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Notation – Bridging infrastructure and runtime code

Hacker News

Notation – Bridging infrastructure and runtime code

Hey everyone, Daniel here. I’m building an open source framework that makes it easy to code and deploy serverless applications – https://github.com/notation-dev/notation | https://notation.dev . I wanted a tool that delivers the benefits of serverless (scalable managed services), but with a first-class developer experience. Here are the main concepts: 1/ Notation provides a set of primary infrastructure resources (eg API gateway, lambda, event bridge etc) that can be composed together into serverless apps. They are fully typed, so slot into place very easily. A lot of annoying cloud implementation details – things like constructing valid ARN strings, and writing JSON policy documents - are handled by the framework. 2/ Secondary infrastructure resources (eg IAM roles, permissions and integrations) are inferred from the relationships between the primary resources, and from explicit user intents. You can see these visualised in a generated graph. 3/ Runtime code (ie the lambda function code) belongs in separate modules – but, these can be imported into and referenced by infrastructure modules. Again, we have end-to-end types, so if a lambda function were configured to accept and API request, but got attached to an SQS stream, the compiler would quickly complain about that. 4/ Notation apps can be deployed directly to your own cloud accounts. If you want to develop locally, you can deploy using watch mode, which hot-swaps infrastructure resources, and provides snappy updates of serverless functions, while you code. The next step is to integrate more AWS services, and then later expand to other cloud platforms (feature requests ofc welcome). You can give Notation a spin at https://github.com/notation-dev/notation . I’d love to hear your feedback on the approach!

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Product HuntOn track for Day 1 leaderboard · Strong signals: apps, user, visual · Missing: mac, agents, macos
88%88% 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: para · Missing: supports, reddit linkedin, podcasting
75%75% 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, ide, io · Missing: https docs, excited, just released
71%71% 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 · Missing: plus, intuitive, reviews
28%28% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: apps, way, para · Missing: mobile apps, ios, personal
27%27% 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
12%12% 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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