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Latest from AI Tech Publishing

81 recent articles

AI Tech Publishing

Apr 20, 2026 · Artificial Intelligence

How Claude Code Achieves 92% Prompt Cache Hit Rate and Cuts Costs by 81% – A Deep Dive

This article explains the mechanics of prompt‑caching for large language models, breaks down static versus dynamic context, details KV‑cache operation and its pricing, and shows how Claude Code’s 30‑minute programming session reached a 92% cache hit rate that reduced inference costs by 81%, concluding with three production‑grade design rules.

AI agentsAnthropic APIClaude Code

0 likes · 13 min read

How Claude Code Achieves 92% Prompt Cache Hit Rate and Cuts Costs by 81% – A Deep Dive

AI Tech Publishing

Apr 19, 2026 · Industry Insights

AI Will Replace Programmers? Why You Should Stay Calm

Amid widespread claims that AI will replace programmers, the article argues that current AI demos are limited to simple apps, while real software engineering requires deep problem definition, architecture design, and operational judgment—skills AI cannot replicate, making programmers' cognitive expertise more valuable than ever.

AIAutomationIndustry Insight

0 likes · 7 min read

AI Will Replace Programmers? Why You Should Stay Calm

AI Tech Publishing

Apr 19, 2026 · Artificial Intelligence

How to Build Production‑Ready Agent HITL: State Machines, Event Sourcing, and Distributed Coordination

The article presents a detailed engineering guide for deploying production‑grade AI agents with Human‑in‑the‑Loop, covering a three‑layer decoupled architecture, tool‑level and hook‑level interception, a six‑state session state machine with event sourcing, robust timeout handling using CAS, and cross‑node coordination for multi‑agent workflows.

AgentDistributed CoordinationEvent Sourcing

0 likes · 17 min read

How to Build Production‑Ready Agent HITL: State Machines, Event Sourcing, and Distributed Coordination

AI Tech Publishing

Apr 17, 2026 · Artificial Intelligence

Why Your AI Agent Crashes: 7 Hosting Patterns Compared

The article explains why AI agents fail when deployed with the wrong hosting model, presents a systematic comparison of seven patterns—Cron, Reactive, Daemon, Pipeline, Service, Adaptive, and Mesh—detailing their problem scope, typical scenarios, concrete Python or TypeScript implementations, when to choose each, and the trade‑offs, while warning against the common mistake of over‑engineering from the start.

AI agentsEvent-drivenMulti-Agent Mesh

0 likes · 21 min read

Why Your AI Agent Crashes: 7 Hosting Patterns Compared

AI Tech Publishing

Apr 16, 2026 · Cloud Native

Deploying a Stateful AI Agent on a Stateless Web Architecture: Challenges, Solutions, and Code Walkthrough

This article analyzes the fundamental conflict between stateful AI agents and the inherently stateless, distributed nature of modern web services, explores time, state, and execution model mismatches, and presents a practical Agent‑as‑API solution using FastAPI, Redis, SSE, and Kubernetes to achieve scalable, fault‑tolerant deployments.

AI AgentFastAPIKubernetes

0 likes · 30 min read

Deploying a Stateful AI Agent on a Stateless Web Architecture: Challenges, Solutions, and Code Walkthrough

AI Tech Publishing

Apr 15, 2026 · Artificial Intelligence

8 Critical Harness Design Issues That Threaten Long‑Running Agent Accuracy

The article systematically breaks down why autonomous agents lose control during long‑running engineering tasks—missing context, short‑sighted planning, context anxiety, and plan drift—and shows how a well‑designed harness layer can preempt these problems without changing the underlying model.

AI EngineeringContext ManagementHarness

0 likes · 11 min read

8 Critical Harness Design Issues That Threaten Long‑Running Agent Accuracy

AI Tech Publishing

Apr 14, 2026 · Artificial Intelligence

12 Harness Design Patterns from Claude Code: Memory, Workflow, Tools, and Automation

The article dissects twelve concrete harness design patterns uncovered in the leaked Claude Code source, organized into four categories—memory & context, workflow & orchestration, tools & permissions, and automation—detailing their use cases, trade‑offs, and implementation costs for building production‑grade AI agents.

Agent DesignAutomationClaude Code

0 likes · 14 min read

12 Harness Design Patterns from Claude Code: Memory, Workflow, Tools, and Automation

AI Tech Publishing

Apr 13, 2026 · Artificial Intelligence

12 Core Components of a Production-Grade Agent Harness and Framework Comparison

The article explains why production issues often stem from the agent harness rather than the model, defines the harness concept, breaks down its twelve essential components, shows a full execution loop, compares Anthropic, OpenAI, LangChain and other frameworks, and discusses key design trade‑offs for building robust AI agents.

AI agentsAgent Harnessframework comparison

0 likes · 21 min read

12 Core Components of a Production-Grade Agent Harness and Framework Comparison

AI Tech Publishing

Apr 12, 2026 · Artificial Intelligence

How Hermes Agent’s Multi‑Layer Memory Beats OpenClaw’s Simple Markdown Store

The article dissects Hermes Agent’s four‑store memory architecture—declarative, procedural, situational, and persona—deterministic routing, frozen snapshots, nudge‑driven persistence, security scanning, dual‑peer modeling, skill management, and three‑phase context compression, showing why it outperforms OpenClaw’s breadth‑first design.

Context CompressionHermes AgentLLM Agents

0 likes · 17 min read

How Hermes Agent’s Multi‑Layer Memory Beats OpenClaw’s Simple Markdown Store

AI Tech Publishing

Apr 9, 2026 · Artificial Intelligence

Engineering‑Focused Guide to Training and Inference of Large Language Models

This article walks engineers through the full LLM stack—from tokenization and positional encoding to transformer blocks, efficient fine‑tuning, quantization, and production‑grade inference techniques such as KV‑cache, FlashAttention, PagedAttention, continuous batching, and speculative decoding—highlighting trade‑offs, toolchains, and practical workflow steps.

LLMLoRATransformer

0 likes · 13 min read

Engineering‑Focused Guide to Training and Inference of Large Language Models