Building on HF

Ujjwal Tyagi

Ujjwal-Tyagi

AI & ML interests

Chief Scientist at Shirova AI, Senior ML Engineer and Computer Scientist. Passionate about open-source AI, with AI ethics and safety at the heart of everything I build.

Recent Activity

replied to AbstractPhil's post about 2 hours ago

GLIP - Geometric Linear Interpolative Patchwork aka geolip. https://github.com/AbstractEyes/glip-autoencoder This is the repo that will contain the next experimental stage, which is based entirely on the research and structural boundaries applied by said research. It'll be a little rigid while I get Claude set up. In order to directly train these layered topological response patchworks you must install and use the geovocab2, geofractal, and wide_compiler repos. This is due to the wide_compiler's wide_linear high-speed efficiency for ensemble processing, the geovocab2 factory structure with multiple formulas including highly efficient designs meant for kernel compilation, and a series of reusable utilities in geofractal including some of the more complex losses and difficult to optimally tune gate structures surrounding them. Many of the underlying formulas are outlined here; https://huggingface.co/AbstractPhil/geometric-experiment-history/blob/main/FORMULAS.md Utilization and training USING the pretrained or untrained geolip patchwork will be as simple as loading the model in pytorch and will not require external dependencies of the geolip package, numpy, or pytorch depending on the task. It will come packaged with recommended losses but I encourage experimentation because I simply cannot cover all spectrums. Experiments show you can train the patchwork directly with task losses and it retains some useful cohesion, but it will lose all identity without the correct losses making it difficult to task-orient the geometric behavior down the chain. More details to come as development progresses. The system is coming together and the state of the utilizable autoencoder will be ready within a couple weeks. The entire system is built for convenience and reusability, so the structure will be built similarly to autoencoder systems that currently exist, with a few tweaks here and there for important elements - so the interface will be familiar to those who use it.

replied to nyuuzyou's post about 2 hours ago

🌍 Street-Level Imagery Dataset https://huggingface.co/datasets/nyuuzyou/streetview 934,191 image records index Eastern Europe and Northern Asia. Temporal links map historical views at identical coordinates across nine years. Key Stats: - 905,940 unique images - Coverage spanning 2016 to 2025 - Average 14.3 historical links per location Geographic bounds span 20.49° E to 152.32° E. Urban centers show higher data density.

reacted to nyuuzyou's post with 👍 about 3 hours ago

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Public reports allege that Anthropic gobbled up trillions of tokens of copyrighted material and public data to build their castle. 🏰📄 Now that they're sitting on top, they're begging for special laws to protect their profits while pulling the ladder up behind them. 🪜🚫

But the hypocrisy meter just broke! 📉 They are accusing Chinese labs like DeepSeek, Minimax, and Kimi of "huge distillation attacks. The Reality is that You can't just loot the entire internet's library, lock the door, and then sue everyone else for reading through the window. Stop trying to gatekeep the tech you didn't own in the first place. Read the complete article on it: https://huggingface.co/blog/Ujjwal-Tyagi/the-dark-underbelly-of-anthropic

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The Dark Underbelly of Anthropic: How the "Responsible" AI Pioneer Built Claude on Pirated Data, Hypocrisy, and Hidden Dangers

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