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Leap – Full-stack AI developer agent that deploys to AWS

Hacker News

Leap – Full-stack AI developer agent that deploys to AWS

We just launched Leap, an AI developer agent that helps you build and deploy apps with real backends to your own AWS or GCP cloud. We built it because every AI app builder we tried fell short. They were fine for prototypes, but not for real systems. No support for proper backend infra, no isolated test environments, and sometimes shockingly no way to own your code. We build Leap to be more appropriate for developers: - Connect GitHub to put your code in a repo you control - Work with code generation using pull request–style revisions with diffs - Get Architecture diagrams and API docs for your app as you build - Develop using an isolated preview environment - Deploy either via open source tooling or the integration with Encore Cloud for automated deploys to AWS/GCP Leap uses Claude 4 Sonnet and runs on our open-source framework Encore.ts[1], which provides declarative infrastructure (like a cloud-agnostic CDK-layer). We found the framework to be a good fit for LLM code generation as both application logic and infra is defined in the same context, and the declarative nature makes it less error prone. Right now we think Leap is best for starting new projects or building new isolated services in existing larger systems. The biggest challenge right now is dealing with larger codebases as models struggle to cope with large contexts. And while we're doing a few things to optimize this, we intentionally made it so that you can switch to your IDE any time and keep working there. To run your app locally you only need the open Encore CLI installed, which also can be used to build the app as a docker container. We're just getting started and would love your feedback if you try it. And also it would be helpful to understand what would make this useful for you? - You can try it here: https://leap.new - Here's the launch blog post and video: https://leap.new/launched [1] https://github.com/encoredev/encore

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Actual performance

22points
2comments
Made the leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: agent, claude, model · Missing: mac, agents, macos
98%98% 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
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: exist, open source, existing · Missing: https docs, excited, just released
70%70% 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, video, way · Missing: mobile apps, ios, personal
41%41% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: builder · Missing: plus, platform, intuitive
22%22% 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
18%18% 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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