Without relying on off-the-shelf solutions, build your own AI gateway: how to normalize requests, consolidate key management, measure token usage, implement rate limiting and billing within streaming responses, and deploy model routing. This article thoroughly explains the core pipeline, offering actionable module breakdowns and data structures.
Repeatedly sending identical system prompts and knowledge base prefixes is a hidden driver of inflated AI bills. Prompt caching stores these segments; once cached, subsequent hits trigger significant price reductions. This article clarifies the differences between OpenAI's and Claude's caching mechanisms, quantifies potential savings, and explains how to structure prompts for higher cache hit rates.
The MCP Server ecosystem is exploding, but only a handful of categories are useful to developers every day. This guide explains 10 essential types—from files, GitHub, and databases to retrieval, browsers, and documents—and what to consider before using each one.
Build a file-search-capable MCP Server in roughly 100 lines of TypeScript, ready for immediate use in Claude Code and Cursor. This end-to-end guide covers tool definition, parameter validation, path security, integration configuration, and debugging techniques.
Connecting an AI to an MCP Server is akin to installing a plugin capable of reading from and writing to real-world systems—amplifying both capabilities and risks. This article outlines four primary security concerns associated with MCP: excessive authorization, prompt injection, malicious or spoofed servers, and data exfiltration, while providing a pre-integration security checklist.
Reading the MCP documentation often leaves readers confused by the terms 'Host,' 'Client,' and 'Server.' This article uses a clear role diagram to explain what each component is responsible for, how messages flow between them, and why this separation exists. After reading it, you will never again confuse Client with Server.
Function calling is a model capability, while MCP is a tool distribution protocol; they operate at different layers and do not replace one another. This article clarifies their relationship, explains how MCP addresses the M×N integration challenge, and offers guidance on choosing between them across three scenarios: building proprietary applications, integrating with ecosystems, and distributing tools externally.
Multiple agents collaborating sounds more capable than one, but many multi-agent systems are simply slower, more expensive, and harder to debug. This guide explains where they offer real value, their hidden costs, and one pragmatic rule: optimize a single agent before splitting the work among several.
When an Agent runs away, stalls, or returns the wrong answer, which layer actually failed? This guide groups eight causes across task definition, tools, context, and loop control, then gives diagnostic signals and fixes for each so you can locate the specific fault instead of deciding that the Agent simply does not work.
Enabling AI to click through web pages, fill out forms, and book flights relies on a 'observe → decide → act → re-observe' loop. This article clarifies the two pathways agents use to perceive web content (visual screenshots versus DOM structure), explains how actions are executed, and explores why these systems can be slow and prone to failure.
What enables tools like Claude Code and Cursor Agent to independently read codebases, modify multiple files, and run tests? This article dissects the internal loop of a Code Agent: how it locates relevant code within context windows too small for entire projects, formulates plans, executes edits, verifies outcomes, and rolls back changes—clarifying both its capabilities and limitations.
Agent memory is not simply a record of every conversation. This guide separates memory into task state, cross-session long-term memory, and vector retrieval, then explains what each layer should store, how entries are written and retired, and how to prevent memory poisoning.
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