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mirrord – run your local code in the context of your cloud environment

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mirrord – run your local code in the context of your cloud environment

Hey everyone, I’m Eyal, co-founder and CTO of MetalBear, the company behind mirrord. We’ve recently released version 3.0 of mirrord - it’s the first version of the product that we believe can provide daily value for backend developers. mirrord is a free, open-source (MIT) product that cuts your development loops by making it easier and faster for your local code to “meet” your cloud environment. mirrord lets you “plug” your local process into your Kubernetes cluster. When you run your process with mirrord, it completely wraps your process in the context of the corresponding service on your cluster - it essentially proxies all of your process’ input/output to the remote pod, from network traffic to file access to environment variables, so that you can keep running your process from the familiar comfort of your local environment, but with input, configuration, and state from the cloud. While mirrord can be used in a lot of ways, our ideal use case is for it to be used for concurrent work on a shared, mature development or staging environment. Instead of maintaining multiple environments, each with its own state, maintenance, and cloud costs, organizations can now leverage a single well-maintained production-like environment for use by multiple developers or teams. mirrord is written in Rust, and the technology behind it is quite interesting (and we actually wrote some blog posts about it, see in our company blog [1]): - mirrord injects itself into the executed process, hooking most of the libc APIs, then decides what to run locally and what to run remotely - This lets us run only a specific process in the context of the remote environment, unlike similar solutions which use VPN or edit system files - In order to hook functions, it uses the fantastic Frida framework As for our business model, we plan to launch a managed service to complement the open-source offering of mirrord. The service is intended to help you manage the use of the product in a team/enterprise context. We would love your feedback, and would appreciate your support by: - Trying out mirrord [2]! - Joining our Backend Engineers Discord community [3] - Telling your friends - Contributing to our codebase or opening issues [4] Let me know what you think! [1] https://metalbear.co/blog/ [2] https://mirrord.dev/docs/overview/quick-start/ [3] https://discord.gg/pSKEdmNZcK [4] https://github.com/metalbear-co/mirrord

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, context, single · Missing: mac, agents, macos
93%93% 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: vpn, organizations · Missing: supports, reddit linkedin, podcasting
87%87% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
66%66% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
50%50% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
20%20% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
17%17% 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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