Best English Chat App

[Wynellewe Gr]

Hi @marc2 - I created a how-to video on implementing a free peer-to-peer video chat which may be of interest (link below). I also have some videos on creating private messaging functionality but there are so many design nuances with messaging/chat to consider. Below are screenshots of both Messaging and Chat plugins; look at those as they may help you figure out what works for your needs.

Hi @kenlaji - thanks for reaching out. I did see that Gruveo made an update. Unfortunately they no longer allow the service for free. Let me take a look at creating a new video though using their free demo credentials.

Got it working in the Hobby plan by manipulating and overriding the CCS for the Chat. They also have video, but I have not enabled it. I think they are using Firebase which is a fast key/value store, perfect for offloading Bubble.

Look at the best 10 chat API and messaging SDK providers classified based on their Communication Style, Features, Cloud Service, Backend/Front-end Technologies, Technical stacks, Pricing and reputation in the market.

With the ever-flourishing of messaging users to grow around 2.48 billion users which are predicted to reach the Global Chat App market value around $100 billion in 2019. Through a rich variety of features and third-party integrations have emerged to...

Mirrorfly - Chat,Video,Voice Solutions Provider (One Time Licence Cost SaaP)
Apphitect - Instant Messaging Solution Provider
Pubnub - Chat,Video,Voice Solutions Provider (Monthly Re curing SaaS)

Whenever you are looking the best chat app, here you can use the best video chatSDK app to use for long purpose. It makes everything cool to consider right solution to build chat app. -video-conferencing-software/

Asking AI to design the best prompt for you to use with AI to achieve your desired AI behavior, and then asking it to further improve it, before using that very same prompt with AI, is what I describe as meta-prompting."

I know, I saw it a few days ago, thanks for sharing! Here is a related quote from that paper that draws some contextual parallels to my definition of Meta-prompting from the book, albeit in a far less technical manner than this, and perhaps only coincidentally. Of course, the context is totally different:

Hello everyone
I would like to know which database you prefer for chat applications?
I do not know if traditional databases are preferable or maybe there is some other option that works well with Elixir / Phoenix?

We are getting very good results with Risk and its integrated SOLR search functions. There is a small delay while SOLR indexes update to the Risk database, but otherwise very scaleable with multiple writers and readers. It really depends on how complex your data model is, and how much traffic you expect. PG will be fine for anything under 1k simultaneous users for sure, and can go higher with effort.

ChatGPT apps are software made for smartphones, tablets, or computers that act as a medium between you and ChatGPT. You can use them to ask for help with meal planning, get clarifications about complex topics, and just randomly chat with them.

ChatGPT (Chat Generative Pre-training Transformer) is an AI chatbot based on large language models (LLMs) that uses natural language processing (NLP) and machine learning (ML) to communicate like a human.

Usually, the ChatGPT apps are versatile. They aim to enhance productivity but they also help businesses with content creation, or draft engaging social media posts and act as virtual tutors for students.

In the quest to build the best possible products for customers, Guru teamed up with OpenAI to create Guru GPT, a private, AI-powered assistant that searches across all your connected internal content and information directly within ChatGPT. Create context-based content and seamlessly draft Guru Cards from your conversations.

Like other chatbots, it works through text-based chat, meaning that it uses NLP to create AI-generated responses or commands in real time. Moreover, Chat AI provides easy access to the best AI models currently available, such as AI21, Cohere, and Hugging Face.

Fireflies.ai is an AI voice assistant that helps you summarize meeting notes and transcribe them by integrating with leading conferencing platforms like Zoom, Google Meet, Webex, and Microsoft Teams.

Fireflies automatically captures recordings and syncs your notes, recordings, and transcripts with your existing systems, such as Zapier, HubSpot, etc. But Fireflies can also transcribe pre-recorded audio files and summarize them for you.

Frank combines ads-free search with text and image creation to bring you a powerful chat AI bot that uses ChatGPT and voice technology to understand human language and bring you the most relevant results.

TextAI has 110+ AI tools you can select from, for everything from writing emails or finding synonyms to writing an apology message. Whether you want to write a love letter or handle a difficult conversation, TextAI has your back.

Most modern businesses and organisations today are using some kind of team chat application. The usual suspects are Slack, HipChat, Discord, Mattermost, Rocket Chat, Riot, and Gitter, to name a few. While chat is immediate and primarily synchronous,...

Absolutely, you add the chat-integration plugin, then set your conditions between categories and tags to notify to any number of chat services. This works totally awesome for our community. Different levels of customization are available for various chat services supported. Enjoy, and happy holidays.

I'm building a chat app and I want a full history off all messages ever sent in the chat conversation. At the moment I am storing each message as a single row in a table called 'messages'. I am aware that this table could grow huge as even small messages like 'Hi' would have their own database record.

If you are still worried for the size, you could apply some optimizations to group messages, like adding a buffer to your application that you only push after some time (like 1 minute or so); that way you would avoid having only 1 line messages

You could create a database for x conversations which contains all messages of these conversations. This would allow you to add a new Database (or server) each time x exceeds. X is the number conversations your infrastructure supports (depending on your hardware,...).

The problem is still, that there may be big conversations (with a lot of messages) on the same database. e.g. you have database A and database B an each stores e.g. 1000 conversations. It my be possible that there are far more "big" conversations on server A than on server B (since this is user created content). You could add a "master" database that contains a lookup, on which database/server the single conversations can be found (or you have a schema to assign a database from hash/modulo or something).

You know the one. Like I said, it's a sacred construct. A lifeline to your best friends, an outlet for the thoughts and questions and breadcrumbs of internet humor that you just can't send to anyone else. A constant companion, antagonist, distraction, delight.

The dataset for this project is, of course, my Group Chat. Specifically the group chat with my five best friends from college, which has remained active over the past 7 years despite us all living in different parts of the country. How active?

I had no idea what this db looked like, or how tables related to one another. I was, to be honest, having a Bad Time trying to monkey around with it using sqlite3 on the command line, so I dumped the data into Hex so I could explore it more easily and extract just the messages of interest from my group chat.

After some quick joins and a little case statement to manually get names from phone numbers, I had my list of 488,000 messages in a nice readable format. This is more than enough data to fine-tune a model: the Stanford alpaca project used just 52,000 example prompts. I just had to massage it into the right format for an LLM.

Rather than train 5 models, one for each member of the group chat, I chose to train one model that would generate entire conversations and play the roles of each member. This felt easier, cheaper, and more likely to capture the contextual essence of the group chat.

If you want to run this process yourself against your chat.db, you can clone this Hex project and do it mostly automatically. Be advised though: This requires uploading your chat.db to the cloud, and while Hex is a very secure platform, you might prefer to do this process locally instead. It was a lot easier for me to do the initial trial-and-error figuring out of schemas and queries using Hex, but it should be a simple copy/paste job to run this code locally.

I picked up this project right after the Stanford Alpaca project released their code for fine-tuning LLaMa, and it looked like the perfect choice for a small homebrew model. This was state-of-the-art at the time, 3 weeks ago! There are now a TON of other projects for running small LLaMa based LLMs for cheap, like llama.cpp and Alpaca-LoRa. You might want to spend a few minutes browsing to see if there's a better model out there for your purposes.

I already had all this in an S3 bucket, so it was easy to just download to my machine with the s3 CLI. If you don't have LLaMa weights, there's a ton of places to get them including the official form.

Once you've got your custom prompt dataset and your converted weights, you can begin a training run with the following command. Replace the placeholders that look with your ports, directories, data paths, etc. It should take just a few hours.

Quick plug: I cannot say enough good things about Modal, a tool that lets you write code locally and deploy it to the cloud without managing any infrastructure or config. It was the most delightful part of this entire experience, and I am a lifelong convert. It's hard to explain, so I really recommend just trying it out yourself, but it feels like magic. Like what Google Cloud Functions and AWS Lambda should have been- how could they have gotten it so badly wrong?

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