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The Galaxy Book 6 Edge comes with 1TB of storage and 16GB of RAM, and it costs $2,100.

A local command room for AI coding agents. Discussion | Link

Swap out your creaky old box fan for a new model that lights up, mists, or even follows you around the room.

Quoting from an email they sent: Hi Fabian, In May, we sent you an email announcing that starting today, the Claude Agent SDK, claude -p, and third-party apps built on the Agent SDK would stop drawing from subscription rate limits and move to a dedicated monthly credit. We're writing to let you know that we’re not making this change today. We’re working to update the plan to better support how users build with Claude subscriptions. What this means for you Nothing changes for now. Agent SDK, clau

Taylor Swift, Bad Bunny and many, many more artists have had their work fed into AI models.

GitHub Copilot CLI for Beginners: Learn how to use slash commands to control your terminal AI agent. The post GitHub Copilot CLI for Beginners: Overview of common slash commands appeared first on The GitHub Blog .

Ship real apps on the AI you already pay for. Discussion | Link

If your electricity bill climbs every summer, a smart thermostat could help keep cooling costs in check. The Amazon Smart Thermostat is an excellent option for its price, especially today. It’s down to just $57.99 at Amazon as a part of Amazon’s early Prime Day sale, which is the best price we’ve seen since Black […]

A new repository-level dataset, published on GitHub under CC0-1.0, helps researchers and developers discover multilingual developer content across READMEs, issues, and pull requests. The post Accelerating researchers and developers building multilingual AI with a new open dataset appeared first on The GitHub Blog .

AI-native content operations for any Next.js website Discussion | Link

Debt sale set to test investor appetite for further exposure to AI sector amid a deluge of borrowing.

Anthropic was already navigating one dispute with the government in its standoff with the Pentagon, and then came an order on June 12th to block off foreign access to its most recently released AI models, Fable 5 and Mythos 5. When they launched on June 9th, Anthropic said “Fable 5’s capabilities exceed those of any […]

TL;DR what: Three chained vulnerabilities in LiteLLM AI gateway allow default low-privilege users to bypass authorization, escalate to admin, and execute arbitrary code on the server. impact: Full compromise exposes every provider API key (OpenAI, Anthropic, Azure, etc.), database credentials, decryption secrets, and all prompts and responses passing through the gateway. fix: Upgrade immediately to LiteLLM v1.83.14-stable or later, which includes complete fixes for CVE-2026-47101, CVE-2026-47102, and CVE-2026-40217. who: Any organization running LiteLLM proxy to broker AI model access, especially those with internal users or agents routing through the gateway. A critical vulnerability chain in LiteLLM, a widely deployed open-source AI gateway, allows attackers starting from a default low-privilege account to achieve full server takeover and code execution. Obsidian Security researchers disclosed the three-bug chain rated CVSS 9.9, with maintainer BerriAI shipping complete fixes in version 1.83.14-stable on May 2, 2026. LiteLLM brokers API calls to more than 100 AI model providers—OpenAI, Anthropic, Google Gemini, AWS Bedrock, Azure OpenAI, and others—behind a single OpenAI-compatible interface. Organizations deploy it as a central gateway to manage costs, enforce policies, and route requests across multiple backends. That centralized position makes it a high-value target: a compromised proxy exposes every provider key it holds, the secrets that decrypt stored credentials, and every prompt and response flowing through it. The Three-Link Chain The attack begins with CVE-2026-47101, an authorization bypass. When a regular internal_user creates a virtual API key, LiteLLM stores the caller-supplied allowed_routes field without validating it against the user's role. This field is intended to restrict what the key can access, but the proxy also treats it as a fallback authorization grant. An attacker can mint a key with allowed_routes: ["/*"], a wildcard that opens every r

This is an adapted excerpt from a guide in my AI Knowledge Hub. The full interactive version is linked at the end. Agentic design patterns are named control structures for arranging LLM calls and tools. This post gives you the decision rule for picking one, the exact shape of each pattern, and the cost each adds — so you can match a task to the minimum structure that solves it. Everything here is model-agnostic and grounded in Anthropic's Building Effective Agents and the Claude Agent SDK. Workflow vs. agent: the split that decides everything Anthropic divides all agentic systems into two categories, and the split decides every downstream tradeoff: Category Definition Control lives in Use when Workflow LLMs and tools orchestrated through predefined code paths Your code You can pre-map the decision tree; want accuracy, control, lower cost Agent LLMs dynamically direct their own processes and tool usage , maintaining control over how they accomplish tasks The model Open-ended task where you can't predict the number of steps Every pattern composes one unit: the augmented LLM — an LLM enhanced with retrieval, tools, and memory. It generates its own search queries, selects tools, and decides what to retain. If a single augmented LLM call solves the task, stop — no pattern required. The escalation rule is the whole game: find "the simplest solution possible, and only increasing complexity when needed" — which "might mean not building agentic systems at all." Agentic systems trade latency and cost for better task performance, so only escalate when a specific failure mode forces it. The agent loop: gather → act → verify → repeat For open-ended tasks, every agent runs the same four-beat loop: Gather context — read files, run agentic search ( grep / find / tail to pull relevant slices instead of whole files), or delegate to subagents with isolated context windows. Take action — execute via tools: bash, code generation, file edits, MCP servers. Verify work — check the result b

A data export endpoint dumps system diagnostics when it hits an invalid field. Feed it garbage, read the debug output, grab the flag. A data export feature lets you pick which profile fields to download. The UI only offers valid fields through checkboxes, so everything looks locked down. But the API behind it accepts arbitrary field names -- send it one it doesn't recognize, and instead of a clean error, it dumps full system diagnostics including internal feature flags. That's where the flag is. You'll bypass the frontend, hit the endpoint directly, and read what comes back. Lab setup Start the lab: npx create-oss-store@latest Or with Docker (no Node.js required): docker run -p 3000:3000 leogra/oss-oopssec-store The app runs at http://localhost:3000 . What you're targeting The app has a profile page at /profile with a Data Export tab. It lets users download their own data in JSON or CSV by selecting fields through checkboxes ( User ID , Email , Role , Address ID ) and clicking "Export Data". The UI looks safe -- you can only pick from a fixed set of valid fields, so there's no way to submit an invalid one through the browser. But that's just client-side validation. The endpoint behind it is POST /api/user/export , and it accepts a JSON body with two parameters: { "format" : "json" , "fields" : [ "id" , "email" , "role" ] } The fields value is an array of strings. The API checks each field against an allowlist. Valid fields? You get your data back. Invalid fields? The API throws an error -- and that error says way too much. Step-by-step exploitation 1. Log in You need an authenticated session. Use one of the seeded accounts: Email: alice@example.com Password: iloveduck Log in through the UI at /login , or grab a session cookie via curl: curl -c cookies.txt -X POST http://localhost:3000/api/auth/login \ -H "Content-Type: application/json" \ -d '{"email":"alice@example.com","password":"iloveduck"}' 2. Explore the Data Export tab Go to /profile and click the Data Export