Coincidex
Transfer-aware, replay-free continual learning for PyTorch
What this is
A drop-in PyTorch layer for continual learning that lets similar tasks share sub-networks for transfer while keeping dissimilar tasks isolated, avoiding both forgetting and the need for a replay buffer.
Our one-line summary — not the founder’s tagline.
- How it works
- Learns task similarity from context so similar tasks share sub-networks while dissimilar ones stay isolated, with no replay buffer.
- What's different
- Reproducible benchmarks disclose where the method works and where it doesn't.
- Pricing
- Apache-2.0 license (open source).
- Best for
- PyTorch users needing continual learning without catastrophic forgetting.
What we measured
Nobody else publishes this — it comes from knocking on the door every day.
- Watched by us
- 7 days
- Last checked
- 1d ago
Where it shows up
Every group here is a page of its own — each one checked daily.
The numbersIs it still shipping, is it overpriced, where did it land — and what the price is built from.
League price & chart
No money. No seat. It goes on your record — and in 28 days reality settles it.
Calls are closed while we rebuild accounts. You can still read every one of them.
AI. Launched 7d ago on Product Hunt, picking up 12 votes. Today, the site itself changed 3d ago — evidence it's still shipping, not just announcing.
Quiet for 2 days — no penalty yet, but quiet doesn't stay free.
We check this site every day, ourselves. A founder can post “still working on it” — a claim like that doesn't price. What we price is what we can verify from the outside: evidence, not announcements. The real question isn't “will this be huge?” — it's “will they still be moving in four weeks?”
The market viewHow this launch is priced and ranked in our league — the investor side of the page.
ranked by the reality anchor, not the market price
It launched on Product Hunt with 12 votes.
No matter how much money goes in. There is no pump here — you can't make yourself right by buying more. The line only moves on things that actually happened: an award, revenue that grew, a new platform, code that shipped — or silence.
2 quiet days in between are left out — nothing happened on them. A launch that goes quiet eases down a little at a time — never a cliff you could have run from the night before.
Daily tracking just started for this launch — the first point is on the board. A second reading lands with tomorrow's run, and the curve builds from there.
How the launch is moving on its own board, day by day — the crowd's attention.
A flat line is normal: votes stop within a day or two of launch, on every board. What's unusual — and what actually counts — is a launch that keeps pulling votes long after its day is over.
About
Most continual-learning methods either forget (fine-tuning) or isolate and forfeit transfer (fixed masks, adapters). Coincidex does both: similar tasks share sub-networks for forward transfer, dissimilar ones stay isolated so old skills survive — with no replay buffer. A tiny, drop-in PyTorch layer (torch + numpy, CPU-friendly) that learns task similarity from context. It's also honest: reproducible benchmarks and a clear map of where it works and where it doesn't. Apache-2.0 license.
Where it launched
1 platform| Platform | Votes | Counts toward price | Link |
|---|---|---|---|
| Product Hunt | 12 | sets the price | ↗ |
The board it did best on sets the price. Every other board only adds to it if the launch also placed high on that board too — because just showing up somewhere isn't an achievement. Listing on twelve directories is free; placing well on them isn't.
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