SoftBank Dumps Nvidia for AI

Baidu AI Beats GPT-5 & Gemini

Author

Altiam Kabir
November 12, 2025

In partnership with

AI PlanetX

Welcome to another edition of AI PlanetX.

SoftBank exits Nvidia for full AI push; Baidu challenges GPT-5 with efficient model; Anthropic plans $50B U.S. data center expansion.

Inside This Edition: 💎

  • Hottest AI News

  • Top AI & SaaS Tools

  • AI Tutorial: Automate Coding Tasks with Jules

  • Top AI & Tech News

  • AI Art Spotlight

  • Prompt of the Day: Make Vibe Coding Worth It

  • AI Video Tutorial

  • Course of the Day: Design, Develop & Deploy Multi-Agent Systems

Hottest AI News

SoftBank

SoftBank Dumps Entire Nvidia Stake to Go All In on AI

SoftBank founder Masayoshi Son has made another audacious move, selling his entire $5.8 billion Nvidia stake to double down on AI investments. It's a classic Son play—bold, risky, and impossible to judge until the dust settles.

Details:

  • Son's career is a rollercoaster of extremes. During the dot-com bubble, his net worth hit $78 billion before he lost $70 billion in the crash. But his $20 million Alibaba investment in 2000, decided after six minutes with Jack Ma, grew to $150 billion

  • The Vision Fund era brought chaos. He raised $45 billion from Saudi Arabia and kept the partnership after Khashoggi's murder. WeWork became his biggest disaster—he overrode objections, valued it at $47 billion, then watched it collapse. He called it "a stain on my life"

  • Currently, he's selling Nvidia at $181.58 per share—14% below its peak—to fund $30 billion for OpenAI and a reported $1 trillion AI hub. The twist? SoftBank's 2019 Nvidia sale of $4 billion is worth over $150 billion. Markets reacted nervously, with Nvidia down 3%

His track record offers no clear answer. With Son, you never know if it's genius or disaster until it's already happened.

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Baidu

Baidu's Open-Source AI Claims to Beat GPT-5 and Gemini Using Far Less Power

Baidu just released ERNIE-4.5-VL-28B-A3B-Thinking, an open-source multimodal AI model that claims to outperform Gemini 2.5 Pro and GPT-5-High on vision-related benchmarks—while using less power.

Details:

  • The model can zoom in and out of images dynamically, mimicking how humans analyze visuals. This lets it perform better at document analysis, visual grounding for industries, and video understanding while linking to external tools like image search

  • It uses a Mixture-of-Experts system, activating 3 billion of its 28 billion parameters at once. That efficiency allows it to run on a single 80GB GPU instead of many high-end accelerators. Baidu trained it using multimodal reinforcement learning and dynamic difficulty sampling

  • The model is released under Apache 2.0 for full commercial use. It includes developer tools and can be implemented in about 30 lines of Python. This raises competition, as its efficiency challenges models from US

This marks a major shift in AI, showing that efficiency and open-source availability could rival the current “bigger is better” mindset.

Top AI & SaaS Tools

  • Fabrile (Life-time Deal): No-code AI chatbot builder that trains on your data to automate support, qualify leads, book meetings, customize tone/behavior, and deploy across web and apps

  • Emma App: Scans labels and barcodes to expose hidden sugars, allergens, and 500+ risks—snap a photo in-store to get a clear health score [F-R-E-E]

  • Logo dev: Provides reliable brand logos for any company, stock ticker, or crypto symbol—ideal for dashboards, SaaS, fintech apps, and internal tools [F-R-E-E]

  • Chad IDE: Code editor designed to make AI wait times productive by integrating entertainment and micro-tasks (gaming, scrolling, cashback) directly into the coding loop [F-R-E-E]

  • ElevenLabs Scribe v2: A highly accurate real-time speech-to-text model with ~150 ms latency that supports 90+ languages [F-R-E-E]

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Delivered daily, it breaks down tools, prompts, and real use cases—so you can implement AI without wasting time.

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AI Tutorial

How to Automate Coding Tasks with Jules

Shipping code does not have to mean babysitting every commit. In this guide you will use Google’s async development agent Jules to queue up tasks, let it work in the background, and publish clean branches when it is done.

  1. Go to Jules and sign in with your Google account

    This ensures you can securely connect to the async agent and its integration features.

  2. Connect GitHub account

    Authorize Jules to have permissions to your repositories so it can create branches, commit changes, and submit updates automatically.

  3. From the dashboard, choose the specific repository you want to work on

    Use the dropdown menu to also select the correct branch (for example, dev or feature-x) so changes are applied in the right place.

  4. Open the chat interface inside Jules

    This is where you describe what you want the async agent to do.

    Type your request clearly.

    For example:

    • Generate unit tests for the payment module

    • Update the API documentation

    • Resolve the issue where file uploads fail for large files

  5. Review the draft plan generated by Jules

    It will outline the steps it intends to take, such as editing specific files, creating fresh ones, or running checks.

    If needed, refine your instructions.

    You can clarify details like:

    • Just update the frontend code

    • Use Python 3.11 syntax

    Once the plan looks correct, press Approve. Jules will run the changes asynchronously—meaning you don’t have to wait in real time.

  6. Monitor progress in the activity log

    You’ll see which files are being modified and whether tests are being run in the background.

  7. When Jules completes the work, preview the changes

    Check the diff or run the updated code locally if you want to verify results.

  8. Tap Publish Branch to push the finished work into your repository

    From there, you can merge it into your main branch through a pull request like normal.

Note: Break tasks into smaller steps for higher accuracy, and don’t approve until the plan looks right. After publishing, review the diff so nothing slips through unnoticed.

Top AI & Tech News

  • Anthropic announced plans to invest $50 billion in building data centers across the U.S. to expand its AI infrastructure

  • Samsung is rolling out Vision AI Companion, a generative-AI upgrade to Bixby, across its 2025 TV lineup

  • Google is launching Private AI Compute, a cloud platform like Apple’s Private Cloud Compute that offloads heavy AI tasks while keeping user data private

  • ElevenLabs launched an Iconic Voice Marketplace offering 28 licensed, rights-holder-approved AI celebrity voices for brands, spanning actors, athletes, historical figures, and more

AI Art Spotlight

Create Your Own Photo Like This Using the Prompt Below:

High-fashion, cinematic, half-length portrait of the person from the input photo in a dreamlike editorial style. Multi-exposure motion blur with ghosting and vertical streaks. Clean gradient background from deep azure (#0033A0) to cyan (#00CCFF). Subject wears a plain white oversized T-shirt with soft folds catching colored lights. Dramatic lighting: a cool blue wash with an orange/yellow edge glow from camera left/backlight. Expression is neutral and introspective, with eyes softly lit or slightly shadowed. Shot on a 50–85 mm lens at f/1.8, with shallow depth of field, cinematic blur, and slight chromatic separation. Skin and fabric are crisp in focus, with a surreal, minimal, glitch-inspired fashion mood.

Model: Gemini 2.5 Flash Image (Nano Banana)

Prompt of the Day

The Prompt That Makes Vibe Coding Worth It

This prompt is the foundation of true vibe coding — where structure meets flow. It keeps every line purposeful, every file aligned with the architecture, and every decision context-aware. It’s not just for writing code; it’s for building with rhythm, clarity, and intent. Without it, vibe coding loses its direction.

Top AI Video Tutorial

33 Incredible Google AI Features That Are Completely F-R-E-E

AI Course of the Day

Design, Develop, and Deploy Multi-Agent Systems with CrewAI

This hands-on course teaches you to build multi-agent systems that automate complex, end-to-end workflows. Across four modules, you’ll design intelligent agent teams that plan, reason, and collaborate using tools, long‑ and short‑term memory, and robust guardrails, then learn how to scale and monitor them for production.