Tag Archives: chatgpt

Holographic woman labeled AI AGENT leaps through futuristic city with text NEW WORLD GATEWAY.
AI, Business

Anthropic Accidentally Leaked the Blueprint for AI Coding Agents

Or as Elon said “Anthropic is now officially more open than OpenAI“. On this fine April Fools’ Day, the joke isn’t that AI is replacing developers. The joke is that the playbook for doing it just… slipped onto the internet.

Anthropic didn’t intend to publish a step-by-step manual for building AI coding agents.
But through a mix of repos, prompts, and system design breadcrumbs, they effectively did exactly that.

The TL;DR or Key Takeaways from Claude Code’s Source:

  1. Prompts in source code: Surprisingly, much of Claude’s system prompting lives directly in the codebase — not assembled server-side as expected for valuable IP.
  2. Supply chain risk: It uses axios (recently hacked), a reminder that closed-source tools are still vulnerable to dependency attacks.
  3. LLM-friendly comments: The code has excellent, detailed comments clearly written for LLMs to understand context — a smart practice beyond just AGENTS.md files.
  4. Fewer tools = better performance: Claude Code keeps it lean with under 20 tools for normal coding tasks.
  5. Bash Tool is king: The Bash tool stands out, with heavy deterministic parsing to understand and handle different command types.
  6. Tech stack: Entirely TypeScript/React with explicit Bun bindings.
  7. Not open source: The source is “available” but still proprietary. Do not copy, redistribute, or reuse their prompts — that violates the license.

Overall impression:

  • It’s a very well-organized codebase designed for agents to work on effectively.
  • Human engineering is visible, though some parts (like messy prompt assembly) feel surprisingly low-level for Anthropic.
  • The fact that core prompts ship in the CLI tool itself is the biggest surprise.

Let’s take a step back… It is all started with this:

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AI, Business

OpenClaw: Redefining Productivity with Autonomous Skills

OpenClaw isn’t interesting because it chats.
It’s interesting because it acts.

If you haven’t internalized that yet, you’re still thinking in “LLM as assistant” mode. OpenClaw is closer to a junior operator with insomnia and root access.
In early 2026, the ecosystem around OpenClaw (which evolved from Clawdbot and Moltbot) has exploded with community-built “skills.” The real shift? These skills run locally and have a heartbeat. They wake up. They check things. They move.

Let’s break down the most popular ones — and more importantly, how to actually build and use them without turning your machine into a chaos engine.

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AI, Business

Why Claude’s Code Security Offering Doesn’t Replace Real SMB Cybersecurity

There’s been a lot of noise lately about AI (=Claude Code Security) replacing large chunks of cybersecurity.

Let’s slow down and separate what AI is actually good at from what actually keeps small and mid-sized businesses safe.

AI tools that scan code?
Impressive.

AI that reads configs and flags obvious misconfigurations?
Useful.

AI that can reason over static artifacts and suggest fixes?
Absolutely real progress.

But here’s the uncomfortable truth: most SMBs are not losing sleep over static code scanning.

They’re losing sleep over this:

  • “Why did our Microsoft 365 tenant just send 8,000 phishing emails?”
  • “Why is our bookkeeper’s laptop beaconing to an IP in Eastern Europe?”
  • “Why did our backup silently fail for 12 days?”
  • “Why did we pass compliance last quarter and now suddenly we don’t?”

That’s where EspressoLabs lives.

LLMs are extraordinary pattern recognizers.
They are very good at analyzing text, code, logs — when you give them the data in a clean, structured way. But SMB security isn’t clean. It’s messy, inconsistent, human, political, and operational.

EspressoLabs provides value in places LLMs simply cannot operate — at least not yet:

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AI, Chrome, webdev

Transforming Recipe Chaos with SeasonApp

Some projects start with ambition.

This one started with annoyance.

I was tired of juggling recipes across bookmarks, screenshots, messages, and the occasional scribble in a notes app.
A normal person would’ve organized things.
I opened Cursor.

The plan was simple: a quick weekend hack.
Nothing serious. Just a tiny tool to help me stop losing recipes.

But then it worked. And I liked using it.
Then I showed it to a couple of friends.
Then my family started using it.
Then those friends shared it with their friends.

That’s when the “weekend hack” quietly transformed into SeasonApp—a small but mighty full-stack platform for cooking, powered by AI and built to remove friction from the kitchen.

Why SeasonApp Exists

If you cook regularly, your digital life eventually turns into a disorganized pantry. Tabs everywhere. Screenshots mixed with flight confirmations. Recipe blogs where you scroll past a childhood memoir before finding the ingredient list. And once you finally want to cook something, you can’t find the right recipe—or you’re missing one ingredient and the whole plan collapses.

SeasonApp brings order to that chaos.

It gives recipes a home.
It helps you create new ones.
And it actually understands what you want to do with whatever’s in your fridge.

The more people around me used it, the more obvious the need felt.
Everyone had the same pain; they just tolerated it.
SeasonApp gives them a better way.

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AI

Gemini 3: Your New AI Coding Assistant

Every developer has that moment where they stare at the screen and wish for a magic wand.
Something that can unscramble a legacy codebase, sketch a UI without endless Figma tabs, or summarize a 300-page API doc that reads like… and create some good tests out of nothing.

Google just dropped something dangerously close.

Gemini 3 isn’t another “slightly better benchmark” release. It’s a real step forward—especially for people who build things for a living.

Here’s where it gets interesting:

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Chrome, JavaScript, webdev

Building a Real-Time Pull-Up Tracker: How I Taught The Browser to Count Our Pain

It started as a simple idea my son brought up: Can we make a web app that counts our pull-ups during our pull-up games?

Turns out, teaching a machine to recognize human suffering is both hilarious and complicated.
What began as a “let’s make a quick pull-ups app” spiraled into an intense journey through computer vision, browser quirks, and a few accidental infinite loops that made our laptop sound like a jet engine.

The “Simple” Goal

I wanted to automatically count pull-ups using a web camera.

Easy, right?

Just detect a human, see when they go up and down, and count.

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JavaScript, webdev

The Future of Coding: LLMs as Collaborators

The rise of large language models (LLMs) has been one of the most transformative developments in software engineering in decades. Tools like GPT4.1, Gemini 2.5 Pro, Claude Opus 4, and various AI-powered code editors such as Cursor (or CoPilot) promise to change the way we build software.

But as these tools evolve and mature, the real question isn’t if we should use LLMs—it’s how.

There’s an emerging split in philosophy between two approaches: full automation through AI agents and IDE integrations, or human-led development using LLMs as intelligent partners.

Based on real-world experiences and a critical review of LLM-based coding tools, the most effective path today is clear:

LLMs are best used as powerful amplifiers of developer productivity—not as autonomous builders.

Let’s break down why.

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Business

Leveraging AI for Efficient Code Reviews

In today’s fast-paced development environment, leveraging AI tools for code reviews can significantly enhance productivity and code quality. As developers, we often work in isolation or wait hours (sometimes days) for our colleagues to review our pull requests. Large Language Models (LLMs) like GPT-4, Claude, and others can provide immediate feedback, spot potential issues, and suggest improvements within your favorite IDE.

This blog post explores how to craft effective prompts for LLMs when reviewing your code in VSCode, with specific examples for backend Node.js/Express developers and React frontend developers.

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JavaScript, webdev

The Power of Many: Why You Should Consider Using Multiple Large Language Models

Large Language Models (LLMs) have taken the world by storm. These AI systems can generate text, translate languages, write different kinds of creative content, and answer your questions in an informative way. But with so many LLMs available, each with its own strengths and weaknesses, how do you choose the right one for the task? 

The answer might surprise you: it’s about more than picking just one. Here’s why using multiple LLMs can be a powerful approach.

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