Our inboxes contain dozens (if not hundreds) of newsletters we subscribed to during moments of curiosity, but we seldom read most of them. Manually unsubscribing is tedious: open each email, scroll to the bottom, click unsubscribe, confirm … repeat 50+ times.

This post covers a personal project developing an AI agent using the ReAct pattern to analyse newsletters I have subscribed to and recommend the ones to unsubscribe based on my reading behaviour.

While large language models (LLMs), such as GPT-4 and Claude, are capable of extracting structured information from text, small language models (SLMs) have historically struggled to do so reliably. Previously, the only viable approach was to fine-tune a larger open-weights model using distillation. A week ago, there was an announcement, which appears to an alternative.

Researchers have identified a cyber threat known as slopsquatting, also referred to as package hallucination, in which malicious actors exploit large language models (LLMs) tendency to generate non-existent package names during code generation. These hallucinated package names, when registered by attackers with malware payloads, create a new vector for software supply chain attacks—particularly within AI-assisted development workflows.

Large language models (LLM) are made up of billions of parameters, thus posing challenges when loading them onto GPU memory for model inference or fine-tuning. This post briefly explains the challenges and describes a solution to load Mixtral 8x7B, a State-of-the-art (SOTA) LLM, onto consumer-grade GPUs, followed by using the model for NLP tasks such as Named Entity Recognition (NER), Sentiment Analysis, and Text Classification.

A few simple ways to achieve 1.3x to 970x speedup of Python for loops with minimal effort.