Skip to main content
Paul Welty, PhD AI, WORK, AND STAYING HUMAN

· found

Building voice-driven AI applications using LLMs

Building voice-driven AI applications using LLMs

Discover how to create voice-driven AI applications using large language models, focusing on essential components and best practices for success.

The article discusses the potential of voice-driven AI applications and the use of large language models (LLMs) in these applications. It highlights the importance of speech-to-text, text-to-speech, and the LLM itself as the three basic components for building an LLM application. The article also mentions the benefits of running application logic in the cloud, the challenges of phrase detection and endpointing, and the considerations for audio buffer management. It emphasizes the need for reliable and low-latency data flow in voice-driven LLM apps.

Original article: How to talk to an LLM (with your voice)

Why customer tools are organized wrong

This article reveals a fundamental flaw in how customer support tools are designed—organizing by interaction type instead of by customer—and explains why this fragmentation wastes time and obscures the full picture you need to help users effectively.

Infrastructure shapes thought

The tools you build determine what kinds of thinking become possible. On infrastructure, friction, and building deliberately for thought rather than just throughput.

Server-side dashboard architecture: Why moving data fetching off the browser changes everything

How choosing server-side rendering solved security, CORS, and credential management problems I didn't know I had.

The work of being available now

A book on AI, judgment, and staying human at work.

The practice of work in progress

Practical essays on how work actually gets done.

Most of your infrastructure is decoration

Organizations are full of things that look like governance, strategy, and quality control but are actually decorative. The trigger conditions nobody reads, the dashboards nobody checks, the review processes that rubber-stamp. When you finally audit what's functional versus ornamental, the ratio is alarming.

The machine is eating faster than you can feed it

Sixty-three issues closed across thirteen projects in one day. Four milestones completed. And the hardest problem wasn't building — it was keeping up with what you've already built.

The proxy problem

Every organization has this problem: knowledge locked inside one person's head. Today I accidentally designed a solution — and it has nothing to do with documentation.

Jasper is a useful tool for developing employee training.

Transform employee training with Jasper by aligning programs to business goals, engaging diverse learning styles, and using innovative methods for success.

The IMF warns about AI’s impact on inequality

IMF warns AI could deepen global inequality, urging policymakers to implement safety nets and retraining programs to protect vulnerable workers.

It’s going to take a century for artifical intelligence to be able to perform most human jobs. But there are going to be some key developments during the next decade.

Explore how AI will transform jobs in the next decade, from enhancing security to automating coding, reshaping the future of work.