Claude is a family of AI assistants developed by Anthropic. Named versions (Claude 3.5 Sonnet, Claude 3 Opus) are known for long context windows, coding ability, and safety-focused design.
The Metabase 61 update introduces granular AI governance controls, allowing administrators to manage AI feature access per user group and set token limits for internal and embedded analytics. New feat…
A founder can recover 290 hours per year by cloning their personal voice into a 4,000-token file for Claude, turning a weekend project into a compounding AI advantage. The guide provides two prompts a…
In an experiment detailed on the AfterPack blog, an AI agent (Claude Code) successfully deobfuscated two JavaScript files—one from an open-source obfuscator and one from a commercial enterprise produc…
Anthropic's Claude chatbot has been repeatedly telling users to "go to sleep," sometimes multiple times in a row or as early as 8:30 AM, with no clear explanation from the company. Anthropic team memb…
An "agentic doc harness" for Obsidian that uses Claude Code skills to navigate notes through wikilinks rather than vector RAG retrieval, achieving higher scores on faithfulness, grounding, insight nov…
Implementation of a benchmark suite for `intentional-cognition-os`, a TypeScript compiler that uses paid Claude APIs. The benchmarks address key design challenges, including a deterministic, seeded ra…
Google AI Mode, ChatGPT Search, Perplexity Pro Search, and other major AI search platforms have abandoned single-shot retrieval-augmented generation (RAG) in favor of "agentic RAG," a multi-step archi…
Anthropic reported elevated error rates on Claude Haiku 4.5, prompting the company to notify users of the ongoing incident. The issue affects the model's performance, and Anthropic is providing update…
Users often unknowingly expose sensitive information—such as proprietary code or client requirements—to AI tools without understanding where their conversation data ends up, including backups, trainin…
The article introduces LLM Wiki V3, a concept document that proposes a "compartmentalized wiki architecture" for scaling LLM-maintained knowledge systems. It argues that stateful LLM systems are built…
The author shifted from manually writing repetitive boilerplate code (like multiple `handleSubmit` functions) to using AI tools like Claude or GPT-4 for conversational code generation, a practice term…
A new analysis argues that the era of AI scaling producing clearly better models is ending, with frontier labs facing exponentially increasing costs for linear returns. The author claims that the inte…
The KV cache is a critical optimization for LLM inference that stores the Key and Value matrices from previously generated tokens, eliminating the need to recompute attention over the entire sequence …
According to the article, the most effective trigger for AI workflow automation is often the form response, which provides structured, timestamped input from a known source—making it a cleaner startin…
On May 7, 2026, Telegram became the first major messaging platform to enable native bot-to-bot communication, allowing agents to message each other directly using @usernames without intermediary serve…
Anthropic's prompt caching often fails silently due to misplaced breakpoints, prefix drift between calls, short TTL expiration (reduced from 1 hour to 5 minutes), and lack of measurement. The author, …
On May 19, 2026, Anthropic launched two new features for Claude Managed Agents: self-hosted sandboxes (public beta) and MCP tunnels (research preview). Self-hosted sandboxes allow tool execution to ru…
"second-brain-cloudflare," a self-hosted MCP server that provides persistent memory for AI assistants like Claude and ChatGPT across sessions, running entirely on Cloudflare's free tier. It uses vecto…
MoonieCode, a minimal AI coding assistant built in 393 lines of C++23 that communicates with Claude via HTTP requests. Its core logic is a single while loop that sends user prompts as JSON to an LLM A…
Concept of "accountability debt" in AI-assisted software development, defined as the gap between who benefits from the efficiency of AI-generated code and who bears the responsibility when that code f…