How I Plan, Track & Organize My Finances In Notion
Plus, more links to make you a little bit smarter today.
Making a new video every week until I make $5 million - Week 2
Best Practices for Using Terraform on Infrastructure as Code
Terraform, developed by HashiCorp, has become one of the most popular tools for Infrastructure as Code (IaC). It allows you to define, provision, and manage cloud infrastructure for your projects by using declarative configuration files. As teams scale and infrastructures grow more complex, following best practices in Terraform becomes critical for maintaining solid and secure infrastructure. This article outlines the key best practices that ensure you’re getting the most out of Terraform!
LauraGPT: Listen, Attend, Understand, and Regenerate Audio with GPT
Generative Pre-trained Transformer (GPT) models have achieved remarkable performance on various natural language processing tasks, and have shown great potential as backbones for audio-and-text large language models (LLMs). Previous mainstream audio-and-text LLMs use discrete audio tokens to represent both input and output audio; however, they suffer from performance degradation on tasks such as automatic speech recognition, speech-to-text translation, and speech enhancement over models using continuous speech features. In this paper, we propose LauraGPT, a novel unified audio-and-text GPT-based LLM for audio recognition, understanding, and generation. LauraGPT is a versatile LLM that can process both audio and text inputs and generate outputs in either modalities. We propose a novel data representation that combines continuous and discrete features for audio: LauraGPT encodes input audio into continuous representations using an audio encoder and generates output audio from discrete codec codes. We propose a one-step codec vocoder to overcome the prediction challenge caused by the multimodal distribution of codec tokens. We fine-tune LauraGPT using supervised multi-task learning. Extensive experiments show that LauraGPT consistently achieves comparable to superior performance compared to strong baselines on a wide range of audio tasks related to content, semantics, paralinguistics, and audio-signal analysis, such as automatic speech recognition, speech-to-text translation, text-to-speech synthesis, speech enhancement, automated audio captioning, speech emotion recognition, and spoken language understanding.
Batteries: how cheap can they get?
How dirt cheap batteries will completely transform our electricity grid, paving the way for solar and wind and replacing grid reinforcements with grid buffers.
How I Plan, Track & Organize My Finances In Notion
As a product creator, I love creating micro tools to improve my personal and work productivity.
This quarter, I’m focused on improving how I track money and manage personal finances.
Large Language Models in Finance: A Survey
Recent advances in large language models (LLMs) have opened new possibilities for artificial intelligence applications in finance. In this paper, we provide a practical survey focused on two key aspects of utilizing LLMs for financial tasks: existing solutions and guidance for adoption.
First, we review current approaches employing LLMs in finance, including leveraging pretrained models via zero-shot or few-shot learning, fine-tuning on domain-specific data, and training custom LLMs from scratch. We summarize key models and evaluate their performance improvements on financial natural language processing tasks.
Second, we propose a decision framework to guide financial professionals in selecting the appropriate LLM solution based on their use case constraints around data, compute, and performance needs. The framework provides a pathway from lightweight experimentation to heavy investment in customized LLMs.
Lastly, we discuss limitations and challenges around leveraging LLMs in financial applications. Overall, this survey aims to synthesize the state-of-the-art and provide a roadmap for responsibly applying LLMs to advance financial AI.