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- 🤖 NVIDIA’s Scaling AI Inference To Power Next-Gen AI
🤖 NVIDIA’s Scaling AI Inference To Power Next-Gen AI
PLUS: The Largest Cloud Computing Deal Ever Made

Welcome back AI enthusiasts!
In today’s Daily Report:
🔋NVIDIA’s Scaling AI Inference To Power Next-Gen AI
☁️The Largest Cloud Computing Deal Ever Made
🛠Trending Tools
🥪Brief Bites
💰Funding Frontlines
💼Who’s Hiring?
Read Time: 3 minutes
🗞RECENT NEWS
NVIDIA
🔋NVIDIA’s Scaling AI Inference To Power Next-Gen AI
NVIDIA just introduced “Rubin CPX,” a specialized computer chip that enables advanced AI models to comprehend massive amounts of information all at once.
Key Details:
A context window refers to the number of tokens an advanced AI model can process or “remember” when generating an output.
A token is a word or part of a word. For example, “yesterday” might be broken into three tokens: “yes,” “ter,” and “day.”
AI inference refers to how well an advanced AI model can apply the information it was trained on to generate useful outputs.
AI inference becomes more challenging as the context window expands. In other words, the more information you give an advanced AI model to work with, the harder it is for that advanced AI model to quickly, accurately, and consistently generate useful outputs.
So, why does this happen?! When advanced AI models have to process huge amounts of tokens, they hit a computational wall: a point where the GPUs running the advanced AI models simply can’t keep up.
“Rubin CPX” solves this issue by combining everything an advanced AI model needs into a single, super-efficient specialized computer chip that’s optimized for AI inference.
Why It’s Important:
“Rubin CPX” enables advanced AI models to process significantly larger amounts of tokens without compromising on speed or accuracy. It delivers over 30 PetaFLOPs of compute; that’s 30 quadrillion mathematical calculations per second!
This breakthrough paves the way for enabling advanced AI models to handle more complex tasks while maintaining continuous context to deliver smarter, smoother, and stronger performance.
ORACLE & OPENAI
☁️The Largest Cloud Computing Deal Ever Made

Image Source: Canva’s AI Image Generators/Magic Media
OpenAI has reportedly signed a historic cloud computing deal with Oracle, committing $300 billion over the next five years starting in 2027.
Key Details:
On Wednesday, Oracle’s stock rose 39.95% to $328.33 per share, signaling the software company’s largest single-day gain since 1992. To put this into perspective, Oracle CEO Safra Catz got $412 million richer in less than 6 hours.
Oracle gained $244 billion in “market cap” (i.e., the total value of a publicly traded company’s shares) within a single day, making Oracle worth more than $922 billion.
It’s all because Oracle announced it signed four multi-billion-dollar contracts to provide cloud computing services for AI developments. As a result, the software company expects AI-related cloud computing revenue to reach $144 billion by 2030.
Why It’s Important:
Here’s the catch: OpenAI must raise tens of billions of dollars every year to afford Oracle’s cloud computing services. At the same time, Oracle needs to spend billions of dollars every year to build, maintain, and upgrade the AI infrastructure OpenAI depends on.
In other words, they’re both stuck in a billion-dollar spending spree, and if either side stumbles financially, the whole thing collapses.
🩺 PULSE CHECK
Will OpenAI raise enough money or generate enough revenue to pay Oracle?Vote Below to View Live Results |
PROMPT ENGINEERING TIPS
⚙️The Bias in Your Beliefs!
Our beliefs serve as mental shortcuts, allowing us to quickly categorize, interpret, and evaluate complex concepts to draw seemingly logical conclusions.
For example, beliefs are often concerned with understanding the causes of things: “If {Y} closely followed {X}, then {X} might be assumed to have been the cause of {Y}.”
These beliefs or mental shortcuts allow us to interpret and predict, helping connect dots and fill in gaps. However, it also leads us to oversimplify or misinterpret complex realities.
This simple prompt turns ChatGPT into your personal belief awareness coach:
Context: I want to better understand how my beliefs influence the way I interpret information,
Clarity: but I often struggle to identify how my biases guide, direct, or inform the assumptions I make.
Guidance: Can you help me analyze my {Insert Specific Opinion} regarding {Insert Specific Topic} based on {Insert Specific Worldviews}, and identify any potential hidden biases that may be shaping my assumptions?
I want to better understand how my beliefs influence the way I interpret information, but I often struggle to identify how my biases guide, direct, or inform the assumptions I make. Can you help me analyze my {Insert Specific Opinion} regarding {Insert Specific Topic} based on {Insert Specific Worldviews}, and identify any potential hidden biases that may be shaping my assumptions?
🛠TRENDING TOOLS
✨vidvoi adds captions and voiceovers to your videos.
📼Rewind remembers and recalls everything you’ve said.
🛡️Rafter finds and fixes vulnerabilities in AI-written code.
🗣️Dumai transcribes your voice into searchable summaries.
🌪️Spiral bing-reads millions of customer reviews to analyze them.
🧰 Browse our Always Up-To-Date AI Toolkit.
🥪BRIEF BITES
Alterego unveiled “Alterego,” the world’s first near-telepathic device that enables silent communication at the speed of thought.
Alibaba Cloud released “Qwen3 ASR,” a robust speech recognition service that accurately transcribes accents, emotions, and cross-talk.
Google introduced “NotebookLM Learn,” which helps students create flashcards, generate quizzes, and interact with peer-reviewed textbooks.
OpenAI published “Why Language Models Hallucinate,” revealing that language models often present false information as fact because they’re trained to guess confidently rather than admit uncertainty.
💰FUNDING FRONTLINES
💼WHO’S HIRING?
Block (Remote): Software Engineer Intern, Summer 2026
NVIDIA (Austin, TX): AI Developer Tech. Engineer, Entry-Level
Microsoft AI (Mountain View, CA): Brand Designer, Mid-Level
Meta (London, UK): Data Engineering Manager, Analytics, Senior-Level
📒FINAL NOTE
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