class YashChindam:
def __init__(self):
self.name = "Yash Chindam"
self.role = "AI / ML Engineer"
self.focus = ["LLMs", "RAG Systems", "Computer Vision", "NLP", "Deep Learning"]
self.passion = "Building AI systems that solve real-world problems"
self.currently = "Exploring Generative AI & multimodal research"
self.hf_profile = "https://huggingface.co/yashchindam"
def say_hi(self):
print("Thanks for dropping by! Let's build something intelligent together 🚀")
me = YashChindam()
me.say_hi()
github/spec-kit — GitHub's Spec-Driven Development toolkit
Core contribution · PR #4250 merged into main
Presets could not declare that they depend on an extension, so installing one without its companion left users with a workflow that silently did nothing. I added
requires.extensionsto the preset manifest with strict PEP 440 validation, plus an install-time check that warns — and names the exact remediation — for missing, stale, disabled, corrupted, and version-mismatched dependencies.
Community catalog · published and listed in github/spec-kit
| Package | What it does |
|---|---|
speckit-inventory |
Read-only extension that derives every live FR-/NFR-/SC-/T- ID from existing specs and returns focused per-task context packs instead of whole-file dumps |
inventory-alignment |
Preset that makes the agent classify each requirement against the live set before writing, so a reworded requirement is updated rather than duplicated |
Python · CLI tooling · agent workflows · zero runtime dependencies
Languages & Frameworks
AI / ML
LLMs & Models
Vector Databases & RAG
Data & Tools
Results: RMSE
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Accuracy: 85–92% · Speed: 30–60s/doc |
Accuracy: Entity F1 80–92% |
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Pipeline: 9-step automated analysis · Parallel multi-key processing |
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Supported: 8+ document types · FNOL · Proof of Loss · Damage Photos |
🗂️ More Projects (click to expand)
| Project | Description | Stack |
|---|---|---|
| RAG w/ LLaMA2 + LangChain + ChromaDB | End-to-end RAG pipeline using LLaMA 2 | LLaMA 2 ChromaDB LangChain |
| PDF Chatbot with RAG | Conversational PDF Q&A with RAG architecture | RAG FAISS LLMs |
| Conversational Q&A Chatbot — Mistral Nemo | Contextual, multi-turn chatbot using Mistral Nemo | Mistral Nemo LangChain |
| Image Captioning | Deep learning-based automatic image captioning | PyTorch CNN LSTM |
| License Plate Recognition | Automatic license plate detection and OCR | OpenCV OCR Python |
| Text Summarization — BART | Abstractive text summarization with BART | BART Transformers HuggingFace |
| Anime Title Generator — Flan-T5 | Fine-tuned Flan-T5 for creative anime title generation | Flan-T5 HuggingFace |
| Research Paper Title Generator — BART | Fine-tuned BART for academic title generation | BART Transformers |
| Movie Title Generator — Flan-T5 | Flan-T5 fine-tuned for cinematic title generation | Flan-T5 HuggingFace |
| Predicting Credit Card Approvals | ML classifier for credit card approval prediction | scikit-learn Pandas |
| Google Play Store Trend Analysis | EDA and trend analysis of Play Store app data | Pandas Matplotlib Seaborn |
| RAG Implementation & Prompt Optimization | Benchmarking and optimizing RAG prompt strategies | RAG LLMs Python |
Published models & datasets on Hugging Face
| Resource | Link |
|---|---|
| 🧬 Drug-Protein Interaction Model | yashchindam/Drug-Protein-Interaction-Prediction-Using-CLIP-and-Deep-Learning |
| 📦 Drug-Protein Dataset | datasets/yashchindam/Drug-Protein-Interaction-Prediction-Using-CLIP-and-Deep-Learning |
"The best way to predict the future is to invent it." — Alan Kay