent code (Codex, Claude, Devin) to human code on bug rates and review cycles, finding unique failure modes for each agent.
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Spec-Driven Development (SDD) is the disciplined alternative to 'vibe coding.' It teaches developers to write detailed specifications (the 'what') to guide coding agents, ensuring maintainable code and structured feature development.
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Coding agents are great at generating code, but verification is the hard part. Learn how to use test oracles, anti test slop, and ADRs to ensure AI-generated code is reliable.
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AI code generation creates a bottleneck in traditional review processes. The talk explores how rapid agent output strains human maintainers, arguing that review is about aligning mental models, not just quality control.
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AI is shifting the focus of software reviews from line-by-line implementation details to validating business outcomes, requirements, and overall intent. This marks a major evolution in development workflows.
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To safely adopt AI agents, focus on verification first. Use detailed plans starting with a 'Why,' encode successful patterns into deterministic flows, and make communication attention-aware by marking human vs. AI text.
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Tessl Code Review addresses the bottleneck of trusting AI-written code by reading PR intent, using customizable 'review lenses' scoped via globs, and allowing teams to own their review standard in a versionable repo file.
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Code review must evolve beyond checking code diffs. The focus needs to shift from semantic accuracy (bugs) to preserving alignment, mentorship, and architectural intent by capturing session decisions.
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Wayve's agentic PR review system moves beyond guesswork by building structured feedback loops, collecting sentiment and usage data to improve prompts and ensure reliable multi-agent behavior.
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Harness engineering is a discipline focused on building automated loops of checks and maintenance agents to delegate more work to AI. This process involves improving three dimensions: autonomy, automation, and quality.
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The rise of AI agents is flooding open-source projects with 'slop'—low-quality, unvetted code. Maintainers must implement new guardrails like requiring issues first and using automated systems to manage contributions.
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