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Diagify – CLI to convert natural language into technical diagrams

Hacker News

Diagify – CLI to convert natural language into technical diagrams

Hello HN, I've created Diagify, a CLI for converting natural language to technical diagrams. There are a bunch of diagram-as-code tools out there and I had the idea that it should be possible to use LLMs to generate the diagram code based on a natural language description, execute it, and return the generated image. This is essentially what Diagify does. More specifically, Diagify generates code for the Mingrammer library which is specifically for technical diagrams. It also uses the OpenAI API to generate the Mingrammer code from the natural language description. The workflow is first OpenAI generates the Mingrammer python code based on the description, then it does some basic error checking. It was found that often the generated Mingrammer code had incorrect imports so this is checked. If incorrect imports are found, OpenAI is called again for correction with the incorrect imports identified and also some suggested imports to replace them with. Then the Mingrammer code is executed. If it is executed successfully the corresponding image is generated. If there is a runtime error then OpenAI is called again with the identified error in an effort to correct it. It's become somewhat reliable at this point. My reasons for creating Diagify are two-fold 1) to see if it even would work and 2) creating technical diagrams by hand or even using diagram-as-code tools can be tedious so using a simple natural language interface could be helpful.

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Indie HackersFits the IH revenue-focused audience · Strong signals: created · Missing: supports, reddit linkedin, podcasting
73%73% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: openai, using, code · Missing: mac, agents, macos
73%73% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
65%65% 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
45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: interface · Missing: plus, platform, intuitive
42%42% 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
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
3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

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