Building AI-native Applications

AI is completely reshaping the way products are built and used. The latest advancements in Large Language Models (LLMs), Generative AI, and other deep learning techniques are redefining the boundaries of what technology can do. Economies of scale and breathtaking amounts of venture capital funding are leading to a democratization of AI resources for companies of any size. At the same time, bridging the gap between cutting-edge research and real-world production software engineering is more challenging than ever. With new computing paradigms, what is the best possible user experience? How can applications benefit from AI capabilities? How can we make AI-enabled software production-ready?

Mar 12, 2023

Generating Subtitles in Real-Time with OpenAI Whisper and PyAudio

Last night, I started watching a recent show which includes dialogues in multiple languages, so naturally, I wondered if I could use OpenAI’s Whisper model to transcribe and translate audio to subtitles in real time. I hadn’t used it in the past, so there was some initial research and fiddling around until it worked, let’s check it out!...Machine Learning

Jun 18, 2023

On the future of coding blogs in a world of LLMs

In recent months, with the general availability of increasingly powerful large language models (LLMs) like GPT4 I’ve noticed a change in my work-related browsing behavior, away from blogs and forums like StackOverflow. The reason for this is simple: Whereas the good old internet required you to get really good at coming up with search queries to find sites describing roughly equal problems and somewhat feasible solutions, the new age of information retrieval is based on natural language as a tool for getting personalized answers. With GitHub Copilot, line-by-line code suggestions reduce lookups for specific libraries. With ChatGPT, understanding complex scenarios for individual cases is remarkably easy. While there is a chance of hallucinations or outright incorrect code being generated, in most cases, it just works....Productivity

Jul 09, 2023

Writing damn good decks with LLMs

I’ve always loathed working on tasks that don’t create actual value for anyone, such as preparing read decks for potential investors. You know that nobody really takes their time to read what you spent days of your life working on, but traditional institutions and government programs with a low propensity to risk-taking want to offer resources to startups, so you better come up with a business plan and read deck. The content is usually strongly based on requirements such as market sizing (TAM, SAM, SOM with CAGR), as well as other carefully-researched (read: 50% market reports and 50% made up) metrics so far from reality, they could as well be omitted....Startups

Jul 26, 2023

From regression to neural networks

While ML is all the rage right now, it is important to understand the fundamentals powering the incredible breakthroughs in fields like natural language processing (NLP), computer vision (CV), and generative AI in the past years....Neural Networks, Deep Learning