🚀 Partnerships - Prediction Markets
This article announces a partnership between SIRE, WeBuildScore, and Kalshi, focusing on prediction markets and the introduction of two new tools: αLink and αVault.
Key Points:
• SIRE and WeBuildScore are partnering with Kalshi.
• The partnership positions the companies at the core of prediction markets.
• αLink, a new terminal, is designed for advanced users.
• αVault provides yield across various markets.
🔗 Resources:
• mxmsbt ↗ - SIRE Partner
• WeBuildScore ↗ - Partner in prediction markets
• Kalshi ↗ - Prediction market platform
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💡 Eurographics 2026 - Submission Deadline
This article reminds researchers of the upcoming abstract and submission form deadline for Eurographics 2026.
Key Points:
• Deadline is September 22nd.
• Submit all required information.
• Information will be fixed later for bidding and review.
🔗 Resources:
• Justus Thies ↗ - Eurographics 2026
• #Eurographics2026 ↗ - Eurographics 2026 hashtag
💡 Parenting - Late-Night Learning
This article describes a humorous situation where the author watches a parenting class late at night after completing household chores.
Key Points:
• Author watches "difficult kid - gentle parenting" class at 11:45 pm.
• This follows completing household chores.
• The class is for a 3-year-old child.
🔗 Resources:
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🤖 Computer Vision - Keypoints Explained
This article explains the concept of keypoints in computer vision and how they're used for scene reconstruction and precise real-world coordinate determination.
Key Points:
• Keypoints act as stable anchors in image reconstruction.
• They enable reconstruction with precise real-world coordinates.
• Miners will need to produce them in future applications.
🔗 Resources:
• mxmsbt ↗ - Explained keypoints in computer vision
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🤖 Computer Vision - Keypoint Scoring
This article discusses a new scoring mechanism for keypoints in computer vision, focusing on how precisely keypoints anchor a pitch to real-world coordinates at scale.
Key Points:
• New scoring mechanism focuses on keypoint precision.
• Measures how well predicted keypoints reproject a global template.
• Aims for precise, scalable measurement.
🔗 Resources:
• mxmsbt ↗ - New scoring mechanism for keypoints
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🚀 Education - Tinygrad Coding Challenges
This article announces the launch of hands-on coding challenges for learning Tinygrad on their platform.
Key Points:
• Hands-on coding challenges are available.
• First Tinygrad problem launched.
• Contributions to expand the question library are welcome.
🔗 Resources:
• __tinygrad__ ↗ - Tinygrad platform
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💡 AI Summit - Discussion on AI Limitations and World Models
This article summarizes a discussion at an AI summit, covering the limitations of current AI systems and the potential of world models in robotics.
Key Points:
• Discussion on limitations of current AI systems.
• Focus on world models like Genie 3.
• Exploration of world models' role in robotics.
🔗 Resources:
• friedberg ↗ - AI summit participant
• theallinpod ↗ - AI summit host
• Demis Hassabis ↗ - AI expert
🤖 Camera Identification - Critical Analysis
This article expresses skepticism about a claim regarding camera identification using lens blur field estimation. It highlights the gap between technical achievement and the claim's reliability and lack of research support.
Key Points:
• Lens blur field estimation is a technical achievement.
• Equating this to a reliable fingerprint for camera identification is irresponsible.
• Claim lacks research support.
🔗 Resources:
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🤖 GPT-OSS-20B - GPS Distance Intuition
This article presents results on how well the GPT-OSS-20B model estimates the distance between two GPS points, testing various model configurations and using OpenAI's message format.
Key Points:
• Base model without Harmony: 86% accuracy.
• Base model with Harmony: 22% accuracy.
• Fine-tuned model with Harmony: 77% accuracy.
• Fine-tuned model with Harmony and forced structure: 68% accuracy.
🔗 Resources:
• OpenAI ↗ - Provider of message format
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🤖 GPT-OSS-20B - Harmony Formatting Experiment
This article describes an experiment exploring the impact of using or not using Harmony formatting on the performance of a GPT-OSS-20B model for GPS distance estimation.
Key Points:
• 72% of results are close to the target.
• Harmony formatting improves accuracy over non-Harmony.
• Further investigation is needed to optimize performance without Harmony.
🔗 Resources:
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