What does it mean to create dataset for ChatGPT according to you? My view is “you understand every personas and how tricky they can prompt a question” and how flawless model can respond to it… 😇

reddit.com/r/tensorflow/s… #DeepLearning #AI

Came across this library for building AI agents with semantic sense of enforcement. Pretty cool 😎 parlant.io

Excited to share Aryabhatta 1.0, our leading model that scores 90.2% on JEE Mains, outperforming frontier models like o4 mini and Gemini Flash 2.5 Trained by us at AthenaAgent , in collaboration with Physics Wallah (PW), using custom RLVR training on 130K+ curated JEE problems

GPU costs holding back your killer idea? Apply for free inference for your startup. (credits worth $100K) - Credits apply to open/closed source models (Anthropic, etc) - Use thousands of models with a single API Don’t just dream it. Build it. 👇

Indiranagar Kicks in :)

Some fun things people may have missed from Gemma 3 270M: 1. Out of 270M params, 170M are embedding params and 100M are transformers blocks. Bert from 2018 was larger 🤯 2. The vocabulary is quite large (262144 tokens). This makes Gemma 3 270M very good model to be hyper

The growth of LLM context length with time: - GPT-3.5-turbo → 4k tokens - OpenAI GPT4 → 8k tokens - Claude 2 → 100k tokens - Llama 3 → 128k tokens - Gemini → 1M tokens Let's understand how they extend the context length of LLMs:

diabrowser.com/skills/graham Evaluate a person online like investor Graham Duncan. Just use the prompt and see the :)

What we are certainly missing in the AI first space is “precision engineering” Solving for it puts your upfront :)

Every Human is like an LLM, we just have to know right way to prompt and simulate :)

Revived to the core 🍺

coupling Dating + Background verification = Neo modernism That’s it🫨 #viral

Next month is product release: PM: Lets dogfood it DEV: God forbid ...

What should i ask to anthropic startup’s team? Anthropic Lightspeed

Google NotebookLM is what you need for your life Google DeepMind Google

You're not depressed, you just lost your quest.

Finding God at MidNight

The laziness to aggregate high quality data pushes the demand of LLMs(API calls) Why? - We can train SLM on high quality data and serve at scale in long run. -Data under our control, model weights under our control. But: We are lazy 🥸

🫥