🧠 PewDiePie builds a $20,000 supercomputer to run local AI models

One of the world’s biggest streamers, PewDiePie, has taken a sharp turn from YouTube entertainment to AI independence. After more than a decade of gaming videos and vlogs, he’s now built a personal supercomputer worth around $20,000 to run local large language models (LLMs) entirely offline.

🔸 The setup includes 10 GPUs: 8 custom RTX 4090s (48 GB each) and 2 RTX 4000 Ada cards, powerful enough to handle models like Llama 70B, gpt-oss-120B, and Qwen 245B or up to 64 smaller models simultaneously.
🔸 He even created his own local AI interface and a “council of models” that debate his questions in real time.
🔸 PewDiePie says his goal is simple: keep full control of his data and avoid AI corporations like OpenAI, criticizing them for storing user chats including deleted ones.

From Let’s Plays to local LLMs, PewDiePie’s pivot marks a new era where creators aren’t just using AI, they’re owning it.

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⚡️ Inside Tesla’s first Optimus production line

Tesla has quietly set up its first pilot production line for the humanoid robot Optimus. It’s a small-scale test run for what the company hopes will evolve into a full manufacturing system by 2026, when a larger line is planned.

🔸 The current line assembles and tests robots directly at Tesla’s factory and R&D labs, where Optimus units are already performing basic tasks.
🔸 The company’s target is ambitious, a $20,000 cost price per robot, putting it in the same range as a compact car.
🔸 This phase remains experimental, aimed at refining motion control, battery integration, and safety routines before ramping up.
🔸 Musk’s bet: mass-producing humanoids could one day be as routine as building cars — though testing them next to car lines raises the question, what could possibly go wrong?

Tesla’s robot factory may be small, but symbolically, it marks the moment the company moved from demo mode to industrial ambition.

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📰 Bad news: AI now writes more articles than humans

A new analysis shows that, for the first time, AI-generated articles have overtaken human-written ones on the web. The gap is still small but growing fast.

🔸 Researchers sampled 65,000 web articles (2020–2025) from the CommonCrawl dataset and analyzed them with Surfer AI Detector.
🔸 If over half of an article’s text was flagged as AI-written, it counted as machine-made.
🔸 Cross-checks by publication date helped avoid false positives older pre-ChatGPT articles, for example, were assumed to be human.
🔸 The tipping point came in November 2024, when AI-made content first surpassed human output; after a brief dip, AI now dominates again.

Not all “AI-written” pieces are fully automated, many show signs of human editing or partial rewriting and such content still appears less often in top search results.

Still, the web’s content balance has flipped. The internet’s new authors may not be people, just machines that learned to sound like them.

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🚀 OpenAI rolls out GPT-5.1 with “Instant” and “Thinking” modes

OpenAI has released GPT-5.1, a major update introducing two new models designed for different user needs, one for fast, natural conversation and another for deep reasoning.

🔸 GPT-5.1 Instant brings back the warmth and humor users missed from GPT-4o, offering quick, engaging replies that feel more human.
🔸 GPT-5.1 Thinking is optimized for complex reasoning, it adapts its response time to question difficulty, thinking longer on hard problems and speeding through easy ones.
🔸 The update responds directly to user feedback that GPT-5 felt “cold” and overly mechanical, restoring balance between personality and precision.
🔸 Older models remain in the legacy section for three months, while the new lineup rolls out first to paid users.

With GPT-5.1, OpenAI is clearly splitting its path, one branch for warmth and speed, the other for depth and intelligence.

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🎙 Fireflies founders faked their AI for a year, now it’s a $1B unicorn

In 2016, the co-founders of Fireflies, now a unicorn meeting intelligence startup, promised clients an AI that could automatically join and transcribe calls. There was just one catch, the AI didn’t exist yet.

🔸 For the first year, the two founders personally joined every client call, manually tracking who spoke and what was said.
🔸 The service cost $100 per month, covering about 100 meetings, while customers believed an autonomous system was behind it.
🔸 A year later, they built the real automation, eventually raising $19M and reaching a $1B valuation.

Today it’s a legendary MVP story, but it raises the timeless startup question: when does “fake it till you make it” cross from hustle to scandal?

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⚙️ You can now steer ChatGPT mid-response

OpenAI just added a small but powerful feature, you can now update your prompt while ChatGPT is responding, instead of stopping and starting over.

🔸 Simply hit “Update” to tweak your query on the fly, add missing details, correct instructions, or shift direction (“Use Java instead of Python,” “Make it less formal,” etc.).
🔸 Works even in Deep Research mode, where responses take longer and restarting would waste time.
🔸 It feels minor, but it transforms how you interact with long-running or complex tasks, making conversations more iterative, flexible, and human-like.

A quiet update, but one that makes ChatGPT feel less like a black box and more like a real-time creative partner.

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🌍 World Labs makes its 3D world-generation model “Marble” available to everyone

World Labs, the spatial-AI startup co-founded by Fei-Fei Li, has opened access to Marble, a generative model that can create full three-dimensional worlds from text, images, video, or existing 3D layouts.

🔸 Marble can generate persistent, editable 3D environments and export them as meshes, Gaussian splats, or video for use in games, film, and simulation.
🔸 The model accepts multimodal inputs from a single photo to multi-view video and includes an editor called “Chisel” for sketching rough 3D structures before applying style and detail.
🔸 World Labs positions Marble as a step toward richer “spatial intelligence,” enabling designers, creators, and robotics teams to build virtual worlds far faster than traditional workflows allow.

Generative AI is moving beyond text and images into full spatial understanding, turning world-building into a prompt-driven process.

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