From Zero to TechBit: The 60-Day Journey of Building in Public
I woke up with a question that had been eating at me for weeks:
What's actually broken in tech?
Not the polished pain points you see on LinkedIn. Not the blog posts that show up months after a polished launch. I wanted the real stuff. The raw frustration developers, founders, and PMs feel when nothing's working.
That morning, something clicked. I opened my laptop. And I started building.
I had no idea I was about to go on the most intense 60 days of my life.
Day 1: Just Me, Python, and a Wild Idea
April 26, 2025. That's when it all started.
It started embarrassingly simple: a Reddit scraper using PRAW.
No ML. No fancy UI. Just a script pulling posts from subreddits like r/programming, r/startups, and r/devops. I remember scrolling through raw threads and thinking:
"This is where the real pain lives."
People weren't posting product feedback forms. They were venting. Dropping gold. Insights buried in chaos.
Then came Twitter (X). I built a real-time scraper to catch rants, threads, and pain points as they happened. My apartment became a mess of code, coffee mugs, and open terminals.
But here's the thing about experimenting early - I burned through all my API credits testing different approaches. Had to pause Twitter ingestion pretty quickly, but the learning was worth it.
No roadmap. No team. Just momentum.
When Reality Slapped Me Hard
A few weeks in, I had thousands of posts… and no idea what they meant.
So I started small: I used all-MiniLM-L6-v2, built a basic semantic similarity checker, and layered in rule-based matching.
It sort of worked. Until it didn't.
I needed to go deeper — to figure out why something felt painful. So I built a custom quality scoring system. Added severity detection, user role cues, context tagging, and grouped pain points into domains like AI, SaaS, Infra, DevTools.
That's when the system began to shape up.
Still solo. Still grinding. Still obsessed.
May 25: Waitlist Goes Live
Exactly 30 days after I started building, I opened up TechBit for early access.
No marketing. No growth team. Just a link and a note.
And people joined.
Not just joined — they shared feedback. They told me what felt off. They spotted tagging issues, weird UI bugs, and inconsistencies in pain classification.
One message stuck with me: "Love the idea. But the categories don't make sense. A deployment issue tagged as UX? A design bug marked Infra?"
They were right.
The rule-based approach was no longer enough. It was time to scale — and get smarter.
The Model Mayhem: Hunting for Precision
After launching the waitlist, I started testing open-source LLMs to improve tagging and summarization.
Attempt 1: LLaMA 8B Instruct via Groq
✅ Super fast
❌ Too shallow — misclassified deep technical pain as generic noise
Attempt 2: Qwen3 235B A22B FP8 via Together AI
✅ Better context
❌ Still inconsistent — especially in nuanced product pain
For explanations: I reused LLaMA 8B instruct from Together. Sometimes it worked beautifully. Other times? Total miss. Felt like guessing.
At one point, I nearly dropped the ML angle altogether.
Then, my friend Roshan suggested: "Why don't you just try GPT-4.1 mini? It dropped recently and would categorize the best."
He was right.
GPT-4.1 mini just clicked.
✅ Fast
✅ Sharp
✅ Human-like outputs
✅ Accurate tagging
✅ Readable summaries
Suddenly, the categorization started making sense. The summaries felt intentional. The signal was clear.
Not just a better model — a better product.
The Backend Breakup (from Railway to Google Cloud)
While I was fixing my ML stack, another monster crept in: my backend.
I'd built the whole system on Railway using FastAPI + Supabase.
Worked fine during the waitlist. But as traffic grew and ingestion scaled, everything broke. Requests crashed. Memory overflowed. Deployments failed during critical moments.
I spent days debugging broken deployments.
One night, the entire system crashed during a live feedback session. I was mortified.
That's when I made the call to migrate everything to Google Cloud Run.
I rewrote the middleware, structured async tasks for scraping and classification, added autoscaling, and finally brought stability.
Now, it runs clean. Supports real-time ingestion from Reddit and processes everything asynchronously. ML runs smoothly. And no more panic when I hit deploy.
What TechBit actually became is a platform where founders, PMs, engineers, and builders can discover real-time tech pain points, categorized and actionable. It's where you go to understand what's actually frustrating developers, admins, and users across the tech ecosystem - all in one place.
The Demo That Changed Everything
This part matters most to me.
I was wrapping up my internship at Back Market in Paris. Just 20 days before the end of my internship, I decided to share TechBit with the team.
Picture this: 24-year-old intern, standing in front of the CPO, CTO, VP of Product, VP of Insights, and two PMs, nervously demoing a side project.
The UI wasn't polished. The ML wasn't perfect. But I walked them through it — how I scraped Reddit, filtered real product pain, and cleaned the noise.
When the VP of Product leaned forward and said: "You're onto something real here." That moment will never leave me.
Building in Public: What It Really Taught Me
I shared everything. The bugs. The backend meltdowns. The failed prompts. The wins.
People followed along. They tested features. Pointed out bugs. Gave unfiltered feedback.
And it made the product 10x better.
Building in public taught me:
✅ Show up daily — even when you're tired
✅ Ship before you're ready
✅ Iterate in the open
✅ Feedback is gold
✅ Your first idea will suck — fix it anyway
The internet became my co-founder.
After the Waitlist: The Final Sprint
After the May 25 launch, I entered a different mode. The product wasn't done — it was just beginning.
Here's what changed:
💡 Added animated cards and better transitions
💡 Rewrote tagging logic for more precise roles & severity
💡 Migrated backend to GCP for better uptime
💡 Built search + filter that actually made sense
💡 Improved speed, UX, and desktop layout
💡 Finalized OpenAI integration for production ML
💡 Documented the entire PRD + roadmap in Notion
💡 Removed Twitter and LinkedIn ingestion temporarily - those early API credit burns taught me to be more strategic about costs
Looking Ahead: July 10 Public Release
It's almost time. On July 10, 2025, TechBit goes public.
The waitlist release taught me everything. The feedback, the crashes, the confusion, the hope.
Now, it's stable. Sharp. Honest.
A platform built to show the real pain in tech — not hidden behind slides, but written in public threads, rants, and discussions.
If you're a founder, builder, PM, or just someone who cares about the future of products — I built this for you.
Final Reflections
You don't need a team to start. You don't need permission to build.
You need curiosity, obsession, and enough coffee to outlast your own doubt.
This product began with a question. It became a habit. And now, it's something people actually use.
Thanks to everyone who followed along. Thanks to those who tested early builds. And thanks to the pain points that reminded me what really matters in tech.
This is TechBit, and I'm just getting started.
With love,
Vineeth
P.S. If you want to follow along with what happens next, you know where to find me. This is just the beginning.