Popular GitHub Repositories for Pain

Don't feel pain to use Deformable Convolution

Scrape Reddit for marketing pain points using GPT — built to fuel Cronlytic's growth.

This is an RTS-like game written in Python 3 with arcade library. This is my most ambitious project up to date, since I try to overcome Python's shortcomings and make as efficiently-running game as possible. It will cover pathfinding, UI, AI, and graphics, using relatively slow and inefficient language. Main goal is to make it running sufficiently fast for p1layers to be able to actually play the game without pain. I do not have any good idea for the name of the game, so, for now it is just an RTS.

Localize your services without pain ? GPT is under the hood

The traditional machine learning models give a lot of pain when we do not have sufficient labeled data for the specific task or domain we care about to train a reliable model. Transfer learning allows us to deal with these scenarios by leveraging the already existing labeled data of some related task or domain. We try to store this knowledge gained in solving the source task in the source domain and apply it to our problem of interest. In this work, I have utilized Transfer Learning utilizing BertForSequenceClassification model. Also tried RobertaForSequenceClassification and XLNetForSequenceClassification models for Fine-Tuning the Model.

? This is MultiAgentOrchestration. We built it to [short goal — e.g., speed up dev, solve X pain point, play with AI, etc.]. If you’re someone who loves to tinker, break things, experiment, or you’re just another geek → welcome aboard!

PainDiffusion: A model that can generate pain reaction to stimuli.

An intelligent GenAI assistant that acts as a PwC Technology Consultant—aware of all team-built GenAI assets—and actively helps in meetings by matching client pain-points with PwC Labs solutions. Built to eliminate manual discovery, augment discussions, and evolve into a full-fledged fine-tuned assistant.

Repository for self-hosted work

This project ingests customer feedback (e.g., complaints about surge pricing) and uses LLMs to summarize them into actionable insights. It helps Uber’s operations, pricing, and product teams better understand user sentiment and pain points around dynamic pricing.