Browsing: LLMs

As LLMs integrate into our daily lives, understanding their behavior becomes essential. In our ongoing efforts to study model behavior and alignment, we present this work…

You probably use Google on a daily basis, and nowadays, you might have noticed AI-powered search results that compile answers from multiple sources. But you might…

Image by Author   # Introduction  Remember when building a full-stack application required expensive cloud credits, costly API keys, and a team of engineers? Those days are…

Transformers power modern NLP systems, replacing earlier RNN and LSTM approaches. Their ability to process all words in parallel enables efficient and scalable language modeling, forming…

Image by Author   # Introduction  Last month, I found myself staring at my bank statement, trying to figure out where my money was actually going. Spreadsheets…

When running LLMs at scale, the real limitation is GPU memory rather than compute, mainly because each request requires a KV cache to store token-level data.…

Image by Editor   # Introduction  Hallucinations are not just a model problem. In production, they are a system design problem. The most reliable teams reduce hallucinations…

Conclusion Several larger conclusions emerge from this test case. The two models that drew from curated databases of experimental literature, NotebookLM and our custom-built tool, outperformed…

In recent times, many developments in the agent ecosystem have focused on enabling AI agents to interact with external tools and access domain-specific knowledge more effectively.…

Google has officially released Android Bench, a new leaderboard and evaluation framework designed to measure how Large Language Models (LLMs) perform specifically on Android development tasks.…