AI Dev Tools — The Crazyrouter Podcast

A practical episode on why AI model catalogs need operational discipline: exact aliases, access rules, pricing visibility, endpoint examples, deprecation notes, and release checks that keep developer workflows from breaking as model lists change.

A practical episode on launching new Claude-family models in an API gateway: model aliases, unified endpoints, token allowlists, docs language, test emails, request examples, and how to help developers try a new model without changing their whole stack.

A practical episode about production video generation APIs: async task submission, polling, model availability, endpoint drift, archived video URLs, and why teams should test Wan, Veo, Seedance, and Kling with real calls before writing integration docs or choosing a provider.

A practical episode about GLM-5.2, long-horizon coding models, 1M-token context, reasoning-token budgets, unlimited RPM promises, and what engineering teams should measure before moving agent workflows into production.

A practical episode about turning vision model benchmarks into production decisions: accuracy, latency, tail latency, cost per successful image, media handling, usage signals, failure modes, and user-facing routing strategy.

A practical episode about image understanding APIs, URL image inputs, Gemini and Claude inline media conversion, Qwen and OpenAI-style URL passthrough, and why production AI gateways need media-aware routing instead of just model-aware routing.

A practical episode about DeepSeek official concurrency limits, cloud-provider TPM and RPM quotas, and why AI gateways need structured capacity routing instead of a single rate-limit number.

A practical episode about why AI operations dashboards should use service accounts for GA4 and Search Console, how expired OAuth refresh tokens break growth reports, and why analytics credentials are part of production infrastructure.

A practical episode about the reliability work behind AI API products: regional base URLs, routing, failover policy, balance-related pauses, support clarity, and turning repeated support cases into better developer infrastructure.

A practical episode about using coding agents for prediction workflows: deterministic probability models, model-written explanations, JSON validation, and why AI apps need evidence checks instead of vibe-based answers.

A practical episode about why model quality includes payload compatibility, endpoint fit, retries, and real workflow success rather than only benchmark headlines.

A practical episode on how a Claude Code guide repository can become developer growth infrastructure: correct base URL rules, UTM discipline, searchable docs, setup scripts, FAQs, and multi-platform content distribution built from validated developer onboarding.

A practical episode on why GPT-5-style reasoning models need cleaner request payloads, how to handle max_tokens versus max_completion_tokens, when to use reasoning_effort and verbosity, and why config-only Claude Code onboarding is better for users who already installed the CLI.

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 Image, Imagen, Qwen Image, and FLUX-style models before choosing production routes.

One-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 checklists turn fragile AI tool setup into repeatable developer onboarding infrastructure.

API 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 paths turn small configuration details into support load — and how guides, generators, and troubleshooting pages convert support questions into growth assets.

Dynamic 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 get expensive; and how model routing through an API gateway helps teams control cost, latency, and evidence.

API 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 production teams should measure cost per successful task instead of raw token price.

API 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 separately from model failures — and how gateways make authentication problems easier to diagnose.

AI 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 cost per successful task instead of raw token price.

MCP 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-aware routing across the full agent workflow.

AI 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 model pricing should become a living routing layer inside your AI infrastructure.

Gemini 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 tasks to stronger models. This episode explains how to use lightweight models in production without trusting them blindly.

A 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 episode explains why exact-payload health checks, fallback routing, and cost per successful task are now core AI infrastructure.

Distribution 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 networks, and podcasts — with platform-aware syndication, UTM tracking, and measurable outcomes instead of random posting.