A knowledge base that logs daily experiences and learning journeys, sharing them with you to grow together.
This article explains what an Agent Loop is, how it differs from a simple while loop by using intelligent LLM‑driven exit conditions, compares three mainstream design patterns—deterministic, SDK‑level, and multi‑agent orchestration—and offers guidance on selecting the right approach for various AI tasks.
This article analyzes why direct SDK integration breaks down as LLM usage scales, outlines the governance problems a gateway solves—including unified interfaces, smart routing, failover, cost control, security, and observability—compares major open‑source and hosted gateway solutions, and provides step‑by‑step guidance for building, configuring, and operating a production‑grade LLM gateway.
This advanced guide explains why real‑world LLM deployments must focus on user‑perceived latency, streaming chunk handling, timeout and retry strategies, concurrency, batch processing, token optimization, caching, and observability rather than just model accuracy.
This advanced tutorial explores how LLM APIs handle multimodal inputs and produce structured outputs, detailing format differences, image‑generation parameters, JSON vs JSON‑Schema responses, platform‑specific quirks, practical code examples, and best‑practice strategies for building reliable production pipelines.
This guide explains the three core Function Calling parameters—tools, tool_choice, and parallel_tool_calls—showing how to design tool schemas, control model autonomy, choose parallel execution, and avoid common pitfalls, with concrete Python and JavaScript examples and a cross‑platform comparison.
This article explains how inference‑oriented LLMs differ from standard models, details the three key reasoning‑control parameters (reasoning_effort, thinking, max_completion_tokens), compares their usage across OpenAI o1, Anthropic Claude, DeepSeek‑R1 and Kimi, and provides practical guidance on selecting values, managing costs, and deciding when to employ reasoning models.
This guide explains how temperature, top_p, top_k, and min_p control LLM output, compares their effects, warns against common pitfalls, and provides concrete configuration recommendations and step‑by‑step tuning workflows for tasks such as code generation, QA, dialogue, translation, and creative writing.
This article provides a detailed side‑by‑side comparison of core API parameters such as temperature, top_p, top_k, penalties, max_tokens, tools and response_format across OpenAI, Claude, Gemini, DeepSeek, Kimi, MiniMax and Yi, explains common migration pitfalls, and offers practical guidance for selecting and adapting LLM services.
While many newcomers think using an LLM API is as simple as picking a model and feeding a prompt, the real control lies in parameters such as temperature, top‑p, top‑k, max_tokens, penalties, stop, and stream, each of which dramatically influences output quality, length, cost, and behavior.
This article walks through a six‑step, AI‑augmented review‑driven development workflow—using Spec‑Kit commands, devil‑style iterative reviews, and TDD—to turn a simple "user login failure lock" requirement into a robust, well‑tested implementation while catching design flaws early.
