go

gocryptfs, an aspiring successor to EncFS, now in v1.0

Hacker News

gocryptfs, an aspiring successor to EncFS, now in v1.0

Author of gocryptfs and co-maintainer of EncFS here. gocryptfs is a FUSE overlay filesystem for Linux like EncFS. But it is written from scratch in Go, uses modern crypto and fixes the security issues of EncFS while providing equivalent speed. Some of the key differences to EncFS are: * Just one security level roughly equivalent to EncFS "paranoia" mode, at the speed of "default" mode. * A design so simple it fits on one page ( this one: https://nuetzlich.net/gocryptfs/security/ ) * Explicit filesystem creation using "gocryptfs -init". No configuration prompts. * Long file names up to 256 bytes (with zero performance impact <= 176 bytes) * Stress-tested using fuse-xfstests * No reverse mode (yet?). Sorry. I'm quite happy with how the project turned out and have released v1.0 a few days ago. If you are using EncFS, you should probably switch. * Website: https://nuetzlich.net/gocryptfs/ * Github: https://github.com/rfjakob/gocryptfs * Binaries: https://github.com/rfjakob/gocryptfs/releases/tag/v1.0 * Comparison to other projects: https://nuetzlich.net/gocryptfs/comparison/ PS: If you are on OSX, check out this ticket: https://github.com/rfjakob/gocryptfs/issues/15 (TLDR: seems to mostly work) PPS: If you are on Windows: cppcryptfs is a C++ re-implementation for Windows: https://github.com/bailey27/cppcryptfs . The author is very active and would sure love to see testers. PPPS: If you are wondering, the other "aspiring successors" are CryFS and securefs. Both are in the comparison table and both are worth looking at. The big difference is that they implement their own directory databases stored in files while gocryptfs relies on the underlying FS as much as possible.

Share card

Actual performance

20points
Made the leaderboard

Launch Intel predictions

Analyze your own launch →
Indie HackersFits the IH revenue-focused audience · Strong signals: para · Missing: supports, reddit linkedin, podcasting
81%81% 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.
AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive
50%50% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: filesystem, io · Missing: https docs, excited, just released
47%47% 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 HuntUnlikely to reach the leaderboard · Strong signals: using · Missing: mac, agents, macos
41%41% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: para · Missing: mobile apps, ios, personal
35%35% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: active · Missing: arr, mrr, revenue
16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: crypto · Missing: web3, chat, cryptocurrency
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

Similar products

Go
Golongpoll v1.1 released33%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Golongpoll v1.1 released

Hacker News3
Ne
Neodoc v1.0.0-rc.128%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Neodoc v1.0.0-rc.1

Hacker News6
Re
Revivejs v1.1.028%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Revivejs v1.1.0

Hacker News1
Go
Go-Featureprocessing v1.0.028%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Go-Featureprocessing v1.0.0

Hacker News1
Py
Pyinfra v1.428%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Pyinfra v1.4

Hacker News3
Am
Amfora v1.9.028%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Amfora v1.9.0

Hacker News1
Oh
Ohayo v1 (2017)21%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Ohayo v1 (2017)

Hacker News2
Ar
Ark v1.0.045%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Ark v1.0.0

Hacker News10
Lo
Logto v1.2.028%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Logto v1.2.0

Hacker News8
In
IntroJS v1.0.0 released33%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

IntroJS v1.0.0 released

Hacker News6