MicroVM daemon, deploy any Docker image

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https://github.com/herd-core/herd Lately, I have been trying to understand the security aspect of docker containers, and what I have realized is that all docker containers share the host's kernel. Any zero day vulnerability in the kernel can be used to gain access to the host os. In order to deal with this, I did some research turns out Amazon has open sourced the core technology behind their serverless technology lambda. But in its current state its very hard to setup, let alone run anything securely. This technology is called firecracker microvm's It started off as a go library, for creating process pools to just do a simple firecracker spawn, turned into a full fledged host side daemon. deploying a microvm through a docker image is now as simple as running `herd deploy --image postgres:latest -p 5432:5432 -e POSTGRES_PASSWORD=postgres` with boot times ~500ms PS - I recently lost access to `Greptile` free code review for OSS projects, my repo stars (38) < 50. Could you guys please bump them up slightly :))

Visit product View on Hacker NewsAug 29, 2026

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Product HuntOn track for Day 1 leaderboard · Strong signals: dock, code, open · Missing: mac, agents, macos

66%66% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Indie HackersFits the IH revenue-focused audience · Strong signals: started · Missing: supports, reddit linkedin, podcasting

66%66% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: open source, code review, ide · Missing: https docs, excited, just released

63%63% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoStrong fit for a featured deal · Strong signals: host · Missing: plus, platform, intuitive

51%51% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

33%33% 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

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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