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Engineering Leadership - Page 21
AI decisions through the lens of teams and execution. Curated tldw.news briefings about engineering leadership, with practical engineering takeaways from long-form AI and developer-tool videos.
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IBM TechnologyWhen AI Guardrails Lock Out the Good Guys
Open models unshackle attackers but block defenders with guardrails. CISA’s simpler patch-prioritization model may lack teeth.
Theo - t3․ggThe Rewrite Option Is Back
Bun’s Zig-to-Rust rewrite shows AI can now execute large-scale code migrations, but the real lesson is that community governance is the new hidden risk.
AI EngineerContext Engineering, Not Model Smarts, Is Blocking Production Agents
Atlan’s experiments show managing context as versioned, skill-based systems creates operational headaches—the infrastructure gap scaling teams must close.
AI EngineerAI Skills Without Evals Are Production Time Bombs
AI skills boost agents ~15%, but unevaluated skills silently fail and waste tokens. Here's the eval discipline most teams are missing.
AI EngineerHow Cursor’s FDE Team Avoids the Staff Augmentation Trap
Cursor’s FDE lead shares a sharp framework for deciding where high-touch engineering actually drives ROI—and where it’s just expensive staff augmentation.
AI EngineerOwning the Verdict, Not Writing Code
Why the engineer’s scarce skill is no longer coding but evidence-based accountability, and how cognitive debt threatens AI adoption.
GOTO ConferencesAI Agents Need Sandboxing, Not Free Rein
Tau5 experiments with AI agents given temporary access to system internals via MCP—but only inside a sandbox. A safe pattern for dev tooling.
AI JasonThe Agent Harness Is Now Your Codebase
Pi agent’s extensions put the harness under version control, turning guardrails, token filters, and UI into code. You trade polish for full ownership.
AI EngineerAgents Must Prove Safety Before Execution, Not Just Be Aligned
Erik Meijer revives 1990s proof-carrying code to verify agent plans before they mutate the world—shifting safety from hope to check. Is this practical at scale?
AI EngineerThe Real RL Bottleneck Isn't Models, It's Your Workflow Spec
The new open-source RL stack re-centers the AI bottleneck on designing multi-agent workflows, with a $50k, 3-day frontier-model run as proof.
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