Topic
AI Workflows
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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AI EngineerThe Real Differentiator for AI Agents Isn't the Model
Alake's intelligence plane concept reframes agent architecture around memory and ontology, not models. His Oracle-backed demo shows dramatic token savings…
Latent SpaceHow a $64M Firm Runs Wealth Management on AI Agents
Arca's AI agents handle financial analysis, compliance, and client monitoring across 10-day workflows. Trades still require human approval. A deep look at…
AI EngineerWhy a Positive A/B Test Is Not Enough for AI Features
Bumble's PM reveals why a positive A/B test for an AI feature is only the start: low adoption, bad long-tail outputs, hidden costs, and regulatory walls.
AI EngineerVoice agent evals: from 33% agreement to 92% in regulated fintech
Upstart's voice agent evals went from 33% to 92% compliance agreement. The blueprint for deploying AI safely in regulated environments.
Theo - t3․ggHarnesses Don't Matter Anymore: Focus on the Agentic Environment
Theo makes the case that harnesses are fading; the future is ADEs that let you swap harnesses like models. But the evidence is mostly opinion.
David OndrejGoing All-In on AI Coding: The Tradeoffs Most Teams Will Miss
David Ondrej argues that partial AI adoption still leaves humans as the bottleneck. His two-model review loop and autonomous agent loops offer practical…
Dave EbbelaarThe real recipe for AI support: structure, not model size
AI support agents work with structure: cheap classifier, strict schemas, deterministic guardrails, evals. A practical blueprint behind a six-client claim.
Y CombinatorSupabase's agent takeover: what the shift from human to machine users means
Supabase's user base has flipped from developers to agents. The company is betting the entire database market shifts to non-technical builders. The evidence…
InfoQAI in 2026: From Model Selection to System Engineering
AI shifts from model choice to system design. Agents demand orchestration, evaluation, and context engineering. The bottleneck is operational.
AI EngineerThe Cheaper Model That Costs More: Why Custom Evals Matter
Cheaper AI models can be more expensive. Learn why custom evaluations are the only way to right-size model cost without sacrificing performance.
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