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Engineering Leadership - Page 20
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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AI EngineerLoops Won’t Replace Your Engineers—Unless You Let Them
Loops promise speed but demand discipline: verification gaps, cost, and code rot threaten to undercut the hype. Pragmatic teams start small.
AI EngineerYour Model’s Best Feature Won’t Survive a Bad Harness
AI models are getting smarter, but the largest accuracy regressions come from broken harnesses, wrong system prompts, poor quantization—not the model itself.
AI EngineerCode Is Free—Architecture and Security Are Now the Bottleneck
Code writing is commoditized, and human code review may vanish. The real engineering bottleneck shifts to architecture, specification, and security governance.
AI EngineerAI Leverage Is in Org Charts, Not Model Choice
Garry Tan: AI leverage is org design—skill files as employees, resolver tables as org charts—not model choice. Revenue per head records broken.
AI EngineerExecution Is Cheap—Your Eval Design Will Make or Break Success
An AI agent won a coding competition by executing human ideas, signaling execution is automated. High-leverage work shifts to evaluation design, architecture.
Latent SpaceWhy the Next AI Scaling Axis Might Be a Wet Lab
Lila Sciences treats automated labs as verifiers, turning physical experiments into a scaling axis by generating tokens for generalist AI.
GOTO ConferencesThe Alignment Trap: Why Technical Leadership Isn’t Senior Coding
Tech leadership is about alignment, not architecture. Kua’s framework shows how to spot misalignment, own it, and reduce complexity across teams.
InfoQCutting SDK Duplication in Half with a Rust Core
Temporal's shared Rust core halved SDK duplication, but the bridge layer's complexity is the real lesson for leaders scaling multi-language products.
The Pragmatic EngineerDark AI Factories Break: Context Engineering and Slow Loops Work
Dex Horthy explains why his lights-off coding factory failed and how context engineering plus incremental loops are the pragmatic path to reliable AI code.
AI EngineerSoftware engineering is becoming a product taste job
Anthropic’s agents now land 65% of product PRs, shifting engineering to product thinking and raising the bar for automated code review.
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