(Almost) Every infrastructure decision I endorse or regret after 4 years running infrastructure at a startup – in my current role as a CTO of an early-stage startup, I make many choices about tools, programming languages, architecture, vendors, etc. This retrospective view was fascinating not only for the tools themselves but also for the arguments.
Everything You Can Do with Python’s textwrap Module – I have used Python for more than 10 years and never heard of textwrap model. Maybe you, too, haven’t heard of it.
https://towardsdatascience.com/everything-you-can-do-with-pythons-textwrap-module-0d82c377a4c8
It was never about LLM performance – I couldn’t agree more. The performance gaps between different LLMs are becoming neglectable. Now, it is about the experience you build using those models and the guardrails you put in to ensure the experience.
https://read.technically.dev/p/it-was-never-about-llm-performance
How to build an enterprise LLM application: Lessons from GitHub Copilot – the post ends with a summary of 3 key takeaways –
- Identify a focused problem and thoughtfully discern an AI’s use cases.
- Integrate experimentation and tight feedback loops into the design process
- As you scale, continue to leverage user feedback and prioritize user needs
Those takeaways are general and correct for almost every product launch I can think of. The post provides more concrete tips for LLM applications. It is interesting to read about a product on such a scale that I use it on a daily basis.
Speaking for Hackers – public speaking is hard. From choosing a topic, submitting a CFP, preparing your talk and slides, and wrapping it all up. Every step can be tricky, and each of us has other things that are harder for us. This site provides excellent materials for all the parts before, during, and after the talk, making it easier to step out of our shells and share the knowledge.
Tom Ron 