A stunningly concentrated and hefty galaxy cluster, from a time in the universe’s history when such massive structures aren’t expected to have fully formed yet, is challenging cosmic evolution theories. Across a series of three recent papers, a team led by researchers from IPAC—a science and data center for astrophysics and planetary science at Caltech—have revealed that the cluster is the most distant example of strong gravitational lensing with a galaxy cluster. The new results, based on observations from NASA’s James Webb Space Telescope (JWST), were presented in a press conference on June 17, 2026 at the 248th meeting of the American Astronomical Society.
Gemma 4 is now significantly faster in Ollama 0.31 on Apple Silicon via multi-token prediction (MTP), powered by MLX. Performance is now up to 90% faster when used with coding agents, as measured using the Aider polyglot benchmark.
This is my project, Mighty Camera. It is essentially a monocular SLAM camera running entirely on tiny onboard compute. See my past posts for details. Mighty also supports combining multiple cameras and synchronizing them to produce frame-level synced streams. In this setup, I’m using that hardware synchronization to generate depth with SGBM, while it also produces VIO pose. submitted by /u/twokiloballs [link] [Kommentare]
I have building alternative ways explore scientifc literature. The goal was to make the large number of papers published daily easier to keep up with by visualising the macro scopic trend. It is free to use at The Global Research Space for any one interested in giving it a try! How I built it I sourced the latest 11M papers from OpenAlex and Arxiv and ecoded them using SPECTER 2 on titles and abstracts then projecting it down to 2d using UMAP and creating labels within voronoi bounds around high density peaks at increasingly deep depths. There is also support for both keyword and semantic queries, and there's an analytics layer for ranking institutions, authors, and topics etc. I have also more recently added to ability to slide back and forth in time and a daily auto ingestion script to ensure the map is up to date. Feedback or suggestions is very welcome! submitted by /u/icannotchangethename [link] [Kommentare]
Hi everyone, Posted this a while back... a checklist I made while prepping for a CV internship (landed it, hence sharing). It's not a textbook, just a phase-by-phase map of what to actually study for CV/ML interviews: math → CNNs → ViTs → detection → tracking, plus specialization tracks you pick based on the role. After checking on it after a while it got a decent number of stars which surprised and made me happy that people found it useful to save it for later. I decided after that to add more in-demand tracks to help more people after doing some research of the basic internship requirements and maybe a little more. So, just added three new specialization tracks: Segmentation, OCR, and VLMs, on top of the existing ReID and Deployment tracks. Also cleaned up the structure a bit and added proper contributing guidelines if anyone wants to add their own track (3D vision, pose estimation, etc. are open). GitHub: https://github.com/David-Magdy/CVIL Feedback/PRs welcome, especially if something's outdated or miscategorized. And remember to keep it CVIL! submitted by /u/PolarIceBear_ [link] [Kommentare]