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AAAbbas Afsharfarniaabbas-code.hashnode.dev8h ago · 8 min read

Lean AI: Stop Wasting Tokens and Start Building Smarter LLM Apps 🚀

Most AI applications have a token problem. Not because tokens are inherently expensive, but because many applications send far more context than the model actually needs. Massive system prompts. Entir

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KHKevin Hwangkevinhwang.dev1d ago · 9 min read

Extending Agents With Plugins: Improving Codex Memory 🧠

As Benjamin Franklin once said, “Another day, another AI coding agent.” He wasn't wrong, agent products are now dime a dozen, with many orgs running Claude, Codex, Cursor, etc. in parallel — the agent

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Ccodanykscodanyks.hashnode.dev2d ago · 7 min read

Anatomy of a Good Skill: Designing Capabilities That Systems Can Trust

In the previous article, we looked at what a skill actually represents inside an agent system. A skill is not just a collection of instructions or a reusable prompt. It is a defined capability that al

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Mmayaanderssonllmasajudge.hashnode.dev4d ago · 4 min read

I benchmarked 6 prompt-optimization frameworks on the same task. Here is what each one actually optimizes.

TL;DR: I ran six prompt-optimization frameworks against the same task and the same eval metric over a few weeks. They are not interchangeable: some are full programming models, some are single search

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MAMuhammad Azlaan Zubairwait-what.hashnode.dev5d ago · 3 min read

Is Overthinking a Red Flag? We Put AI Reasoning to the Test.

More Thinking Doesn't Always Mean Better Answers Reasoning models are designed to think step by step, and the common assumption is simple: More thinking = better results. I expected reasoning models

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AAAli Almoosawiaikhazna.hashnode.dev6d ago · 6 min read

AI Tools Are Not Enough: Understanding the AI Asset Stack

Most people still think AI adoption starts with finding “the best AI tool.” The real shift is understanding which AI asset fits the task. But that is no longer enough. The AI space has become crowded

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EAElham Ajdarielham-notes.hashnode.devJun 15 · 14 min read

Guidelines for Reducing AI Coding Tool Credit Usage

Executive Summary AI coding tools such as Augment Code, Claude Code, Cursor, GitHub Copilot, and ChatGPT consume credits, tokens, or usage quota based on several factors: The amount of context they p

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XYXiao Yaoblog.appliedaihub.orgJun 11 · 17 min read

How to Build Your First Self-Running AI Agent in 15 Minutes

Most engineering workflows treat LLMs as stateless calculators: input a prompt, copy the snippet, kill the tab. The session ends, the context evaporates, and the model's iterative reasoning capacity —

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HCHelkyn Coellounlockedconsulting.hashnode.devJun 9 · 7 min read

AI Doesn't Think For You, It Thinks Like You

A few weeks ago a Business Manager handed me a 50-page AI-generated technical spec. The document was impressive. The perspective behind it was the problem. I had talked to him a few days earlier about

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FIfor IT theieee-projects.hashnode.devJun 3 · 42 min read

Context Engineering vs Prompt Engineering for Final Year Students: 4 IEEE Papers You Should Read (2026–2027)

Introduction Prompt engineering was, until very recently, the dominant lens through which practitioners understood how to work with large language models. Write better prompts, get better outputs. Tha

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#prompt-engineering

Explore Hashnode

Trending tags this week

Lean AI: Stop Wasting Tokens and Start Building Smarter LLM Apps 🚀

Extending Agents With Plugins: Improving Codex Memory 🧠

Anatomy of a Good Skill: Designing Capabilities That Systems Can Trust

I benchmarked 6 prompt-optimization frameworks on the same task. Here is what each one actually optimizes.

Is Overthinking a Red Flag? We Put AI Reasoning to the Test.

AI Tools Are Not Enough: Understanding the AI Asset Stack

Guidelines for Reducing AI Coding Tool Credit Usage

How to Build Your First Self-Running AI Agent in 15 Minutes

AI Doesn't Think For You, It Thinks Like You

Context Engineering vs Prompt Engineering for Final Year Students: 4 IEEE Papers You Should Read (2026–2027)