Following are my personal thoughts on tech, AI, startups and adoption of AI in Health-Care. You could read more about me here
I recently listened to Pieter Levels on the Lex Friedman Podcast, and it was eye-opening. Pieter has built numerous successful micro-SaaS businesses by running his applications on single server, avoiding cloud infrastructure complexity, and focusing on what truly matters: product-market fit.
My desk setup as of writing this post. I've been working from home for the past 1 year and have been slowly evolving my setup to be more ergonomic and efficient.
Also, I want to use this blog to track changes for my setup over time and share with fellow devs / team-mates.
In the ever-evolving landscape of LLMs, I've observed two distinct camps: the doomsayers who predict a dystopian future and the overly optimistic who claim AI has completely transformed their lives overnight. As for me? I find myself somewhere in the middle – cautiously optimistic about the technology's potential while actively seeking ways to harness it for practical, everyday use.
Understanding observability in AI applications, particularly in Large Language Models (LLMs), is crucial. It's all about tracking how your model performs over time, which is especially challenging with text generation outputs. Unlike categorical outputs, text generation can vary widely, making it essential to monitor the behavior and performance of your model closely.
Recently, there has been a surge of enthusiasm surrounding large language models (LLMs) and generative AI, and justifiably so: LLMs have the power to revolutionize entire industries. Yet, this enthusiasm often gives rise to inevitable hype. It appears somewhat counterintuitive to avoid incorporating “AI” into a product’s presentation, considering the immediate market interest it can generate.
Guest Post on Twilio Blog on how we Vincere used Twilio to Reinforce Healthy Habits That Improve the Lives of Underserved Populations
Guest post on AWS Startups Blog
Smoking is still the leading cause of preventable death, and the pandemic made matters worse: cigarette sales increased in 2020 for the first time in over 20 years. There are over 34 million adult smokers in the US, many of whom are lower income. Reaching them and providing effective support at this scale while keeping costs down can only be achieved with the aid of technology and carefully designed patient experience.
Vincere uses incentives. behavioral nudges and evidence-based interventions for healthy behaviors and making healthy choices. This requires us to track multiple events for users and nudging them through reminders/notifications towards better health
Understanding FlexBox can be challenging in the start. But once you start understanding FlexBox it is really awesome. There are some really good resources out there on FlexBox. This one is the best that I found on medium. In this article, I will cover the concepts to create your own configurable Grid framework based on FlexBox.
The JavaScript community is going through the phase of evolution. There are so many frameworks, techniques and ideas that have been going on. It is really exciting but keeping up with it all is really a hard thing to do.