Context Window vs Output Limit vs Reasoning Tokens
Understand the difference between AI context windows, maximum output tokens, and reasoning tokens, including how these limits interact in modern reasoning models.
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How to Design a Custom OpenAI-Compatible Provider Connection
A practical architecture for custom OpenAI-compatible connections: base URLs, credentials, headers, model discovery, validation, local HTTP, and safe diagnostics.
Read articleHow to Debug AI Tool-Calling Loops
Debug invalid arguments, repeated tool calls, missing results, stuck multi-round loops, and provider-specific tool state with structured traces and deterministic limits.
Read articleHow to Debug Local AI Connection Failures
Diagnose local AI connectivity systematically across DNS, IP addresses, bind interfaces, ports, firewalls, iOS permissions, HTTP/TLS, authentication, endpoints, and model IDs.
Read articleDeepSeek API Integration Guide
Integrate DeepSeek safely with OpenAI-style clients while handling thinking mode, reasoning_content, tools, streaming, model discovery, and compatibility differences.
Read articleHow to Delete AI App Data Correctly
Design complete AI app deletion flows that remove conversations, attachments, credentials, indexes, caches, backups, provider-side resources, analytics identifiers, and queued work without leaving hidden copies.
Read articleHow to Design AI Conversation Search
Design private AI chat search with local lexical indexes, semantic retrieval, filters, branch awareness, ranking, snippets, attachments, and predictable deletion behavior.
Read articleHow to Design Good AI Tool Schemas
Design tool names, descriptions, JSON schemas, enums, required fields, error contracts, and side-effect boundaries that models can use reliably and applications can validate safely.
Read articleHow to Design Model and Reasoning Controls Without Confusing Users
Design capability-aware model, reasoning, sampling, output, and tool controls that stay understandable across providers without exposing unsupported states or pretending native settings are equivalent.
Read articleDesigning Offline and Degraded Modes for AI Apps
Design honest offline and degraded AI modes with cached chats, local models, provider outages, queued work, capability loss, recovery, and explicit privacy-aware fallback.
Read articleHow to Detect AI API Capability Mismatches Before Sending
Prevent avoidable AI API failures by validating model, endpoint, modality, tools, reasoning, structured output, and request settings before network execution.
Read articleDirect-to-Provider AI vs Proxy Servers
Compare direct AI API calls with application proxy servers across privacy, credentials, billing, CORS, reliability, observability, policy, and architecture.
Read articleHow to Estimate AI Request Cost Before Sending
Learn how to estimate AI API request cost from input, output, cached, reasoning, and multimodal token budgets without pretending preflight estimates are exact.
Read articleHow to Estimate Per-Conversation AI Cost
Estimate AI conversation cost across input, cached input, output, reasoning, tools, retries, provider changes, and background work without pretending historical estimates are exact billing records.
Read articleHow to Execute Parallel AI Tool Calls Safely
Run independent model-requested tools concurrently without breaking dependencies, permissions, ordering, cancellation, idempotency, or deterministic result correlation.
Read articleHow to Export AI Conversations Portably
Design portable AI conversation exports with Markdown, JSON, attachments, branches, tool events, metadata, usage, and versioning without leaking credentials or locking history to one provider.
Read articleHow File Attachments Flow Through AI APIs
Understand how AI clients move files from local storage into model context through inline payloads, provider uploads, retrieval, extraction, lifecycle tracking, and portable conversation state.
Read articleFunction Calling vs Tool Calling vs Agents
Understand the difference between function calling, tool calling, and agents, and where execution loops, permissions, state, and autonomy actually belong.
Read articleGoogle Gemini API Explained for Chat Client Developers
A practical guide to Gemini's current Interactions API and generateContent API, including state, tools, streaming, multimodal input, caching, and client architecture.
Read articleHow to Handle App Backgrounding During AI Generation
Design AI generation for mobile app backgrounding with lifecycle-aware persistence, partial streams, bounded background execution, remote jobs, cancellation, and recovery after suspension or relaunch.
Read articleHow to Handle Untrusted AI-Generated URLs
Safely handle URLs generated by models, tools, search results, and retrieved content using parsing, scheme restrictions, network-boundary checks, redirect policy, user confirmation, and SSRF-style defenses.
Read articleHealth Scoring AI Providers Without Fake Precision
Measure AI provider health with scoped recent signals, sample confidence, latency distributions, transient failures, and circuit state without inventing meaningless 0-100 scores.
Read articleHosted Tools vs Client-Executed Tools
Compare provider-hosted tools with tools executed by your own AI client across state, privacy, portability, latency, authorization, and reliability.
Read articleHow AI Model Discovery APIs Work
Learn how model-list endpoints, metadata, capability discovery, caching, filtering, and custom-provider validation should work in multi-provider AI clients.
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