Anagha

One more brick in the wall — portfolio site is live on desktop ✅. Mobile and tablet version next. Every pixel counts. #BuildInPublic 🔗malusree-2001.github.io/anagha-p.githu…

Maps stack powered by OpenStreetMap github.com/headwaymaps/he…

City2Graph Transform geospatial relations into graphs for Graph Neural Networks and network analysis. github.com/c2g-dev/city2g…

Integrates machine learning with geospatial data analysis github.com/opengeos/geoai

The word "car" is meaningless to an LLM. It is just a string of characters. The first thing a transformer does is convert every token into a vector. Somehow this high-dimensional vector encodes the meaning of the word. GPT-3 uses 12,288 dimensions. But here’s the cool part: the geometry of the space actually means something. E(king) − E(queen) ≈ E(man) − E(woman) In this example, one specific direction in 12,288-dimensional space encodes gender. Another encodes plurality, and so on. Nobody explicitly programmed this. The model learned it simply by reading enough text. There is a special matrix called the embedding matrix, which stores the “definition” of all words as vectors. In GPT-3 its size is 12,288 × 50,257: one column per word in the vocabulary.

@aditiloves29 @X Let's connect

🚀 Hey @X algorithm, I'm looking to #CONNECT with people interested in: 🎨 Frontend 💼 Backend 👩‍💻 Gen AI ✨ Full Stack 🧑‍💻 DevOps ✅ DSA 🧠 AI/ML 🧱 Web3 📊 Data Science 💸 Freelancing 🐍 Python 🔨 Startups Let’s grow, learn & build together! 🔥

@trawasthi_ai Great resource

A popular curated list of resources to get into LLMs (>65k GitHub ⭐) The LLM course is divided into 3 parts: 🧩 LLM Fundamentals: Covers basics about Maths for ML, Python, and Neural Networks. 🧑‍🔬 The LLM Scientist: Focuses on LLM architectures and the techniques to build LLM(Pre-training, Post-training, SFT, Evaluation, Quantization, etc.) 👷 The LLM Engineer: focuses on creating LLM-based applications(Building Vector DB, RAG, Advanced RAG, Agents, Inference Optimization) and deploying them. (link in the comment)

@codewithsu lets connect

@shivi1026 Let's connect

Are you into tech? If yes, let’s connect :) #buildinpublic #100DaysOfCode #100DaysOfDSA #TUFWinterArc

@Zephyr_hg Ideas

I spent weeks researching 2025's hottest AI opportunities. Found 50 business ideas that actually make money using current tech. Each idea includes revenue paths, MVP scope, and exact tech stack needed. Perfect for solopreneurs ready to capitalize on the AI boom before everyone else catches on. Skip months of market research and failed experiments. Comment "IDEAS" and I'll DM it to you (must be following)

Bad ML dev → grinds through theory & code → starts training small models → applies them to real data → builds & deploys useful AI systems → great ML dev

@atullchaurasia Happy birthday 🎂

Today is my birthday

Late-night grind session: code, calm, and coffee. Because dreams don't debug themselves. 💻🌙

@__theSeyram instagram

You must delete 1 app, which one?

One of the coolest tricks in computer vision for spotting fake images 👀 Check the light sources and reflections Mirrors and shadows don’t lie, if perception says “something’s off,” the pixels probably are 🪞✨

Your turn!!!!! Scikit-learn or XGBoost: Which one’s your ride-or-die? Drop a project or trick you’ve pulled off with either! #MachineLearning #DataScience #ScikitLearn #XGBoost

Scikit-learn is your go-to for fast ML workflows and experimentation, while XGBoost is the champ for squeezing every drop of performance out of structured data.

⚔️Scikit-learn vs. XGBoost: The Ultimate ML Smackdown Building a killer predictive model in 2025? Which tool’s your MVP?

The hardest part of coding is not the syntax It’s explaining to your future self what you were thinking when you wrote that line 🫠