I

I made a tool that speeds up TCP/IP by eliminating packet loss

Hacker News

I made a tool that speeds up TCP/IP by eliminating packet loss

I have been chipping away at the problem of packet loss during TCP/IP data transfer for the last 19 years. I am new to HN – Greetings I created this tool (Netpump Data) to optimise the size of packet segments and use nonlinear data transfer to reduce the risk of access congestion, enable accelerated delivery, and access the latent capacity of available bandwidth. By “nonlinear data transfer”, I mean that files are segmented and transmitted through different ports (or sockets), as opposed to TCP/IP, which is a linear transfer through a single port. I’ve found that I can get speeds usually around 2x to 5x faster than TCP/IP (but up to around 15x). I am looking for some people to test my observations / assumptions, provide feedback and explore use-cases with me. Netpump Data must be installed on both ends of the transfer, so it is most appropriate for enterprise networks. Theoretically, it could become a consumer product, but this has not yet been built. I have a Demo User Guide [1] which provides instructions on how to use Netpump Data in a test environment I’ve set up across 5 servers on the Azure Cloud. There is also an Admin Guide [2] which goes into further detail concerning implementing Netpump Data in other TCP/IP environments. To give you dedicated time on our test environment to demo the software, if you’re interested, please get started using this Typeform [3] and I will provide you a unique User ID and password to gain access to our Azure servers. My website, contact information and FAQs are at: https://www.netpump.com/ If you’d like to know how I made this, have any feedback, or discuss how it might be used, I’ll be around to respond to any queries and comments. [1] https://www.pacbyte.com/user_guides/Netpump%20Data%20Azure%2... [2] https://www.pacbyte.com/user_guides/Netpump%20Data%20-%20Adm... [3] https://7zwtzlao1bg.typeform.com/to/YNhhLKgI

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Indie HackersFits the IH revenue-focused audience · Strong signals: created, started · Missing: supports, reddit linkedin, podcasting
75%75% predicted probability of success on Indie Hackers, 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, io · Missing: https docs, excited, just released
64%64% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Product HuntOn track for Day 1 leaderboard · Strong signals: user, new, single · Missing: mac, agents, macos
55%55% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
44%44% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
32%32% 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
10%10% 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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