GuidesRate Limits and Retries: Backoff That Does Not Make It Worse
Token buckets, jitter, retry budgets and circuit breakers for LLM APIs — how to stay under the limit instead of discovering it, and why naive retries amplify outages.
ReadNotes on models, agents, and what inference costs
Practical writing for developers building with large language models — how they work, how to pick one, and how to keep the bill predictable.
Hands-on setup for the tools developers actually use.
Guides
GuidesRoo Code Setup: Custom Endpoint and Profiles Per Mode
Configure Roo Code against an OpenAI-compatible base URL, then use API configuration profiles to give each mode its own model. Includes the tool-calling caveat.
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GuidesRunning LLMs in CI Without Flaky, Expensive Builds
Putting a model call inside CI adds a non-deterministic network dependency to your build. How to keep it cheap, secret-safe on forks, and incapable of blocking a merge.
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GuidesStreaming LLM Responses: SSE, Buffering, and Why Output Stalls
Streaming is what makes an AI feature feel fast. It is also where proxies, buffers and framework defaults quietly break things. A practical guide.
ReadGuidesWhat "OpenAI-Compatible" Actually Means (And Where It Breaks)
Most AI tools speak one API shape. Understanding what compatibility covers — and the four places it usually leaks — saves hours of debugging a base URL swap.
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