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)
The agent-shaped org chart
Every real org has the same topology: principal, role-holder, specialists. Staff AI maps onto it, node for node, and the cost collapse shows up in the deliverables that were always just human-handoff overhead.
AI as staff, not software
Two frames for what AI is doing to work. The tool frame makes tools smarter. The staff frame makes roles unnecessary. Those aren't the same product, the same company, or the same industry.
Knowledge work was never work
Knowledge work was always coordination between humans who couldn't share state directly. The artifacts were never the work. They were the overhead — and AI just made the overhead optional.
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.
Shopping is the last mile
Every meal planning app treats cooking as the hard problem and shopping as a logistics detail. They have it backwards. Cooking is mostly solved. Shopping is the last mile.
Watch what they buy, not what they say
Forms ask people to declare preferences. Receipts record what they did. The gap between the two is where revealed preference lives, and it's wider than most product teams admit.
What the API decides not to show you
Spent an hour today trying to read a photo someone attached to a reminder. The bytes are right there on disk. Apple won't let me see them. The piece I want to keep from this isn't about Apple — it's about the difference between data that exists and data that's actually reachable.
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.