So We Shipped An AI Product. Did it Work?
- Phillip Carter tl;dr: “Like many companies, earlier this year we saw an opportunity with LLMs and quickly but thoughtfully started building a capability. About a month later, we released Query Assistant to all customers as an experimental feature. We then iterated on it, using data from production to inform a multitude of additional enhancements, and ultimately took Query Assistant out of experimentation and turned it into a core product offering. However, getting Query Assistant from concept to feature diverted R&D and marketing resources, forcing the question: did investing in LLMs do what we wanted it to do?”featured in #454
LLMs Demand Observability-Driven Development
- Charity Majors tl;dr: “Many software engineers are encountering LLMs for the very first time, while many ML engineers are being exposed directly to production systems for the very first time. Both types of engineers are finding themselves plunged into a disorienting new world—one where a particular flavor of production problem they may have encountered occasionally in their careers is now front and center. Namely, that LLMs are black boxes that produce nondeterministic outputs and cannot be debugged or tested using traditional software engineering techniques. Hooking these black boxes up to production introduces reliability and predictability problems that can be terrifying.“ Charity believes that the integration of LLMs will necessitate a shift in development practices, particularly towards Observability-Driven Development, to handle the nondeterministic nature of these models.featured in #450
Lessons From Building A Domain-Specific AI Assistant
- Eric Liu tl;dr: Eric Liu, Engineer at Airplane, discusses how the Airplane team built a domain-specific AI assistant, the lessons they learned along the way, and what's next for the future of AI assistants.featured in #447
featured in #446
5 AI Tools For Developers To Help Boost Your Productivity
- Lewis Cianci tl;dr: (1) Phind: A developer-focused search engine that provides detailed answers and related links for coding questions. (2) Bloop.ai: Helps developers understand the structure of GitHub repositories quickly. (3) Codeium: Offers real-time code suggestions within various IDEs. (4) ColPat: Design tool that helps in creating color palettes and themes for apps and websites. (5) RegExGPT: Generates regular expressions based on natural language prompts.featured in #444
featured in #443
featured in #441
AI Is Better At Writing Code Than Reading Code. Here’s Why.
- Daksh Gupta tl;dr: Have you ever been handed a new codebase at your job and been completely lost? I have. While LLMs have been generating code for months, the problem of reading, understanding and navigating existing large codebases remains unsolved. In this article, I explore why.featured in #438
What We Don't Talk About When We Talk About Building AI Apps
- Vicki Boykis tl;dr: Vicki shares her experience and pain points when building AI applications, highlighting several aspects often not discussed in conversations: (1) Slow iteration times, (2) Build times, (3) Docker images, and more.featured in #432
Five Reasons To Trust Prolific Participants With Your AI Training Tasks
- George Denison tl;dr: Finding engaged and reliable participants for your AI training tasks can be a challenge. Here are five reasons why you can trust Prolific participants with your AI training tasks. Prolific participants are: (1) Engaged. (2) Diverse. (3) Treated fairly and ethically. (4) Understand their crucial role. (5) Satisfied.featured in #430