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Engineering Leadership - Page 3
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 EngineerArca's agents run the business. Most teams still assist humans.
Arca runs its business on agents that own outcomes end to end. The architecture for long-horizon, human-in-the-loop work is production-proven. The…
No Priors: AI, Machine Learning, Tech, & StartupsYour engineering teams are about to spend 50% time on security
Cloudflare CTO Dane Knecht says 50% of engineering time will shift to security for 18 months. The fix? More red teams and workflow redesign, not feature…
Theo - t3․ggLLM distillation demand turns API keys into prime targets for attackers.
PHP CVEs and API key thefts hit the industry; the real motive may be model distillation. What leaders should do now.
AI EngineerModel Choice Barely Matters: Fix Engineering Culture, Not AI Tools
AI model choice explains under 1% of developer output variance, per Jellyfish's 50,000-engineer dataset. The levers leaders control matter more.
Latent SpaceGPU capacity is a shape problem, not a supply problem
GPU capacity is sold in rigid three-year blocks, but AI demand is bursty. Modal's founder explains the mismatch and what it means for compute strategy.
Latent SpaceAI engineers are the whole team now, a leadership risk
AI engineers are told they are the team. A Moshi founder argues recommendation design, not model quality, decides whether AI tools get used.
Weights & BiasesHuman behavior simulation is close to usable, but check the 85% claim
Simulated users predicting behavior at 85% accuracy could change product testing, but the claim is vendor-generated and the ceiling is too convenient.
No Priors: AI, Machine Learning, Tech, & StartupsOpen-Weight Agents Are Giving Every Attacker Nation-State Reach
Open-weight models are collapsing the adversary pyramid. CrowdStrike's CEO wants defenders to fight AI with AI, but the numbers need scrutiny.
AI EngineerStop evaluating models. Start evaluating the risk around them.
1,368 AI failures later: deployment controls, not model quality, determine real-world harm. Failure-driven evals follow. No production proof yet.
AI EngineerTesting AI Agents: Plant the Defect, Then Find It
No benchmark for your AI agent? Air Canada generated 275 contracts with planted violations, cut expert grading from weekly to daily, and warns against LLM…
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