Aug 15, 2026 LLMs Can Suggest Metamorphic Relations. They Just Can't Guarantee Them. Large language models are decent brainstorming partners for discovering test oracles, but they hallucinate properties and miss domain constraints. metamorphic-testingllmtest-oraclesai-engineering
Jul 19, 2026 Your Claude Thread Is Already Documentation. It Just Dies in Twelve Hours. LLM conversations contain intent, rejected alternatives, and working code. That's exactly what documentation should be. literate-programmingllmdocumentationai-engineering
Jul 13, 2026 An LLM Can Pre-Inspect Your Code. It Cannot Run the Meeting. Fagan inspections need four to six people and two hours to review 250 lines. fagan-inspectionsllmcode-reviewai-engineering
Jun 12, 2026 Five Implementations and No Majority: How to Actually Pick the Best One N-version programming sounds simple: run multiple implementations and pick the best answer. In practice, 'best' is a lot harder to define than 'most common.' n-version-programmingimplementation-selectiondifferential-testingai-engineering
Jun 11, 2026 The Same LLM Can Write Five Versions of Your Function. Here's How to Make Them Actually Different. N-version programming with LLMs doesn't require multiple models. n-version-programmingllmdiverse-implementationsai-engineering
May 27, 2026 The Validation Spectrum: Why AI Codes Backend Best AI coding follows a validation gradient. Backend validates in milliseconds. Web takes minutes. Mobile takes hours. The feedback loop determines where AI coding works. ai-engineeringbackendwebmobilevalidationai-codebase
May 26, 2026 Fear vs. Steamroll: The Two Realities of AI Coding AI coding works for elite teams who recover from any mess. Everyone else gets fear, failed CI, and abandoned experiments. The gap isn't the model. ai-engineeringsoftware-architectureproductionai-codebaseteam-dynamics
May 24, 2026 AI Coding in Production: Why Most Teams Quit Most teams try AI coding, ship code that fails QA, and quit. The problem isn't the model—it's the absence of guardrails that make AI output trustworthy. ai-engineeringsoftware-architectureproductionautotomyai-codebasebest-practices
May 23, 2026 Fuck-u-code: The Deterministic Quality Gate Your AI Pipeline Forgot You have type checking, linting, and architecture rules. But your deterministic stack is blind to complexity, duplication, and naming disasters. Here's the $0 fix. ai-engineeringdeterministicguardrailscode-qualitystatic-analysisci
May 22, 2026 Why Version One Is Never the Problem: AI Coding and Long-Term Maintenance AI coding tools excel at generating version one. The real engineering challenge starts at version four, when the team needs to change something without breaking everything else. ai-engineeringsoftware-architecturerefactoringai-codebaselong-term-maintenance
May 22, 2026 AI-Generated Code and the Replaceability Principle The real measure of AI-generated code quality is not whether it works on day one. It is whether you can replace it on day thirty without rewriting everything else. ai-engineeringsoftware-architecturereplaceabilityai-generated-codemaintainability
May 22, 2026 Deterministic Guardrails for AI Codebases Human review is inconsistent. AI review is worse. The only scalable defense for AI-generated codebases is deterministic enforcement: rules that fail the build, not suggestions that get ignored. ai-engineeringsoftware-architectureguardrailssemgrepci-cdcode-review-automation
May 17, 2026 In the AI Era, Code Review Becomes Specification Review When AI can generate implementation, tests, and contracts from a spec, the highest-leverage human work moves upstream. ai-engineeringcode-reviewspecificationsliving-specificationsci
May 16, 2026 The AI Safety Stack: Types, Contracts, Property Tests, and Mutation Gates If you want AI-generated code to survive production, do not rely on code review alone. ai-engineeringsafety-stacktypesdesign-by-contractproperty-based-testingmutation-testing
May 16, 2026 Meteor Development Is Real. Your Codebase Does Not Have to Collapse With It The meme is funny because teams really do build against moving targets, fixed launch dates, and half-decided scope. ai-engineeringsoftware-architectureautotomyproduct-developmentguardrails
May 16, 2026 Stanford CS146S Is Right About AI Coding. The Missing Subject Is Architecture A Stanford CS146S review and critique: the inaugural Fall 2025 course gets AI coding right, but the missing lesson is AI architecture and replaceability. ai-engineeringsoftware-architecturestanfordcs146sguardrailsagentic-development
May 15, 2026 Why Great Engineering Ideas Stayed Niche Until AI Made Them Economical Design by contract, property-based testing, mutation testing, and model checking were not bad ideas. They were too expertise-heavy to sustain. ai-engineeringsoftware-reliabilitydesign-by-contractproperty-based-testingmutation-testing
