Topic
AI Workflows - Page 5
How engineering teams turn AI tools into repeatable work. Curated tldw.news briefings about ai workflows, with practical engineering takeaways from long-form AI and developer-tool videos.
583
breakdowns
Page 5 of 59
AI EngineerThe architecture that makes AI stop inventing financial numbers
Kepler's architecture forces LLMs to plan answers but never write numbers. A post-processing filter stripped 10,981 unverifiable citations from a single eval…
AI EngineerYour Agent Fix Just Broke Something Else – Agent Regression Testing
Prompt changes break agents in invisible ways. Use production traces and automated evals to catch regressions before shipping.
AI EngineerBridgewater's data platform: humans need precision, agents need scale.
Bridgewater's 100M-doc Vault serves both humans and agents by designing for their different needs—precision for people, scale for AI. Plus: how they migrated…
Y CombinatorInference costs 12x training: the new COGS problem for AI teams
Inference costs dominate AI budgets 12x over training. DeepSeek's margins prove it: optimization of the serving layer is now the primary lever for cost and…
AI EngineerHow a Hedge Fund Measures AI ROI Without Waiting for P&L
A hedge fund's Head of AI shares how they AB test AI workflows and measure cost per useful insight—a practical model for proving AI ROI when P&L impact is…
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…
AI EngineerAI agents need identity-first security, not better sandboxes
Six real AI agent attacks and what would have stopped them. The surprising priority is identity management, not sandboxing.
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…
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.
Y CombinatorVibecoded Sites All Look Alike: Conversion Taste Is the New Moat
AI design tools make output fast, but the website reviews show conversion and trust are the real battlegrounds.
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.