u/AnshMNSoni
r/AIDevelopmentSpace r/projects r/WebApps r/AIQuality r/website r/redis r/LocalLLM r/PythonProjects2 r/ollama r/searchengines r/FastAPI r/MarsSociety r/dsa r/FlutterDev r/LangChain r/u_AnshMNSoni r/learnrust r/Cloud r/indieweb r/AIDiscussion r/PythonLearning r/rust r/AISystemsEngineering
Why are AI companies suddenly open-sourcing so much?
DeepSeek, Meta, Google, Sarvam and Anthropic are all opening parts of their AI stack.
I started wondering: why would billion-dollar companies give away technology that costs millions to build?
The answer goes far beyond “helping developers.”
I explored the business strategy behind it — ecosystems, distribution, standardization, developer adoption, competition and where AI companies actually intend to capture value.
Would love to hear what you think.
u/AnshMNSoni — 10 days ago
Built a multi-agent AI system for B2B cable tender quoting - looking for architecture loopholes, not UI feedback
Built RFP Agent AI - automates the 2-5 day manual tender quoting process for wire/cable manufacturers into 10 seconds using a 5-agent pipeline (PDF parsing → SKU matching → LME pricing → risk analysis → quote generation).
Known problems I've already found:
- Catalog only has ~6 SKUs, real world needs 5000+
- Vector search giving 46% match scores - wrong approach?
- No live LME API yet
- No human checkpoint on low confidence matches
Is multi-agent overkill here? Is vector search right for structured spec matching? What would you do differently?
GitHub: https://github.com/AnshMNSoni/B2B-RFP-Agent.git
Would love brutal honest feedback - not looking for encouragement, looking for loopholes, wrong assumptions, and better architectural approaches. If you've built something similar or work in procurement/manufacturing tech, your perspective would be especially valuable.
u/AnshMNSoni — 12 days ago
I'm building an open-source Python library focused on reusable building blocks.
Visit: https://github.com/AnshMNSoni/PythonSTL.git
Instead of adding more algorithms, I want to solve real problems Python developers repeatedly face.
I'm curious:
What utility do you find yourself implementing over and over?
What functionality do you wish Python's standard library already had?
What do you usually copy from previous projects?
Examples could be caches, schedulers, retry logic, rate limiters, streaming utilities, search structures, etc.
I'd love to understand the pain points rather than propose solutions.
Let's discuss...
u/AnshMNSoni — 23 days ago
Built TransitOps in Odoo Hackathon 2026 – Looking for honest feedback
Hey everyone,
Our team recently participated in Odoo Hackathon 2026, where we built TransitOps.
The idea was simple: create a system that helps streamline public transportation operations using Odoo.
Like every hackathon project, we had limited time, so we focused on building an MVP instead of trying to make everything perfect.
GitHub: https://github.com/AnshMNSoni/TransitOps
We're not here to promote it - we're genuinely looking for feedback.
Some questions we'd love your thoughts on:
- What features would you expect in a transit operations platform?
- Is there anything in the architecture you'd improve?
- What would you build differently if you had 48 hours?
- Any suggestions that could make this useful beyond the hackathon?
Constructive criticism is more than welcome.
Thanks!
u/AnshMNSoni — 1 month ago
I built PythonSTL - a DSA learning library for Python devs who miss C++ STL [Open Source]
Before anyone says it - yes, I know. Python already has lists, deques, and heapq. You don't need this to solve LeetCode problems.
But here's what I noticed: when someone is *learning* DSA in Python, the named abstractions are missing. There's no `stack()`, no `queue()`, no `priority_queue()`. You either build them yourself or chain together built-ins and hope the concept sticks.
PythonSTL gives those concepts a proper home.
It mirrors C++ STL interfaces exactly - same method names, same semantics - built on top of Python internals using the Facade Design Pattern. It also ships with full type hints, time-complexity annotations in docstrings, and zero external dependencies.
What it's for:
- Learning DSA concepts clearly in Python
- C++ devs switching to Python who want familiar footing
- Conceptual clarity over syntactic shortcuts
What it's NOT for:
- Competitive programming
- Replacing any standard library
- Production use
It's sitting at 5,500+ downloads with ~1,000/month, so clearly some people find it useful.
Feedback, criticism, and contributions welcome.
u/AnshMNSoni — 1 month ago
6 Myths About PythonSTL, Busted
Built a Rust-backed STL library for Python - and quickly learned people assume a lot about what "Rust-powered" actually means. Here's the reality check:
Myth 1: "Useless since LeetCode/Codeforces block external imports."
Reality: True, but that's not the point. PythonSTL is for local prototyping - helping C++ devs translate their STL mental model into Python and sharpen structure design before interviews.
Myth 2: "Python's built-ins make the Rust backend pointless."
Reality: Python has no native sorted set/map (dict/set are unordered hash tables) and heapq is min-heap only. PythonSTL adds true O(log n) BTreeSet/BTreeMap and flexible heaps - the same hybrid-performance pattern used by Polars, Pydantic, and cryptography.
Myth 3: "Rust backend = always faster."
Reality: Not for O(1) ops like single push/pop - FFI crossing overhead dominates. Rust wins on compute-heavy work: sorting, partitioning, binary search on large data.
Myth 4: "Rust makes it thread-safe."
Reality: No. Containers hold PyObjects and still go through the GIL. Concurrent mutation without a threading.Lock still risks data races.
Myth 5: "stl_set/stl_map replace Python set/dict."
Reality: Different tools. Python's are O(1) unordered hash tables; PythonSTL's are O(log n) sorted trees for when you need order or range queries (lower_bound/upper_bound).
Myth 6: "Rust backend avoids memory/refcounting issues."
Reality: False - PythonSTL still holds PyObject references and plays by CPython's refcounting and GC rules, including circular references.
Building this taught me as much about Python internals as it did about Rust.
For more visit: https://pypi.org/project/pythonstl/
⭐ Don't forget to visit: https://github.com/AnshMNSoni/PythonSTL.git
u/AnshMNSoni — 2 months ago
Why adding Rust to my Python library made it 194x faster... and 5x slower.
I’ve been optimizing PythonSTL - a library that replicates C++ STL containers and algorithms in Python. To boost performance, I built a compiled Rust backend using PyO3 and Maturin.
After running benchmarks comparing Pure Python vs. Python + Rust vs. Pure C++ (O3), I encountered an amazing systems design paradox.
Here are the numbers and the engineering behind them:
The Wins (CPU-Bound Workloads):
• Bubble Sort (10k items): Pure Python took 5.4891s. Python + Rust took 0.0283s (a 194x speedup!).
• Binary Search (5k queries on 1M items): Python + Rust was 5.3x faster (0.0182s down to 0.0034s) by utilizing direct memory indexing without copying the list.
The Losses (FFI-Bound & Algorithmic Workloads):
• Stack (500k push/pop cycles): Python + Rust was only 1.47x faster (0.2324s vs 0.1581s). Why? Because calling push/pop from Python space crosses the Python-Rust FFI boundary 1 million times. The constant-factor cost of argument checking and GIL coordination dominates the runtime.
• Sorted Sets & Maps: Python + Rust was 5x slower! Why? Python’s native set/dict are highly optimized unordered O(1) hash tables. C++ STL compliance requires sorted keys, which we replicate using Rust’s B-Trees (running in O(log N) time) and calling back into the Python VM for comparisons.
The Core Takeaway:
Rust binary extensions are not a magic wand for performance. If your application does highly granular operations that frequently cross the FFI boundary, the boundary overhead will eat your performance gains.
But if you can bundle heavy calculations to run inside Rust with a single boundary crossing-it is an absolute game-changer.
Check out the project: https://github.com/AnshMNSoni/PythonSTL
I'd love to hear from other hybrid systems developers: how do you manage FFI overhead in your PyO3/Maturin libraries?
Thankyou.
u/AnshMNSoni — 2 months ago
I Added Redis to My URL Shortener and Got Almost No Speedup
I recently built a URL shortener using FastAPI, PostgreSQL, Redis, and Docker.
The goal was simple:
Add Redis → benchmark redirects → see performance improvements.
Instead, my first benchmark looked like this:
- Redis cache hit: 12.47 ms
- PostgreSQL cold lookup: 7.36 ms
Redis was effectively slower than PostgreSQL.
After digging through the request lifecycle, I found that cache hits were still executing synchronous analytics queries:
- SELECT url_id
- INSERT click record
The cache was working correctly, but PostgreSQL was still sitting on the critical path.
I fixed it by:
- Caching
url_idalongside the destination URL - Moving click logging to FastAPI BackgroundTasks
After the changes:
- Median cache-hit latency dropped from 12.47 ms to 5.22 ms (-58%)
- Redis became ~41% faster than PostgreSQL lookups
The interesting part wasn't Redis itself.
The interesting part was realizing I wasn't benchmarking Redis at all—I was benchmarking my entire request path.
I wrote a detailed breakdown covering the investigation, benchmarks, tradeoffs, and why I chose BackgroundTasks over Kafka/Celery.
Read Full Blog: https://anshmnsoni.in/blogs/linklite
Curious if others have run into similar situations where the "optimized" component wasn't actually the bottleneck.
u/AnshMNSoni — 2 months ago
build my own social media web app
Not exactly, but yes - have you ever imagined a personal portfolio that feels like a social media platform?
I recently launched my personal portfolio website designed to be more interactive and engaging than a traditional portfolio.
Instead of simply listing projects and skills, I tried to create an experience where visitors can explore my work, journey, and achievements in a more dynamic way.
Some features:
- Social-media-inspired design
- Interactive project showcase
- Responsive UI
- Modern user experience
- Personal branding focus
This project was built as an experiment to see how far a portfolio can go beyond being just a resume website.
I'd genuinely appreciate any feedback on:
- UI/UX
- Performance
- Overall experience
- Things you'd improve
Website: https://anshmnsoni.netlify.app
"Your portfolio shouldn't just show your projects - it should showcase your creativity."
Thank you!
u/AnshMNSoni — 2 months ago
We turned our NASA Space Apps Challenge finalist project into a live platform
Hey everyone,
A few months ago, our team built Prithvi Netra during the NASA Space Apps Challenge 2025, where we became Zonal Finalists.
At the time, it was only a localhost hackathon prototype. Since then, we've continued developing it and have finally deployed it publicly.
- Project: Prithvi Netra
- Live: https://prithvi-netra-nleb.onrender.com
- GitHub: https://github.com/AnshMNSoni/Prithvi-Netra
The goal is to make Earth observation and geospatial insights more accessible through an interactive platform powered by modern AI and data-processing techniques.
We'd love feedback on:
- UI/UX
- Performance
- Features we should add
- Open-source contributions
- Overall usefulness of the platform
This is our first time taking a hackathon project all the way to deployment, so any feedback is highly appreciated.
Thanks!
u/AnshMNSoni — 2 months ago
▲ 1 r/searchengines
A Search Experiment
What happens when you build a search engine focused on distributed query processing instead of AI?
I've been working on a project called Disee, and it's finally live on AWS.
The project is intentionally simple:
- No AI
- No caching layer
- No LLM integration
- Just distributed query processing across multiple nodes
The current dataset comes from Wikipedia and Stack Overflow, and the main objective is to explore distributed systems concepts such as query distribution, node coordination, scalability, and execution flow.
This isn't competing with Google or modern AI search systems. It's primarily an engineering experiment and learning project focused on understanding how distributed search infrastructure works under the hood.
Live Demo:
https://disee.xyz
GitHub:
https://github.com/AnshMNSoni/Disee
I'd appreciate any feedback, architecture suggestions, performance ideas, or distributed systems discussions.
Every large-scale system starts as a small experiment.
Thankyou.
u/AnshMNSoni — 2 months ago
▲ 0 r/FlutterDev
Built a console-based-Instagram in Dart 😁
Hey everyone, I just finished a small side project: a terminal-based Instagram simulation written in Dart.
It lets you create a profile, search for other users, and follow them, with validation to prevent following the same profile twice. The main challenge was handling edge cases in user input, like entering strings where numbers are expected.
It is a beginner-to-intermediate level project but a good exercise in structuring a Dart CLI app. Single account only for now, and messaging is not yet implemented. Planning to add multi-account support next.
Check it out here: https://github.com/AnshMNSoni/Console-Based-Instagram
Feedback and suggestions welcome.
u/AnshMNSoni — 2 months ago
I built an AI agent that automates B2B RFP processing for the wires & cables industry - here's what I've built so far
The Problem:
Procurement teams manually read dense RFP documents, cross-reference SKU catalogs, and build quotes by hand. It's slow, error-prone, and costs real business deals.
What RFP Agent AI does:
Paste an RFP. Get a structured quote in 10–15 seconds.
Tech Stack & Architecture:
• 3-agent pipeline: Sales Agent → Technical Agent → Pricing Agent
• Each agent powered by Google Gemini Pro with rule-based fallback
• Sales Agent: extracts voltage, material, insulation, compliance specs from unstructured text
• Technical Agent: matches specs to SKU catalog with weighted scoring (Voltage 40%, Material 30%, Insulation 30%) + AI reasoning
• Pricing Agent: recommends quantities, generates cost breakdown + strategic analysis
• Deployed on Render
Current Status:
✓ Working demo — still actively building this out
✓ Handles ~500 RFPs/day on Gemini free tier
✓ Graceful degradation if AI fails
⚠️ First load has a ~30s cold start (Render free tier) — worth the wait!
🔗 Live demo: https://rfp-agent-ai.onrender.com
🔗 GitHub: https://github.com/AnshMNSoni/B2B-RFP-Agent.git
Would love feedback from anyone working on agentic AI or enterprise automation!
u/AnshMNSoni — 2 months ago
AI systems are shaped by the companies and people who build them.
That doesn’t automatically make them malicious — but it does mean they are not perfectly neutral.
Transparency, incentives, and accountability matter.
(Conversation screenshot from Claude by Anthropic.)
u/AnshMNSoni — 3 months ago
But how LLMs thinks...
I wrote a technical breakdown of how LLMs work internally—from tokens and embeddings to self-attention, transformer blocks, and next-token generation.
Tried to keep it engineering-focused and easy to follow without oversimplifying.
Would love feedback from people building with LLMs, RAG, or agents—anything I should explain deeper or differently?
Read Full Blog: https://medium.com/@anshsoni702/how-does-an-llm-actually-think-what-really-happens-inside-the-model-part-1-afe58d2c8350
u/AnshMNSoni — 3 months ago
I recently wrote a blog explaining why AI agents are far more than “just chatbots.”
A lot of people use ChatGPT daily but still don’t understand what makes AI agents fundamentally different — especially when it comes to memory, planning, tool usage, and autonomous execution.
I tried to explain the concepts in a simple and beginner-friendly way.
Would genuinely appreciate feedback from this community:
https://medium.com/@anshsoni702/they-called-it-just-a-chatbot-they-were-wrong-1da0f85ddba4
Thankyou.
u/AnshMNSoni — 3 months ago
Build an Email-Agent Using Langchain + Ollama. Repo: https://github.com/AnshMNSoni/email-agent.git
u/AnshMNSoni — 3 months ago
Crossed 3000+ Downloads 🙌
Hey everyone,
PythonSTL recently crossed 3000+ downloads, and I’m genuinely happy to see people using it.
I’ve open-sourced the project and added a few issues for anyone interested in contributing or exploring the codebase.
GitHub Link: https://github.com/AnshMNSoni/PythonSTL.git
Would love to hear feedback or suggestions from the community. Thanks a lot :)
u/AnshMNSoni — 3 months ago
I turn 21, and instead of celebrating achievements, I ended up reflecting on my entire journey.
I grew up as a curious kid who used to break toys just to understand how they worked.
Then slowly life became:
marks,
rankings,
JEE,
competition,
pressure,
internships,
comparison,
and constantly trying to prove myself.
Somewhere during JEE prep, I randomly asked myself:
>
And honestly, that question never left me.
I wrote a blog about growing up, parental sacrifices, fake internships, engineering life, self-doubt, and slowly learning to compete with myself instead of others.
Not trying to sound motivational or inspirational — just honest.
Maybe someone here will relate to it.
Read Full Blog 👇
21 Years of Becoming Myself https://medium.com/@anshsoni702/21-years-of-becoming-myself-0c68c53d9be1
Thankyou.
u/AnshMNSoni — 3 months ago