137 stories
Claude vs ChatGPT for marketing, decided by the work you do: copy, research, long docs, connectors, and the email HTML test that trips both up. What the evidence actually shows.
Join my free newsletter → https://www.sandeepswadia.com/newsletter Take us on your morning run or commute, follow us on Spotify: https://open.spotify.com/show/033nSSACs3S4g5NUIrXnHx Most people believe programming is reserved for an elite group of people who can decipher green text on black…
The August 2026 MCP Client Changelog. See what changed in Cline, Goose, Claude Code, Codex, Claude, AgentCore, GitHub Copilot, and VS Code, and what to test in your MCP server.
Read the July 2026 MCP Client Changelog. Discover critical updates for Claude, ChatGPT, Goose, Codex, Cline, and AgentCore, and how to test your MCP servers.
I signed off a hallucination fix as verified. We now had zero out of five fabricated responses, clean run, done. Then I ran it again under harder conditions and got 66.7%. The number I'd trusted enoug
A 48-minute video walkthrough of token sampling, watermark detection, and removal
Short illustration of how Claude's text watermarking is supposed to work based on Anthropic's released materials.
Claude is one of the best AI models available. But a conversation that resets every time is not the same as an agent that works for you. Here is what to use when you need more.
Anthropic confirmed on August 11 that it is embedding invisible watermarks into everything Claude writes, and it isn't limiting the change to European users.
Follow along in Colab: Building applications with Large Language Models doesn’t have to be complicated. A lot has been going on recently to simplify the development, so you can utilize already pre-trained models and support even complex pipelines with a few lines of code.
Everyone’s “doing AI” these days. Many products or platforms try to solve genuine problems, while others just add the magic button with stars graphics and hope people won’t ask what it actually does.
Claude Code relies on a CLAUDE.md file for instructions on build, test, and style conventions. Learn how to write, structure, and maintain a perfect CLAUDE.md that keeps your AI agent on track.
Pointing an AI at raw kubectl works, badly. Radar's built-in MCP server gives Claude, Cursor, and friends a token-optimized, RBAC-aware view of your cluster.
In early 2023, I was interning at a US-based company, long before agentic AI became part of everyday development. We had tools like ChatGPT, Gemini, and Copilot, but they were mostly chat interfaces:
Anthropic connected their data to Claude and asked it real questions. The answers looked great. The kind you’d forward to your team. Then they checked. Claude was right 21% of the time. So they fed it thousands of past queries and confirmed it read them. Accuracy moved less than a point.
Your competitor showed up in ChatGPT. You didn't. Same industry, same kind of product. Why? The honest answer involves four signals AI models actually weight when they compose recommendations — and three myths to stop chasing.
Before you pay anyone to measure your AI visibility, you can do the basic audit yourself in under 10 minutes. Here's the exact methodology: which queries to run, which platforms to check, and how to score the result.
Your robots.txt tells Google what to crawl. Your llms.txt tells AI models what your site is actually about. Most sites have one. Almost none have the other — and the ones that do are getting cited more.
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