Geolocate any picture down to its exact coordinates (web version)

Reddit - Artificial Intelligence 1 min read

About this article

Hey guys, Thank you so much for your love and support regarding Netryx Astra V2 last time. Many people are not that technically savvy to install the GitHub repo and test the tool out immediately so I built a small web demo covering a 10km radius of New York, it's completely free and uses the same pipeline as the repo. I have limited the number of credits since each search consumes GPU costs, but if that's an issue you can install the repo and index any city you want with unlimited searches. I...

You've been blocked by network security.To continue, log in to your Reddit account or use your developer tokenIf you think you've been blocked by mistake, file a ticket below and we'll look into it.Log in File a ticket

Originally published on March 28, 2026. Curated by AI News.

Related Articles

Machine Learning

[D] MXFP8 GEMM: Up to 99% of cuBLAS performance using CUDA + PTX

New blog post by Daniel Vega-Myhre (Meta/PyTorch) illustrating GEMM design for FP8, including deep-dives into all the constraints and des...

Reddit - Machine Learning · 1 min ·
UMKC Announces New Master of Science in Artificial Intelligence
Ai Infrastructure

UMKC Announces New Master of Science in Artificial Intelligence

UMKC announces a new Master of Science in Artificial Intelligence program aimed at addressing workforce demand for AI expertise, set to l...

AI News - General · 4 min ·
[2603.15159] To See is Not to Master: Teaching LLMs to Use Private Libraries for Code Generation
Llms

[2603.15159] To See is Not to Master: Teaching LLMs to Use Private Libraries for Code Generation

Abstract page for arXiv paper 2603.15159: To See is Not to Master: Teaching LLMs to Use Private Libraries for Code Generation

arXiv - AI · 4 min ·
[2602.07374] TernaryLM: Memory-Efficient Language Modeling via Native 1.5-Bit Quantization with Adaptive Layer-wise Scaling
Llms

[2602.07374] TernaryLM: Memory-Efficient Language Modeling via Native 1.5-Bit Quantization with Adaptive Layer-wise Scaling

Abstract page for arXiv paper 2602.07374: TernaryLM: Memory-Efficient Language Modeling via Native 1.5-Bit Quantization with Adaptive Lay...

arXiv - AI · 4 min ·
More in Ai Infrastructure: This Week Guide Trending

No comments

No comments yet. Be the first to comment!

Stay updated with AI News

Get the latest news, tools, and insights delivered to your inbox.

Daily or weekly digest • Unsubscribe anytime