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Slaide, open-source Markdown slides AI writes and PowerPoint opens

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Slaide, open-source Markdown slides AI writes and PowerPoint opens

I am trying to replace PowerPoint with a file format better suited for AI. But let me fill you in... So, I pitch a lot these days, and "make the deck" had become the worst part of my week. I don't know about you, but automation hasn't really worked for me here. Every AI deck tool I tried failed the same way. After various trial and errors, it ends up being fine on screen, but a broken approximation on export, and not one of them would touch my company master. So I was thinking that perhaps the solution is to give AI a format that's just easier to handle - the XML is really bloated and complex, maybe Markdown & YAML would do better. So, I gave it a shot and it worked. It is called .slaide. It is basically a somewhat customized Markdown to match text with theme defined placeholders and a theme defined in YAML. The markdown has some custom elements for charts, svg, html, etc. to make it valid. All files are then packaged in a zip file (.slaidec). It renders to a web deck, a PDF and a .pptx. None of the underlying formats are new to AI and that's good about it. First time I tried it, the agent (here my local claude code) managed to come up with some great decks. I only had to tweak the skills a bit to push it from "good" to great. And then I had to think about a hosted version for those without a local agent (or who just want to try it all out for a day) - I added that, too, as well with a manual editor/viewer. Anyway, the end result is a surprisingly small deck file which renders great on its own and has many of the features covered you'd expect from a presentation viewer. I added a few of them on our website https://getslaide.com for anyone to try. I also made it so that you can import from PPTX and export to PPTX or PDF if you need to. I'd say it is 95% or so accurate (there are some differences in how browsers and pptx renders pixels, hence the difference). The core is Apache-2.0: language, renderer, CLI, the pptx import and export, the Rust viewer, an MCP server and an agent skill. That skill is the whole reason the format is text at all. Everything local is free. Only the hosted version costs money, because we are paying for the agent on our end, image generation and all. So, all in all, you can just install it via npm or check out the source on Github: https://github.com/aivorynet/slaide or simply run "npm install -g @aivorynet/slaide", then "slaide install", and just use your local cli to work with the format. It should support claude, codex, gemini and cursor out of the box, but happy to get some feedback on it (I primarily focused on my hosted build and claude code to be honest - limited resources and all that). Anyway, would be cool to hear your thoughts. At least for me it did really well across the board, so curious to see if anyone else has the same result.

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Product HuntOn track for Day 1 leaderboard · Strong signals: agent, cursor, claude · Missing: mac, agents, macos
94%94% 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: gemini · Missing: supports, reddit linkedin, podcasting
88%88% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
43%43% predicted probability of success on TrustMRR, 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
34%34% 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: host · Missing: plus, platform, intuitive
24%24% 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
23%23% 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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