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.
Merged fixes · 3 additional PRs into main
| PR | What it fixed |
|---|---|
#4424 |
The workflows reference guide had drifted from the shipped workflow on four points — version, minimum Spec Kit version, supported integrations, and the default integration. The guide told readers the default was copilot when it was actually auto. Reconciled the documented definition with the real one and added a test that fails if they diverge again. |
#4397 |
setup-plan emitted a JSON key named SPECS_DIR that held the per-feature directory, while every sibling script used SPECS_DIR for the specs root — the same name meaning two different paths. Renamed it to FEATURE_DIR across the Bash, PowerShell, and Python ports with a parity test. |
#4396 |
Template composition in common.sh looped forever when the resolved core content contained a literal {CORE_TEMPLATE}, because the loop re-tested the string it had just rewritten. Matched the already-correct PowerShell and Python semantics instead of inventing new ones. |
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