Topic
AI Infrastructure
Model platforms, cost, latency, and operational trade-offs. Curated tldw.news briefings about ai infrastructure, 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…
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 EngineerThe Concurrency Trap: Why Self-Hosting AI Models Is Harder Than You Think
IMEC's benchmark of thousands of SWE-bench Pro runs reveals self-hosting AI models can cut costs by 25%, but concurrency constraints make larger models…
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.
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…
AI EngineerLet Your Agent Pay? 6 Attack Vectors You Need to Patch First
X402 offers a path for agents to pay for data in 10-cent increments. Ishween Kaur shows six ways those payments fail—and how to catch them.
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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