Forma ONNX Graph Viewer and Editor

Hacker News

Hey all! Most ONNX tooling stops at just "looking". For those of you who know about Netron, it allows you to read the graph but the moment you want to change something you're back in Python either using onnx-modifier or onnx.helper scripts. I wanted to click a node, change an attribute or rewire a connection and actually know whether the model still runs, within a web-page. This is exactly what Forma is! Simply drop an .onnx or .tflite file on the page and a full graph renders. From there you can do multiple different things (for more info have a look at the README in repo) MIT Licensed repo Live web-app: https://forma-ml.vercel.app

Visit product View on Hacker NewsSep 13, 2026

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, using · Missing: mac, agents, macos

62%62% 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

56%56% 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: io · Missing: https docs, excited, just released

41%41% 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 · Missing: mobile apps, ios, personal

40%40% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

33%33% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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