Topic
Engineering Leadership - Page 17
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.
229
breakdowns
Page 17 of 23
GOTO ConferencesWhy AI Coding Demands Slowing Down
AI coding tools tempt teams to rush features, but Kent Beck warns: without building optionality, they lose the ability to change code at all.
Theo - t3․ggOpenAI’s Secret: Grug-Speak Reasoning Slashes Token Costs
OpenAI’s token efficiency exposes a hidden AI cost war. Leaked traces show models think in terse “grug speak,” slashing task costs but killing transparency.
David OndrejMulti-Model Orchestration Beats Frontier Closed AI—at a Price
Mixture of agents combines models for frontier-level results but with high cost and latency. Is this AI's future for serious engineering?
AI EngineerEscaping Skill Hell: A Framework for Teams
When agent skills fail unpredictably, poor design is often at fault. This framework helps leaders enforce quality and tradeoff awareness across skill libraries.
GOTO ConferencesWhen Platforms Become Innovation Engines, Not Just Shared Services
Most platforms fail as glorified infrastructure or rigid standards. Gregor Hohpe shows how to build ones enabling speed and innovation.
InfoQWhy Local-First Is Ready to Challenge Your Cloud-Only Stack
Heroku co-creator Adam Wiggins explains why mature CRDT sync engines make local-first a practical choice for CTOs.
Latent SpaceYour AI Agent Strategy Should Start with One Person at a Time
NanoClaw’s founder explains why giving every employee a personal AI agent is the missing step before automating team workflows.
AI EngineerYour LLM Bill Is a Model Selection Problem
Most features don't need frontier models; evaluate SLMs with a golden dataset and prompt engineering to match quality and eliminate inference costs.
AI EngineerAI Made Coding Cheap—Now Requirements Are the Moat
AI makes building software cheap, so building the wrong thing is cheaper and more dangerous. Winners master the old skill of deciding what to build.
AI EngineerWhy AI Agents Need a Control Plane, Not Better Prompts
Agents break traditional infrastructure. The real risk isn’t model errors—it’s retry storms and cost explosions. A deterministic control plane is the fix.
Get TL;DW
Too Long; Didn't Watch.
A concise breakdowns of the AI and devtools videos that actually matter for engineering leaders.
Free. Weekly. No hype.