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Qborfy AI

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Latest from Qborfy AI

71 recent articles

Qborfy AI

Jul 11, 2026 · Artificial Intelligence

Why Does Your AI Agent Forget Mid‑Run? Understanding Token Window Limits and Context Management

The article explains that an AI agent’s “memory loss” is caused by the finite token context window, describes three concrete symptoms—repeating actions, forgetting constraints, and giving contradictory answers—and evaluates three engineering solutions (sliding‑window truncation, context compression, and external memory) with their trade‑offs, plus practical tips such as using CLAUDE.md for persistent rules and session_id for resume.

AI agentsClaudeLLM

0 likes · 20 min read

Why Does Your AI Agent Forget Mid‑Run? Understanding Token Window Limits and Context Management

Qborfy AI

Jul 9, 2026 · Artificial Intelligence

What Happens to a Message Inside an AI Agent Loop?

This article walks through the full lifecycle of a user message in Claude Code's Agent SDK, explaining each processing stage, the crucial tool‑use decision, parallel tool execution, error handling, and how Hooks let developers extend the loop without modifying core logic.

AI AgentAgent LoopClaude

0 likes · 16 min read

What Happens to a Message Inside an AI Agent Loop?

Qborfy AI

Jul 8, 2026 · Artificial Intelligence

Build a Working AI Agent Loop in Just 50 Lines of Python

This tutorial walks through a minimal 50‑line Python implementation of an AI Agent Loop, covering the core four‑step cycle, dual termination strategies, deterministic vs. autonomous designs, tool registration, and a complete runnable example.

AI AgentAgent LoopDeterministic vs Autonomous

0 likes · 13 min read

Build a Working AI Agent Loop in Just 50 Lines of Python

Qborfy AI

Jul 7, 2026 · Artificial Intelligence

Why Agent Loop Is the Overlooked Core Engine Behind AI Applications

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.

AI AgentAgent LoopClaude

0 likes · 10 min read

Why Agent Loop Is the Overlooked Core Engine Behind AI Applications

Qborfy AI

Jul 3, 2026 · Operations

When to Deploy an LLM Gateway? Complete Guide to Architecture, Routing, Cost, and Security

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.

LLM gatewayLiteLLMOne API

0 likes · 23 min read

When to Deploy an LLM Gateway? Complete Guide to Architecture, Routing, Cost, and Security

Qborfy AI

Jul 2, 2026 · Artificial Intelligence

How Streaming Responses and Performance Tuning Boost LLM API Production

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.

LLMapicaching

0 likes · 23 min read

How Streaming Responses and Performance Tuning Boost LLM API Production

Qborfy AI

Jun 27, 2026 · Artificial Intelligence

Advanced Guide to LLM API: Multimodal Input and Structured Output

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.

JSON SchemaLLMPython

0 likes · 19 min read

Advanced Guide to LLM API: Multimodal Input and Structured Output

Qborfy AI

Jun 26, 2026 · Artificial Intelligence

Mastering Function Calling: Deep Dive into tools, tool_choice, and parallel_tool_calls for LLMs

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.

AI agentsFunction CallingLLM APIs

0 likes · 18 min read

Mastering Function Calling: Deep Dive into tools, tool_choice, and parallel_tool_calls for LLMs

Qborfy AI

Jun 25, 2026 · Artificial Intelligence

Understanding Reasoning Control Parameters in Large Model APIs: A Deep Dive into reasoning_effort, thinking, and max_completion_tokens

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.

API parametersLLMModel Comparison

0 likes · 18 min read

Understanding Reasoning Control Parameters in Large Model APIs: A Deep Dive into reasoning_effort, thinking, and max_completion_tokens

Qborfy AI

Jun 16, 2026 · Artificial Intelligence

LLM Sampling Parameter Tuning: Best Practices for Temperature, Top‑p, Top‑k, and Min‑p

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.

LLMSamplingmin_p

0 likes · 22 min read

LLM Sampling Parameter Tuning: Best Practices for Temperature, Top‑p, Top‑k, and Min‑p