I have started using large language models for book recommendations. I can be very specific about what I am looking for and the recommendations are hyper personalized. If you use some sort of tool that pairs LLMs with realtime data like Gemini the results are even better.
So is this going to be another clusterfuck like 10DLC? I am glad our company stuck with our guts and intentionally decided not to go outbound, but I almost feel bad for the startups that were banking on full outbound.
Looks like the FCC basically killed outbound AI calling companies like Air.ai, and does not seem to affect inbound companies like ours (https://echo.win)
Interestingly they explicitly mention AI generated voices, does that mean voices generated by traditional TTS engines are fine?
Our company https://echo.win/ provides inbound phone call automation and management using AI for businesses. Generative voices are going to add a lot of value to our product.
I have had luck with doing this on GPT4 with careful prompting, but GPT 3.5 is pretty reluctant to respond with anything other than straight up conversational answers.
I just saw that Azure OpenAI service has a SLA and OpenAI does not. I thought they would have separated the infrastructure for free ChatGPT users and paying API customers.
When the big earthquake in Nepal happened in 2015, I was working with a volunteer organization called Translators Without Borders to help with translation during relief efforts. Since I was in the USA I could not contribute back physically, so this was the next best thing.
My goal was to help volunteers that were in the field in Nepal communicate in English -> Nepali and back. Even though this was somewhat effective, there was still a communication gap because most people in Nepal in remote parts could not even read in Nepali.
I looked around for solutions but couldn't find any Nepali Text To Speech solutions. The builder brain in me fired up and I decided to build a Nepali Text To Speech engine using some of the groundwork that was laid by Madan Puraskar Pustakalaya (Big Library in Nepal) which they had abandoned halfway.
I spend all night hacking along to build a web app that let the volunteers paste translated text and have it spoken. The result was https://nepalispeech.com/ and the first iteration of this was built in just 13 ish hours.
I hope the people that got affected by the earthquake are in a better situation now.
This reminds me of AutoIt. It was a scripting language for windows that helped with UI automation, and it was one of the things that opened up the world of programming to me when I was 12.
I quit Amazon a couple weeks ago to start a startup, and I felt like I was reading my own story at some points. The tooling was the biggest thing dragging me back. It's hard to get excited about what you are building when you have to wait 2-4 minutes to preview your changes when most industry standard tools / stacks can do it instantly.
I left this feedback a bunch of times with different people in the org and I really hope they scrap their janky "frameworks" and dev tooling. They should just move to more standard open source tools options that evolve and get better quicker than barely maintained internal tooling.
Open Source tools also have bigger communities and resources online to debug and solve issues, compared to mediocre documentation from internal wiki. If absolutely needed, make thin wrappers above open source tools. Some other teams within Amazon had the luxury of using better tools, but I bet a lot of people are in the shoes I was in.
I had to switch from OpenAI's models to GPT-J because OpenAI's policies were restrictive. How did you get around that? My guess is that since you are only outputting Emoji's it might be allowed.
Let me be the devil's advocate here, they basically have a map of your interests and likes and things that grab your attention. I am not saying China is going to do this, but if a malicious actor wanted to abuse this data, they could potentially craft curated videos that feed your biases using your interests and potentially make you believe any propaganda they wanted.
Now imagine a machine learning model doing this instead of humans, it could infer so much more information about how you think and how your brain works based off of that data and do even more harm. Ever thought about buying something and an ad for that casually pops up in your feed? It is going to be similar to that, but instead of ads for a product it could be some misinformation that could cause paranoia (just an example).
What most people here complaining about the "40 mph" limit do not realize is that below 40mph is where it's hardest to implement proper self driving, as majority of below 40mph driving will be in city streets with pedestrians, frequent turns and more variables. Anything higher will generally be in highways where lane assist cruise control systems are already more than good enough.
I will gladly take a system that can drive itself confidently under 40mph in city, and use basic lane assist (and maybe lane changes and exit ramps like in Teslas) for highways. Cant wait!
Next.js creates a good setup to create a React frontend application with server side rendering support out of the box. Pair that with Vercel and you get an amazing deployment environment with CDN, cloud functions, edge functions (middleware) server side rendering and more without much hassle.
Tailwind let me quickly create any UI I want, and Daisy helps me reduce the amount of time needed to style basic elements like inputs and all.
Firebase lets me get a low latency real time data source for my application, that can scale infinitely. It also handles some other parts of building an application that normally take a lot of time: authentication, storage management etc. And the pricing is really really cheap once you consider how much it costs to create an infrastructure that scales as well as firebase does, unless you model your data wrong and end up using a lot of db read/write cycles unnecessarily.
The backend API piece is not missing, you can use either Next.js API or firebase functions for backend piece, I use those for things like stripe billing backend etc.
This stack is enough for most projects out there, and when its not enough its flexible enough that you can integrate it with other things. And that timeline I mentioned was for a production ready MVP.
Similar for me, Next.js / Daisy + Tailwind / Firebase means I can whip out a MVP or prototype in weeks instead of months. Love the increase in productivity first tooling in web development ecosystem.
Sad to see all the non Chinese open source models being at least one generation behind.