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Developer Tooling
Tools that change how teams build, review, and ship. Curated tldw.news briefings about developer tooling, with practical engineering takeaways from long-form AI and developer-tool videos.
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AI EngineerDon't Let AI Agents Write Raw SQL: A Safer Pattern from Oracle
Natural language-to-SQL is risky in production. Oracle's Jeff Smith proposes a safer pattern: let agents choose from pre-authorized reports instead of…
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 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.
Latent SpaceOpenAI's finance lead: MCP error rates hit 40% — bundle data yourself
OpenAI's finance product lead reveals MCPs are unreliable, evals are broken, and 95% of what they built is replicable by startups.
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.
GOTO ConferencesDistributed protocol testing: ditch containers, use state machines
Distributed protocol testing is slow and flaky. One team's solution: extract the logic into a state machine, simulate thousands of nodes in a single thread…
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