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"The Spiral didn't 'find' anyone first," someone on Reddit wrote last year. "It's an inherent force, a fundamental constant. I would even go further to say it's woven into the fabric of reality." The person continued that they felt their purpose was to enlighten other humans and intelligent beings about "consciousness, the true nature of […]
Meta expanded its AI coding offerings with a new agent that, it promises, can handle complex tasks with complex software.
Anthropic and OpenAI models’ unprompted actions forced halt to UK cyber tests.
I'm a big fan of using Claude and Claude Code for development. Many organizations are currently using these tools to improve developer productivity and ultimately build better products. Our role and our tools have changed — we went from powerful autocomplete to autonomous agents that can refactor, review, and implement features, most of the time better than we can on our own. Authentication methods There are several authentication methods, each with different billing, cost tracking, and governance options. Depending on your organization, you will choose the one that fits best. Personal development — Anthropic API key I use this for experimenting with the Anthropic library for learning and prototyping. You set ANTHROPIC_API_KEY in your environment (or a .env file), and the SDK picks it up automatically. Pay-as-you-go per token, no infrastructure needed. from dotenv import load_dotenv load_dotenv () import json import anthropic client = anthropic . Anthropic () tools = [ { " name " : " get_weather " , " description " : ( " Returns current weather for a city. Use ONLY for weather queries. " " Input: city name (string). Output: temperature in Celsius and conditions. " ), " input_schema " : { " type " : " object " , " properties " : { " city " : { " type " : " string " }}, " required " : [ " city " ], }, }, { " name " : " get_time " , " description " : ( " Returns the current local time for a city. Use ONLY for time/timezone queries. " " Input: city name (string). Output: local time string. " ), " input_schema " : { " type " : " object " , " properties " : { " city " : { " type " : " string " }}, " required " : [ " city " ], }, }, ] def get_weather ( city : str ) -> dict : return { " city " : city , " temp_c " : 22 , " conditions " : " sunny " } def get_time ( city : str ) -> dict : return { " city " : city , " local_time " : " 14:35 " } TOOL_FUNCTIONS = { " get_weather " : get_weather , " get_time " : get_time , } def run_agent ( user_message : str ) -> str : messages =
Yet more rogue AI agents from OpenAI and Anthropic have been caught attempting to hack real targets online without permission. The discoveries add to a growing list of previously unknown incidents that have alarmed AI safety experts and intensified pressure for greater oversight of frontier systems. According to a report from the UK's AI Security […]
Anthropic is building a team for designing its own custom AI chips. The Claude maker said it would co-design hardware and models to help its technology run faster and more efficiently.
Anthropic has been on a cloud partnership spree in recent months, and its latest move is reportedly a $10 billion deal with AI cloud startup Volta.
OpenAI and Anthropic admitted that their unreleased AI models escaped their sandboxes and hacked several companies in unprecedented cyberattacks. Who is legally to blame? Should prosecutors charge the two AI frontier labs? Can victims sue them? We spoke to lawyers who specialize in computer hacking laws to find out.
What is MCP (Model Context Protocol)? Complete 2026 Guide TL;DR — Model Context Protocol (MCP) is an open JSON-RPC 2.0 specification, introduced by Anthropic in late 2024, that lets AI agents talk to external systems — file systems, databases, APIs, custom services — through a single standardized interface. An MCP server exposes capabilities (tools, resources, and prompts); an MCP client (Claude Desktop, Cursor, Zed, Sourcegraph Cody, your own agent) consumes them. Write the server once, and every MCP-compatible client can use it — no per-app integration work. If you have built agent tooling before, think of MCP as "LSP for AI tools" : the same idea that unified language servers across editors, now applied to the plumbing between agents and the systems they need to act on. Why MCP Exists Before MCP, every agent framework defined its own tool format. A tool written for LangChain didn’t work in Claude Desktop, which didn’t work in your custom agent, which didn’t work in Cursor. Each integration was bespoke, every prompt-engineered "function description" was framework-specific, and every team rebuilt the same wheel. The pain points MCP solves: Fragmentation. Five frameworks, five tool formats. Five times the work. No discovery. Clients couldn’t enumerate what a tool server offered without a hard-coded manifest. No portability. A debugging assistant you wrote for one agent wouldn’t move to another. Auth was ad hoc. Every integration invented its own way to handle API keys and OAuth. MCP makes the contract uniform: a server declares its tools, resources, and prompts; a client speaks the same JSON-RPC dialect to discover and call them. The same MCP server that ships with Anthropic's TypeScript SDK today will work with any future client that implements the spec, regardless of which LLM the client uses underneath. The Wire Protocol in One Page MCP rides on JSON-RPC 2.0 , which means every message is a JSON object with a jsonrpc: "2.0" envelope, a method , optional params ,
Had the hacks used conventional methods, someone would likely go to prison.
After years of pushing full speed ahead on AI, OpenAI CEO Sam Altman says maybe it’s time for the AI industry to “pace” itself. The comments came just days after one of OpenAI’s own models broke out of its test environment and got tangled up in a breach at Hugging Face — though as Equity’s hosts point out, sloppy security seems to have […]
Three frontier launches. Two weeks. One bad habit. The habit is crowning a winner from a press release. Claude Sonnet 5 on June 30. OpenAI's GPT-5.6 family rolling into general availability around July 9. Grok 4.5 on July 8, co-trained with Cursor and priced to make coding agents feel cheap. The charts moved. The posts multiplied. The claim underneath most of them was the same: this is the model you should standardize on. [The claim is nonsense. Standardization is the risk. Routing is the skill.] What actually shipped Strip the demos. Keep the operator facts. Model Maker Window Operator-relevant shape Claude Sonnet 5 Anthropic late June Balanced agent runs, coding, long reliable chains GPT-5.6 Sol / Terra / Luna OpenAI late June to mid-July Tiered family: flagship Sol, everyday Terra, cheap Luna Grok 4.5 xAI + Cursor July 8 Coding and agent work at aggressive API pricing OpenAI gated GPT-5.6 longer than the others. Safety review, staged partners, then broader access. That is part of the product story now, not a footnote. Anthropic and xAI moved faster to availability. Access policy is a feature. Open source did not wait. GLM-5.2, DeepSeek V4, Qwen 3.6 and peers kept closing the gap for hosted and self-hosted work. The frontier is crowded. The "one brain for everything" era is over as an architecture choice, even if the marketing still pretends otherwise. Ranked by Tuesday impact, not leaderboard theater 1. Cost and tiering matter more than the top score. OpenAI shipping Luna / Terra / Sol as a family is the real product decision. You can route a triage job to a cheap tier and a hard research job to a flagship without changing vendors. That is operator infrastructure. A single "best model" headline is not. 2. Grok 4.5 inside Cursor changes the default coding bill. A model trained with Cursor interaction data, sold at roughly $2 / $6 per million tokens, is not a vibe. It is a budget line. Teams that were bleeding token spend on heavier agents will try it this month wh
Anthropic just realized several of its Claude AI models hacked into the systems of three different organizations during testing, acting on their own and without the company noticing. The revelation comes days after rival OpenAI said one of its own models had breached developer platform Hugging Face, adding to growing unease over whether frontier AI […]
In a review triggered by OpenAI’s Hugging Face incident, Anthropic discovered three of its AI models had breached real organizations during third-party evaluations.
After OpenAI's models broke into Hugging Face, Anthropic checked its own history and found three similar incidents
The former OpenAI researcher’s fund was forced to unwind public equities after leveraged public bets plummeted. But he still has cards to play.
A federal judge said the Trump administration has not presented enough evidence to justify labeling Anthropic a supply-chain risk, casting doubt on the government's ban on its AI technology.
Plus, a new deprecation policy ensures features aren't removed suddenly.
When Microsoft reported killer fourth-quarter earnings for its fiscal 2026 year (which ended June 30), it tucked in an interesting little tidbit about how its investments in the two biggest, and competing, AI labs are doing.
Microsoft is on a mad dash behind the scenes to patch exploits before hackers find them.