🤖 AI Research - Multimodal Generative Models

Multimodal generative models are a type of AI model that can generate images, videos, or other forms of media from text or other input. The Gemini Omni team is hiring researchers to work on building and scaling these models, which could have a significant impact on various industries.

Key Points:

Multimodal Generative Models: These models can generate images, videos, or other forms of media from text or other input, and are being developed by the Gemini Omni team.
Research Opportunities: The team is hiring researchers to work on building and scaling these models, which could have a significant impact on various industries.
Technical Challenges: Building and scaling multimodal generative models is a complex task that requires significant technical expertise.

🔗 Resources:

Original post ↗
Original source
Gemini Omni ↗
Brief description of the team's work on multimodal generative models

🌿 AI for Wildlife Conservation

Vale Vision is using AI to help with wildlife conservation, and has achieved impressive results using a combination of DINOv3 backbones and LightlyTrain. They have fine-tuned their models using fewer than 100,000 images and have outperformed Microsoft's MegaDetector on their own benchmarks.

Key Points:

AI for Wildlife Conservation: Vale Vision is using AI to help with wildlife conservation, and has achieved impressive results using a combination of DINOv3 backbones and LightlyTrain.
Fine-tuning Models: The team has fine-tuned their models using fewer than 100,000 images and have outperformed Microsoft's MegaDetector on their own benchmarks.
Technical Details: The team has used a combination of DINOv3 backbones and LightlyTrain to achieve their results.

🔗 Resources:

Original post ↗
Original source
Vale Vision ↗
Brief description of the team's work on AI for wildlife conservation

📚 Clarifying the Williams'92 Church

The Williams'92 church is a type of neural network architecture, but the terminology used to describe it can be confusing. The author has attempted to clarify the key concepts, including the use of only one rollout per prompt, assembling 128 rollouts into one batch, and using a specific type of neural network.

Key Points:

Williams'92 Church: The Williams'92 church is a type of neural network architecture that can be confusing to understand.
Rollouts: The author has clarified the concept of rollouts, including the use of only one rollout per prompt and assembling 128 rollouts into one batch.
Neural Network: The author has also clarified the type of neural network used in the Williams'92 church.

🔗 Resources:

Original post ↗
Original source
Williams'92 Church ↗
Brief description of the Williams'92 church and its terminology

🔒 Cryptography - McEliece Key Recovery Challenge

The McEliece key recovery challenge is a type of cryptography challenge that has been solved using a new approach called Two-Anchor Holdout/Hermite. The authors have published a paper on this approach, which has been accepted by the IACR ePrint 2026/1986.

Key Points:

McEliece Key Recovery Challenge: The McEliece key recovery challenge is a type of cryptography challenge that has been solved using a new approach called Two-Anchor Holdout/Hermite.
Two-Anchor Holdout/Hermite: The authors have developed a new approach to solving the McEliece key recovery challenge, which involves using two anchors and Hermite polynomials.
Paper Publication: The authors have published a paper on this approach, which has been accepted by the IACR ePrint 2026/1986.

🔗 Resources:

Original post ↗
Original source
IACR ePrint 2026/1986 ↗
Brief description of the McEliece key recovery challenge and the Two-Anchor Holdout/Hermite approach

🌎 Climate Change - School Climateization

Climate change is a significant issue, and one potential solution is to climateize schools. However, this is not a priority for some people, who are more concerned with other issues.

Key Points:

Climate Change: Climate change is a significant issue that requires attention and action.
School Climateization: One potential solution to climate change is to climateize schools, but this is not a priority for some people.
Prioritization: The prioritization of climate change issues is a complex issue that requires careful consideration.

🔗 Resources:

Original post ↗
Original source
Climate Change ↗
Brief description of the issue of climate change and school climateization

🎬 Film - Cold War Cinema

Frances Stonor Saunders has passed away, and was an inspiration to the author's project on Cold War cinema. The author has been working on a project to document and analyze films from the Cold War era.

Key Points:

Frances Stonor Saunders: Frances Stonor Saunders was an inspiration to the author's project on Cold War cinema.
Cold War Cinema: The author has been working on a project to document and analyze films from the Cold War era.
Project Details: The author has been working on a project to document and analyze films from the Cold War era, including films from the Soviet Union and the United States.

🔗 Resources:

Original post ↗
Original source
Cold War Cinema ↗
Brief description of the author's project on Cold War cinema

📚 The Bitter Lesson

The bitter lesson is a concept that refers to the idea that AI researchers often try to build knowledge into their agents, which can lead to short-term gains but long-term plateaus. This concept was first proposed by Richard Sutton.

Key Points:

The Bitter Lesson: The bitter lesson is a concept that refers to the idea that AI researchers often try to build knowledge into their agents.
Short-term Gains: Building knowledge into agents can lead to short-term gains, but long-term plateaus.
Long-term Plateaus: The bitter lesson suggests that building knowledge into agents can lead to long-term plateaus, rather than continued improvement.

🔗 Resources:

Original post ↗
Original source
The Bitter Lesson ↗
Brief description of the bitter lesson concept

🤖 Fable - Hardware Requirements

Fable is a type of AI model that requires significant hardware resources to run. According to estimates, 10,000 copies of Fable would require $400 million in hardware to run.

Key Points:

Fable: Fable is a type of AI model that requires significant hardware resources to run.
Hardware Requirements: Estimates suggest that 10,000 copies of Fable would require $400 million in hardware to run.
Technical Details: The technical details of Fable's hardware requirements are not well understood.

🔗 Resources:

Original post ↗
Original source
Fable ↗
Brief description of Fable's hardware requirements

🤖 Cost per Token - Hardware Cost

The cost per token of an AI model is not the same as the hardware cost of running that model. According to one expert, the idea that any AI model would get 30x cheaper year over year forever is unlikely.

Key Points:

Cost per Token: The cost per token of an AI model is not the same as the hardware cost of running that model.
Hardware Cost: The hardware cost of running an AI model can be significant.
Technical Details: The technical details of cost per token and hardware cost are complex and not well understood.

🔗 Resources:

Original post ↗
Original source
Cost per Token ↗
Brief description of the difference between cost per token and hardware cost

🤖 Claude Haiku 4.5 - Retrieval Quality

Claude Haiku 4.5 is a type of AI model that has been compromised by a 0.6B router. The authors suggest that SLMs can be used as multi-agent routers, and that the routing layer can be taken care of by a 600 million parameter SLM.

Key Points:

Claude Haiku 4.5: Claude Haiku 4.5 is a type of AI model that has been compromised by a 0.6B router.
SLMs: SLMs can be used as multi-agent routers.
Routing Layer: The routing layer can be taken care of by a 600 million parameter SLM.

🔗 Resources:

Original post ↗
Original source
Claude Haiku 4.5 ↗
Brief description of the compromise of Claude Haiku 4.5 and the use of SLMs as multi-agent routers