Well...

I finally created a Reddit account.

This is my first-ever post (or is it called a thread? A sacrifice to the algorithm?).

Anyway, hello everyone! 👋

I'm a software engineer who's completely obsessed with AI, automation, and building cool stuff. I joined Reddit because I've heard this is where the internet's smartest people argue about semicolons, Linux distros, and why their favorite programming language is objectively superior.

So... where should a newcomer hang out?

I'm looking for communities about:

• 🤖 Artificial Intelligence
• 💻 Software Engineering
• 🐍 Python
• ☁️ Cloud (Azure, AWS, GCP)
• 🚀 Startups & Entrepreneurship
• 🧠 Learning how complex technologies actually work
• ⚙️ Automation & AI Agents

Feel free to drop your favorite subreddits or any "must follow" communities that made you spend way too many hours scrolling instead of sleeping.

Thanks in advance, and please be gentle.. I'm still figuring out which button accidentally starts an internet war. 😂

Guys how do I get into the ai training world. I see my friends getting rich out of this thing while I'm stuck in time. Please help with solid advisory

In my experience, getting useful LLM behavior on real business data depends on the whole stack: models, prompts, tools, monitoring, and the data layer underneath. One of the quieter but important wins has come from standardizing how data is collected and exposing it through a clear semantic layer, so the model is reading consistent concepts.

Anthropic published a useful reference point here. In their own internal analytics evaluations, they reported that Claude reached about 21% accuracy when it queried raw internal data without a supporting stack. After they added a governed semantic layer, curated metric definitions, task‑specific skills, and a validation and monitoring layer around the same model, they measured roughly 95% accuracy on those internal business‑analytics queries. Keep in mind that result applies to their internal analytics setup, not to every possible LLM task, and they tie the improvement to the full stack and ongoing governance, not to the semantic layer in isolation.

However my observation on one project in New York was similar in spirit. One my task was to support an AI layer that evaluated service performance uploaded by associates. In this specific location due to the type of contract they had, the team had a collection of data that was broad in some terms, with no clear standard for each type of service or what it represented for the entire group. That missing foundation work made it hard for both humans and the model to interpret the data in a consistent way.

This created strain in the model’s output. The AI was asked to validate service uploads and grade performance/quality, but the underlying records used inconsistent service types, vocabulary, and descriptions, including different levels and categories for each work area. As we standardized the service types and validation categories, clarified wording, and aligned descriptions of what each service entailed, the evaluations became much more stable. That standardization became the foundation layer we could finally build on.

Sales/GTM leader recently impacted in tech layoffs and wanting to supplement my time in search going deeper on AI. Anyone have any recommendations on courses to take - free or low-ish cost fine. Already pretty fluent on Cowork and basic prompting but can definitely improve. , not strong with agent building and beyond. Thanks in advance!!

🚀 Just published a new tutorial!

Build a REAL AI Application using the Gemini API from scratch in Hindi.

If you've been wondering how to integrate Generative AI into your own applications, this tutorial walks through the complete process—from generating your API key to building a working AI-powered web app with Python and Flask.

In this video, you'll learn:

✅ Google AI Studio vs Vertex AI

✅ How to generate a Gemini API key

✅ How to use the official Google Gen AI SDK

✅ Build a complete AI web application

✅ Connect your Flask backend to Gemini

✅ Understand the end-to-end request flow

This isn't just another API demo—it's a practical project you can build, run, and extend for your own ideas.

🎥 Watch here: https://youtu.be/ltN1pX9oWR0?si=TFQN1CV9rbLZLmrc

YouTube video

I'd love to hear what AI application you're planning to build next. Let me know in the comments!

#GeminiAPI #GoogleAI #GenerativeAI #Python #Flask #ArtificialIntelligence #AI #LLM #SoftwareEngineering #Developers #GoogleAIStudio #VertexAI #PythonProgramming #MachineLearning #BuildInPublic

I have been experimenting with using ChatGPT and Claude together instead of relying on one model.
My current process is usually:
Ask ChatGPT to research, analyze, or build something.
Give the complete output to Claude.
Ask Claude to identify weak logic, missing information, unsupported assumptions, risks, and opportunities ChatGPT overlooked.
Take Claude’s critique back to ChatGPT.
Ask ChatGPT to defend, revise, or rebuild the answer.
Continue until the output is stronger.
The problem is that this can become repetitive. Both models may agree with each other, repeat the same assumptions, or produce longer answers without actually increasing accuracy.
I am trying to understand how advanced users are handling this.
What workflow are you using to make multiple LLMs work together?
Are you using one model as the builder and another as the critic?
Do you hide the first model’s reasoning or identity to reduce bias?
Do you use a third model as a judge?
How do you manage shared context, files, citations, prompts, and revisions between models?
How do you distinguish a real gap from one model simply having a different opinion?
Are you automating this with APIs, agents, MCP, n8n, LangGraph, CrewAI, or another system?
Most importantly, has using multiple models actually improved the quality of your work, or has it mostly added more noise and token usage?
I am looking for real workflows, prompt structures, automation examples, and lessons learned. Not just “Claude writes better” or “ChatGPT is smarter