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Your weekly breakdown of AI development tools, API gateways, model pricing, and building with GPT, Claude, Gemini, and DeepSeek. Hosted by Crazyrouter — one API key for 627+ AI models.
Unknown Host hosts AI Dev Tools — The Crazyrouter Podcast.
Image generation pricing is misleading if teams only compare cost per request. This episode explains cost per accepted image, why model demos are not enough, and how developers can use a repeatable test matrix across GPT
Show notesOne-click setup scripts are more than convenience. This episode explains how WorkBuddy-style custom model configuration, local models.json files, Base URL normalization, backups, API key handling, and troubleshooting che
Show notesAPI Base URL mistakes are one of the most common AI developer onboarding failures. This episode explains why missing /v1, wrong environment variables, UTM parameters in API endpoints, region endpoints, and unclear error
Show notesDynamic workflows are changing AI coding from one long agent chat into structured orchestration. This episode explains planner, implementer, adversarial reviewer, and verifier packets; why ultracode-style workflows can g
Show notesAPI billing is no longer just an accounting detail. This episode explains why Anthropic and Claude API cost depends on payload design, long context, output length, retries, agent loops, and fallback strategy — and why pr
Show notesAPI key hygiene is a production AI reliability issue. This episode explains why invalid tokens, missing Authorization headers, rotated credentials, stale workers, and forgotten environment variables should be tracked sep
Show notesAI gateway reliability is more than uptime or HTTP 200s. This episode explains provider availability, payload compatibility, output validity, task success, and cost stability — and why production AI teams should measure
Show notesMCP gives AI agents access to tools, but production teams need more than direct tool wiring. This episode explains why MCP needs gateway discipline: identity, policy, approvals, tool shaping, observability, and cost-awar
Show notesAI pricing pages show unit costs, but production teams need routing decisions. This episode explains why real AI cost depends on success rate, retries, latency, payload compatibility, and task-level economics — and why m
Show notesGemini 2.5 Flash Lite is best understood as a lightweight automation tier: fast, cheap, and useful for classification, extraction, summarization, routing, validation, and first-pass analysis before escalating harder task
Show notesA real Claude Jupiter v1-p vs GPT-5.5 benchmark showed that model quality is not just intelligence. Payload compatibility matters too: Jupiter failed with temperature=0 but passed once the payload was adjusted. This epis
Show notesDistribution is infrastructure for AI developer products. This episode explains how to build a content distribution graph across your main blog, developer communities, Git-based pages, open source platforms, social netwo
Show notesToken price is only the beginning of AI cost. This episode explains why production teams should optimize for cost per successful task, including retries, invalid outputs, fallback calls, latency, and user completion — an
Model orchestration is the practical layer between a prompt and a production AI system. This episode explains how to route by task instead of brand, validate outputs beyond HTTP 200, design fallbacks based on failure typ
Show notesGemini 3.5 Flash is best understood as a strong fast-tier production model, not a direct Claude Opus replacement. We break down real API test results against Claude Haiku and Sonnet-style routes, the max_tokens pitfall t
Show notesMCP gateways are becoming the control layer for production AI agents. We break down why tool access needs identity, permissions, approvals, and observability, and how gateway thinking helps teams make agents capable with
Show notesGPT-series pricing is changing, and that is a reminder that model pricing is product infrastructure. We break down Crazyrouter's GPT discount adjustment, why upstream cost changes happen, and how developers can design AI
Show notesFallback routing is the reliability layer that makes production AI apps feel dependable. We break down why fallback is not just a backup model, how to design task-aware routing rules, when not to fallback, and why loggin
Show notesAI image generation is moving from novelty to product infrastructure. We break down why developers should evaluate image models by use case, not hype: text rendering, product accuracy, latency, edit support, routing, fal
Show notesMCP is moving from integration convenience to production governance. We break down why MCP gateways are becoming the control layer between AI agents and external tools, how they mirror the API gateway pattern for microse
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