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"Just fine-tuned LLaMA 3.2 using Apple's MLX framework and it was a breeze! The speed and simplicity were unmatched. Here's the LoRA command I used to kick off training: ``` python lora.py \ --train \ --model 'mistralai/Mistral-7B-Instruct-v0.2' \ -

Meta has released Llama 3.1, the first frontier-level open source AI model, with features such as expanded context length to 128K, support for eight languages, and the introduction of Llama 3.1 405B. The model offers flexibility and control, enabli

When AI changed the rules, cloud computing had to change too. And that’s exactly where Oracle took the lead. Most cloud giants like AWS, Azure, and GCP still rely on virtualization — where resources like CPU, GPU, and memory are shared across users.

LLM Post-Training: A Deep Dive into Reasoning LLMs This survey paper provides an in-depth examination of post-training methodologies in Large Language Models (LLMs) focusing on improving reasoning capabilities. While LLMs achieve strong performance

Why Grok AI Outperformed ChatGPT & Gemini — Without Spending Billions In 2025, leading AI companies invested heavily in R&D: ChatGPT: $75B Gemini: $80B Meta: $65B Grok AI, developed by Elon Musk's xAI, raised just $10B yet topped global benchmar

3B LLM outperforms 405B LLM 🤯 Similarly, a 7B LLM outperforms OpenAI o1 & DeepSeek-R1 🤯 🤯 LLM: llama 3 Datasets: MATH-500 & AIME-2024 This has done on research with compute optimal Test-Time Scaling (TTS). Recently, OpenAI o1 shows that Test-

💀 99% of AI Startups Will Die by 2026 – Here's Why 1. LLM Wrappers ≠ Real Products Most AI startups are just pretty UIs over OpenAI’s API. No real backend. No IP. No moat. They're charging ₹4,000/month for workflows that cost ₹300 if done directly

🚨 Open AI is an Wrapper👀🤯 Hot take, but let’s break it down logically: OpenAI is not a full-stack AI company — it’s a high-level wrapper over Azure and NVIDIA. Here’s why that matters 👇 🔹 1. Infra Backbone = Microsoft Azure Almost 90%+ of Op

"A Survey on Post-Training of Large Language Models" This paper systematically categorizes post-training into five major paradigms: 1. Fine-Tuning 2. Alignment 3. Reasoning Enhancement 4. Efficiency Optimization 5. Integration & Adaptation 1️⃣ Fin

Top 10 AI Research Papers Since 2015 🧠 1. Attention Is All You Need (Vaswani et al., 2017) Impact: Introduced the Transformer architecture, revolutionizing natural language processing (NLP). Key contribution: Attention mechanism, enabling models