I

I made an app to help my gf cook experimentally

Hacker News

I made an app to help my gf cook experimentally

I like to cook, my gf does not - so usually I do the cooking. But sometimes, she wants to, except then we often have fairly random ingredients at home. She is someone who needs to follow a recipe, so I made an app where you can take a photo of the ingredients you have and chatGPT will come up with a reasonable looking recipe. I did also want to learn LLM integrations, so there was an ulterior motive ;) You can try it out for yourself, but you need to create an account (currently the requests to openAI cost me money). Stack is React + material UI, talking to FastAPI with the openAI python package as main LLM interface, all hosted on digitalocean. I did check out langchain, may decide to integrate that in the future. I am somewhat disappointed by the openAI API, you can supposedly specify JSON responses only by setting the relevant request parameter, but that does not always work(!) Additionally, the API will seemingly randomly decide to reject requests, both GPT-Vision and GPT3.5-turbo (and GPT4), for content violations. And these violations don't always set the relevant field in the response object, sometimes the response looks fine but the message string is some variation of "error: contentViolation" (never consistent). I tested this many times using the same exact inputs, both image and text. The API will definitely complain if the ingredients are too diverse, e.g. soy sauce and apples will usually raise a content violation. I am setting up prompt context (you are PantryBot, an AI recipe generator... etc.) using sets of system prompts as specified in their API docs, but who knows. Probably the most reliable part of the whole system is the ingredient recognition - it usually even translates German or other language packaging into English descriptions. The usual shortcomings of LLM outputs apply to the recipes, YMMV. I did also develop automatic illustrations for recipes - both method step-by-step illustrations and one of the final dish. This was extremely unreliable both by generating content violations but also just completely unrelated images, so it is disabled for now. One day I may decide to self host an LLM with some amount of additional fine tuning or RAG or whatever. Finally - I delayed posting this because I originally had the donation link pointing to buymeacoffee, but they disabled my account for fraud (after I had already had some donations...) and will not reply to the appeal. So now I'm using ko-fi, let's see how long that lasts. Looking forward to your feedback!

Share card

Actual performance

1points
Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →
Product HuntOn track for Day 1 leaderboard · Strong signals: apple, context, chatgpt · Missing: mac, agents, macos
89%89% 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: para · Missing: supports, reddit linkedin, podcasting
82%82% 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, 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.
TrustMRRFits verified-revenue profile · Strong signals: way, para · Missing: mobile apps, ios, personal
52%52% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: host, interface · Missing: plus, platform, intuitive
37%37% 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
15%15% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

Similar products

Co
Cook for Mom56%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Cook for Mom

Hacker News8
Swate
Swate32%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

When to cook what you have

Indie Hackers1food-drinks
No
Noodles. Cook better55%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Noodles. Cook better

Hacker News3
Chefpilot
Chefpilot19%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Stop asking "What's for dinner?" Cook what you have.

Indie Hackers1ai
A
A roster help programmers find some interesting naming67%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

A roster help programmers find some interesting naming

Hacker News1
He
Help me crowdsource a definition of Americanness34%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Help me crowdsource a definition of Americanness

Hacker News2
He
Help a Scientist38%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Help a Scientist

Hacker News1
We
We're trying to help eradicate preventable blindness32%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

We're trying to help eradicate preventable blindness

Hacker News1
He
Help us with the MIT Tetris hack64%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Help us with the MIT Tetris hack

Hacker News37
So
Socket.im Get Help Immediatly.54%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Socket.im Get Help Immediatly.

Hacker News2