shreeyans.vichare

Implemented a web calling interface. So if maybe someone doesn’t want to download the andriod app, they can use the voice calling interface. Turns out its really hard to do public iOS testing unless you have a paid Apple Developer account. So now the product consists of 2 services, the mobile app and the calling simulator. It would’ve been cool if I could rent a toll free number and connect it there but that’s fine for now. Finally everything that was planned to be implmented has been completed. Really feel good about what the PragatiConnect Product has come to be!!!!

Trying to get the price estimator to work so people can upload images of their craft and get a fair price for it. It’s not been processing quite properly yet, but at least the user can add any image and send it to the backend, where the AI should analyse it. Also got the email authentication working, through the SMTP library so it sends and validates OTPs.

Added support for a few more Indian languages (Gujarati and Punjabi). The backend is a lot to configure, but testing has been relatively smooth. The folder and all the files are very messy and hard to keep track of. The text-to-speech and speech-to-text keeps breaking often, trying to work on that with the Google API’s. The voice assistant would lose context everytime a new message was being sent, now the memory persists until either the language is chosen or the voice assistant session is closed.

Worked on the iOS and Andriod application. We have a lot of features to implement, currently working on one thing at a time. The frontend UI looks good, the backend AWS and Google Cloud part is a little tricky to deploy. Regardless we’re moving forward!!

Seems like I’ve hit a wall, rather the wall has hit me. The model is producing incomprehensible text. I’ve come to understand that it’s a scaling problem. Right now, all the data together is ~30 MB. I need to gather wayy more data, more importantly gather clean data. Seeing the Loss drop did feel good, the model is doing the best it can with the amount of data it has but it doesnt have too much to work on. Regardless, I’m not abandoning this, I hope I can come up with and find more data for it to be comprehensible!! Onwards and Upwards we go!!!!

Ok so, I completed the tokenizer. And followed that up by writing and training the model which took a longg time to to train (devlog time does not count for time spent on training). The vocab size is 8000 words and the maximum sequence allowed is 512. All of this was done with PyTorch (No HuggingFace). And after all of that effort, the output I got is in the picture (it is just text that does not make sense)…
Really regret adding Shakespearean text in the training data but hey… you live and you learn!

Ok so I finally got the data together, it took a lot of time but it’s done. Now the actual work. begins. I got the transformers trained, and it is producing a sane amount of tokens and common words have stayed intact. For the data gathering, it was split into 3 parts, Survival Data (5.5MB), Warm-up Data (28.1MB)[English Language], Instruction Data (254KB)[QnA formatted Data]. And it was all combined into a tokenizer corpus (one large .txt file) and trained using sentence piece, next up is the architecture and the actual implementation! I’m really excited!!

To say finding and cleaning and processing data for training has been difficult would be an understatement. There’s always the fear of is this enough? Is this useful? Will this be enough data? And so far, it has been very messy but step by step I think I’m getting closer to actually useable data. However, the folder strucutre is still a mess. An LLM really needs a lot of data and for survival tips, I have been using Wikipedia and openly available survival manuals and PDFs. Data gathering is a really important step and I dont want to take it for granted so let’s hope all of this works!!

This was an existing project that I worked on and finished the deployment finally! The backend is now running on render instead of running locally and the database is connected to a remote Supabase project. It was really fun to work on something that me and my friends could actually use and need. Colleges/Schools keep changing PCs, and chess.com and lichess being blocked was just getting too much. That’s where Veloz Chess comes in. It was fun to work on this project but since this was an existing project I worked on, this will probably be short on the number of devlogs.
Please feel free to mess around with the code and of course play with your friends!! Enjoyy 🥳🥳

Please visit: https://ather-docs.vercel.app for a DETAILED DEMO and DOCUMENTATION

For installation please visit the latest release: https://github.com/Shreeyans2305/AtherOps/releases/tag/v1.0.7 and follow the steps there

(So far, I have only tested for MacOs and that works on the latest version however the builds for Ubuntu and Windows were also successful)
The objective is to create a smart and user friendly system that benefits the E-commerce platforms and SaaS businesses with transitions.
Broken API endpoints, payment gateway errors are real and wayy too common. These small bugs end up costing businesses a lot of money.
To resolve this, AtherOps introduces a Smart Multi-Agent Pipeline that monitors all the signals, logs, messages, console, and tickets from a website and provides a smart diagnosis and self fixes the issues based on severity. For harder issues, it sends an email to Engineers and Merchants informing them of the error.
This system vastly optimizes the current monitoring services and ticketing systems saving companies a lot of money and resources by providing a 24/7 support team of agents!!
The setup for a business is as easy as adding a JavaScript SDK through a code snipped in the script tag of the HTML file!!
Really proud of this project and I hope it goes on to make real world impact.
Thank you for your time.

PS: I finally got the package working so now you can download it on your own system and run it. Just make sure to install ollama first and read the setup steps. Have Fun!! I hope you enjoy it.

Some finishing touches were added and I quickly whipped up a documentation page since deploying something like this is really tedious and would need money and probably a large team. The locally running light weight LLMs give this page a huge boost, seperating them from the exisiting diagnosis platforms, something like AtherOps is in a completely different category since it’s smart and self healing! Really proud of the way this project turned out and hope it can be shipped to real companies in need of monitoring during a transition to headless architecture and also as a general smart tool!

The core of the project has been built out. After an error/ticket/API endpoint failure, the site correctly diagnoses and fixes the issue, identifies patterns, notifies related authorities (via email), and asks for human intervention on serious for critical errors. This project requires the installation of a lot of dependencies, so I am thinking I might just use a demo video instead of the working agentic pipeline. The setup is rather easy but heavy. I’ll probably include everything in the README file. Make sure to checkout the README file!!

Created the frontend and the backend. The purpose of this is to monitor a website’s console, websocket data and customer support tickets to provide real time healing and assistance. So far, the data about a business’ website such as API errors, Payment Failures, Websocket errors go into the backend and follow an Agentic AI Pipeline which observes and reasons abouts these errors and establishes a pattern. In this devlog, I completed the implementation of the entire site. I will be sharing the theoretical framework behind this soon. Stay Tuned!!

I got a simple demo orbit simulation running with Pygame. Still exploring the physics side of it all but for now this is a good base line to start from. I am planning to make it more accurate and better looking. Mimicing Newtonian Physics for now but might upgrade to use general relativity based on how much I am able to grasp and implement because some of the topic matter is really dense. Anyway that’s it thanks for following along with the journey!

I finally finished the Project!
It took a lot of time to perfect, added dark mode and I really like how it turned out!
The progress bar feels smooth while reading a post! Can’t wait to put more cotent and perhaps in the future, my own merch store (as teased on the website)
Managed to make the categories section a little more interactive, added social links for contacting and tons and tons of tiny details that had to be worked out but I am super proud of it.
Hope you’ll enjoy reading my blogs as much as I’ll enjoy writing them

(P.S. I am still unable to upload a video in the devlog, to checkout the implementation please visit the demo link, though I have tried my best with GIFs but they don’t fully convey the UI,thank you!!)

So I have completed the page for individual posts, and specific categories of post.
The design is kept consistent throughout the entire website to give it a better feel.
The Specific category page conditionally renders by parsing through the md files so I don’t have to manually add anything while uploading blogs. I also ended up writing a blog which was a fun break!
I really feel good about how this project is coming along and now all I have left to implement is the Light Mode/Dark Mode switching and the ability to contact me and like individual posts!
(P.S. I am having an issue uploading videos in devlogs, so if you want to see how the final page looks like please check the demo link)

Perfecting the landing page is taking longer than I expected! But I am all for the customizability, that’s the fun part about making a blog website on your own, the possibilities are endless. While there aren’t any insane animations/transitions, I have done my best to make the website feel interactive and lively.
A lot of useful implementation was added in the duration of this session. The blog posts read from MarkDown files instead of JSON. The data and cotent and extracted using the React Markdown and gray matter pacakge. Powerful yet subtle animations have been added using the Framer Motion Library and GSAP.
I’m really proud of how this page is turning out to be and hope to keep adding features as I keep adding blog posts.
What’s left for now is the page for specific categories, adopting a mobile friendly design and light mode/dark mode integration.
Thanks for reading!

I’m working on my first project! This is so exciting. I can’t wait to share more updates as I build.