In

Interval, CLIs in browser with no front end code

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Interval, CLIs in browser with no front end code

Hey HN, I'm Alex, founder of Interval [0]. We’re bringing internal tools written like CLIs to the browser to make them more powerful, through a Node.js SDK which attaches them to a hosted dashboard without any frontend code. Large companies have full teams that work on internal dashboards and tooling. Interval brings that infrastructure to anyone. In our previous project we had ~65 CLI scripts for tasks like provisioning user accounts, moderating content, and migrating data. These were quick to write and powerful. They also turned into an ongoing timesuck. Non-technical coworkers needed to bother an engineer every time to run one. Some of them were powerful enough to be footguns, and because we had to rewrite validation every time, we were always a bit scared of them. We wanted to bring the power and speed of cranking out CLI scripts into the world of modern software development: testable, easy to expose to colleagues via a URL, and works with Everything Else by default. That’s why we made Interval. We built: - A Node.js + TypeScript SDK – this embeds in your backend codebase and provides APIs for defining tools + collecting input + displaying output. These APIs are simple awaitable functions that return parsed, validated, and soundly-typed user input. You put your Interval actions in source control, test them, run CI/etc exactly like you do for all your other backend code. - A hosted UI, which handles I/O for the scripts in a less brittle way than command line arguments do, while also taking care of auth, permissions, and audit logs. This lets you take your CLI commands out of an engineer’s terminal and share them with the whole company. Give support the ability to ban spammers but not drop the prod DB. Require two people for sensitive actions. Echo commands to a Slack channel. It’s a powerful set of primitives out of the box and lets you do anything else you want in the code you’re already writing, without making you have to spin up a second company just to support your internal tools. Under the hood, your CLIs and the hosted dashboard talk through a 2-way message passing system. We felt this approach was the best of both worlds: - UIs are hosted by Interval: We build, host, and maintain the part most people don't care about for internal tools. - Backends are self-hosted: We can't see your business logic, secrets, etc. We're in public beta today. I hope you like it and I'm happy to answer any questions/feedback in the comments. [0]: https://interval.com

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Product HuntOn track for Day 1 leaderboard · Strong signals: slack, user, tasks · Missing: mac, agents, macos
97%97% 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 · Missing: supports, reddit linkedin, podcasting
89%89% 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
80%80% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRFits verified-revenue profile · Strong signals: way · Missing: mobile apps, ios, personal
53%53% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: host · Missing: plus, platform, intuitive
42%42% 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
15%15% 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.

Correct prediction on native model

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