LLMs
Unsloth and NVIDIA Partner on Faster LLM Fine-Tuning
Unsloth leverages custom CUDA kernels and 4-bit quantization to deliver up to 30x faster LLM fine-tuning with significantly reduced memory overhead.
Author
Author: Praveen Pandey
Software engineer and AI researcher with 10 years of experience in machine learning systems and distributed computing. Writes about LLMs, agentic AI architectures, developer tooling, and open-source ML.
AI Research
How Unsloth Breaks the VRAM Wall in LLM Fine-Tuning
Unsloth revolutionizes LLM fine-tuning by bypassing the VRAM wall through custom CUDA kernels, enabling long-context training on consumer-grade hardware.
AI Research
Google DeepMind Scales AlphaEvolve for Algorithm Optimization
AlphaEvolve leverages Gemini models and evolutionary computation to automate algorithm discovery, significantly optimizing Google's production infrastructure.
AI Research
My Take on AlphaEvolve: The Engineer’s Role After AI Coding
AlphaEvolve shifts LLMs from simple coding assistants to autonomous optimization engines using Gemini Flash and Pro to evolve high-performance algorithms.
AI Research
AlphaEvolve and the Changing Role of the Senior Engineer
AlphaEvolve shifts AI from coding assistant to autonomous optimization engine, using Gemini models to evolve high-efficiency code through genetic selection.
AI Research
Turning Raw LLM Activation Vectors into Readable Prose
Anthropic's Natural Language Autoencoders (NLAs) bridge the gap between raw activation vectors and human-readable prose to decode LLM internal states.
AI Research
How Natural Language Autoencoders Decode LLM Embeddings
Natural language autoencoders bridge the gap between high-dimensional math and human cognition, transforming opaque LLM activations into readable semantic insights.