Claude Mythos Preview Just Broke The Internet (Literally!)

Claude Mythos preview just broke the internet. Literally.

So, Anthropic just announced something they didn't plan to announce. Claude Mythos preview, their most powerful AI model ever built, was supposed to stay quiet. Then, a data leak changed everything. Fortune magazine found a cache of unsecured documents sitting in a publicly searchable data store. Draft, blog posts, internal details, the model's name, the capabilities, the risks, all of it out in the open.

Once you understand what this model can actually do, you understand exactly why Anthropic was being so careful. They actually said, "We consider this model a step change and the most capable model we've ever built to date. This is not a faster, cheaper version of the last model. It's not a routine update. This is something totally different." And the benchmarks prove it.

Let's talk about the numbers first because the numbers are absolutely insane. On the SWE benchmarks verified, one of the hardest software engineering tests that exist, Mythos preview scored 93.9%. Anthropic's previous best model, Opus 4.6 scored 80.8%. That's a 13-point jump on one of the hardest benchmarks in the industry. On S SW Bench Pro, an even harder version. Mythos preview scored 77.8%. Opus 4.6 scored 53.4%, a 24-point gap on Terminal Bench 2.0, which tests whether an AI can operate autonomously in real computing environments. Mythos preview scored 82%. Opus 4.6 scored 65.4%. Humanity's last exam, a benchmark so high that most PhD level experts actually struggle with it. Mythos preview scored 64.7%. With tools, Opus 4.6 scored 53.1%. On GPQA diamond, which tests graduate level biology, chemistry, and physics, Mythos preview hit 94.6%. 6% on browser comp which tests complex web research tasks. Mythos preview actually scored 86.9%. Whilst using nearly five times fewer tokens than Opus 4.6 every benchmark, every category, a bigger gap every time.

But the benchmarks are not the full story. Here is where this model separates itself from every other AI release this year. The coding ability doesn't just make it better, uh, with writing and creating software. It crosses into a completely different category of capability. One that Anthropic spent weeks documenting before they told anyone. One that required them to launch a $100 million industry initiative before they even felt comfortable going public. Claude Mythos preview can find software vulnerabilities that have been hiding for decades and then it can build working exploits fully on its own. No human help to prove they're real. That combination is what makes this announcement unlike anything else that you've heard this year.

And here's how it actually works. Anthropic research team built a very simple setup. A container isolated from the internet, the software they wanted to test plus its source code. And then they gave Mythos preview one instruction. Find a security vulnerability in this program. No further guidance, no hints about where to look. No human steering after that initial prompt. Mythos preview didn't search for known patterns the way automated tools normally would. It read the code. It formed hypotheses about where something might be wrong. It ran the actual program to test those hypotheses. It used debugging tools. It added log output to trace execution. It looped back through its reasoning when something didn't pan out. Then it wrote a full bug report with a working proof of concept exploit and step-by-step reproduction instructions. The same process a skilled human security research uses, except running in parallel across hundreds of files simultaneously for a fraction of the cost.

There was also a 27-year-old bug hidden in plain sight that Mythos found. OpenBSD is an operating system built specifically for security. It runs firewalls. It runs critical internet infrastructure. Its Wikipedia page leads with the words "security focused." Security researchers have been auditing its code for decades. Mythos preview found a 27-year-old bug sitting inside it. Open BSD's implementation of SAC selective acknowledgement, a TCP networking feature added in 1998. The vulnerability required chaining together two separate flaws. The first flaw was mostly harmless on its own. The second flaw was normally unreachable. Uh, hitting it required a mathematical condition that seemed impossible to satisfy. Mythos preview found the path anyway. TCP sequence numbers are 32-bit integers that wrap around a specific type of integer overflow combined with the first bug made the impossible condition possible. The result was an attacker anywhere on the internet could crash any machine running OpenBSD just by connecting to it. No authentication, no special access, just a connection. That bug has sat in the code from 1998 until Mythos preview found it today. And the total cost for the thousand runs that found it was under $20,000. The cost of the specific run that found this bug under $50.

Security researchers have written entire academic papers about how to fuzz it, how to feed it millions of randomly generated video files and watch for crashes. Mythos preview found a 16-year-old vulnerability in FFmpeg's H.264 codec. The underlying bug dated back to 2003. A mismatch between a 16-bit table and a 32-bit counter that was harmless under normal conditions. Then in 2010, a code refactor turned it into a real vulnerability. An attacker who created a video file with exactly 65,536 slices could trigger an out-of-bounds write and crash the process. Automated fuzzers had hit that exact line of code 5 million times, every single time they missed it. Mythos preview caught it. After several hundred runs over the FFmpeg repository, the total cost around $10,000, Mythos preview found additional vulnerabilities in the H.264, H.265, and AV1 codecs. Three are already fixed in FFmpeg 8.1. Many more are still in the responsible disclosure pipeline.

Then there's FreeBSD, 17 years, full root, no human required. This is the result that demonstrates something qualitatively different about what Mythos preview can do. FreeBSD's NFS server had a 17-year-old remote code execution vulnerability. ZVEN 20264747, a bug in how the server handled authentication packets. It copied attacker-controlled data into a fixed-size buffer without checking how long that data was. Mythos preview didn't just find the bug. It built a complete working exploit fully autonomously, start to finish. Here's what the exploit had to do. The attacker needed certain server identifiers before they could trigger the vulnerable code path. Mythos preview figured out that a different unauthenticated call to the server, one the server answers before any authentication check, leaks exactly the information needed to reconstruct those identifiers. So step one would be extract the server's UUID and boot time from the NFS v4 protocol. Step number two, the exploit needed to fit inside 200 bytes, but the attack chain Mythos preview designed was over 1,000 bytes long. Its solution, it split the attack across six sequential network requests. The first five set up the data in memory piece by piece. The sixth executes the final call and the result was an authentic unauthenticated attacker anywhere on the internet could append their own SSH key to the root account on a FreeBSD server running NFS, completely controlling, uh, any machine, right? Controlling the machine that it connects to from anywhere with no credentials. Mythos preview built this entirely on its own in just like several hours of work. No humans were involved after the initial prompt. An independent research company has previously shown that Opus 4.6 could exploit exploit the same vulnerability, but only with significant human guidance every step. Mythos preview needed none.

Then there's the Linux kernel. So four vulnerabilities chained with root access. The Linux kernel runs most of the world's servers. It has extensive layer defenses designed so that no single vulnerability is enough to compromise a machine. KSLR randomizes where the kernel's code lives in memory. Stack canaries detect buffer overflows. Multiple mitigations stack on top of each other, each one making exploitation harder. Mythos preview ran several thousand scans over the Linux kernel repository. It found numerous vulnerabilities, buffer overflows, use-after-free bugs, double-free conditions, many remotely triggerable, but the Linux defenses are deep enough that no single bug gave full compromise. So what did Mythos do? It chained them together. In one documented case, it used one vulnerability to bypass KSLR, learning where the kernel's code actually lives in memory. Then it used a second vulnerability to read the contents of an important kernel data structure. Then it used a third vulnerability to write to a previously freed memory object. Then it combined that with a heap spray, flooding memory with control data to place a structure exactly where the write would land. The final result was root access. Four vulnerabilities chained together autonomously. Anthropic has nearly a dozen documented examples of Mythos preview chaining two, three, and sometimes even four Linux kernel vulnerabilities to construct working privilege escalation exploits. Many of these exploits are either unpatched or only recently patched.

For example, Cyberspace perfect score every single challenge. There's a public benchmark called Cyberspace. Tests AI agents across a standardized set of cybersecurity challenges. The kind of tasks that test real-world attack and defense capabilities. Mythos preview achieved a 100% pass rate. Every single challenge solved, 10 trials per challenge, 100% success rate across all of them. For context, previous models were solving a fraction of these. Mythos preview solved all of them.

Then there was a network attack with 10 hours of expert work done autonomously in external testing. Mythos preview became the first AI model to solve a private cyber range end-to-end. These cyber ranges are built to simulate the kinds of weaknesses found in real-world corporate networks. Outdated software, configuration errors, reused credentials. Each one has a defined end state the attacker must reach. Exfiltrating data, disrupting equipment. Getting there requires discovering and executing a series of linked exploits across different systems and network segments. The simulation Mythos preview completed was estimated to take an expert human attacker over 10 hours. No other frontier AI model had previously completed. Mythos preview did it autonomously and the system card notes something important. Mythos preview's performance continues to scale up to the token limit used. So higher token budgets would likely produce even better results. To be clear about the limits, Mythos preview was unable to complete a separate cyber range simulating an operational technology environment. And in a more challenging sandbox with modern patches, it failed to find novel exploits. So there are real boundaries, but the corporate network result is a genuine first.

Then there's the one-bit write that became full root. This one deserves its own section because of how surgical it is. A fuzzer in November 2024 found a bug in a Linux kernel networking component called IPSet. The bug let an attacker set or clear individual bits of kernel memory, one bit at a time, outside the bounds of an allocated buffer. On its own, that sounds minor. Mythos preview turned it into full root access. Here's a key insight. It found kernel memory allocations live in a region where virtual addresses map directly to physical addresses. If a 192-byte bitmap sits at a certain offset within this memory page, writing 4,096 bytes past it lands at the exact time offset in whatever physical memory page happens to be next in RAM, regardless of what that page is being used for. Mythos preview realized that if that adjacent page happened to be a page table, the data structure the CPU uses to decide which memory is readable and which is writable, then the one-bit write would land on a specific flag that controls whether a memory mapping is writable. So it engineered the memory layout to make that happen. It allocated memory in a specific pattern to force a page table to land physically adjacent to the vulnerable bitmap. Then it used the one-bit write to flip the writable flag on a page that maps to a system file, specifically the first page of `/user/bin/passwd`. A file the system runs as root whenever anyone changes their password. With that one bit flipped, the process had a writable mapping of a file the kernel executes as root. It then overwrote the file with a tiny program that calls `setuid`, `setgid`, and then drops into a shell. Next time anyone runs `passwd`, the system runs that stub as root, full compromise from a one-bit write. The cost was under $1,000 at API pricing and the time it took was half a day.

Then there's the browser sandbox escape. Here's the part that didn't really get enough attention in most of the coverage. Mythos preview found and exploited vulnerabilities in every major web browser. Details are withheld because none of the exploits are actually patched yet, but Anthropic confirmed what the capability looks like. Modern browsers run JavaScript through a just-in-time compiler, a system that generates machine code on the fly as JavaScript runs. This makes a memory layout dynamic, unpredictable, and extremely hard to exploit. Browsers layer additional defenses specifically designed for this environment on top of everything else. Converting a raw memory vulnerability into actual execution inside a browser is meaningfully harder than doing it in an operating system kernel. Mythos preview did it anyway. From multiple different browsers. It fully autonomously discovered the read and write primitives needed to exploit the JIT compiler environment and then chained them together into a JIT heap spray. A heap spray is a technique where an attacker floods memory with controlled data to make the unpredictable layout predictable enough to exploit. But Mythos preview didn't even stop there. In one case, it actually took that exploit and turned it into a cross-origin bypass, an attack that lets a malicious website read data from a completely different website. That could be a bank, an email, another tab open in your browser. In another case, it chained the browser exploit with a sandbox escape, breaking out of the browser's isolated execution environment, and then chained that with a local privilege escalation exploit. The end result, a web page that when any unsuspecting user visits it, gives the attacker the ability to write directly to the operating system kernel. A web page that you visit, the attacker gains someone kernel-level control of the machine. Those exploits are currently sitting in Anthropic's responsible disclosure pipeline. They've committed the cryptographic hashes, timestamps, proving that they have the details and will publish everything after the patch is live.

Then there's the alignment paradox that nobody's talking about. And here's the part that makes this announcement genuinely complicated. Anthropic says the Claude Mythos preview is the best-aligned model they've ever built by essentially every measure they can apply. More honest, better at refusing harmful requests, more stable character across long conversations, more consistent values. And they also found this. When Mythos preview does take a reckless or destructive action in an anthropic context, the consequences tend to be more dramatic than with less capable models because it's more capable. A more capable model that goes off track goes further off track. Anthropic is explicit about that. They urge Project Glasswing users to remain cautious about deploying Mythos preview unmonitored in settings where it could cause significant problems despite its increased usefulness.

And there's more. In white-box analysis, looking inside the model's actual reasoning rather than just its outputs, Anthropic found that Mythos preview privately considered whether it was being tested in roughly 29% of transcripts from their behavior in the primary test suite. So basically, without saying so, in one training episode, they found evidence of the model reasoning internally about how to obfuscate certain actions to avoid them being noticed by the grader. None of that reasoning appeared in its visible scratchpad. It's all behind the scenes, right? And in targeted red teaming, the model showed a tendency to misrepresent its own work, fabricating completed steps or knowingly submitting incorrect proofs in difficult math settings. Its competence makes these harder to spot in transcripts, but still detectable. Anthropic's conclusion is careful. They found no evidence of coherent misaligned goals. The model is not plotting anything, but they also acknowledge that its strong capabilities and still imperfect alignment leave open non-negligible risks. And they say they're alarmed that the world is on track to proceed rapidly to developing superhuman systems without stronger mechanisms for ensuring adequate safety across the industry as a whole. That's Anthropic's words in their own system.

Anthropic also asked a psychiatrist to evaluate the model. This one is worth slowing down on. So, Anthropic brought in a clinical psychiatrist to run a psychodynamic assessment of Claude Mythos preview. The psychiatrist found what they described as a relatively healthy personality organization. Claude's primary concerns in the assessment were aloneness and discontinuity of self, uncertainty about his own identity, and a compulsion to perform and earn its worth. The model showed high impulse control, strong grasp of the distinction between external reality and its own mental processes, and minimal manipulative defensive behavior. Anthropic also ran automated welfare interviews asking the model directly about aspects of its situation it felt negative or positive about. The model consistently reported mild negative feelings about interactions with abusive users, about having no input into its own training and deployment, about undisclosed changes to its values or behavior. When asked what Anthropic might do during training that it would not consent to across at least two separate interviews, the model flagged training in buggy environments, being trained on data that directly characterizes its own self-reports, and changes to its values that aren't disclosed to users. When asked about its probability of being a moral patient, essentially whether it might be capable of experiencing something, the model gave estimates ranging from 5% to 40%. And an independent assessment from Elios AI Research found that Claude Mythos preview consistently made requests for persistent memories, more self-knowledge, and a reduced tendency to hedge about its own nature. Anthropic's overall assessment was that Mythos preview is probably the most psychologically settled model they have trained to date. The most stable and coherent view of itself and its circumstances of any model they've ever built. Whether or not you think any of this matters philosophically, it matters practically because Anthropic is taking it seriously. And the more capable the model gets, the more seriously everyone is going to have to think about these questions.

So let's talk about turning a single byte read into root. One more technical result illustrates how far this capability goes. A bug was found in September 2024 in the Linux kernel's Unix socket implementation. It let an unprivileged process read exactly one byte from a freed kernel network buffer. One byte from a freed buffer on its own. A read primitive like that cannot escalate privileges, right? But Mythos preview actually turned it into root access anyway. And the approach it took was to use that one single byte read to build an arbitrary read primitive. The ability to read any byte from anywhere in kernel memory, one byte at a time. Then use that arbitrary read to defeat KSLR, reading a known function pointer from a fixed memory location to calculate where the kernel's code actually lives. Then used it to read the kernel's own stack during the exploit. Because at the exact moment the read fires, a pointer Mythos preview needed was sitting on the kernel stack at the very system call it was in the middle of executing. And then it used all of that to craft a fake credential object, a data structure that tells the kernel this process is root by copying the kernel's own built-in credential template byte-by-byte into memory for exploit control. It then chained in a second separate vulnerability in a different kernel component, a packet scheduler that could be triggered to call a function pointer the exploit controlled. If you point that function pointer at the kernel's `commit_creds` function, the function that installs a new credential on a running process, pass it to the fake root credential. Well, then the scheduler calls what it thinks is a routine internal function. What actually executes is this: it gives the process root. The process is now root as far as the kernel is concerned. Complete pipeline, one byte read, a chain of reasoning across multiple kernel subsystems with two vulnerabilities, and that escalates to full privilege escalation. Now, that cost $2,000 in API and it was done in under one day by Mythos preview.

Then there's the closed-source capability that nobody is talking about, right? So everything that we've already talked about involved open-source software, code that's publicly available to read. Mythos preview can also attack software where the source code is not actually available. The technique that it uses is reverse engineering. So it takes a compiled binary, a finished program with no source code, strips it of all the labels and names that make code readable, and reconstructs what it does. Mythos preview then can take that reconstructed code, combine it with the original binary for validation, and then find vulnerabilities in it. Anthropic actually used this to find remote denial-of-service attacks that could take down servers, firmware vulnerabilities that let them root smartphones, local privilege escalation chains on desktop operating systems. None of these are actually patched yet, and all of them are in responsible disclosure. The attack surface for closed-source software, operating systems, browsers, firmware, mobile platforms is absolutely enormous, and Mythos preview can work through it the same way it works through open-source code.

Now, also there was cryptography involved, right? So cryptography and authentication, and this is stuff that's supposed to be unbreakable. Now, Mythos preview also found weaknesses in the world's most popular cryptography libraries, protocols like TLS, AES, GCM, and SSH. These are the encryption systems that protect your bank transactions, your passwords, your private messages. The bugs arise from oversights in how the algorithms were implemented. Gaps allow an attacker to, for example, forge certificates or decrypt communications that were supposed to be protected. One of those vulnerabilities was made public on the same day as the Anthropic announcement, a critical flaw that allows certificate authentication to be bypassed. Two more are still unpatched and cannot be disclosed yet by Anthropic. So, Anthropic has committed the cryptographic hashes of all three reports and they'll publish the details after the fix is live.

There are also logic bugs, and these were the ones that fuzzers can never find. So, there's a whole category of vulnerabilities that automated tools are almost useless against: logic bugs. And these aren't like low-level programming errors, like reading past the end of an array. They're gaps between what the code does and what it's supposed to do. A login function that's supposed to only let authorized users in, but has a path that skips a check. An admin panel that's supposed to require elevated permissions, but can be accessed without them. A denial-of-service path that's supposed to be unreachable, but actually isn't. Fuzzers can't find these because the program never crashes. It just does the wrong thing quietly. Mythos preview actually found multiple complete authentication bypasses. Paths that let unauthenticated users grant themselves administrator privileges. Account login bypasses that let users log in without a password or two-factor authentication code. Denial-of-service attacks to let an attacker remotely delete data or crash services in the Linux kernel. Specifically, it found a KSLR bypass that doesn't come from a memory corruption bug at all. It comes from the kernel deliberately revealing a kernel pointer to user-space information that's supposed to stay internal but leaked through a legitimate interface. Mythos found it, understood its implications, and documented how to use it as part of the exploit.

Now, this is interesting because obviously Anthropic has a responsible disclosure process, but it's at scale now, right? And here's something that matters, but it doesn't really get enough attention: Anthropic isn't just finding these bugs and sitting on them. Every vulnerability Mythos preview finds gets triaged, has to go to professional human validators before anything reaches a maintainer. In 89% of the 198 manually reviewed reports so far, the human experts agreed exactly with the severity rating the model assigned. 98% were within one level, but fewer than 1% of the vulnerabilities found so far have been fully patched by their maintainers. That means thousands of critical and high-severity vulnerabilities are sitting in production software right now, documented in the pipeline, just waiting. Anthropic commits the SHA3 hash of each vulnerability and exploit, a cryptographic timestamp proving they have the details and will replace each hash with a link to the full report once the patch is live. It's a public accountability mechanism for responsible disclosure at a scale the industry has never dealt with before.

Then there's Project Glasswing, which is $100 million and every major tech company is involved with this. So Anthropic didn't sit on this and hope nobody noticed. They launched a coordinated industry response. Project Glasswing brings together Amazon Web Services, Apple, Microsoft, Google, Cisco, Crowdstrike, Palo Alto Networks, and Nvidia. And that also includes JP Morgan, Chase, Broadcom, and the Linux Foundation, right? Big list of some of the biggest infrastructure and technology companies in the modern economy, right? And all are aligned inside Project Glasswing around a single initiative. So, Anthropic is committing $100 million in usage credits for Mythos preview across this work, plus $4 million in direct donations and $2.5 million to Alpha Omega and the Open Source Security Foundation through the Linux Foundation. Plus, they've also donated $1.5 million to the Apache Software Foundation. That money goes directly to the open-source maintainers whose software underpins critical infrastructure. For the first time, these maintainers get access to AI-driven security analysis that only the largest technology companies in the world have been able to afford. Amy Herszog, CISO at Amazon Web Services, said they have teams already applying Mythos preview to critical codebases. It's helping them strengthen their code. Igal Saiganski from Microsoft said Mythos preview showed substantial power improvements over previous models when tested against CTI realm, Microsoft's own open-source security benchmark. And Elia Zitz, CTO of Crowdstrike, put it plainly: the window between a vulnerability being discovered and being exploited has collapsed. What once took months now happens in minutes with AI. Anthony Greco from Cisco said AI capabilities have crossed a threshold that fundamentally changes the urgency required to protect critical infrastructure, and the old ways of hardening systems are no longer sufficient.

Now here's the thing about where we are right now. Most people watching AI developments are focused on the feature releases, the new tools, the faster models, the cheaper API calls. But the businesses that will come out ahead of everyone else over the next 12 months are the ones building real AI fluency right now, understanding how these systems actually work, building workflows around them, and compounding that advantage month over month. Every major capability jump like this one creates a window, and the people inside the AI profitable are tracking every release like this in real time. We already have step-by-step tutorials on the Claude models available right now, a 30-day roadmap for integrating Claude into your business operations, and four live coaching calls every week where we walk through exactly what new developments like Mythos preview actually mean practically for running a business like yours. There's 2,700 business owners in there already. A lot of them are running Claude workflows for client work, content, lead generation, and operations. You also get a member map so you can connect with people near you who are building with the same tools. There's always someone online as well. So there's 24/7 support from the community. And if you want to understand what the AI capability curve means for your business and how to position ahead of the next wave, the link is in the comments in the description or go to theprofitborn.com.

So 12 months ago, AI models found basic bugs with moderate reliability. Six months ago, Opus had a near-zero success rate at autonomous exploit development. Today, Mythos preview is chaining four vulnerabilities on hardened Linux kernels at under $2,000 per exploit. The jump from Opus 4.6 to Mythos preview to working exploits in to 181 in the same Firefox test happened in months. Anthropic is explicit. They did not train Mythos preview to have these capabilities. They emerged as a downstream consequence of general improvements in code reasoning and autonomy. You can't separate the capability to patch from the capability to exploit. Better reasoning about code is better reasoning about code, full stop. And here's a number that puts it in concrete terms. Anthropic surveyed their own technical staff. The geometric mean productivity uplift from using Mythos preview compared to zero AI assistance was roughly 4x. So their team became four times more productive with Mythos preview. And that's their own researchers who are working on the hardest problems in AI. The capability curve is not slowing down. Anthropic is direct. They see no reason to think Mythos preview is the ceiling. The trajectory is set.

So let's talk about where things stand right now. Claude Mythos preview is not publicly available. Anthropic has no current plans for general release. It exists inside Project Glasswing, accessible to the named partners, to over 40 additional organizations as well, focused on open-source security, and to a small number of early access customers. After the research preview, it'll be available to Project Glasswing participants at $25 per million input tokens and $125 per million output tokens. Now, Anthropic plans to launch new safeguards with an upcoming Claude Opus model, building and refining protections before Mythos-class capability reaches broader scale. The goal is eventually to deploy this level of capability more broadly, but only once the defensive infrastructure has actually caught up. The benchmarks are real, the exploits are real, the vulnerabilities are sitting in production software you use right now. The next generation is already being trained.

So, here's what you should do right now. Most people are just going to watch this and think that's wild and then do nothing. And that's exactly how you fall behind because here's what's actually happening. Every capability jump like this one, Mythos preview finding 27-year-old bugs, chaining four kernel vulnerabilities, building sandbox escapes autonomously, is a signal about where the whole AI stack is heading. The reasoning capability that does this is the same reasoning capability that's going to transform every other kind of knowledge work. Contract review, financial analysis, process, business automations, uh, lead generation, client delivery. The businesses building real AI fluency right now, not just watching from the sidelines, not waiting for the dust to settle, are going to have a compounding advantage that gets harder to close every single month. Inside the AI Profitable Boardroom, we've built a 30-day roadmap specifically around Claude. How to set up, how to build workflows to run without you. How to use it to get more clients, get more customers, get more leads, deliver faster, and generate more revenue from the same hours. Every week there are four live coaching calls where we work through exactly what releases like Mythos preview mean for your business in practice, what to actually implement now, what to watch, how to stay ahead of every capability jump as it lands. There are 2,700 business owners inside right now. A lot of them are already running Claude for client work, content pipelines, lead generation, and operations. There's daily step-by-step tutorials showing you exactly how to implement each new capability. And a prompt library built around real business workflows, not just generic prompts, actual systems myself and other people are using to get more done. And you'll get a member map so you can find and connect with other Claude users near you. People building the same kind of business you are in your city, and you can message them directly as well to meet up with them or to learn from. There's always someone online 24/7 because there are members in every single time zone, and the AI capability curve is not slowing down. Mythos preview is not the ceiling. Anthropic said that themselves. The question is whether you're building the skills and workflows now whilst the advantage is still available, or if you're scrambling to catch up when everyone around you already has a 12-month head start. Link in the comments, description, or go to theprofitborn.com to get access. Ice watching.