Show HN: I Built a Fair Alternative to Product Hunt for Indie Makers Like You
14 by lakshikag | 3 comments on Hacker News.
I’m an indie maker, just like many of you. A few months back, I launched a product on one of the big platforms, and... nothing. It got buried under dozens of other launches within hours. All that work, all that excitement is gone in the blink of an eye. No one even saw it. It stung. I wasn’t mad, well, maybe a little but mostly, I just felt invisible. The truth is, indie makers like me don’t have big teams or budgets to fight for visibility. We rely on genuine support and connections. I couldn’t stop thinking about how many great ideas never get the attention they deserve because they’re overshadowed. So, I decided to build something different: https://itslaunched.com Here’s the idea: • 10 launches per day, max. Limiting the number of daily launches ensures that every product gets its moment in the spotlight. • 2 votes per user, per day. This isn’t a popularity contest. You only get two votes, so people have to really think about which products they want to support. It’s quality over quantity. • “Under Radar” feature. This one’s my favorite. If a product doesn’t get much love on its launch day, it gets a second chance to shine the next day. Because timing shouldn’t be the only thing standing between you and success. There’s more like badges, comments, streaks but the heart of it is simple: a fair shot for indie makers. I built this because I believe every product deserves to be seen, especially the ones built by solo makers and small teams putting their heart into something they truly care about. And I didn’t build this to compete with Product Hunt. I built it to give indie makers the platform they deserve, one where their creativity truly gets noticed. If this sounds like something you’d want to check out, I’d love your thoughts. I’m still tweaking and improving it every day based on feedback. Let me know what you think and if you’ve got a product you’re proud of, I’d love to see it shine.
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New top story on Hacker News: Show HN: SemHash – Fast Semantic Text Deduplication for Cleaner Datasets
Show HN: SemHash – Fast Semantic Text Deduplication for Cleaner Datasets
11 by Pringled | 2 comments on Hacker News.
We’ve just open-sourced SemHash, a lightweight package for semantic text deduplication. It lets you effortlessly clean up your datasets and avoid pitfalls caused by duplicate samples in semantic search, RAG, and machine learning. Main Features: - Fast and hardware friendly: Deduplicate datasets with millions of records in minutes, on a CPU. - Flexible: Works on single or multiple datasets (e.g., train/test deduplication), and multi-column data (e.g., Question-Answering datasets). - Lightweight: Minimal dependencies (largest is NumPy). - Explainable: Easily inspect duplicates and what caused them, and view the lowest similarity duplicates to adjust the threshold based on your dataset. We found that text deduplication is more complex than it appears, so we built SemHash to simplify the process. Duplicate samples can skew model training, reduce generalization, and cause train-test leakage—leading to unreliable results. Techniques like minhash handle exact or near-exact duplicates, but semantic deduplication also catches semantically redundant samples, which we believe is an important aspect of deduplication. Furthermore, it’s not trivial to see why something was removed with minhash, which we also believe is important. We already found some interesting results on some well known datasets in our benchmarks which are included in the repo. We are curious to hear your feedback! Do you currently deduplicate your datasets before training, and what techniques do you use?
11 by Pringled | 2 comments on Hacker News.
We’ve just open-sourced SemHash, a lightweight package for semantic text deduplication. It lets you effortlessly clean up your datasets and avoid pitfalls caused by duplicate samples in semantic search, RAG, and machine learning. Main Features: - Fast and hardware friendly: Deduplicate datasets with millions of records in minutes, on a CPU. - Flexible: Works on single or multiple datasets (e.g., train/test deduplication), and multi-column data (e.g., Question-Answering datasets). - Lightweight: Minimal dependencies (largest is NumPy). - Explainable: Easily inspect duplicates and what caused them, and view the lowest similarity duplicates to adjust the threshold based on your dataset. We found that text deduplication is more complex than it appears, so we built SemHash to simplify the process. Duplicate samples can skew model training, reduce generalization, and cause train-test leakage—leading to unreliable results. Techniques like minhash handle exact or near-exact duplicates, but semantic deduplication also catches semantically redundant samples, which we believe is an important aspect of deduplication. Furthermore, it’s not trivial to see why something was removed with minhash, which we also believe is important. We already found some interesting results on some well known datasets in our benchmarks which are included in the repo. We are curious to hear your feedback! Do you currently deduplicate your datasets before training, and what techniques do you use?