Tyler Williams's picture

Building on HF

Tyler Williams PRO

unmodeled-tyler

·

https://quantaintellect.com

AI & ML interests

AI research engineer & solo operator of VANTA Research/Quanta Intellect

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repliedto raghu298's post about 3 hours ago

who sml is best for text summarization?

reacted to TravisMuhlestein's post with 🧠 about 4 hours ago

Routing and trust are becoming coupled problems in multi-agent systems As agent-based systems scale, two challenges start to converge: routing and trust. Routing determines which agent should act. As the number of specialized agents increases, selecting the right one efficiently becomes non-trivial.But selecting an agent is only part of the problem. In production systems, you also need to verify who that agent is before allowing it to execute. Without identity and verification, routing decisions are made on components that may not be trustworthy. This creates an interesting architectural split: -routing → decides what gets executed -identity → determines whether it should be trusted GoDaddy’s ANS (Agent Name Service) introduces a model where agents are tied to domain-based identity and can be cryptographically verified before interaction. This suggests a shift where identity becomes part of the underlying infrastructure, similar to how DNS and TLS evolved for the web. Curious how others are thinking about: -routing strategies (static vs dynamic vs learned) -identity layers for agents -verification and trust in production systems 🔗 https://www.godaddy.com/resources/news/intelligent-ai-routing

reacted to lakj7's post with 🔥 about 4 hours ago

Neural Gas is a classical unsupervised learning algorithm for vector quantization and topology learning, introduced in the early 1990s. It maintains a set of prototype vectors that move through the data space and gradually approximate the underlying distribution by ranking samples and adapting all units accordingly. While the original formulation is algorithmically elegant, most existing implementations remain procedural and non-differentiable, which limits their integration with modern deep learning systems. This project introduces a **differentiable** implementation of Neural Gas in PyTorch: https://github.com/francesco-p/ngas-pytorch The key idea is to reinterpret the update rules in a way that is compatible with autograd, allowing the algorithm to be embedded inside end-to-end trainable pipelines. This shift enables several directions that are difficult or impossible with standard implementations: - joint optimization of Neural Gas with neural networks - inclusion of topology-learning modules inside differentiable models - gradient-based tuning of algorithm parameters - hybrid architectures combining representation learning and vector quantization The repository provides a clean PyTorch implementation and focuses on making the core mechanism usable as a first-class differentiable component, rather than a standalone preprocessing step. In parallel, an interactive playground was built to visualize the behavior of Neural Gas during training and better understand how prototypes adapt to the data distribution: https://francesco-p.github.io/res/neural-gas/playground.html The goal is to revisit a well-known algorithm and make it compatible with current machine learning workflows, where differentiability is a central constraint rather than an afterthought.

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