28 posts
A practical playbook for restaurant and hotel groups: what to automate with AI when replying to Google reviews, and what a human must still handle.
Vercel's skipMiddlewareRequestBody saves milliseconds but strips context your agent's tool chain needs. Here's the coupling it creates — and how to debug it.
EmbeddingGemma 2 makes on-device multimodal RAG real — but local embeddings quietly turn async retrieval into a sync bottleneck. Here's when it's worth it.
Agents hallucinate task completion because they trust their own confidence. Here's how to assert external state in the loop — and architect retries that actually work.
AutoSynthData scales agent training data without annotation — but synthetic distributions hide failure modes your evals won't catch. Here's the tradeoff.
Vercel's Is Agentic site-type views prove monolithic agent evals hide production failures. Here's how to route eval signal per interaction surface.
Vercel cut Sandbox routing from 62ms to 3.4ms. The real win isn't speed—it's variance collapse, and that's what keeps 6-tool agent chains inside SLA.
Pachocki's 'Alien Mind' treats alignment as governance. For teams shipping 12-hour agent loops, it's an observability problem. Here's how we engineer it.
WeatherNext 3 beats generic LLMs by specializing. Here's the architecture fork every agent team faces — and how to design fallbacks that survive it.
BenchMIRT shows LLM benchmarks miss what breaks agents in production. Here's the closed-loop eval harness shipping teams need instead of leaderboards.
Vercel's Instagram adapter for Chat SDK hides Instagram's constraint model. Here's where agent latency, state, and retries break — and how to fix them.
Vercel's ECH hides hostnames, but agent timing, token counts, and error signatures still leak your tool graph. Here's what to harden instead.
Vercel Connect now shows token lifecycles and correlation IDs — but only per connector. Here's the agent-scoped logging pattern that closes the gap.
DeepMind's WeatherNext replaced physics with sequence prediction. Here's when your agent should defer to a specialized time-series model instead of RAG or reasoning.
OpenAI's cyber-eval disclosures expose a gap every agent team shares: the eval pipeline is an attack surface. Here's how to isolate it before it burns you.
Qwen 3.8 Max collapses vision and text into one model with 1M context. Here's the routing logic you can now delete — and what still earns its keep.
OpenAI's scientific computing report reveals the real ship-blocker for code agents: stateless execution loops. Here's the fork/snapshot architecture that fixes it.
Better simulators make physical AI look close. Here's why your LLM agent architecture, evals, and observability won't survive the jump to embodied tasks.
Vercel logs *why* a cache missed, not just that it did. Here's why agent tool calls and retrievals need the same granularity — or you're flying blind.
Seedream 5.0 Pro's text rendering is a leap forward, but production agents still need SVG+image hybrid pipelines. Here's how we architect them.
HF's revamped Kernels aren't just about custom hardware. For agent teams, the real win is a faster loop from inference bottleneck to shipped optimization.
Vercel's AI Gateway routing rules move failover out of code deploys. Here's what they actually solve for agents at scale—and what they don't.
ScarfBench doesn't rank models—it exposes where agents break on real codebases. Here's why enterprise Java migration predicts your production agent's reliability.
HP's OpenAI Frontier partnership isn't a model story. It's an operations story. Here's the agent observability and routing infrastructure enterprises actually need.
Frontier ASR still fails 2-3x harder on bilingual speech. The fix isn't waiting for better models — it's runtime code-switch detection and language routing.
Endava cut requirements analysis from weeks to hours with Codex. The real story isn't agents—it's the workflow engineering most teams skip.
Nemotron-style diffusion LLMs cut decoding time, but agent latency lives in retrieval and tool calls. Here's what actually changes in production.
Benchmark recall lies. Here's how we diagnose retrieval quality in production RAG systems before users start filing tickets at 2am.
