AI Engineering10 min read
How I'd Learn AI Engineering in 2026
A practical path from coding-agent speed to the architecture, evaluation, and failure-analysis skills real AI engineering work requires.
Topic
AI Agents
Agents are useful when the loop is designed well. This topic collects the articles where I break down the difference between demos, workflows, memory, tools, evals, and systems that survive real constraints.
agentagentsagenticworkflowworkflowsloop engineeringmemorytool usemcpharness
AI Agents13 min read
How I Automated 90% of My Content Workflow With AI Agents
The architecture, agent loops, writing workflow, visual system, and title tests behind a content pipeline that cut my workload from two or three days to about five hours.
AI Agents6 min read
Graph Engineering vs Loop Engineering: What Actually Changed
Why the loops-versus-graphs debate misses the point, what actually changed in agent orchestration, and which parts of your system should stay deterministic.
AI Agents4 min read
How to Build a Memory Your AI Agents Can Actually Reuse
The useful part is not giving agents more context. It is making your research, notes, and sources available again in the next session.
AI Agents11 min read
Loop Engineering Explained
A practical way to stop babysitting coding agents: design their context, tools, checks, budgets, and stop conditions.
AI Agents17 min read
Coding Changed Forever
What changed in AI coding, why generated code can feel faster, and where engineering judgment still matters.
AI Agents8 min read
How to Control Bias in AI Agents
The guardrails, audits, and human review loops that actually work
AI Agents9 min read
Harness Engineering: The Missing Layer Behind AI Agents
Prompting tells an agent what to do. The harness controls its tools, permissions, tests, traces, and failures.
AI Agents6 min read
Stop Building Agent Demos
What separates a polished AI agent demo from a system your team can evaluate, debug, deploy, and trust.
AI Agents14 min read
The 12 Questions That Decide Your AI Architecture
A cheatsheet to avoid costly rework in agent systems.
AI Agents9 min read
Multi-agent is becoming the new overengineering
You’re not building agents. You’re building workflows (and that’s fine)
AI Agents23 min read
How to Clean Up AI-Generated Drafts Without Sounding Like ChatGPT
How to Spot and Remove “AI Slop” from Your Writing
AI Agents10 min read
Deep Research Agents: The Next Big Leap Beyond ChatGPT
OpenAI’s Deep Research Explained
AI Agents10 min read
2025: The Year of AI Agents
Million-Token Context? Cheap Tools? Perfect Time for Agents
AI Agents3 min read
How to Really Build on Top of LLMs
(full training session) (typical path for companies)
AI Agents5 min read
How FlashMLA Cuts KV Cache Memory to 6.7%
DeepSeek's Game-Changer for LLM Efficiency
Large Language Models7 min read
Research With AI Tools
Research and Writing: Why AI Tools Change the Workflow
Large Language Models6 min read
Long Context Models Explained: Do We Still Need RAG?
Retrieval-Augmented Generation vs. Long Context: A Comprehensive Comparison
AI Agents7 min read
Mitigating AI Hallucinations: Exploring MetaGPT's Collaborative Framework
What is MetaGPT? LLM Agents Collaborating to Solve Complex Tasks
AI Agents4 min read
Deepmind's new model Gato is amazing!
Gato: A single Transformer to RuLe them all! The first generalist RL agent using transformers!