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:

Helping non-profits is one of the smartest ways to start in AI automation. You get real-world problems to solve, not theoretical ones. You sharpen your execution, build systems that actually get used, and learn what breaks outside of controlled environments. At the same time, you’re contributing to something that matters. The upside compounds: - Stronger portfolio with real outcomes - Referrals from trusted networks - Exposure without paid acquisition - Faster skill development under real constraints If you’re early, don’t wait for perfect clients. Go where the problems are real and the stakes matter. That’s where capability gets built.

Huge Win as I continue to apply the 1% effort rule daily. The biggest lie I tell myself is "I'll do this when I have time." Time never shows up. Inertia does. The fix is the 1% rule. Not 1% better. 1% effort. The smallest deliberate action you can take on the thing you're avoiding. In practice: - Open the project you've been avoiding. Read one paragraph of the docs. Close it. - Send one outreach message to one prospect on your list. - Write one line of the email you've been drafting in your head for a week. - Refactor one function. One. Then stop. - Test one new tool you've been meaning to try. Hit one button. Done. That's it. No streaks. No accountability hacks. No new app. Three things this actually does: 1. Breaks the resistance loop. Starting is the hard part. The 1% bypasses resistance because there's nothing to resist. 2. Keeps the project alive. A task touched today does not decay into avoidance. A task ignored for two weeks becomes a different beast. 3. Compounds quietly. One paragraph a day is a finished doc by month-end. One outreach a day is a real pipeline by quarter-end. The point is consistency, not volume. The mistake is thinking you need a clear afternoon and full motivation. You don't. You need 90 seconds and the discipline to stop after. Try it on the thing you've been avoiding the longest. Set a 90 second timer. Do the smallest version of it. Close the laptop. What's the thing you'd run this on?

Someone this week dropped a 14-page client SOW into Claude and got back: - A one-page plain-English summary for the client - A scoped build checklist (.docx) for the dev team - A milestone breakdown (.xlsx) for ops One source. One prompt. Real .docx and .xlsx files attached in the response. The pattern works for anything you have to translate for multiple audiences: - One messy CRM export, versions for sales, marketing, and finance - One vendor contract, versions for the client, legal, and procurement - One SOP, versions for new hire, veteran, manager - One audit report, versions for internal team, executive, regulator Two things make it work on Opus 4.7: 1. It reads the source closely. Dense tables, footer terms, attached schedules, scanned pages, fine print. You don't have to pre-extract the section you want. Drop the whole thing in. 2. It produces finished files. Not "here's what your spreadsheet could look like." Actual .docx and .xlsx, ready to open. First pass usually comes back complete. The prompt structure that holds up: - Attach the source (PDF, scan, photo, or doc) - Attach the rules each version must follow (audience profile, length limits, required fields) - List what you want produced, and what stays the same vs. what changes across versions - Close with: "List the assumptions you made, the terms you simplified, and anything from the source you left out." That last line is what lets you trust the output. You review the decisions Claude made instead of comparing every line against the source. If you've been running this pattern on Sonnet, try it once on Opus 4.7. The files come back more complete. The model swap is worth it when the input is dense and the output has to be finished, not draft. What's the source document in your work that always gets rewritten three times for three audiences? Drop it in the comments. We can workshop the prompt together.

From AI roles and first clients to live receptionist systems and enterprise training deals - this week inside AIS+ showed what happens when builders stop watching and start executing. 🚀 Standout Wins of the Week inside AIS+ 👉 @Griffin Maklansky went from being laid off to landing an AI Workflow Builder role in just 1 month. 👉 @Ahmed Bin Faisal landed another $2,000 USD client — an interior design firm — and broke down exactly what led to the close 👉 @Narsis Amin built a working AI restaurant receptionist handling bookings, availability, and CRM logging end-to-end. 👉 @Josh Holladay closed a $4.5K (+$1K) client with half up front today — and dropped his top 10 lessons from the close 👉 @Dion Wang received his first official testimonial, validating real client impact and around 40 hours/month saved. 🎥 Super Win Spotlight | @Duy Nguyen Duy started as an engineer who was curious about AI — but unsure how to turn that curiosity into something real. After joining AIS+, he went from learning passively to building his own AI-operated business, Sharper Automations. Since then, he has: • Built a 24-agent AI business operating system • Landed 2 local paying clients through word-of-mouth • Created a system that improves itself weekly through feedback loops • Started moving toward his goal of leaving his corporate job His biggest shift? From “Can I really do this?” → to building a real business around AI automation.