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Superblocks AI – AI coding assistant for internal apps

Hacker News

Superblocks AI – AI coding assistant for internal apps

I’m Frank, the Head of AI Engineering at Superblocks. We’re a platform for enterprise developers to build and host mission-critical internal apps. Today we launched Superblocks AI and are excited to hear feedback from the HN community! Superblocks AI combines the power of the Superblocks drag-and-drop App Builder with robust AI code generation, code optimization, code explanation, mock data generation, and API call generation across SQL, Python, JavaScript, JSON and HTML. We built Superblocks AI with the intention to streamline some of the repetitive and often confusing aspects of building internal tools, here’s a brief overview: Generate Code: LLMs like ChatGPT have quickly become a critical part of developers' lives and we wanted to bring that experience natively into our product. The response is streamed for the best UX, similar to Chat-GPT, and can be modified before use. Explain Code: Understanding someone else’s code for large internal engineering teams is often a challenge. Highlight code and Superblocks AI offers an explanation that you then have the ability to add as a comment. Edit Code: You can make alterations to code by highlighting it and choosing prompts like optimize performance, enhance readability, or something custom. We've added an inline code diff view for easy verification of changes. Generate API Calls: You often want to make an API call but need to spend 30 minutes looking up the documentation and crafting the correct call in Postman. We provided a way for you to give a prompt, and Superblocks AI will generate REST and GraphQL queries for APIs like Stripe, Salesforce, your favorite SaaS provider, and more. Generate Mock Data: This enables faster UI development by generating mock data for components like tables, charts, grids etc., which can be especially handy during prototyping. I'd love to hear feedback from the HN community on how you think this feature should evolve over the next few months!

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Product HuntOn track for Day 1 leaderboard · Strong signals: apps, stripe, chatgpt · 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 · Missing: supports, reddit linkedin, podcasting
94%94% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: excited, ide, io · Missing: https docs, just released, exist
62%62% 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: apps, month, way · Missing: mobile apps, ios, personal
50%50% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform, host, builder · Missing: plus, intuitive, reviews
49%49% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: saas · Missing: arr, mrr, revenue
19%19% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency
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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