AI Dev Tools — The Crazyrouter Podcast

Епизоди

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  EP196: AI API Security Posture — Turn Controls Into Continuous Evidence 29.10.2026 6мин

  A practical guide to AI API security posture: inventory exposure, verify controls continuously, protect keys and data, constrain tools, and turn security assumptions into evidence that operators can act on.
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  EP195: AI API Policy Drift — Detect When Production Rules Stop Matching Intent 28.10.2026 7мин

  A practical guide to policy drift in AI APIs: version routing and safety rules, detect stale enforcement, compare intended and observed behavior, and restore control before small gaps become incidents.
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  EP194: AI API Dependency Mapping — Know What a Model Change Can Break 27.10.2026 7мин

  A practical guide to dependency mapping for AI APIs: connect models to prompts, schemas, tools, tenants, budgets, and user journeys so teams can assess blast radius before changing production behavior.
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  EP193: AI API Rollback Design — Restore Safe Behavior Without Losing State 26.10.2026 6мин

  A practical guide to rollback design for AI APIs: restore compatible behavior, preserve operation state, stop unsafe traffic, reconcile billing, and verify that recovery is complete before reopening exposure.
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  EP192: AI API Release Readiness — Decide Whether a Model Change Is Safe to Ship 25.10.2026 7мин

  A practical guide to AI API release readiness: define workload contracts, evaluate quality and reliability, verify cost and capacity, stage exposure, and make go or no-go decisions with evidence.
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  EP191: AI API Change Failure Analysis — Find Why Safe-Looking Changes Break Production 24.10.2026 7мин

  A practical guide to analyzing AI API change failures: trace intent to impact, distinguish code from configuration and provider drift, improve rollout evidence, and make future changes safer to reverse.
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  EP190: AI API Post-Incident Learning — Turn Failures Into Durable Reliability Improvements 23.10.2026 8мин

  A practical guide to learning from AI API incidents: build a precise timeline, separate causes from conditions, prioritize corrective actions, improve tests and runbooks, and verify that reliability actually improves.
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  EP189: AI API Recovery Verification — Prove the System Is Healthy Before Declaring Victory 22.10.2026 7мин

  A practical guide to verifying AI API recovery: test real user journeys, validate routing and billing state, drain queues safely, compare quality and latency, and avoid declaring an incident over before the system is truly healthy.
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  EP188: AI API Recovery Objectives — Turn Reliability Goals Into Operating Decisions 21.10.2026 8мин

  A practical guide to recovery objectives for AI APIs: define acceptable data loss and recovery time, prioritize workloads, preserve routing policy, reconcile state, and test restoration before an incident.
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  EP187: AI API Graceful Degradation — Preserve the Core User Journey Under Pressure 20.10.2026 8мин

  A practical guide to graceful degradation for AI APIs: define essential outcomes, reduce optional work, preserve contracts, and keep user journeys useful when models or providers are slow, expensive, or unavailable.
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  EP186: AI API Admission Control — Keep Overload From Becoming the User Experience 19.10.2026 6мин

  A practical guide to admission control for AI APIs: classify work, reserve capacity, shed safely, protect interactive traffic, and make overload decisions visible before queues become outages.
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  EP185: AI API Quotas — Separate Fairness From Mere Rate Limits 18.10.2026 8мин

  A practical guide to AI API quotas: separate requests from tokens and concurrency, allocate fair tenant budgets, handle bursts, prevent noisy neighbors, and make quota decisions visible and reversible.
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  EP184: AI API Request Hedging — Cut Slow Tails Without Paying Twice 17.10.2026 9мин

  A practical guide to request hedging for AI APIs: identify true tail latency, launch a bounded backup safely, avoid duplicate side effects and charges, protect streaming responses, and measure whether the extra capacity is worth it.
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  EP183: AI API Prompt Versioning — Ship Prompt Changes Like Code 10.10.2026 8мин

  A practical guide to prompt versioning for AI APIs: keep templates reproducible, separate content from code, test changes, roll out safely, preserve rollback, and connect prompt releases to quality, cost, and incidents.
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  EP182: AI API Observability — Trace Every Request From Gateway to Model 09.10.2026 8мин

  A practical guide to AI API observability: define request context, trace multi-provider calls, measure useful latency and quality signals, protect sensitive data, correlate retries and spend, and make production failures diagnosable.
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  EP181: AI API Error Taxonomy — Make Failures Actionable 08.10.2026 8мин

  A practical guide to AI API error taxonomy: classify failures by retryability and ownership, preserve provider context, return stable client errors, prevent unsafe retries, and turn failure data into better operations.
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  EP180: AI API Budgets — Put Spend Controls Where They Matter 07.10.2026 8мин

  A practical guide to AI API usage budgets: set tenant and workflow limits, reserve spend, enforce model-aware controls, handle streaming and retries, expose useful feedback, and keep budget enforcement reliable during incidents.
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  EP179: AI API Idempotency — Stop Duplicate Work and Charges 06.10.2026 7мин

  A practical guide to idempotency for AI APIs: design request keys, persist results, handle retries and timeouts, protect tool effects, scope deduplication, and make duplicate work observable.
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  EP178: AI API Load Testing — Find Capacity Before Production Does 05.10.2026 6мин

  A practical guide to load testing AI APIs: model realistic workloads, separate concurrency from rate limits, protect budgets, measure accepted results, rehearse provider failures, and turn test data into capacity decisions.
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  EP177: AI API Streaming Backpressure — Keep Tokens Flowing Without Melting Clients 04.10.2026 7мин

  A practical guide to streaming backpressure for AI APIs: bound buffers, propagate cancellation, handle slow clients, protect concurrency, resume honestly, and make token delivery observable.