AI in production. Reliability in practice.
Latest edition ·The latest analysis
A client-side SSE probe separates first answer text from normal completion. Fixed arrivals and outcome counts show whether a serving change delivers more useful answers.
Read the storyFrom the publication
Infermeld makes a mixed-GPU llama.cpp launch inspectable. Its recorded failures show why separate device allocations and completed workload checks matter more than adding VRAM capacities.
Anthropic’s October 7 credits add a Console-funded path for eligible subscribers. Subscription access remains, while scheduled agents still need task budgets spanning retries and verification.
Instinct brings personal tasks into connected services. A hypothetical booking and notification worker show how permission, provider confirmation and uncertain results affect what happens next.
Kimi K3 can keep a larger incident record in view. Its practical value depends on evidence handling, agent integration and the cost of useful investigation.
Field guides
Follow a request. Review the code. Find out what actually happened.
Explore the guidesKeep the traces that explain a failure
Tail sampling selects recorded traces by error, latency and other evidence. A checkout timeout shows how routing, waiting time and memory affect the trace a responder can retrieve.
Give AI code review a useful job
CodeRabbit brings AI feedback into pull requests, editors and the CLI. A retry loop that exceeds its caller’s deadline shows the kind of finding that can repay review time.
Recover when a tool call times out
A missing response can leave a worker already created. Durable operation IDs and the destination’s idempotency contract let a replacement executor recover the original request.
Put it into practice
Free reliability tools. No account needed.
Open the workbenchThe AI tools guide
Coding assistants, code review and agent supervision.
Browse the guideCompanies & products
