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AI Tech Publishing

In the fast-evolving AI era, we thoroughly explain stable technical foundations.

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Latest from AI Tech Publishing

90 recent articles
AI Tech Publishing
AI Tech Publishing
Jul 10, 2026 · Artificial Intelligence

How I Collaborate with AI at Work: 5 Practical Principles

The article outlines a systematic approach to working with AI by treating context as infrastructure, encoding preferences in configuration files, front‑loading validation, progressively delegating larger tasks, and closing the feedback loop, with concrete examples of directory organization, CLAUDE.md onboarding, skill files, hooks, and session monitoring.

AI collaborationClaudeWorkflow Automation
0 likes · 18 min read
How I Collaborate with AI at Work: 5 Practical Principles
AI Tech Publishing
AI Tech Publishing
Jul 7, 2026 · Artificial Intelligence

From Prompts to Loops: Four Claude Code Loop Patterns and Their Usage Limits

Claude Code defines a loop as an agent repeatedly executing a work cycle until a stop condition is met; this article explains four loop patterns—round‑driven, goal‑driven, time‑driven, and proactive—detailing their triggers, stopping criteria, ideal tasks, token‑control strategies, and practical examples.

AI automationClaude Codedynamic workflows
0 likes · 13 min read
From Prompts to Loops: Four Claude Code Loop Patterns and Their Usage Limits
AI Tech Publishing
AI Tech Publishing
Jun 29, 2026 · Artificial Intelligence

Understanding the Full LLM Inference Pipeline: From Tokenization to Streaming Output

The article walks through every stage of LLM inference—from tokenization and embedding, through transformer layers, prefill (compute‑bound) and decode (memory‑bound) phases, KV‑cache management, attention redesign, quantization, and serving infrastructure—explaining how each step consumes time and resources and how to identify bottlenecks.

Attention optimizationKV cacheQuantization
0 likes · 14 min read
Understanding the Full LLM Inference Pipeline: From Tokenization to Streaming Output
AI Tech Publishing
AI Tech Publishing
Jun 29, 2026 · Artificial Intelligence

Productionizing LLM Agent Harness: Architecture, Backend Design, and Optimization

The guide explains how to turn a basic LLM call into a production‑ready multi‑agent system by introducing the Agent Harness architecture—five components (Orchestrator, Subagents, Skills, Backend, Context Engineering)—and detailing backend state handling, isolated sub‑agents, caching layers, token optimization, async task queues, and observability best practices.

Agent HarnessAsync TasksCaching
0 likes · 27 min read
Productionizing LLM Agent Harness: Architecture, Backend Design, and Optimization
AI Tech Publishing
AI Tech Publishing
Jun 24, 2026 · Fundamentals

How Modern Browsers Work: Inside Chromium, Blink, V8 and Multi‑Process Architecture

This article walks through the complete lifecycle of a web page in modern browsers, covering network fetching, HTML/CSS parsing, layout calculation, painting and GPU compositing, the V8 JavaScript engine pipeline, module loading, multi‑process isolation, and key differences among Chromium, Gecko and WebKit implementations.

Browser architectureChromiumJavaScript engine
0 likes · 47 min read
How Modern Browsers Work: Inside Chromium, Blink, V8 and Multi‑Process Architecture
AI Tech Publishing
AI Tech Publishing
Jun 22, 2026 · Product Management

Why Product Managers Should Master Loop Engineering After Prompt Engineering

The article explains how product managers must move beyond writing better prompts to building repeatable, evidence‑driven loops that continuously improve long‑term assets such as PRD review rules, interview summarizers, and release checklists, outlining the five loop components, practical examples, and common pitfalls.

AI AgentsProduct Managementiterative improvement
0 likes · 13 min read
Why Product Managers Should Master Loop Engineering After Prompt Engineering
AI Tech Publishing
AI Tech Publishing
Jun 19, 2026 · Artificial Intelligence

Designing a Durable Agent Loop Architecture for Reliable AI Workflows

The article dissects the agent loop concept, explains why durability must span the entire execution layer, presents a three‑layer architecture (loop, skill, orchestrator), shows concrete Inngest code, discusses error handling, observability, and how this model compares to existing tools, concluding with guidance for building production‑grade agent loops.

AI AgentsAgent LoopInngest
0 likes · 21 min read
Designing a Durable Agent Loop Architecture for Reliable AI Workflows
AI Tech Publishing
AI Tech Publishing
May 1, 2026 · Artificial Intelligence

5 Counterintuitive Design Principles for Prompt Caching in Claude Code

The article details five counterintuitive design principles for Claude Code's prompt caching—optimizing prompt layout, using message‑based updates, never switching models or tools mid‑conversation, safely compressing context, and monitoring cache health—backed by concrete examples and up to 90% cost savings.

AI engineeringClaude CodeContext Compression
0 likes · 10 min read
5 Counterintuitive Design Principles for Prompt Caching in Claude Code