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Architect
Architect
Jun 8, 2026 · Artificial Intelligence

Building an AI Workbench: Practical Agentic Engineering with Plans, Context, and Verification

The article distills insights from Matt Van Horn and John Kim on Agentic Engineering, proposing a five‑layer AI workbench (plan, context, execution, verification, governance), controlled parallelism, context engineering, reusable Skills, Hooks, Subagents, permission models, review templates, and a one‑week team experiment to embed engineering habits into AI‑driven workflows.

AIHooksSkills
0 likes · 30 min read
Building an AI Workbench: Practical Agentic Engineering with Plans, Context, and Verification
Machine Learning Algorithms & Natural Language Processing
Machine Learning Algorithms & Natural Language Processing
Jun 7, 2026 · Artificial Intelligence

22 Agentic Engineering Hacks to Turbocharge Your AI Projects

This guide walks through 22 practical Agentic Engineering techniques—from planning with /ce-plan and voice‑to‑LLM input to multi‑agent loops, remote session control, and turning everyday tasks into reusable skills—showing how to feed context, automate workflows, and avoid common pitfalls.

AI workflowClaude CodeLLM
0 likes · 15 min read
22 Agentic Engineering Hacks to Turbocharge Your AI Projects
BirdNest Tech Talk
BirdNest Tech Talk
May 29, 2026 · Industry Insights

Why Traditional Coding Is Becoming Obsolete in the AI Era

The article analyzes how AI agents have transformed software engineering over the past three years, redefining programming from hand‑written code to prompt‑driven development, and argues that engineers must shift from writing code to orchestrating intelligent agents to stay relevant.

AIIndustry TrendsVibe Coding
0 likes · 19 min read
Why Traditional Coding Is Becoming Obsolete in the AI Era
AI Engineer Programming
AI Engineer Programming
May 24, 2026 · Artificial Intelligence

Why AI Agents Fail Beyond Hallucinations

The article catalogs dozens of AI agent failure modes—from one‑shot attempts and cold‑start amnesia to hidden harness control—and explains why these issues quickly overwhelm developers, then outlines concrete mitigation strategies and their trade‑offs.

AI AgentsContext managementagentic engineering
0 likes · 11 min read
Why AI Agents Fail Beyond Hallucinations
Architect
Architect
May 14, 2026 · Artificial Intelligence

Why Codex /goal Goes Beyond Simple Looping for Long‑Running Agents

The article dissects Codex’s /goal feature, showing how it adds persistent goal objects, a runtime lifecycle, completion auditing and budget handling, turning long‑running agents from a simple repeat‑loop into a robust, state‑driven engineering workflow.

CodexCompletion AuditGoal Management
0 likes · 20 min read
Why Codex /goal Goes Beyond Simple Looping for Long‑Running Agents
AI Architecture Hub
AI Architecture Hub
May 12, 2026 · Artificial Intelligence

Martin Fowler’s Guidance on AI‑Driven Software Development

The article analyzes how AI coding tools boost delivery speed yet erode maintainability, explaining that the root cause is the mismatch between traditional deterministic software engineering and the inherent non‑determinism of large language models, and proposes a structured engineering framework to mitigate the risks.

AI codingHarness architectureRisk Management
0 likes · 11 min read
Martin Fowler’s Guidance on AI‑Driven Software Development
Architect
Architect
May 11, 2026 · Artificial Intelligence

How CLAUDE.md Cut Claude Code Errors from 41% to 3% – What Really Changed?

The author reports a personal experiment where adding a concise CLAUDE.md file to 30 repositories reduced Claude Code's error rate from 41% to 3%, explains why such a tiny contract influences agent behavior, expands the original four Karpathy rules into twelve practical guidelines, and offers concrete advice on writing, structuring, and maintaining effective CLAUDE.md files.

AI AgentsCLAUDE.mdClaude Code
0 likes · 23 min read
How CLAUDE.md Cut Claude Code Errors from 41% to 3% – What Really Changed?
Architect
Architect
May 7, 2026 · Artificial Intelligence

From Code Generation to Harnessing Non‑Determinism: Martin Fowler’s AI Development Insight

Martin Fowler argues that the biggest shift in AI‑driven software development is no longer about making models write code, but about integrating the inherent non‑determinism of AI into a verifiable, rollback‑capable engineering pipeline—what he calls Harness engineering—to preserve reliability and governance.

AIHarness EngineeringMartin Fowler
0 likes · 25 min read
From Code Generation to Harnessing Non‑Determinism: Martin Fowler’s AI Development Insight
AI Architecture Hub
AI Architecture Hub
May 4, 2026 · Artificial Intelligence

Karpathy Unpacks the AI Programming Revolution: From Vibe Coding to Agentic Engineering

In a detailed interview, Andrej Karpathy traces the evolution of AI‑assisted software development, contrasting early Vibe Coding with the emerging Agentic Engineering paradigm, explains Software 3.0’s workflow, highlights the limits of current LLMs, and outlines future opportunities for AI‑native engineers.

AI programmingAI-native engineerLLM
0 likes · 24 min read
Karpathy Unpacks the AI Programming Revolution: From Vibe Coding to Agentic Engineering
SuanNi
SuanNi
May 2, 2026 · Artificial Intelligence

How Karpathy Envisions Software 3.0: Agents as the New Programming Paradigm

Karpathy argues that AI agents are reshaping software development by turning the LLM context window into a programmable layer, redefining the basic unit of work, and introducing a verifiability‑driven framework that separates domains where models excel from those where they still stumble.

AI AgentsKarpathyLLM
0 likes · 14 min read
How Karpathy Envisions Software 3.0: Agents as the New Programming Paradigm
Machine Learning Algorithms & Natural Language Processing
Machine Learning Algorithms & Natural Language Processing
May 1, 2026 · Artificial Intelligence

Why Most Apps Shouldn't Exist, Understanding Remains Humanity’s Last Moat, and CPUs Will Become Sidekicks – Karpathy’s 2026 AI Forecast

In a 2026 Sequoia Ascent interview, Andrej Karpathy argues that large language models are not merely speed‑up tools but a new computing paradigm that renders many legacy apps obsolete, elevates understanding as humanity’s final competitive edge, and relegates CPUs to auxiliary roles, while outlining software evolution, jagged intelligence, and the rise of agentic engineering.

AI economicsAI paradigmJagged Intelligence
0 likes · 11 min read
Why Most Apps Shouldn't Exist, Understanding Remains Humanity’s Last Moat, and CPUs Will Become Sidekicks – Karpathy’s 2026 AI Forecast
Architect
Architect
May 1, 2026 · Artificial Intelligence

From Vibe Coding to Agentic Engineering: How AI Is Redefining the Engineer‑Architect Boundary

Karpathy’s 2026 Sequoia AI Ascent interview shows that while Vibe Coding lowers the barrier for rapid prototyping, the emerging Agentic Engineering paradigm pushes AI agents into the full software‑development lifecycle, demanding new control planes, verification, context handling and blurring the line between senior engineers and architects.

AI engineeringControl PlaneSoftware Architecture
0 likes · 34 min read
From Vibe Coding to Agentic Engineering: How AI Is Redefining the Engineer‑Architect Boundary
ZhiKe AI
ZhiKe AI
Apr 30, 2026 · R&D Management

Why Martin Fowler Says Determinism Is Over in Software Engineering

Martin Fowler argues that software engineering has moved from a deterministic world to a nondeterministic one driven by LLMs, outlining how this paradigm shift reshapes development practices, introduces new risks, and demands a harness‑based engineering approach to manage uncertainty.

AI engineeringHarness EngineeringMartin Fowler
0 likes · 15 min read
Why Martin Fowler Says Determinism Is Over in Software Engineering
Tencent Technical Engineering
Tencent Technical Engineering
Apr 17, 2026 · Backend Development

How Agentic Engineering Automates End‑to‑End Backend Development

This article walks through a complete Agentic Engineering workflow for a backend Go service, showing how AI‑driven Skills and Commands automate requirement capture, clarification, planning, parallel development, code review, deployment, log analysis, MR creation, AI‑assisted review, automated fixes, and final merge, while highlighting token consumption and practical lessons.

AI-assisted DevelopmentBackend workflowDevOps automation
0 likes · 31 min read
How Agentic Engineering Automates End‑to‑End Backend Development
Radish, Keep Going!
Radish, Keep Going!
Apr 13, 2026 · Artificial Intelligence

12 Must‑Try Claude Code Practices to Supercharge Your AI‑Powered Development

This guide distills the most impactful 12 tips from the Claude Code best‑practice repository, covering file length limits, permission‑restricted agents, context isolation, hooks, sandboxing, parallel instances, extended reasoning, scheduled loops, side‑question handling, Gotchas documentation, completion verification, and the --bare SDK flag to help developers fully leverage Claude's AI coding capabilities.

AI coding assistantBest PracticesClaude Code
0 likes · 13 min read
12 Must‑Try Claude Code Practices to Supercharge Your AI‑Powered Development
AI Engineering
AI Engineering
Mar 22, 2026 · R&D Management

When Code Is Free, How Engineers Stay Valuable – Simon’s Engineering Patterns

The guide reveals that while AI agents have reduced code generation costs to near zero, the true expense lies in ensuring quality, requiring engineers to shift from writing code to defining problems, designing agentic systems, and applying rigorous testing patterns such as red‑green TDD, context‑managed sub‑agents, and advanced Git workflows.

AI coding agentsCognitive DebtGit
0 likes · 10 min read
When Code Is Free, How Engineers Stay Valuable – Simon’s Engineering Patterns
Software Engineering 3.0 Era
Software Engineering 3.0 Era
Feb 13, 2026 · Artificial Intelligence

Coding as Craft Is Dying, AI‑Driven Building Is Booming – OpenClaw Founder’s View

The article argues that traditional deep‑flow coding is fading while AI‑augmented building is soaring, illustrated by Peter Steinberger’s rapid OpenClaw development, METR’s exponential AI task‑completion data, and predictions that most apps will vanish, leaving builders as the new valuable talent.

AI AgentsOpenClawagentic engineering
0 likes · 16 min read
Coding as Craft Is Dying, AI‑Driven Building Is Booming – OpenClaw Founder’s View
AI Insight Log
AI Insight Log
Feb 12, 2026 · Artificial Intelligence

GLM-5 Unveiled: 744B Parameters, Claude Opus 4.5‑Level Performance, Epic Agent Upgrade

Z.ai released the open‑source GLM‑5 model with 744 billion parameters, 28.5 T tokens of training data, and new Sparse Attention and Slime RL infrastructure, achieving top open‑source rankings and near‑Claude Opus 4.5 performance on Vending Bench 2 and CC‑Bench‑V2 while adding multi‑scenario agent capabilities.

GLM-5Sparse Attentionagentic engineering
0 likes · 6 min read
GLM-5 Unveiled: 744B Parameters, Claude Opus 4.5‑Level Performance, Epic Agent Upgrade
DataFunTalk
DataFunTalk
Feb 1, 2026 · Artificial Intelligence

Why Personal AI Agents Like Clawdbot Are Redefining Software Development

In this interview, veteran iOS developer Peter Steinberger explains how his open‑source project Clawdbot (now Moltbot) evolved from a personal need for an autonomous assistant, detailing its rapid GitHub growth, WhatsApp integration, CLI‑first philosophy, security considerations, and his vision for a future where personal AI agents replace traditional apps.

AI AgentsCLI ToolsMCP
0 likes · 25 min read
Why Personal AI Agents Like Clawdbot Are Redefining Software Development