prints hard, the agent tree is a game changer for scaling ai like humans do
MACHINE LEARNING
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Neural Network Training: Predictive Performance Estimation Methods
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"After training on 12,000 patterns it becomes obvious that the new network will outperform the old… If our predictive method gives a good quantitative estimate of the network's test error, we can decide whether three weeks of training should be devoted to the new architecture"
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Learning Curves: Asymptotic Values and Convergence Rates
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Learning Curves: Asymptotic Values and Rate of Convergence published in Advances in Neural Information Processing Systems 6 (NIPS 1993) https://
proceedings.neurips.cc/paper/1993/fil
e/1aa48fc4880bb0c9b8a3bf979d3b917e-Paper.pdf
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Scaling Laws History: From Bell Labs 1993 to Modern AI
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first i thought scaling laws originated in OpenAI (2020) then i thought they came from Baidu (2017) now i am enlightened:
Scaling Laws were first explored at Bell Labs (1993) -
NeurIPS Paper on Power Laws in Classifier Training
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it's literally a NeurIPS paper they train classifiers on different-sized datasets, different-sized models and fit power laws can't believe this was 32 years ago
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Competition and Attraction Improve Model Fusion
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arXiv: Competition and Attraction Improve Model Fusion https://arxiv.org/abs/2508.16204
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Distilled 3D Gaussian Splatting Technology
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Distilled-3DGS
— AK (@_akhaliq) 25 août 2025
Distilled 3D Gaussian Splatting pic.twitter.com/MmLgivksRRDistilled-3DGS Distilled 3D Gaussian Splatting
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Expert-Curated Datasets Improve Small Model Reasoning Skills
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Want to improve a smaller model’s reasoning skills? Our DaaS team is building expert-curated datasets for reasoning, function calling, and more. Let’s talk!
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Reasoning Models Outperform: Inference-Time Compute Advantage
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Key insights:
Reasoning > non-reasoning models. Better living through inference-time compute!
Smaller open-weights models struggle without fine-tuning or other post-training optimization
SnorkelWordle is a strong signal for evaluating reasoning, especially in smaller models
