👋 I'm Ananya, a CS student documenting my journey to become an AI Engineer.
Building projects, solving DSA, exploring AI/ML, joining hackathons, and sharing everything I learn along the way.
Learning in public. Building consistently. 🚀
Curious if others here have already dug into AIKosh. What did you find that felt uniquely useful (or surprisingly missing)?
Link:- https://t.co/qNgRnan4Wj
@OfficialINDIAai
Just spent some time exploring AIKosh (India’s official AI datasets & models platform) and I’m genuinely fascinated by how Indian it feels.
There are things that feel rooted in the actual texture of the country.
A couple that stood out:
https://t.co/qNgRnan4Wj
This diversity is what makes it interesting. From datasets which can be used to solve civic issues to commerce and finance. This feels like a utopia for developing solutions to India-centric problems.
Came across it while hunting ideas IBM Z Datathon
A little late, but here's my Day 1 submission for the Girls Who Yap 2.0 Fellowship 🤍@connectdoradao
I hope this is the first of many posts documenting my fellowship journey, lessons, and future tasks. Here's to building, learning, and growing.
Still doesn’t feel real.
I’m in GirlsWhoYap Fellowship 2.0 with @connectdoradao
Chaos, late-night builds, and strangers becoming family.
Stepping in as a Builder. #MainCharacterMonth
PS: Just realised DoraDAO is named after Dora’s relentless curiosity. That stays with me.
@misschopra Java
Primarily because it was my first programming language and it helped me build many concepts from scratch which I could have missed with Python
This is just the beginning.
Next up: Tokenization (how text becomes tokens)
Full repo with explanations + code: in the top post
Would love your thoughts! 🧵
I'm learning AI Engineering by building core concepts from scratch.
First module: A tiny next-word predictor that shows how LLMs actually generate text.
https://t.co/JIEc74o0ot
Building intuition, one bite at a time.
#AI#MachineLearning#LLM#LearnInPublic
This is the core intuition behind modern LLMs:
They don’t “understand” language magically. They predict the next token based on patterns learned from massive data.