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AI Workflows - Page 33
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.
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Theo - t3․ggOpenAI’s Secret: Grug-Speak Reasoning Slashes Token Costs
OpenAI’s token efficiency exposes a hidden AI cost war. Leaked traces show models think in terse “grug speak,” slashing task costs but killing transparency.
David OndrejMulti-Model Orchestration Beats Frontier Closed AI—at a Price
Mixture of agents combines models for frontier-level results but with high cost and latency. Is this AI's future for serious engineering?
Latent SpaceYour AI Agent Strategy Should Start with One Person at a Time
NanoClaw’s founder explains why giving every employee a personal AI agent is the missing step before automating team workflows.
AI EngineerAgent Costs Are Rising — Composition Is the Answer
Intelligence costs are rising. Domain-specific agents could boost token efficiency 80%+ via composition, but production tooling lags.
AI EngineerAI Made Coding Cheap—Now Requirements Are the Moat
AI makes building software cheap, so building the wrong thing is cheaper and more dangerous. Winners master the old skill of deciding what to build.
AI EngineerWhy AI Agents Need a Control Plane, Not Better Prompts
Agents break traditional infrastructure. The real risk isn’t model errors—it’s retry storms and cost explosions. A deterministic control plane is the fix.
AI EngineerWhen Coding Agents Retire Your Platform
Spec-first agentic design with deterministic simulation shifts platform value to specifications, changing how teams build software.
AI EngineerWhen Not to Automate: RL for ETL Failure Remediation
An RL system cuts ETL recovery from days to minutes but deliberately escalates when unsure—reserving human judgment for novel failures.
AI EngineerYour Agent Failed in Prod. Good Luck Reproducing It.
Agent production failures are irreproducible. Temperature zero is a myth. Here's how replayability—not determinism—solves debugging.
AI EngineerVoice-In, Visuals-Out: The Latency Hack That Makes Agents Work
Latency kills voice-agent projects. Allen Pike's fix: drop voice output, serve visuals under 1s, and use a fast model with aggressive prefix caching.
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