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G-Script – Visual Scripting for the Web

Hacker News

G-Script – Visual Scripting for the Web

Hi all, I'm a PM by day who taught themselves to code over COVID. One of the things I enjoyed during that process was learning how to make basic games in Unreal Engine using Blueprints. I found visual scripting was such an intuitive way to express what I call "mid-tier complexity" logic and I felt there really wasn't a great equivalent for the web - so I built one over the last few months and this is my MVP. Tools like Zapier etc are great and easy to use, but they're heavily limited when it comes to expressing any kind of complex logic or trying to follow coding principles like DRY. On the other end of the spectrum, serverless setups like Cloudflare workers or Firebase functions give you all the power of code, but there's atleast 20-30 minutes of additional overhead involved in just getting the things live. G-Script is designed to land exactly in the middle of these two options. It's "Lower level" than tools like Zapier and supports most of the code-level primitives you NEED in order to express logic. Examples being: - Conditional Logic - Loops - Static Typing - Objects & Arrays - Version Control - Reusable logic/functions On the flip side, it's much less friction that writing an actual serverless function, both up-front and ongoing. Examples: - You don't need to know "Code", just how to express your logic visually. - No need to manage even basic deployments or use a CLI, it's all done via a UI. - Every Workflow is a seperate little microservice with it's own URL that you can call via HTTP. - The super simple version control system makes it easy to role back to earlier versions of a workflow if you break something, or make changes to your logic without impacting what executes and the Workflow URL until you're ready to deploy your final iteration.

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90points
32comments
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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: visual, using, coding · Missing: mac, agents, macos
82%82% 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.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
67%67% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Indie HackersFits the IH revenue-focused audience · Strong signals: supports · Missing: reddit linkedin, podcasting, created
63%63% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: month, way · Missing: mobile apps, ios, personal
49%49% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: intuitive · Missing: plus, platform, reviews
45%45% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: arr · Missing: mrr, revenue, profit
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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