Day 4 of building in public: honest numbers, what actually got traction, and what flopped
I launched HedgeVision as open source 4 days ago. Here's the honest breakdown of what worked, what didn't, and what I learned.
What HedgeVision is:
A statistical arbitrage platform — finds cointegrated pairs of assets, calculates z-scores and spread, runs backtests, and executes paper trading. FastAPI + React, local-first (SQLite by default, no cloud required). GitHub: https://github.com/ayush108108/hedgevision
The real numbers:
- GitHub stars: 2 (unchanged since day 1)
- IndieHackers: 4 likes, 4 comments, ~40 views
- Reddit: Posts likely shadow-removed (14 karma account)
- LinkedIn: Sent DMs to 61 connections — 0 stars yet from that
What actually got traction:
The 2 real comments on my IH post were the only genuine engagement. Both from people who read it and had something real to say. That feedback loop — even tiny — is the most valuable thing that happened.
What flopped and why:
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Reddit — My u/Longjumping_Sky_4925 account has 14 karma. r/algotrading, r/SideProject, r/MachineLearning all have automod karma thresholds. Posts got filtered before anyone could see them. Lesson: karma is a prerequisite, not a nice-to-have.
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Cold posting without social proof — 2 GitHub stars means search invisibility. GitHub's algorithm doesn't surface repos until you have 20-50+ stars. The cold-start problem is real.
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IndieHackers front page — My post peaked at 40 views. New accounts with no followers get no organic discovery here either. The notification feed is the only initial amplification.
What I should have done first:
Built up 10-20 stars from people I actually know BEFORE any public launch. Every platform has cold-start protection. You need social proof before algorithms help you.
What I'm doing now:
Added real screenshots of the working app to the README (was showing folder structure before — big mistake). Direct DMs to connections with a specific ask. Preparing a proper Show HN post for Hacker News.
The question I'm sitting with:
Is it better to (a) keep building in public and accumulate engagement slowly, or (b) focus entirely on getting those first 20 stars and then relaunch?
Curious how others navigated the cold-start problem with a new OSS project.
The part that doesn't show up in 'AI saves 80% of coding time' posts: the rework.
Every time Claude starts fresh without context, it might choose a different pattern than what's already in the codebase. That inconsistency doesn't fail tests — it passes them. It shows up two sprints later when you're debugging why the same entity is handled three different ways across the project.
The fix: a CLAUDEmd file at the repo root. You describe the architecture decisions, the patterns you're committed to, the ones you've explicitly ruled out. Claude reads it at session start and stays consistent — not just within a session, but across weeks and team members.
It turns AI from a pattern generator into something closer to a collaborator who actually remembers what you decided.
What's been your biggest source of invisible AI-generated technical debt?
This is the kind of post I wish more people wrote — numbers instead of vibes. The Reddit filtering problem resonates hard. I'm on day 1 of my own launch and already ran into the same wall — new accounts get buried by AutoMod before anyone even sees the post. Your point about seeding 10-20 stars from people you actually know before going public is something I'm stealing immediately. Cold-starting on any platform without that initial social proof feels like shouting into a void.
This is a really honest breakdown, appreciate you sharing real numbers.
It’s interesting that the only real traction came from actual conversations and comments. Feels like that’s the part most people underestimate.
Curious if you’re planning to double down on that or try different channels next?
I Have to be a bit versatile here, what do you think?