Cl

Client-Side WASM Vector Search

Hacker News

Client-Side WASM Vector Search

Share card

Actual performance

5points
1comments
Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →
Product HuntOn track for Day 1 leaderboard · Missing: mac, agents, macos
61%61% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Hacker NewsStrong engagement from HN community · Strong signals: ide · Missing: https docs, excited, just released
56%56% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
BetaListSuited for BetaList early-adopters · Missing: web3, chat, crypto
53%53% predicted probability of success on BetaList, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
46%46% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
31%31% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
30%30% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
13%13% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

Incorrect prediction on native model

Similar products

Cl
Client-side vector search with Transformers.js61%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Client-side vector search with Transformers.js

Hacker News1
Ma
Marqo – Vectorless Vector Search77%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Marqo – Vectorless Vector Search

Hacker News62
SQ
SQLite-Vector – Vector search for SQLite, now Apache 2.066%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

SQLite-Vector – Vector search for SQLite, now Apache 2.0

Hacker News2
Cl
Client Vector Search – Embeddings and Semantic Search with 5 Lines87%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Client Vector Search – Embeddings and Semantic Search with 5 Lines

Hacker News11
Fa
Facets – client-side faceted search43%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Facets – client-side faceted search

Hacker News4
C+
C++ virtual_vec vector implementation47%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

C++ virtual_vec vector implementation

Hacker News39
C+
C++23 constexpr n-dimensional Euclidean vector56%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

C++23 constexpr n-dimensional Euclidean vector

Hacker News1
Qu
Quepid now works with vector search50%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Quepid now works with vector search

Hacker News2
SQ
SQLite-vector – Vector search extension for SQLite (no index, 30MB RAM)67%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

SQLite-vector – Vector search extension for SQLite (no index, 30MB RAM)

Hacker News5
Ve
Vector search and reranking SaaS API55%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Vector search and reranking SaaS API

Hacker News8