Wi

Witmo – AI-powered gaming coach from screenshots

Hacker News

Witmo – AI-powered gaming coach from screenshots

Hi HN, I’d like to share a project I’ve been working on: Witmo – an open-source, terminal-based tool that gives you in-game advice by analyzing screenshots from your gameplay. I created Witmo after picking up Elden Ring and constantly running into questions mid-game. I wanted a tool that would let me quickly get advice based on a screenshot — without having to type long explanations for an LLM or repeatedly pull out my phone to retype the same prompt. So, instead of playing, I built this. Witmo connects to your Android phone (via USB and adb), grabs a photo of your game screen, and uses OpenAI’s models to analyze what’s happening. It then gives you contextual advice based on a prompt you chose (from built-in packs or your own), with customizable spoiler controls. Optionally, it can crop images to the game area and read responses aloud using text-to-speech. Repo and install instructions: https://github.com/ymyke/witmo (So far only tested with Windows Terminal, Pixel 8, Python 3.11, but all the code used should be cross-platform.) I had fun building this, and maybe it can help others too. If you have feedback, I'd love to hear it. And if you'd like to contribute to take this further, please do!

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, models, context · Missing: mac, agents, macos
76%76% 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: created · Missing: supports, reddit linkedin, podcasting
75%75% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
50%50% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform · Missing: plus, intuitive, reviews
48%48% predicted probability of success on AppSumo, 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
42%42% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
16%16% 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.

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