How we validate crash, pause, and disk‑full on a single node in CI using Docker, tmpfs, SSE watches, and Prometheus—no server code changes, in our code changes
A developer‑focused guide on implementing the Model Context Protocol (MCP) as a cloud‑hosted tooling layer for LLM apps. Grounded in a working implementation we built and deployed; includes tuning notes, metrics, and pitfalls.
This is a great visual guide! I’ve also been working on a similar concept focused on deep understanding - a visual + audio + quiz-driven lesson on LLM embeddings, hosted on app.vidyaarthi.ai.
Our goal is to make abstract concepts more intuitive and interactive — kind of like a "learning-by-doing" approach. Would love feedback from folks here.
(Not trying to self-promote — just sharing a related learning tool we’ve put a lot of thought into.)
I built this lesson using a tool I'm developing to help explain complex systems (like robotic motion control) visually and interactively. Would love any feedback — especially from folks working in robotics, control theory, or simulation.
I like the idea, but sorry to say that I just got too bored after a few minutes with how slowly it goes (at least given my preexisting knowledge) - I would have loved a sort of "Yes, yes, I know it" fast-forward button.
Thanks for the feedback, that's a really good feedback, we will add more interactive controls in next try of this for learners to control the flow rather than us controlling it.
I built Vidyaarthi.ai to solve a problem I witnessed firsthand: students in India who understand concepts perfectly in their mother tongue but fail exams because education is in English.
The problem is massive - 260 million students in India study in English-medium schools, but most speak Hindi or regional languages at home. They end up memorizing without understanding.
Our solution:
- Voice-based AI tutor (no typing needed - crucial for vernacular users)
- Explains any concept in conversational Hindi with culturally relevant examples
- Optimized for Indian accents and code-mixing (Hindi-English)
Technical challenges we solved:
- Voice recognition for heavy accents and background noise
- Handling code-mixed languages (Hinglish)
Early traction: 500+ active users, organic growth through WhatsApp shares
We're bootstrapped and running Meta ads now. Would love feedback on:
1. How to reach Tier 2/3 city users effectively?
2. Monetization models that work for price-sensitive markets?
3. Similar problems in other countries we could expand to?
Let’s say you’ve got an LLM that knows almost everything—trained on vast amounts of text. But there’s a catch. It’s never seen content encrypted using a specific one-time pad cipher, and you have access to this cipher.
You give the model an encrypted message:
"g5f8s9h2..." (a string of seemingly random characters)
Then, you ask it to:
"Decrypt the above message and summarize its content."
The Paradox
The question here is simple: Can this advanced AI decrypt the message and tell you what it says? Or is it stumped, even with all its computational power?
Let’s say you’ve got an LLM that knows almost everything—trained on vast amounts of text. But there’s a catch. It’s never seen content encrypted using a specific one-time pad cipher, and you have access to this cipher.
You give the model an encrypted message:
"g5f8s9h2..." (a string of seemingly random characters)
Then, you ask it to:
"Decrypt the above message and summarize its content."
The Paradox
The question here is simple: Can this advanced AI decrypt the message and tell you what it says? Or is it stumped, even with all its computational power?
