Awesome List Updates on Mar 28, 2025
8 awesome lists updated today.
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1. Awesome European Tech
Index / VPN
- Unlocator VPN 🇩🇰 - Fast and secure VPN with ultimate streaming freedom.
2. Awesome Neovim
Search / Diagnostics
- prochri/telescope-all-recent.nvim (⭐147) - Frequency and recency sorter for any Telescope picker.
Git / Diagnostics
- yutkat/git-rebase-auto-diff.nvim (⭐25) - Show diff automatically when Git rebase.
3. Awesome Lidar
Libraries
- LAStools - C++ library and command-line tools for pointcloud processing and data compressing.
(⭐979)4. Free for Dev
APIs, Data, and ML
- UniRateAPI – Real-time exchange rates for 590+ currencies and crypto. Unlimited API calls on the free plan, perfect for developers and finance apps.
5. Awesome Polars
Polars plugins / Geographical / Spatial
- polars-st (⭐105) - Polars plugin that provides geographical/spatial operations on Polars DataFrames, Series, and Expressions by @Oreilles.
Polars plugins / Validation
- polars-validator (⭐15) - Polars plugin that makes Polars DataFrames generics by @baggiponte.
Polars plugins / String parsing
- polars-url (⭐6) - Polars plugin to parse/extract fields from urls by @condekind.
- polars_iptools (⭐21) - Polars plugin for IP address parsing and enrichment including geolocation by @erichutchins.
Polars plugins / Text similarity / Fuzzy Matching
- polars-distance (⭐76) - Polars plugin for text similarity/pairwise distance functions by @ion-elgreco.
- polars_sim (⭐13) - Polars plugin that implements fast approximate joins on string columns for polars dataframes by @schemaitat.
Polars plugins / Time series / Datetime
- polars-xdt (⭐211) - Polars plugin with extra-datetime-related functionalities by @MarcoGorelli.
Polars plugins / Machine Learning & Data Science
- polars-ml (⭐13) - Polars plugin for machine learning by @barak1412.
- polars-candle (⭐25) - Polars plugin for running candle (⭐18k) ML models on Polars DataFrames by @wdoppenberg.
Polars plugins / Mathematical & Statistical Functions
- polars_ols (⭐166) - Polars plugin that enables fast linear model Polar expressions by @azmyrajab.
- polars-pairing (⭐12) - Polars plugin that provides pairing functions that encode two natural numbers into a single natural number by @apcamargo.
Polars plugins / General utilities / Performance
- polars-utils (⭐13) - Collection of utilities for data exploration and analysis with Polars DataFrames by @junghoon-son.
- harley (⭐9) - Polars helper methods to enhance developer productivity by @TomBurdge.
- polars-config-meta (⭐9) - Polars plugin for persistent DataFrame-level metadata by @lmmx.
Polars plugins / Miscellaneous
- polars-finance (⭐46) - A collection of Python Polars plugins and functions for market data processing by @ngriffiths13.
- polars_encryption (⭐8) - Polars plugin that extends Polars with encryption algorithm AES-GSM-SIV by @zlobendog.
- polars-bio (⭐69) - Polars plugin for large-scale genomic analyses which is easy to use and considerable faster and more scalabe than existing alternatives by @biodatageeks.
6. Awesome Machine Learning
Books / Misc
- Machine Learning Books for Beginners - This blog provides a curated list of introductory books to help aspiring ML professionals to grasp foundational machine learning concepts and techniques.
7. Awesome Go
Web Frameworks
- Ronykit (⭐34) - Web framework with pluggable architecture and very performant.
8. Awesome Agi Cocosci
Concepts / AI Concept Representation
- ImageBind: One Embedding Space To Bind Them All - CVPR'23, 2023. [All Versions]. [Project (⭐8.7k)]. This work presents ImageBind, an approach to learn a joint embedding across six different modalities - images, text, audio, depth, thermal, and IMU data. The authors show that all combinations of paired data are not necessary to train such a joint embedding, and only image-paired data is sufficient to bind the modalities together. ImageBind can leverage recent large scale vision-language models, and extends their zero-shot capabilities to new modalities just by using their natural pairing with images. It enables novel emergent applications 'out-of-the-box' including cross-modal retrieval, composing modalities with arithmetic, cross-modal detection and generation. The emergent capabilities improve with the strength of the image encoder and this work sets a new state-of-the-art on emergent zero-shot recognition tasks across modalities, outperforming specialist supervised models. Finally, the authors show strong few-shot recognition results outperforming prior work, and that ImageBind serves as a new way to evaluate vision models for visual and non-visual tasks.
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