Large language models (LLMs) transform problem-solving by enabling natural, iterative conversations, ideal for fields like healthcare and legal services. They scale expertise and accessibility but face challenges like reliability and cost.
Labels like "junior" and "senior" often fail to capture a developer's true value. True impact comes from initiative, ownership, and proactive contribution—not just years of experience. We value "pull developers" who actively seek improvement, ensuring the product's success beyond assigned tasks.
A technical moat is often seen as a product's defensive edge, but does every product really need one? For AI products, the choice between building proprietary tech or leveraging existing solutions like OpenAI is complex. True value lies in solving customer problems—not just in owning the technology.
Many product companies are eager to leverage tools like ChatGPT. But how do you go from experimenting to running in production? Let's explore choosing the right large language model (LLM) to understand hosting options, ensuring an efficient and sustainable AI implementation.
Today's digital age may seem hyper-connected, but remote work often lacks genuine human connection. We’ve created a culture of cohesion using dedicated bonding, safe spaces, and custom tools to ensure no one feels isolated.
Being an expert isn't about always having answers. It's about balancing confidence and humility, knowing when to admit you don't know, and valuing collaboration. True expertise grows through listening, learning, and contributing meaningfully, not just knowing everything.
"use cache" is an experimental Next.js 15 Canary feature and may change. In this tutorial, we explore "use cache" for caching, covering fetching and inserting comments with Drizzle to reduce database calls and improve performance while maintaining control.
A fractional CTO can transform a company by addressing urgent technical issues while developing a long-term strategy. Beyond firefighting, a fractional CTO focuses on building a sustainable, self-sufficient team that can thrive independently after their involvement ends. This is how to get started.
In recent audits, I've noticed a less obvious pattern: overly talented start-up teams. While smart, experienced teams thrive early on, they struggle to scale. Scaling requires structure, documentation, and room to grow talent in-house.
