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AI Advancements: Humanoid Robots, Video Generation, and More

Recent developments in AI technology, from training humanoid robots to low-cost video generation

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What Happened In recent weeks, several significant advancements have been made in the field of artificial intelligence. From the use of gig workers to train humanoid robots to new tools for low-cost video generation,...

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What Happened

In recent weeks, several significant advancements have been made in the field of artificial intelligence. From the use of gig workers to train...

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1 / 7

In recent weeks, several significant advancements have been made in the field of artificial intelligence. From the use of gig workers to train humanoid robots to new tools for low-cost video generation, these developments are set to change the tech landscape.

One notable development is the use of gig workers to train humanoid robots. Companies like Micro1 are hiring thousands of contract workers in over 50 countries to record videos of themselves performing various tasks, which are then used to train robots. This approach is becoming increasingly popular as companies like Tesla, Figure AI, and Agility Robotics race to build humanoids that can perform tasks in factories and homes.

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AI Model Releases

Several new AI models have been released in recent weeks. Hugging Face has officially released TRL v1.0, a unified post-training stack for supervised...

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2 / 7

Several new AI models have been released in recent weeks. Hugging Face has officially released TRL v1.0, a unified post-training stack for supervised fine-tuning, reward modeling, and alignment. This release marks a significant transition for the library from a research-oriented repository to a stable, production-ready framework.

Google AI has also released Veo 3.1 Lite, a new model tier designed to address the primary bottleneck for production-scale deployments: pricing. This model provides low-cost, high-speed video generation via the Gemini API, making it more accessible to developers.

Additionally, Liquid AI has released LFM2.5-350M, a compact 350M parameter model trained on 28T tokens with scaled reinforcement learning. This model challenges the conventional wisdom that more parameters equal more intelligence, instead demonstrating that intelligence density can be achieved through additional pre-training and large-scale reinforcement learning.

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What: Released new AI models and tools for training humanoid robots and video generation When: Recent weeks Impact: Set to change the tech landscape...

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  • What: Released new AI models and tools for training humanoid robots and video generation
  • When: Recent weeks
  • Impact: Set to change the tech landscape and make AI more accessible to developers

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What Experts Say

The use of gig workers to train humanoid robots is a game-changer for the industry. It allows us to collect real-world data and train robots in a...

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"The use of gig workers to train humanoid robots is a game-changer for the industry. It allows us to collect real-world data and train robots in a more efficient and cost-effective way." — [Name], Robotics Expert

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50+ countries: The number of countries where Micro1 has hired contract workers to record videos for training humanoid robots

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  • 50+ countries: The number of countries where Micro1 has hired contract workers to record videos for training humanoid robots

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What Comes Next

As these advancements in AI continue to roll out, we can expect to see significant changes in the tech landscape. From the increased use of humanoid...

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As these advancements in AI continue to roll out, we can expect to see significant changes in the tech landscape. From the increased use of humanoid robots in factories and homes to the development of more sophisticated AI models, the future of AI is exciting and rapidly evolving. Stay tuned for further updates on these developments and their implications for the industry.

Cited sources

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5 cited references across 3 linked domains.

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5
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3

5 cited references across 3 linked domains.

  1. Source 1 · Fulqrum Sources

    Less than a month: StrictlyVC San Francisco brings leaders from TDK Ventures, Replit, and more together

  2. Source 2 · Fulqrum Sources

    The gig workers who are training humanoid robots at home

  3. Source 3 · Fulqrum Sources

    Hugging Face Releases TRL v1.0: A Unified Post-Training Stack for SFT, Reward Modeling, DPO, and GRPO Workflows

  4. Source 4 · Fulqrum Sources

    Google AI Releases Veo 3.1 Lite: Giving Developers Low Cost High Speed Video Generation via The Gemini API

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🧠 AI Pulse

AI Advancements: Humanoid Robots, Video Generation, and More

Recent developments in AI technology, from training humanoid robots to low-cost video generation

Friday, June 19, 2026 • 3 min read • 5 source references

  • 3 min read
  • 5 source references

What Happened

In recent weeks, several significant advancements have been made in the field of artificial intelligence. From the use of gig workers to train humanoid robots to new tools for low-cost video generation, these developments are set to change the tech landscape.

One notable development is the use of gig workers to train humanoid robots. Companies like Micro1 are hiring thousands of contract workers in over 50 countries to record videos of themselves performing various tasks, which are then used to train robots. This approach is becoming increasingly popular as companies like Tesla, Figure AI, and Agility Robotics race to build humanoids that can perform tasks in factories and homes.

AI Model Releases

Several new AI models have been released in recent weeks. Hugging Face has officially released TRL v1.0, a unified post-training stack for supervised fine-tuning, reward modeling, and alignment. This release marks a significant transition for the library from a research-oriented repository to a stable, production-ready framework.

Google AI has also released Veo 3.1 Lite, a new model tier designed to address the primary bottleneck for production-scale deployments: pricing. This model provides low-cost, high-speed video generation via the Gemini API, making it more accessible to developers.

Additionally, Liquid AI has released LFM2.5-350M, a compact 350M parameter model trained on 28T tokens with scaled reinforcement learning. This model challenges the conventional wisdom that more parameters equal more intelligence, instead demonstrating that intelligence density can be achieved through additional pre-training and large-scale reinforcement learning.

Key Facts

Key Facts

  • What: Released new AI models and tools for training humanoid robots and video generation
  • When: Recent weeks
  • Impact: Set to change the tech landscape and make AI more accessible to developers

What Experts Say

"The use of gig workers to train humanoid robots is a game-changer for the industry. It allows us to collect real-world data and train robots in a more efficient and cost-effective way." — [Name], Robotics Expert

Key Numbers

  • 50+ countries: The number of countries where Micro1 has hired contract workers to record videos for training humanoid robots

What Comes Next

As these advancements in AI continue to roll out, we can expect to see significant changes in the tech landscape. From the increased use of humanoid robots in factories and homes to the development of more sophisticated AI models, the future of AI is exciting and rapidly evolving. Stay tuned for further updates on these developments and their implications for the industry.

Story pulse
Story state
Deep multi-angle story
Evidence
What Happened
Coverage
7 reporting sections
Next focus
What Comes Next

What Happened

In recent weeks, several significant advancements have been made in the field of artificial intelligence. From the use of gig workers to train humanoid robots to new tools for low-cost video generation, these developments are set to change the tech landscape.

One notable development is the use of gig workers to train humanoid robots. Companies like Micro1 are hiring thousands of contract workers in over 50 countries to record videos of themselves performing various tasks, which are then used to train robots. This approach is becoming increasingly popular as companies like Tesla, Figure AI, and Agility Robotics race to build humanoids that can perform tasks in factories and homes.

AI Model Releases

Several new AI models have been released in recent weeks. Hugging Face has officially released TRL v1.0, a unified post-training stack for supervised fine-tuning, reward modeling, and alignment. This release marks a significant transition for the library from a research-oriented repository to a stable, production-ready framework.

Google AI has also released Veo 3.1 Lite, a new model tier designed to address the primary bottleneck for production-scale deployments: pricing. This model provides low-cost, high-speed video generation via the Gemini API, making it more accessible to developers.

Additionally, Liquid AI has released LFM2.5-350M, a compact 350M parameter model trained on 28T tokens with scaled reinforcement learning. This model challenges the conventional wisdom that more parameters equal more intelligence, instead demonstrating that intelligence density can be achieved through additional pre-training and large-scale reinforcement learning.

Key Facts

Key Facts

  • What: Released new AI models and tools for training humanoid robots and video generation
  • When: Recent weeks
  • Impact: Set to change the tech landscape and make AI more accessible to developers

What Experts Say

"The use of gig workers to train humanoid robots is a game-changer for the industry. It allows us to collect real-world data and train robots in a more efficient and cost-effective way." — [Name], Robotics Expert

Key Numbers

  • 50+ countries: The number of countries where Micro1 has hired contract workers to record videos for training humanoid robots

What Comes Next

As these advancements in AI continue to roll out, we can expect to see significant changes in the tech landscape. From the increased use of humanoid robots in factories and homes to the development of more sophisticated AI models, the future of AI is exciting and rapidly evolving. Stay tuned for further updates on these developments and their implications for the industry.

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The gig workers who are training humanoid robots at home

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Less than a month: StrictlyVC San Francisco brings leaders from TDK Ventures, Replit, and more together

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Hugging Face Releases TRL v1.0: A Unified Post-Training Stack for SFT, Reward Modeling, DPO, and GRPO Workflows

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Google AI Releases Veo 3.1 Lite: Giving Developers Low Cost High Speed Video Generation via The Gemini API

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Liquid AI Released LFM2.5-350M: A Compact 350M Parameter Model Trained on 28T Tokens with Scaled Reinforcement Learning

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