@JeanRemiKing: Introducing NeuralSet: a simple, fast, scalable Python package for Neuro-AI pip install neuralset https://kingjr.github…
Summary
NeuralSet is a new Python package that provides fast, scalable preprocessing and embedding tools for multimodal neuro-AI data including fMRI, EEG, MEG, ECoG, spikes, plus text, audio, video and images.
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OpenAI Microscope
OpenAI Microscope is an open-source tool that systematically visualizes every neuron in commonly studied vision models with fast feedback loops and linkable neurons to support interpretability research. The platform reduces visualization time from minutes to seconds and aims to make neural network analysis more accessible to the research community.
@k1rallik: NVIDIA IS LITERALLY GIVING AWAY FREE AI INFERENCE I literally set it up in 5 minutes and couldn't believe it was free D…
NVIDIA offers free AI inference via DGX Cloud with OpenAI-compatible API for popular models like DeepSeek, MiniMax, Kimi, GLM, and Llama, claimable in 5 minutes.
@dhruvtwt_: Why is no one talking about this? @nvidia is offering around 80 AI models via hosted APIs absolutely for free. You get …
Nvidia quietly provides ~80 free hosted AI model APIs including MiniMax M2.7, GLM 5.1, Kimi 2.5, DeepSeek 3.2, GPT-OSS-120B, ready to integrate with popular dev tools like OpenClaude and Zed IDE.
@AnthropicAI: To support other researchers getting hands-on experience with NLAs, we’ve partnered with Neuronpedia to release NLAs on…
Anthropic and Neuronpedia have partnered to release Natural Language Autoencoders (NLAs) on open models, allowing researchers to gain hands-on experience with this interpretability tool.
Meta-learning In-Context Enables Training-Free Cross Subject Brain Decoding
This paper introduces a meta-optimized approach for semantic visual decoding from fMRI signals that generalizes to novel subjects without fine-tuning, using in-context learning to infer unique neural encoding patterns from a small set of image-brain activation examples. The method achieves strong cross-subject and cross-scanner generalization without requiring anatomical alignment or stimulus overlap.