Friday, April 10, 2026
Anthropic's Mythos model triggers a global security emergency.
April 10 · 8 videos
Anthropic claims Mythos found 27 year old bugs.
The US Treasury met with bank CEOs today.
Meta is rolling out Muse Spark to 3 billion users.
Cerebras hit 1,200 tokens per second.
Local LLMs now run 200B parameters on a workstation.
The agentic web is replacing the static web.
“Mythos may lead to the entire cyber security industry collapsing under the weight of our new god in a box.”
Claude Mythos is too dangerous for public consumption...
Fireship · Fireship · 5 min
Watch on YouTube →Anthropic announces Mythos, a model so dangerous it is gated behind Project Glasswing. It allegedly automates zero-day exploit discovery in foundational software.
- Mythos discovered a 16 year old FFmpeg vulnerability and a 27 year old OpenBSD bug.
- Finding the OpenBSD exploit cost $20,000 in parallel agent compute.
- Anthropic is using a fellowship model to grant exclusive access to trillion dollar companies.
- Skeptics suggest the move is a marketing play to build hype through existential dread.
- The model achieved an 84 percent exploit rate in Firefox within sandbox-disabled environments.
- Security professionals are shifting toward AI-assisted adversarial patching as a primary defense.
AIE Europe Day 2: ft Google Deepmind, Anthropic, Cursor, Factory, Linear, HF, Cerebras & more
David Soria Parra · AI Engineer · 549 min
Watch on YouTube →AI Engineer Europe Day 2 focuses on the transition from chat interfaces to high-bandwidth agentic ecosystems. Speakers highlight the risks of agent-generated technical debt.
- Google DeepMind released Gemma 4 with an Effectively 2 Billion architecture for consumer devices.
- Cerebras Codex Spark achieved 1,200 tokens per second in code generation.
- Cursor deleted 15,000 lines of code by switching to Markdown-based skills.
- The Model Context Protocol ecosystem now sees 110 million monthly downloads.
- Factory introduced Missions, a multi-agent framework capable of running for 16 days.
- Linear advocates for a Zero Bug Policy to prevent AI-generated slop from overwhelming systems.
3 Billion Users About to Get This Al Upgrade (Meta Muse Spark)
Josh · Limitless Podcast · 34 min
Watch on YouTube →Meta pivots to Personal AGI with Muse Spark, leveraging the social graph of 3 billion users. The industry faces a widening divide between AI adopters and resistors.
- Muse Spark is a closed-source model prioritizing visual reasoning over coding benchmarks.
- Meta spent $75 billion on AI capex in the last year to support its family of apps.
- Anthropic's $30 billion ARR is questioned due to cloud reseller revenue accounting practices.
- SpaceX and Intel are building TerraFab to manufacture radiation-hardened chips for space compute.
- Public backlash is manifesting in physical attacks on data center personnel in Indianapolis.
- Tribe V2 research uses brain scans to optimize AI content for maximum dopamine engagement.
Give me 14 minutes and I'll teach you how to read like a PRO
Rob Dial · The Mindset Mentor Podcast · 14 min
Watch on YouTube →Rob Dial presents a high-efficiency reading system to combat the fact that 80 percent of US families bought zero books last year. He argues continuous learning is the ultimate competitive advantage.
- The Two-Sense method uses Kindle and Audible simultaneously at 2x speed to double retention.
- The average CEO reads 60 books per year compared to 33 percent of high school graduates who never read again.
- The Teach-Back method involves color-coding notes to synthesize information for future instruction.
- Reading before bed is recommended as a screen-free replacement for blue-light devices.
- Rereading highlights during small schedule gaps is more valuable than scrolling social media.
- Information intake is directly correlated with professional competitive advantage.
One Registry to Rule them All - Sonny Merla, Mauro Luchetti, & Mattia Redaelli, Quantyca
Sonny Merla · AI Engineer · 22 min
Watch on YouTube →Amplifon manages an agent explosion across 26 countries using a centralized AI Gateway and three specialized registries. This framework standardizes security and budget tracking for global teams.
- The Amplify program uses MCP and Agent Cards to enable automated discovery and lineage tracking.
- A unified AI Gateway manages security via Microsoft Entra ID and tracks granular budget erosion.
- Standardized GitHub blueprints allow developers to publish metadata automatically via CI/CD.
- The model solves enterprise challenges of maintenance and regulatory compliance in fragmented environments.
- Standardization is the prerequisite for scaling AI solutions responsibly across different countries.
- Centralized governance is delivered as a service to simplify developer workflows.
Judge the Judge: Building LLM Evaluators That Actually Work with GEPA
Mahmoud Mabrouk · AI Engineer · 40 min
Watch on YouTube →Mahmoud Mabrouk explains why miscalibrated LLM-as-a-judge evaluators provide false confidence. He introduces GEPA for automated prompt optimization of judges.
- Binary Pass/Fail metrics are superior to 1-5 scales for achieving human-model agreement.
- A naive LLM judge started at 61 percent accuracy but reached 74 percent after GEPA optimization.
- Spending $300 on optimization tokens can save thousands by enabling the use of smaller models as judges.
- Seed prompts for evaluators should be biased toward compliance to avoid internal model bias.
- The speed of production is limited by the speed at which you can evaluate iterations.
- Overfitting on training data early on is a valid strategy to capture the initial signal.
AI Didn’t Kill the Web, It Moved in!
Yohan Lasorsa · AI Engineer · 52 min
Watch on YouTube →The web is evolving into a native host for AI agents rather than just a source of training data. Browser-native APIs now allow 4GB models to run directly on the client.
- Websites should implement LLMs.txt to provide up-to-date documentation for autonomous agents.
- Chrome now supports a local 4GB Gemini Nano model for summarization and proofreading.
- Responsive design for agents is the new mobile-first strategy for maintaining web traffic.
- The Web MCP API allows UI components to become interactive tools for AI agents.
- Edge-based AI reduces operational costs by offloading computation to user hardware.
- Agentic SEO is required to circumvent the lag in traditional model training data.
Running LLMs locally: Practical LLM Performance on DGX Spark
Mozhgan Kabiri chimeh · AI Engineer · 10 min
Watch on YouTube →NVIDIA's DGX Spark workstation allows developers to run 200B parameter models locally using the Grace Blackwell superchip. This shift ensures data residency and eliminates cloud scheduling delays.
- The Grace Blackwell architecture features 128GB of unified memory for local LLM workloads.
- NVFP4 quantization improves Time to First Token by 3.4x compared to base models.
- A 14B model optimized with NVFP4 achieves 20.19 tokens per second on local hardware.
- Local compute provides deterministic latency and cost predictability for rapid prototyping.
- The engineering sweet spot lies in maximizing intelligence per byte rather than raw capacity.
- Using identical software stacks like vLLM enables a seamless workstation to cloud continuum.
References
PeopleDario Amodei · Scott Bessant · Jerome Powell · Sam Altman · Omar Sanseviero · David Soria Parra · Mario Zechner · Armen Ronacher · Tuomas Artman · Alexander Wang · Mark Zuckerberg · Elon Musk · Aaron Tan · Robert Scoble (x.com/Scobleizer) · Tony Robbins · Matthew McConaughey · Andrew Huberman · Sonny Merla · Mauro Luchetti · Mattia Redaelli · Hamel Husain (hamel.dev) · Mahmoud Mabrouk (x.com/mmabrouk_) · Yohan Lasorsa · Olivier Leplus · Mozhgan Kabiri chimeh
ToolsMythos · Project Glasswing · Gemma 4 · MCP (Model Context Protocol) · Codex Spark · Cursor · Linear · Muse Spark · TerraFab · Kindle · Audible · Whispersync · Microsoft Entra ID · GEPA · LLMs.txt · Gemini Nano · DGX Spark · Grace Blackwell GB10 · vLLM