I implement generative AI papers from scratch and document what breaks.

Machine Learning Engineer working on applied ML problems in MedTech, mostly navigating ambiguity and vague ideas. Outside of that I rebuild the models behind modern generative AI, from VAEs and discrete latents to CLIP, GPT-style LLMs, and autoregressive text-to-image, so I understand the mechanism instead of the API. Currently moving into speech and talking avatars.

Learn - Build - Feedback - Iterate

• generative-ai-from-scratch - VAE, VQ-VAE, VQ-VAE-2, CLIP, and DALL·E-1-style autoregressive text-to-image, each implemented by hand and pushed through ablations. Perceptual loss took VQ-VAE-2 from 0.44 to 0.097 LPIPS. The text-to-image write-up tracks a generation bug through two wrong hypotheses before pinning it on data scale.

• attention_to_llm - A GPT-2 style LLM built from tokenizer to DPO. Also holds architecture experiments with plots: pre-norm vs post-norm gradient stress test, RoPE vs absolute position embeddings across context lengths the model never saw, and MoE routing with and without a load balancing loss.

• microscaling-formats - Benchmark of per-tensor, block-32, and microscaling (MX) INT quantization at 8, 4, and 2 bits across 7 vision-language models on VQAv2 and TextVQA. INT8 is essentially free, per-tensor INT4 collapses every model to near zero accuracy because one scale per tensor rounds ~90% of weights to zero, and block-INT4 beats MXINT4 by 3 to 9%. Blog post

• upasak - A flexible, mindful to privacy, no-code/low-code framework for fine-tuning large language models, built around Hugging Face Transformers. Streamlit interface, multi-format dataset support, built-in PII sanitization. Tutorial

• text_recognition - End-to-end OCR pipeline with CRAFT for detection and CRNN for recognition, trained from scratch on ~45,000 images and tracked with CometML.

I am a firm believer of figuring out as we go.

I started my career in 2024 and went deeper into AI to stop treating it as a black box. The further in I got, the more I fell for the fundamental mechanisms and principles behind deep learning architectures.

I'm broadly interested in maths, finance, tech, and ways to stop self-sabotaging. Aiming for generalist status, but my comfort zone has me in a death grip. Don't believe in fluff projects, either you learn something or you solve a problem.

Permanently buried under a backlog of papers to read.