Gamma API- Auto-generate decks, docs and carousels from raw input

Hacker News

Hi HN, I’m Sarafina, an engineer at Gamma, and I’m here to share something we’ve been building the Gamma API, now in public beta. We originally built Gamma as a way to turn rough content into shareable decks and documents, a kind of design layer for non-designers. Over time, we got more automation requests to enable quickly creating new sales pitches, lesson plans, or meeting summaries. The Generate API takes in raw input like text, meeting notes, or CRM data and returns polished content. You can generate: Slide decks Docs Social carousels (LinkedIn-style) All fully designed, brandable, and exportable to PDF or PPTX. How it works: You pass a POST request with the input and config (tone, format, theme, etc.) The API responds with a Gamma link (hosted doc), or export file Works with 60+ languages, customizable themes, and AI image support We’d love feedback, especially from developers thinking about “visualization layer” workflows inside internal tools or automation stacks. AMA! -Sarafina (Gamma Engineering)

Visit product View on Hacker NewsSep 23, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: new, visual, notes · Missing: mac, agents, macos

91%91% 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

85%85% predicted probability of success on Indie Hackers, 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

45%45% 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: way · Missing: mobile apps, ios, personal

41%41% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

40%40% 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

19%19% 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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