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Share compute functions while hiding the implementation

Hacker News

Share compute functions while hiding the implementation

TL;DR: We take the pain away from remote procedure calls. Hi, We are Christoph and Orell from Germany. We made LMRTFY ( https://lmrt.fyi ), a tool to share compute functions running anywhere with others without sharing your code. In engineering sharing algorithms is often done via source code which is cumbersome if you need special resource (GPU, propietary data sets, …) and impossible if you need to protect your IP. We created a a open-core platform to share functions bound to specific resources without sharing the actual implementation. Shared functions can be called from your code. Just like any regular function. Find the open source tool on GitHub ( https://github.com/lmrtfy/lmrtfy ) or dive right into the docs ( https://docs.lmrt.fyi/latest/quickstart ). Unfortunately, we need to require you to sign up because otherwise our servers would be vulnerable and we couldn’t make sure that only you can access your function (E-mail verification is not enforced right now). Alternatively, you can use social logins (google, GitHub). If you like this project, please star us on GitHub! We are very thankful for any feedback! Don’t hold back :)

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Product HuntOn track for Day 1 leaderboard · Strong signals: google, code, open · Missing: mac, agents, macos
75%75% 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.
Indie HackersFits the IH revenue-focused audience · Strong signals: created · Missing: supports, reddit linkedin, podcasting
64%64% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: open source, io · Missing: https docs, excited, just released
61%61% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoStrong fit for a featured deal · Strong signals: platform, calls · Missing: plus, intuitive, reviews
52%52% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: google, way · Missing: mobile apps, ios, personal
39%39% 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
12%12% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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