466K views. 10K bookmarks. Built on a quote that was never said. 466K views. 3.8K likes. 10K bookmarks. All built on a quote that, according to a fact-check, was never said. A viral post this week opened with: "Elon Musk posted an idea that hit 27 million views." Then came the quote, then a step-by-step plan to let a Grok bot run a money-making website for you. Grok itself replied under a quote of the post: no Elon post matches the quote or the 27M views. Recycled hype from promo accounts. Look at the numbers again. Bookmarks are almost 3x the likes. People didn't just enjoy it. They saved it as a plan to follow. Thousands of people filed away a playbook whose first line, by that check, isn't real. And the account behind it? Six months old. Four username changes. A paid blue tick. Readers saw a tick and a famous name, and stopped asking questions. This isn't new, and it isn't only bots. → An MIT study of 11 years of Twitter found false news was 70% more likely to be retweeted than the truth. The cause was people, not bots. False news feels new, and new things get shared. 
→ A Columbia and Inria study found 59% of news links shared on Twitter were never clicked at all. 
→ A satire site once posted "70% of Facebook users only read the headline of science stories." The article body was fake filler text. It was still shared 46,000 times. Here's the irony: A big following works like borrowed trust. Readers stop asking "is this true?" and start asking "is this useful to me?" A blue tick and a famous name in the first line make checking feel unnecessary. And the more useful a post looks, the less people check it. A "prompt to make money" gets saved, not questioned. Even the plan itself deserves a second look. One step says the bot negotiates deals "by email, in your name". That means an AI writing to real people as you, without you reading it first. What I take from this, as someone who posts about AI every day: • A famous name in the first line is a reason to check, not a reason to trust 
• A blue tick means someone pays for X, not that their claims are true
• Bookmarks measure hope, not truth 
• If a claim can't link to its source, it's a story, not a fact Note: I have deliberately left out the account name. The problem isn't one poster. It's 10,000 people saving a claim nobody checked Sources are in the reply 👇

Hermes Agent makes 3D models from text. But the real news is bigger than that. An AI agent that makes 3D models from text? Not new. Meshy, a text-to-3D tool, has an MCP server (a standard way to plug tools into AI agents). Any agent that supports MCP, including Claude and Codex, can already connect to it. Agents asking permission before acting? Also not new. Claude Code and Codex do that all the time. So what did Nous Research actually add with the new Meshy plugin for Hermes Agent? The new part is the price check. A normal approval asks: "Allow this tool?" Hermes asks: "This costs 15 credits. Go ahead?" In the demo, someone asks for a boss enemy for their Godot game. Hermes picks the settings (15,000 triangles, 4K textures, a GLB file for Godot), tells them the cost, and only spends after they say yes. That's a small change, but an important one. Agents are starting to spend real money: credits, API calls, cloud resources. Permission alone isn't enough. You need to know what it costs before you click yes. Nous also added polish on top: → One-line install from the official plugin catalog
→ Models render in the chat, and rigged characters animate there
→ A gallery of every model and prompt you've made
→ Short preview videos on messaging apps, so you can create from your phone
→ 3D-print prep: fixing problems, splitting into parts, handing off to a slicer The catch: you need a Meshy Pro plan and API key, and every model costs credits. What I find interesting as someone building agent tools: as agents start spending money for us, every approval should show the price, not just ask permission. Hermes is one of the first to make that the default. Links in the reply 👇

How do you let an AI agent run shell commands without letting it wreck your computer? That's the headache Vercel just solved. Agents get much more useful with a shell (the command line, like bash). They can list files, read CSVs, run scripts and search code, the same way a developer works. But giving an AI real shell access is risky. One bad command, like deleting files or leaking secrets, and you have a problem. So every team built its own setup: 1Write a custom "run command" tool 2Build or rent a sandbox (an isolated space where the agent can't touch anything important) 3Glue the two together Lots of work, and easy to get wrong. Vercel's AI SDK now ships both parts, ready-made: → A bash tool. One line of code adds shell access to your agent.
→ Pluggable sandboxes. You choose how isolated the agent is. The sandbox options: • In-memory: fast and cheap, with a pretend file system that lives only in memory 
• Full virtual machine: heavier, but a real computer that can install software and run programs Example: put a sales.csv in a restricted in - memory sandbox, give the agent the bash tool, and let it analyse the file. It works only inside the sandbox. It can't reach your actual machine. Why it matters: • Safety by default: the agent only sees what you put in the sandbox 
• Less custom code: no shell tool or isolation layer to build yourself 
• Flexible: start cheap in memory, switch to a full VM later without rewriting your agent. Think of it as a sandpit. The agent can dig and build freely, but it can't wander out into the street. A detail most people will miss: the in-memory option isn't a real shell. It's a fake bash called just-bash, with commands like ls and grep rewritten in TypeScript. That's why it's so cheap, and why it can't run real programs. One note: the function is called experimental_createBashTool, so it's still experimental and may change. Links in the reply 👇

Someone trained an AI on candlestick charts instead of text LLMs learned the language of words. Kronos learned the language of markets. Everyone on crypto Twitter talks about "AI trading". This is what a real research model for it looks like, and it's open source. The problem the paper starts from: Big pre-trained models worked so well for text that researchers built the same thing for time series (data that changes over time, like prices or temperatures). These are called Time Series Foundation Models. But on financial candlestick data, they often do worse than smaller, specialised models. Market data is just too noisy. So the team built one model only for that. How it works: A large language model reads text as tokens and predicts the next one. Kronos does the same with K-lines, also called candlesticks: the bars on a price chart showing open, high, low, close and volume. To Kronos, a price chart is just a long sentence. What makes it interesting: → Trained on data from over 45 exchanges around the world
→ Works zero-shot, meaning no extra training on your data
→ Does price forecasting, volatility forecasting, and generates synthetic price data
→ Tiny by AI standards: 4.1M to 102.3M parameters. Runs on a normal laptop.
→ A live demo forecasts BTC/USDT as a range of possible paths, not one magic price One thing most reposts miss: this isn't new. The paper came out in August 2025. It's resurfacing now because of the AI trading hype. The catch: A model that predicts patterns does not make you money. Markets react to news, people and events that no chart contains. This is a research tool, not a trading bot. What I find interesting as a data engineer: the foundation model idea, one big pre-trained model you reuse everywhere, is moving from text to every kind of data that changes over time. Finance is just the noisiest test. Links in the reply 👇