Topic
AI Workflows - Page 8
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.
376
breakdowns
Page 8 of 38
InfoQHow a bank's top team got tired of AI coding agents
A seasoned team that practices TDD and mob programming tried AI for everything. The result: fatigue, reduced flow, and slower delivery. A grounded…
AI EngineerWhy Warp’s agent platform succeeds on structure, not model power
Warp’s cloud agent platform succeeds by hiding infrastructure complexity and enforcing guardrails across harnesses. The real signal: agents are more about…
AI EngineerThe AI safety net that nods along while patients are harmed
Production AI clinical notes have a 5% serious error rate. Current evaluation systems miss most because they can't judge what matters in context. A…
AI EngineerStop using agent frameworks: Build event-driven pipelines instead
A CEO built his own agent runtime in two weeks. The key: event-driven architecture, YAML definitions, and structured outputs. No frameworks needed.
AI EngineerCoding agents need paved roads, not solo cowboys.
Agent adoption stalls without organizational redesign. Debois argues for shared systems over solo prompting, with platform teams owning paved roads.
AI EngineerCost and latency, not benchmarks: why model routing beats a single model
Benchmarks don't capture real workload cost. DigitalOcean’s inference router optimizes per request, delivering 3x cost savings — but routing isn't a silver…
Latent SpaceDigital twins are 85% accurate—but only if you collect the right data
Simile AI achieves 85% accuracy in digital twin behavior prediction, outperforming frontier models by 2-3x on niche populations. The tradeoff: proprietary…
AI EngineerAgent building is easy. Context is where agents still fail.
Building agents is now trivial. But as Jeff Ng demonstrates, agents without organizational context confidently recommend fixes that caused past outages. The…
AI EngineerWhy design taste is the new competitive moat for AI apps
The secret to making AI apps stand out isn't a better model — it's giving your agent high-quality design references and avoiding the telltale signs of AI slop.
Hugging FaceSmaller AI models can beat frontier models by using them as tools
A small RL-trained model outperforms frontier models at replicating ML research figures by using Codex as a tool—suggesting orchestration matters more than…
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.