Snapdeck – Build slides with open-source LLMs and agent routing

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I’m a student founder, and Snapdeck has been a 6-month sprint from idea to product. In March I hacked together a minimal version and launched it on Product Hunt just to see if anyone else had the same frustration with slides — and people did. A couple of months later I tried a Figma plugin version, which brought in more feedback but still felt limited. Yesterday we launched the first full standalone version on Product Hunt and reached #2 of the day. About 1,000 people have signed up so far, with ~30% retention among the main target users. Under the hood, Snapdeck runs on an orchestration layer we built that routes tasks across multiple open-source language models and commercial APIs, acting as an “agent” to generate structured slides and editable charts. Unlike most AI slide tools, everything stays fully editable — layouts, visuals, and content can be dragged, modified, or updated via natural-language commands. Current output is PDF, with PPTX coming soon. Demo screenshots: https://youtu.be/fmlQ6cccj1w . We’re considering open-sourcing parts of the orchestration layer, and I’d love feedback from HN: is this genuinely useful for real workflows, and what’s still missing? https://snapdeck.site

Visit product View on Hacker NewsSep 23, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: agent, model, user · 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 · Missing: supports, reddit linkedin, podcasting

75%75% 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, 000, io · Missing: https docs, excited, just released

54%54% 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: month, users · Missing: mobile apps, ios, personal

44%44% 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

23%23% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: soon, users · Missing: plus, platform, intuitive

23%23% predicted probability of success on AppSumo, 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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