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ArchiveCache decentralized filesystem topof IPFS

Hacker News

ArchiveCache decentralized filesystem topof IPFS

ArchiveCache is a simple, extensible, decentralized filesystem. Users can create their own folders, share data and discuss. There is no central servers. ArchiveCache currently uses IPFS, but can be extended to use other similar types of tech. ArchiveCache is not an application. It is a framework and a set of tools to use it. It is written in Python, but there are shell command that can be used in scripting. There are just too many ways to use ArchiveCache for it to be a point and click application. ArchiveCache can be used to collaboratively manage large decentralized directory structures. One can use it as a source control system, to share large datasets or any kind of information that sits in directories. Since IPFS handles large files, ArchiveCache can be used to share nn checkpoints as well as docker images or similar. One can also use it for websites and even directly share live runtime environments such as python venv or conda. Package repositories can also be efficiently share on ArchiveCache. ArchiveCache opens up new new ways to collaborate. Due to how ArchiveCache references data it can be used with other networking systems besides IPFS, such as HTTP, FTP, magnet links, Freenode, TCP/IP directly or anything else you can come up with. There are simply no limitations built in to ArchiveCache. The best part about ArchiveCache is that it is very simple and straight forward. ArchiveCache filesystem works like this: Each user has a local version of the filesystem. Users can freely edit their filesystem. Data is stored on IPFS. Users share a link to the root of their filesystem ( an IPFS CID ). Users merge other users filesystems into their own ( rules such as ownership with signatures apply here ). The modifying - merging - sharing cycle is what causes information propagates through the network.

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Actual performance

1points
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Launch Intel predictions

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Hacker NewsStrong engagement from HN community · Strong signals: filesystem, ide, io · Missing: https docs, excited, just released
72%72% 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, dock, new · Missing: mac, agents, macos
72%72% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Strong signals: efficient, users · Missing: plus, platform, intuitive
59%59% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Indie HackersFits the IH revenue-focused audience · Strong signals: efficiently · Missing: supports, reddit linkedin, podcasting
55%55% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: users, way · Missing: mobile apps, ios, personal
43%43% 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 · Strong signals: collaborate · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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