Neural chip decodes interactions between brain regions

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Researchers have developed a neural chip capable of decoding how different regions of the brain interact with one another. The work was reported by EPFL, the Swiss Federal Institute of Technology in Lausanne, as described in a news release. The chip is designed to read signals passing between brain areas rather than only activity within a single region. The source material available for this item is limited, so details on the study's methods, scale, and findings are not included here.

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Roku Moves Developer Repositories from GitHub to GitLab

Roku has relocated its developer code repositories from GitHub to GitLab, according to a company blog post. The announcement was shared on Hacker News, where it gathered 14 points and one comment. The source material provides few additional details about the timing or the reasons behind the switch. Developers who rely on Roku's repositories may need to update their links and workflows to reflect the new hosting location.

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ProgrammingDEV Community ·

DeepSeek-Linked Paper Proposes Engram, a Lookup Memory Module for Transformers

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ProgrammingDEV Community ·

Guide: Running a 4B Qwen3 Chat Model Locally on Windows with a Small NVIDIA GPU

A DEV Community guide explains how to run the 4-billion-parameter Qwen3-4B chat model on a Windows PC with an NVIDIA RTX 30, 40, or 50 series GPU, using a prebuilt zip from the author's open-source Pingala-8 project. The only required install is a recent NVIDIA driver, since the package bundles the CUDA runtime files and avoids the full CUDA Toolkit and any compilation. Users extract the zip and run a batch script that downloads the roughly 1.24 GB model from Hugging Face on first launch, then starts a local llama.cpp server and opens a browser-based chat page at 127.0.0.1:8080. The author reports about 200 tokens per second on an RTX 5070 Ti Laptop GPU and notes that the setup requires roughly 5 GB of free disk space and an internet connection for the initial download.

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ProgrammingDEV Community ·

How to Use AI Tools for Studying Without Losing the Learning

AI tools can speed up studying and sharpen focus, but only if the learner stays in control of the process. Suggested practices include stating a specific learning goal, asking the tool to teach step by step and then explaining the material back in one's own words. Turning notes into practice questions and answering from memory before checking is recommended over rereading. Learners should also verify AI summaries against course notes or trusted sources, since details can be lost or oversimplified. Overall, the guidance is to use AI to support active, regular practice rather than to replace the thinking itself.

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