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Engineering Leadership - Page 30
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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AWS DevelopersTool Protocol Temptation: MCP’s Hidden Context and Security Costs
MCP’s promise of standardized agent-tool integration hides a context-bloat and security tradeoff. Filtering tools becomes a critical harness design lever.
AWS DevelopersThe Real Work in AI Agents: Building the Harness, Not the Model
AI agents aren’t just models—they rely on a harness for tools, context, and guardrails. This is where engineering leaders need to focus.
The Pragmatic EngineerThe AI Hiring Caste System Is Already Here
AI is already creating a hiring caste system. Leaders must evaluate how engineers reason with AI, not just code, before the gap widens.
Y CombinatorGusto’s AI Agent Launch Proves Small, Process-less Teams Ship Fast
Gusto shipped an AI co-founder for SMBs; the real lesson is how a 5-person team built it in 10 weeks with no meetings or specs, using Claude Code.
IBM TechnologyModel Guardrails Won’t Stop AI-Powered Attacks
New AI models emphasize safety guardrails, but open-source competitors and adversarial use render them insufficient. The real defense is system architecture.
AI EngineerStatic Harnesses Fail in Complex Real-World AI—Adaptive Engineering Needed
Fixed harnesses break in real-world complexity; adaptive engineering lets the harness self-organize, trading predictability for resilience.
AI EngineerYour Agent Architecture Might Matter More Than the Model
Harness design can add 20+ points to agents. This talk explores if architecture investment, not models, could slash costs and lock-in — demo early-stage.
AI EngineerWhy Human Reviewers Rubber-Stamp AI—And How to Stop It
Duolingo proctors accepted 50% of false AI flags. A copy change raised rejection 21 pts. Design AI interactions for discernment, or humans become rubber stamps.
Theo - t3․ggOpen-weight models won’t run on your laptop—and that’s fine
Local AI enthusiasts dream of frontier models, but GLM 5.2 needs 400GB+ VRAM. The real value of open-weight is competitive cloud inference.
David OndrejLoRA Turns Trillion-Parameter Fine-Tuning Into an Opex Decision
A $131 LoRA fine-tune of Kimi K2.7 makes frontier-model customization an opex line item, but proof of gains over generic APIs is missing.
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