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PeekoCMS – A Visual CMS / Website Building Platform with AI

Hacker News

PeekoCMS – A Visual CMS / Website Building Platform with AI

Hey folks, I've been working on a small project for the last year or so and figured it was time to share. It's a visual CMS / HTML editing platform with support for Handlebars templating, AI, and visual editing. Features: - Template library - Undo / Redo editor history - Full Monaco code editor in the browser - Localization - Tagged page versions / revisions that can be toggled site-wide - AI Prompting / Editing selections - Custom Web Component Prop / Form Integration - Upload global variables to S3 endpoint and render them in Handlebars I spent ~10 years building websites for a large international cosmetics conglomerate. I saw the struggles and challenges they faced building templates and components that could be integrated with their CMS so non technical users could use it too. I'm trying to build a website builder that isn't super constrained like Wix/Squarespace but still being easy to use. I think in the age of AI, we need to revisit how we give non technical users the ability to build and publish webpages. While my project is not nearly as complete as I would like, it does have a lot of powerful features, including support for automatic UI generation with StencilJS docs. For example, to have a select dropdown when your web component is selected, annotate your Prop with the following: /** * Options that will determine whether the content sits below the media slot or overlapped above. * @type Select * @options below * @options above * @responsive */ @Prop() contentPosition = 'below|above'; By annotating your component props and exporting the Doc JSON from StencilJS, you can create an easy to use UI for all your custom components. If this would be useful for your marketing / ecommerce / project let me know!

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Actual performance

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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: user, visual, code · Missing: mac, agents, macos
93%93% 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: including · Missing: supports, reddit linkedin, podcasting
92%92% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io, including · Missing: https docs, excited, just released
68%68% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoStrong fit for a featured deal · Strong signals: platform, builder, users · Missing: plus, intuitive, reviews
62%62% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: users · Missing: mobile apps, ios, personal
52%52% 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
17%17% 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.

Incorrect prediction on native model

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