Ke

Kedge – Full-stack cloud with forkable VM snapshots and global SQLite

Hacker News

Kedge – Full-stack cloud with forkable VM snapshots and global SQLite

I'm building Kedge, a globally distributed platform for stateful serverless apps. Here's how you make a simple static site: `echo '# Hello world!' | ssh kedge.dev` I helped build Fly.io for 4 years and shared enthusiasm for the founders' vision of a 'global Heroku'. While there, I wrote "The Serverless Server" ( https://fly.io/blog/the-serverless-server/ ) as a study of Lambda and a sketch of a modern serverless product built around lightweight VMs. That essay was the initial inspiration for Kedge. Kedge has a fast VM orchestrator that can create code sandboxes or scale service instances in 3ms, using a combination of forkable VM snapshots and a tree of warm pools (Linux kernel -> base runtime -> app). VMs are memory-dense thanks to shared copy-on-write memory pages. You can run lightweight CGI-style functions, public OCI images, or source code for BuildKit to compile and deploy. Kedge's global control plane sits on an eventually-consistent SQLite database. Taking inspiration from Corrosion and Litestream, I built a local-first, multi-writer CRDT-based replication system backed by object storage, and just recently made it open source ( https://github.com/wjordan/syzy ). You can also use a SQLite client to query `/shared.db` from any instance for a build-in replicated database in your app. This lets Kedge autoscale services close to your users while each instance queries its local replica for eventually-consistent data, with no need to micro-manage instance or volume placement. (There's also a /shared/ filesystem adapter for convenience.) Kedge can even use this same database for stateful, server-rendered HTML apps. Data attributes bind forms, buttons, and values to records in the app database, Kedge compiles the schema and operations at deploy-time, and then queries the local data to serve requests. As a demo, I made a Hacker News clone with story submission, votes, comments and auth in about 60 lines of Markdown, plus CSS ( https://kedge.dev/docs/html-apps#kedger-news ). I've just started collecting public feedback, so please let me know what you think! I'm particularly interested in feedback on the stateful HTML app model, which is the newest (and most ambitious) piece. The preview is currently running in 11 regions for you to kick the tires. There's no billing yet, so the pricing page is an estimate. Thanks for taking a look!

Share card

Actual performance

141points
25comments
Made the leaderboard

Launch Intel predictions

Analyze your own launch →
Product HuntOn track for Day 1 leaderboard · Strong signals: model, apps, user · Missing: mac, agents, macos
95%95% 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: started · Missing: supports, reddit linkedin, podcasting
93%93% 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, filesystem, hacker news · Missing: https docs, excited, just released
87%87% 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: apps, users · Missing: mobile apps, ios, personal
38%38% 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
24%24% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: plus, platform, users · Missing: intuitive, reviews, host
20%20% predicted probability of success on AppSumo, 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.

Correct prediction on native model

Similar products

Stack on Cloud
Stack on Cloud67%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Build backends faster with Stack on Cloud

Indie Hackers1apis
Ou
Our Stack54%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Our Stack

Hacker News5
Ar
Arrived – Stack Overflow for US Immigration52%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Arrived – Stack Overflow for US Immigration

Hacker News244
Th
The OP Stack54%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

The OP Stack

Hacker News4
Po
Pokémon Global Offensive38%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Pokémon Global Offensive

Hacker News3
Co
Codevid-19, a Global and Distributed Covid-19 Hackathon46%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Codevid-19, a Global and Distributed Covid-19 Hackathon

Hacker News9
Gl
Global Terrorism Catalogue – Terrorism Incidents Since 196844%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Global Terrorism Catalogue – Terrorism Incidents Since 1968

Hacker News2
Gl
Global phonebook37%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Global phonebook

Hacker News3
Th
The Global Literary Canon51%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

The Global Literary Canon

Hacker News4
Je
Jekyll Pygments/Redcarpet Global Configs41%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Jekyll Pygments/Redcarpet Global Configs

Hacker News4