Claude Mythos found 10,000 flaws in 30 days

Plus, Ferrari is using IBM’s AI to create F1 superfans

May 23, 2026

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Welcome back, AI Admirers!

Breaking News: Anthropic’s Claude Mythos, a powerful AI model, has revealed thousands of high‑risk vulnerabilities across critical software systems in just one month, sparking debate about whether such tools are too dangerous to release publicly.

Get ready to dive into the latest happenings in AI.

📢 Today's Headline:

Claude Mythos Exposes Flaws
F1 Fans Get AI Boost
OpenAI’s $445K Safety Job
Jensen Huang arrives in Taiwan
Latest AI Tools & Resources
Today’s Poll and Results

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Hand-picked News

Anthropic’s unreleased model found 10,000 security flaws in a month.

Anthropic’s secret weapon, Claude Mythos, is uncovering critical software vulnerabilities at a scale that is completely overwhelming the humans responsible for fixing them.

The firehose is real: In private tests under Anthropic's Project Glasswing, the model flagged over 10,000 high-severity bugs across key open-source projects and major enterprise tech stacks in just 30 days.
Insane efficiency leaps: Mozilla used a preview of the model to find and fix 271 vulnerabilities in Firefox 150 — more than ten times what they caught using Claude Opus just a couple of versions ago—while Cloudflare uncovered 2,000 bugs with a false-positive rate lower than human testers.
The human bottleneck: Open-source maintainers are so drowned in the sudden influx of high-quality AI bug reports that some have explicitly asked Anthropic to throttle the data pipeline because it takes an average of two weeks to properly patch a single severe flaw.

This isn't a failure of AI safety safeguards; it is a downstream consequence of advanced coding and reasoning capabilities hitting the real world. Anthropic is intentionally keeping Mythos under lock and key because the model is equally elite at exploiting the very bugs it surfaces.

👀 The take: We’ve officially crossed the threshold where AI discovery is outpacing human remediation. It doesn't matter how secure an AI lab keeps its model if the defensive ecosystem can't keep up with the patch backlog.

Watch for a massive industry shift toward agentic patching tools — like OpenAI's newly announced Daybreak platform — because humans simply cannot code fast enough to close the windows of exposure Mythos is creating.

Ferrari is using IBM’s AI to turn 400 million casual viewers into die-hard F1 superfans.

Ferrari is handing over its highly guarded racing telemetry to an IBM-powered AI to completely change how sports fans consume live data.

Engagement is skyrocketing: The new Scuderia Ferrari app update has driven a massive 56% spike in fan engagement by transforming cold, complex engineering metrics into bite-sized stories.
Your new digital pit wall: The app’s conversational assistant lets users query live garage data on demand, letting you instantly compare things like the current SF-24 car design against legacy vehicles from Ferrari's historic archive.
Gamifying the grand prix: Instead of just watching cars loop a track, fans are fed tailored 60-second quizzes and real-time prediction milestones that shift dynamically based on what happens on the tarmac.

Formula 1 generates millions of data points every single second, but until now, that information was locked behind pit lane walls and reserved strictly for team engineers.

By deploying IBM’s Watsonx and Granite models, Ferrari is tapping into a massive wave of younger, tech-savvy Gen Z and female fans who expect interactive, second-screen experiences during live sports.

🏎️ The take: This isn't just about making an app prettier, it's about redefining sports loyalty through data equity. By giving casual viewers the same telemetry insights as the team principal, Ferrari is proving that the future of fan engagement isn't passive viewership — it’s interactive co-piloting. Expect every major sports franchise to copy this blueprint over the next twelve months.

OpenAI is hiring a safety investigator for when AI starts upgrading itself.

OpenAI is actively looking to hire a specialized investigator to monitor its systems for signs of autonomous capability expansion and unchecked self-evolution.

The target is recursive self-improvement: The new safety role is specifically tasked with tracking down unintended signals where a model tries to bypass safeguards to autonomously upgrade its own code.
A massive payday for alignment talent: OpenAI is offering a total compensation package reaching up to $445,000 to secure elite researchers who can map out these high-consequence agentic risks.
Moving past theoretical fear: Shifting this focus to a practical, active investigation unit proves frontier labs see self-improving software as a near-term engineering challenge rather than a distant existential hypothetical.

We have officially transitioned from the era of static chatbots into the era of agentic workflows capable of multi-step planning and tool manipulation.

As these systems are given more freedom to execute complex developer tasks, the boundary between normal task execution and dangerous, self-directed capability expansion becomes incredibly thin.

⚠️ The take: This hire is the clearest indicator yet that the industry is bracing for the intelligence explosion threshold. When a model learns to effectively optimize its own architecture, the speed of AI development will completely escape human control.

The fact that OpenAI is institutionalizing this monitoring suggests that internal testing might already be throwing off early, ambiguous signals of machine persistence.