Source: Anthropic, May 22, 2026 Model: Claude Mythos Preview (unreleased) HEADLINE Finding bugs is no longer the bottleneck. Patching them is. Anthropic and ~50 partners have been using Mythos Preview to hunt vulnerabilities in critical software. After one month, the security ecosystem is struggling to keep up with the volume of valid findings. SCALE OF FINDINGS - 10,000+ high/critical-severity vulnerabilities surfaced across partners in one month - Partners reporting 10x increases in bug-finding rate - Cloudflare: 2,000 bugs (400 high/crit), false-positive rate beating human testers - Mozilla: 271 vulns fixed in Firefox 150, 10x the Firefox 148 run with Opus 4.6 - Industry patch volumes spiking: Palo Alto 5x normal, Microsoft and Oracle accelerating EXTERNAL VALIDATION - UK AI Security Institute: first model to solve both cyber ranges end-to-end - XBOW: "significant step up" over all existing models, unprecedented token-for-token precision - Top performer on ExploitBench and ExploitGym (new academic benchmarks) OPEN-SOURCE SCAN RESULTS - Projects scanned: 1,000+ - Total vulns flagged: 23,019 - Estimated high/critical: 6,202 - Triaged so far: 1,752 - True positive rate: 90.6% - Confirmed high/critical: 62.4% - High/crit disclosed: 530 - Patched: 75 - Public advisories: 65 Notable: wolfSSL certificate forgery exploit (CVE-2026-5194), now patched. Would have let attackers host convincing fake bank/email sites. Maintainers are asking Anthropic to slow disclosures. Capacity is the binding constraint. WHAT'S PUBLIC VS. HELD BACK Mythos-class models are NOT being released. Anthropic says safeguards aren't strong enough. Available now: - Claude Security (public beta, Enterprise) - 2,100 vulns patched in 3 weeks using Opus 4.7 - Cyber Verification Program for legit security pros - Tooling release on request: scanning harness, threat model builder, shared skills - Cisco open-sourced its Foundry Security Spec IMPLICATION FOR DEFENDERS The find-vs-fix gap is the new attack surface. Mythos-class capability will proliferate to other labs soon. Anthropic's advice:

In this video, I'll show you how to get started with Codex, the AI coding harness by ChatGPT. We'll cover the essential steps on how to setup Codex primarily using the command line interface, focusing on efficiently integrating this powerful ai coding assistant into your workflow.

From first client wins and live workflows to AI voice agents, portfolio momentum, and production-level fixes - this week inside AIS+ showed what happens when builders keep stacking reps consistently. 🚀 Standout Wins of the Week inside AIS+ 👉 @Michael Garcia closed his first major deal with a wholesale real estate automation engine handling property sourcing, Claude-based deal scoring, and investor pipeline management. 👉 @Luca Giovinazzo delivered his first full client project live — including 11 n8n workflows, CRM systems, Telegram bots, inventory tracking, booking systems, and KPI dashboards for an auto detailing business. 👉 @Paulo Calpatura built a fully automated AI voice receptionist using Vapi, n8n, Claude, Google Maps, Google Calendar, Google Sheets, and ElevenLabs. 👉 Bo Gonzales presented two AI builds internally, stood out in front of 79 employees, and ended up in a 30-minute AI strategy conversation with his CEO. 👉 @Shatadru Majumdar joined just 7 days ago and already completed multiple AIS+ modules while shipping a customer-support workflow using n8n + Claude. 🎥 Super Win Spotlight | @Griffin Maklansky Griffin joined AIS+ after getting laid off and within a month and a half, landed a new AI-focused role. What started it all? Watching Nate’s “Master 95% of Claude Code in 36 Minutes” video and realizing how quickly AI could turn ideas into real products. Since joining, Griffin has: - Built his own personal website to stand out while job hunting - Started learning AI automation seriously despite having no traditional dev background - Used Nate’s templates and systems to level up his Claude workflows - Connected with builders inside the community and started taking real conversations around opportunities - Went from laid off to employed again with a strong salary in under 45 days

Hey everyone, It’s been about two months since I started my AI agency (I started on March 30th), and to be completely honest, I’ve only had one client so far… which was my dad 😅 I wanted to share a few mistakes I made in case it helps other people who are just starting out. At first, I was trying to target everyone and offer everything. I started doing door-to-door outreach and testing different approaches, but I quickly realized I probably needed to focus on one niche only. So I decided to focus on e-commerce, but honestly, I’m still not sure if it’s the right niche. The biggest issue I’m facing right now is that I constantly feel behind. There are new AI tools, new automations, and new strategies coming out every single day. Because of that, I spend way more time learning and consuming content than actually doing outreach and getting clients. I wanted to know if anyone else here has gone through this phase before. And if you managed to get out of it, what helped you the most? Would really appreciate any advice or insights from people who are further ahead. 🙏

If you're building AI agents, I'd urge you to create a template for prompting. Two notable builds in the last month as proof: - I've built an AI agent that has handled over 9,000 emails - Another AI agent that's handling 25k customers. But here's the full setup: - A claude.md file that references a prompting guideline file, it tells Claude how to write prompts. - Once a prompt is approved, I write at the top "approved for production" which tells Claude that it should not make big changes. This makes sure that the prompt does not get destroyed by Claude. - Push the changes to my GitHub to keep track of all changes. This last part is where most people go wrong. When they see a mistake, they ask Claude to write an explicit rule to never do that again. The issue is that Claude will only look for that exact case, and if the next case doesn't match it, Claude will skip it. Instead, what I do is write mental models of the idea, what we're trying to do and why. When you do it this way, Claude has to use more reasoning to figure out which mental model makes sense. You're letting Claude think with some constraints. But this system has cut down my prompting time and also increased my reliability ten fold. And the thing is that I can use this wherever AI agents are used. Sales agent, customer service agent, any type of agent. Because the structure is the exact same every single time. Give me the agent and I'll make it reliable.