ET

ETscript – An experimental interpreter for Salesforce's AMPscript

Hacker News

ETscript – An experimental interpreter for Salesforce's AMPscript

I wanted to take a break from JavaScript and learn a lower-level language. I've always been into musical instruments, so I thought I'd be knee-deep in C++ and building VST plugins by now. But for whatever reason, I ended up going through Robert Nystrom's excellent book, Crafting Interpreters instead. Things started out fine, but I had trouble staying focused since I was just copy-pasting code from the book. Forcing myself to type everything out made things worse. To give myself a challenge, I switched things up by using languages not used by the book. For the tree-walking interpreter, I used Python instead of Java; for the bytecode interpreter, it was Rust instead of C. Doing that made a difference. I didn't stop there, though. Instead of implementing clox (the main subject in the book's second half), I implemented a subset of AMPscript — a DSL I had used back when I worked at ExactTarget/Salesforce. Its domain is primarily email message personalization. Does this interpreter, that doesn't have a domain to operate in, have any use in the real world? No, not really (and it's still missing a garbage collector). But I think anyone curious enough to try it out will at least be able to get a feel for writing AMPscript (since Salesforce doesn't offer trial accounts for their Marketing Cloud product). Also, I'm really bad at bringing hobby projects to any type of conclusion. So as a final challenge, I decided my first "Show HN" would be that for this project. Feedback appreciated!

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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.
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Product HuntOn track for Day 1 leaderboard · Strong signals: email, using, code · Missing: mac, agents, macos
82%82% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
49%49% 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: personal, way · Missing: mobile apps, ios, entrepreneurs
43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
37%37% 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
17%17% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: real world · 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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