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Productivity & Process - Page 2
Process changes that affect focus, throughput, and quality. Curated tldw.news briefings about productivity & process, with practical engineering takeaways from long-form AI and developer-tool videos.
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Y CombinatorAI has killed the pure software moat—hard problems are your only defense
YC partners argue pure software is now a commodity. The real moat? A hard problem—hardware, regulation, or distribution. Engineering leaders must reassess…
AI EngineerRelative Scoring and In-Loop Eval Fix AI Video Quality
Character.ai replaced slow, vibe-based video scoring with a fast distilled model that does axis-specific relative comparisons, embedding evaluation in the loop.
AI EngineerStart with Vibes: The Counterintuitive First Step for Agent Evals
YouTube Ads engineers found 'vibing'—manual, non-scalable checks—uncovers agent failure patterns faster, preventing eval calibration chaos.
Y CombinatorNo Code In 2 Quarters: Datadog's AI Pivot
Datadog says AI will end code writing in 2 quarters, after devs rebuilt 6-month systems in days, forcing reorg and tough questions.
Cole MedinKimi K3's Benchmark Hides a 36% Failure Rate in Real Workflows
Custom benchmarks show Kimi K3 fails on false premises and hidden invariants 36% of the time—4.5x more than Opus. The solution: a hybrid workflow that…
David OndrejCost per task, not token pricing, is the real AI benchmark.
An open-source model outperforms closed giants on front-end and legal tasks, revealing that cost per task—not token pricing—should drive AI budget strategy.
AI EngineerLights-Off Software Factories Fail: Why Code Maintainability Still Requires Humans
AI coding factories promise full automation but degrade codebases. Model training limits maintainability; upfront design keeps humans in the loop.
AI EngineerPerception Agents: A New Bet on Reliability for Unverifiable Work
Agents click but fail at workflows. Amazon's perception tools see your screen and verify, aiming to bridge the trust gap—though still raw.
IBM TechnologyThe Costliest AI Mistake: Using It When You Shouldn’t
Most AI production failures come from choosing the wrong system type, not bad models. A decision framework for agents, rules, or ML prevents costly missteps.
No Priors: AI, Machine Learning, Tech, & StartupsThe Real Scaling Problem for AI Delivery Isn’t Autonomy—It’s Operations
DoorDash’s AI ordering boosts discovery, but their in-house delivery robot reveals hidden ops challenges. Plus, a 20x AI spend spike that forced ROI discipline.
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