u/FlashyAverage26

I started my entrepreneurial journey at 19.

First built an e commerce marketplace for shoes, then somehow convinced myself building a GitHub alternative was a good idea 😂
Funny enough, we still managed to get 4 VC meetings before getting rejected.

Later I built a ComfyUI copilot, made some money from it for a while, then it slowly died off.

After that I kept shipping projects, became more active on X, grew a small audience, and tried building micro SaaS products.
2 failed.

Now I’m working on another one.

One thing I’ve realized in 2026: marketing is probably the highest leverage skill for founders.
A lot of good products die quietly because nobody sees them.

That’s honestly why I came to Reddit.
At first I made tons of mistakes, got posts removed, wrote cringe AI-style content 😭

But slowly I started understanding how communities actually work, and now I’ve crossed 830+ comment karma with a few comments hitting 100+ upvotes.

Looking back, I think one of my biggest problems early on was trying to figure everything out alone without mentors, founder friends, or people who already understood distribution.

Still learning. Still building.

Most RAG demos look impressive until you actually test retrieval quality.

So I started building RAGProbe.

The idea is simple:

upload a document → run automated evaluation → measure:

- retrieval accuracy

- faithfulness

- hallucination rate

Today I finished:

- FastAPI + Next.js setup

- document chunking pipeline

- FAISS retrieval system

- retrieval playground UI

Still building the full evaluation engine.

Build in public day 1.

it's just glimpse how it will be look

https://reddit.com/link/1tfrjkx/video/79xrkmqzmp1h1/player

https://reddit.com/link/1tfktal/video/u4pl8gsw3o1h1/player

When developing AI applications, I discovered that debugging AI agents was a hassle.

There was no way to know:

Prompt sequence

Latency

Tokken consumption

Failure point

This made me develop TraceLLM, an open-source observability framework for large language model applications.

The following things have been developed today:

Python tracer

Trace store for MongoDB

Token or Latency monitoring

Command line trace reports

Every AI call will now create a trace report automatically.

see it's much better

https://reddit.com/link/1tfkse6/video/dfbfc8jm3o1h1/player

When developing AI applications, I discovered that debugging AI agents was a hassle.

There was no way to know:

Prompt sequence

Latency

Token consumption

Failure point

This made me develop TraceLLM, an open-source observability framework for large language model applications.

The following things have been developed today:

Python tracer

Trace store for MongoDB

Token/Latency monitoring

Command line trace reports

Every AI call will now create a trace report automatically.

see it's much better

https://github.com/avikcodes/traceLLM

for the last 2 years i was learning many coding languages and building small projects mostly for fun

but i didn’t build anything that solved a real problem

from 2026 i took a challenge to ship 30 projects that actually solve my own problems and i am using these tools for myself

now i have built my 7th project TinyTrain

upload any csv with a target column it fine tunes a small model and outputs a visual training curve and metrics report

https://reddit.com/link/1t1qpik/video/tuo18sf5dqyg1/player