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Clace – Platform for hypermedia driven internal web tools

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Clace – Platform for hypermedia driven internal web tools

I am building https://github.com/claceio/clace , an open source platform to develop and deploy hypermedia driven web apps for internal tools. For running multiple web applications on a single machine, Clace provides functionality which usually requires stitching together multiple services: reverse proxy like nginx/caddy (for domain/path based routing, TLS certs, static file serving), application server like uwsgi/gunicorn with micro-framework like flask (for API handling, HTML templates) and deployment infrastructure like containers/VMs/k8s (for isolation across apps, versioning and staged deployments, gitops). For internal web apps, Clace provides similar functionality in a single lightweight binary. Clace and its plugins are implemented in go. User applications are developed in Starlark, which uses a python inspired syntax. Clace is intended for use cases where internal tools need to provide access to backend infrastructure like databases, APIs or scripts. Each Clace app runs in a security sandbox, only operations approved by the admin are allowed for each app. App developer can make code changes but they need to stay within the defined rules. For internal tools, where the goal is to glue together external APIs, Starlark works great as the glue code. Being hypermedia driven, with HTMX support, there is no or minimal user javascript required on the frontend. Apps have no build step, so apps can be installed from github url directly. The goal is to build a platform for developing internal tools. Since there are no python/nodejs dependencies to install, easy app distribution is the advantage of Clace for local use. The dev environment for Clace apps including live reload is available without any additional setup (just add the --dev option during app create). Rundeck like jobs for operational use cases are planned. Currently, free-form applications are supported. To install and try it out, run curl -L https://clace.io/install.sh | sh source $HOME/clhome/bin/clace.env clace server start & clace app create --auth-type=none --approve /disk_usage github.com/claceio/clace/examples/disk_usage # This installs a du like webapp clace app create --auth-type=none --approve /book github.com/claceio/apps/utils/bookmarks # This installs a bookmark manager using sqlite for persistence On Windows, use pwsh -Command "iwr https://clace.io/install.ps1 -useb | iex" to install Clace (or use powershell). The installed apps are available at https://localhost:25223/disk_usage and https://localhost:25223/book . Do try it out, would appreciate any feedback.

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Indie HackersFits the IH revenue-focused audience · Strong signals: including · Missing: supports, reddit linkedin, podcasting
97%97% 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: mac, apps, user · Missing: agents, macos, agent
90%90% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: open source, nginx, ide · Missing: https docs, excited, just released
81%81% 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 · Strong signals: platform, host · Missing: plus, intuitive, reviews
39%39% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: apps · Missing: mobile apps, ios, personal
36%36% 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
14%14% 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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