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WinCse – Access Object Storage in Windows Explorer (Now AWS, GCP, etc.)

Hacker News

WinCse – Access Object Storage in Windows Explorer (Now AWS, GCP, etc.)

I previously introduced WinCse on Hacker News (original post: https://news.ycombinator.com/item?id=43947599 ). At the time, it only supported AWS S3, but this update adds compatibility with GCP, OCI, and more object storage providers! Object storages tested for compatibility: - AWS S3 - Google Cloud Storage - OCI Object Storage - Wasabi Hot Cloud Storage - Cloudflare R2 - Backblaze B2 - Storj DCS - IDrive e2 With WinCse, you can access and manage object storage directly from Windows Explorer as if it were local files. If you encounter any bugs, please report them on GitHub. I also welcome suggestions for improvement and will consider implementation where possible. Give it a try! Bug reports, feedback, and contributions are always welcome.

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
66%66% 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.
Product HuntOn track for Day 1 leaderboard · Strong signals: google, new · Missing: mac, agents, macos
60%60% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: hacker news, ide, io · Missing: https docs, excited, just released
58%58% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
36%36% 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
36%36% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: introduce · Missing: web3, chat, crypto
16%16% predicted probability of success on BetaList, 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.

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