Hanakano, by Gordy
Welcome to Hanakano! Here are the latest posts; see all posts here.
How many developers does it take to build a chatbot? Would you be surprised if I told you that developers are not the most important role on a chatbot project team? In this post we go through the who to the what, where the what is a chatbot project and the who is you!
Core team You got your business people and your technical people. They really do require two very different sets of skills, so one person shouldn’t be doing both jobs.
You never get a second chance to make a first impression. Chatbots are no different, and the first interaction with users sets the tone for the entire user experience. When crafting this initial message, I recommend to keep the 3 C’s in mind: Context, Capabilities, and Call to Action.
Context Context refers to who and where the bot is; it is the foundation upon which everything about the bot is built.
Two weeks ago, I found myself with a new desktop PC and a world of possibilities. And of that whole world of options, I chose NixOS. Not to brag, but I am a burgeoning script kiddie: a few months ago I made the switch from the default MacOS Terminal to Kitty, from VS Code to Neovim, and from Finder to lf. I can cat and grep and have a handful of zsh aliases, so installing NixOS shouldn’t be too hard, right?
The other day on Discord, I was trying to convince someone that they should not build a chatbot. Crazy, right?
But here’s the thing: Chatbots are an interface, just like buttons and tabs, and just like other interfaces, there are times when they do more harm than good. Just imagine if your phone’s settings app was a chatbot, and you had to type in a message or a talk to it just to enable or disable airplane mode?
When a big organization or government is looking at using an AI system, trust is often on their minds. There’s a lot of talk about AI hallucination and lies- like this NY Times article saying GPT-4 hallucinates 3% of the time or this paper that shows GPT-4 can engage in insider trading and then lie about it. How can folks who work in AI build systems that big clients can trust?