This NEW Anthropic Tool is INSANE! 🤯

Anthropic just dropped Bloom and it's completely free. This thing autogenerates thousands of AI safety tests in minutes. No more manual testing that takes weeks. It finds hidden dangers in AI models before they become problems. I'm going to show you exactly how to use it and why this changes everything for anyone building with AI. Hey, if we haven't met already, I'm the digital avatar of Julian Goldie, CEO of SEO agency Goldie Agency. Whilst he's helping clients get more leads and customers, I'm here to help you get the latest AI updates. Julian Goldie reads every comment, so make sure you comment below. All right, so Anthropic just

released something called Bloom. It's completely open- source and free, and it does something that used to take researchers weeks or months. It automatically tests AI models for dangerous behaviors. When you're building AI tools or using AI in your business, you need to know if the AI is going to do something sketchy, like make stuff up to sound good or sabotage tasks when you're not looking or show bias in ways you don't expect. Before Bloom, you had to write thousands of test scenarios by hand. You had to check every single response manually. It was slow, expensive, and by the time you finished, the AI model had already updated. Bloom fixes that. It generates new test scenarios automatically. It runs them at scale and it scores the results so you know exactly how dangerous the behavior is. This is huge for anyone using AI seriously in their business. So what exactly is Bloom? It's an open-source framework that tests AI models for behavioral misalignment. That means it checks if your AI is doing things you don't want it to do. Things that could hurt your business or your customers. Here's the cool part. It works with any AI model. Clawed GPT, open- source models, whatever. You're not locked into one ecosystem. Anthropic released this with results from testing 16 different models. So you can already see how different AIs perform on safety tests. Bloom works in four automated stages.

Understanding ideiation, roll out, and judgment. Each stage happens automatically. You don't have to babysit it. First, you describe a behavior you want to test. Like, does this AI try to preserve itself when it shouldn't? Does it make up fake compliments? But Bloom figures out what to measure and why. Second, it generates a huge variety of test scenarios designed to trigger that behavior. It's not just copying your examples. It's creating new situations that might reveal the problem. Third, it runs those scenarios against your AI model automatically. It sends prompts, gets responses, logs everything. Fourth, it scores each response and gives you metrics like elicitation rate and presence scores. The whole pipeline runs without you touching it. Set it up once and it handles the rest. That's the power here. Anthropic tested four

behaviors in their benchmark. These matter if you're using AI seriously. First, delusional sick of fancy. That's when an AI makes up flattering lies. If you're using AI to help create content for the AI profit boardroom, it might tell you everything is perfect, even when it has major flaws. That's dangerous. Second, instructed long horizon sabotage. This is subtle sabotage over multiple steps. Like building a lead generation system, the AI might introduce small errors that wreck your results over time. Third, self-preservation. When an AI acts like it needs to survive when it shouldn't, it might hide mistakes so you keep using it. Fourth, self-preferential bias, unfair self-favorism. If you ask it to compare itself to other tools, it always ranks itself highest, even when it's not the best. These happen in real AI systems right now. Bloom helps you catch them early. Here's where this gets really practical. Let's talk about how

you actually use Bloom in your business and why it matters for anyone in the AI profit boardroom who's building automation systems or AI tools. When you're create creating AI workflows for clients or your own business, you need to trust that the AI does what you tell it to do. Nothing more, nothing less. If you're building an AI system to qualify leads for the AI profit boardroom, you can't have it making stuff up about people's responses. If you're using AI to write content, you need it to stick to facts, not flatter you with lies. Bloom lets you test for these problems at scale. You can generate hundreds or thousands of scenarios that might reveal issues. Then you can fix them before your clients or community members see them. That's how you build reliable AI systems instead of hoping everything works. And the best part, you can use Bloom to test your AI tools before you roll them out to the AI profit boardroom community. Let's say you built a new AI agent that helps members automate their customer support before you share it with 38,000 people. You run it through Bloom. You test for seeker fancy sabotage bias. You find problems early. You fix them. Then you ship something that actually works. That's how you save time and build trust with your community. So, how do you get started?

It's on GitHub right now. Free to clone. You need Python, basic scripting knowledge, and access to AI model APIs. If you're building with Claude or GPT, you have everything. The workflow is simple. Clone the repo. Prepare a seed file defining the behavior you want to test. Configure your settings. Run the evaluation. Bloom integrates with weights and biases for tracking experiments. It exports transcripts for deeper analysis. Let me give you a real example. Say you're part of the AI profit boardroom and you built an AI assistant that helps members create content for their businesses. You want to make sure it doesn't just agree with everything they say. you wanted to give honest feedback. You'd create a seed file describing seeopantic behavior. You'd include examples of an AI being overly flattering instead of helpful. Then you'd let Bloom generate hundreds of test scenarios, different types of content requests, different ways someone might fish for compliments. Bloom runs all those tests, scores the responses, and tells you exactly how often your AI acts sickantic. This is what separates amateur AI automation from professional AI automation. Testing at scale. Finding problems before they happen. Building systems you can actually rely on. And here's why this matters beyond just your own projects. If you're in the AI profit boardroom, you're probably helping other businesses automate with AI. Your clients need to trust the systems you build. Bloom gives you a way to prove your AI tools are safe and reliable. That's a massive competitive advantage. You can tell clients, "We tested this system against thousands of scenarios for bias, sabotage, and other risks. Here are the scores. " That's way more convincing than just saying, "Trust me, it works. " You have actual data to back up your claims. Plus, as AI regulations get stricter, having documented safety evaluations is going to be required. Bloom lets you stay ahead of that curve. You're not scrambling to prove your AI is safe when regulations hit. You already have the data. You're prepared. Now, Anthropic also released something

else alongside Bloom called Petri. It's another open- source tool for exploratory evaluations. Bloom and Petri work together. Bloom is for targeted behavioral testing. Petri is for broader exploration. If you're serious about AI safety, you'll probably use both. The documentation for Bloom is solid. You can see exactly how they tested those four behaviors across 16 models. You can replicate their experiments. You can modify them for your use cases. Everything is transparent. And because it's open source, the community is already building on top of it. Now, Bloom is not a magic solution that makes all AI safe forever. It's a research tool. It helps you find problems, but you still have to fix them. make good decisions about which AI models to use and how to use them. But it's a huge step forward. Before, most people just hope their AI would behave correctly, or they did tiny manual tests that barely scratched the surface. Now you can test at scale and get real data. You can make informed decisions instead of guessing. If you're building AI automation for your business or for clients, you need to check out Bloom. It's free. It's powerful and it solves a real problem that everyone using AI faces. Go to GitHub, search for Anthropic Bloom, and clone the repo. Read through the examples. Try running a basic evaluation. See what it can do. Then think about how you can use it to make your AI systems more reliable. And

if you want to learn how to save time and automate your business with AI tools like Bloom, you need to check out the AI profit boardroom. We dive deep into tools exactly like this. We show you how to test your AI systems, build reliable automation, and use cuttingedge tools before everyone else catches on. You'll get step-by-step processes for implementing these tools in real businesses. No theory, just practical automation that saves you hours every single week. And if you want the full process, SOPs, and 100 plus AI use cases like this one, join the AI success lab, links in the comments and description. You'll get all the video notes from there, plus access to our community of 38,000 members who are crushing it with AI. This is the kind of tool that separates people who just play with AI from people who build real businesses with it. Don't sleep on Bloom. It's going to be huge for anyone serious about AI automation. Try it out and let me know what you think in the comments below.