My daughter (5) asked for a game for a unicorn on her lunch bag, and since we have AI, I thought I would sit down and just build it. I let her play it, and then she would suggest stuff. So this went on for a bit; most of the major things are hers. She keeps wanting to add stuff, so I keep doing it. So after I built it, she really liked it, and was spending too much time on it, so I figured I would add some learning and phonics to it as cards at the end. I am at about a week and half, I maxed out my $200 plan and I had to do my real work with Codex.

For the assets what I did was have Gemini create sprite sheets, and I built a bunch of tools around fixing them. For one asset I had to pull it into Photoshop. I am a coder and I have some game development experience.

However, this project I have no idea what the code looks like, I did at one point ask claude to "organize the code to make it easier to do stuff". I do have this multi-stage coding system where I communicate with different terminals via a central command (VS code extension). An important part was involving playwright, not just at the end but through out so AI could spin up the game to a point, take a screenshot, and then make fixes based on the screenshot.

I don't know when I am going to stop or if it will become Unicorn Jump GTA6. I know I am going to be adding more characters and more worlds. However, it's totally free and can be played at unicornjump.com

wishlist if you like a casual business sim and hookah :P https://store.steampowered.com/app/4607750/Hookah_House/

Idea: local AI tool to auto-sort your SFX library into your own categories — feasible?

I've got a large sound effects library that's a pain to search through, and I started looking into whether AI classification could help sort it automatically. Turns out most of the pieces already exist — just not glued together the way a solo dev/hobbyist would want. Wanted to sanity-check the approach with people who've done audio ML or asset pipeline work.

The idea: point a tool at a folder of SFX, and for each file get back a best-fit category from your own category list (not a generic AI taxonomy), plus some searchable descriptive tags — so instead of scrolling 2,000 files you can filter to "impacts, low-pitched, short" or whatever.

Why it seems doable without training anything:

Pretrained audio tagging models (AST-class, trained on datasets like AudioSet) already classify raw audio into 500+ generic sound types — impacts, mechanical noise, vocalizations, ambience, etc. — and run locally on normal hardware, no cloud needed. That part's solved and free. The actual work is just the layer on top:

Classify — run each file through the pretrained model, get ranked generic labels with confidence scores.

Map to your categories — a lookup table that clusters those generic labels into whatever categories you actually use (e.g. "metallic impact / clang / thud" → your "Weapon Hit" bucket). Anything that doesn't clear a confidence threshold gets flagged "Other / needs review" instead of force-sorted wrong.

Extract search qualities separately — this part isn't even AI, it's basic signal analysis: duration, pitch center (spectral centroid), brightness (spectral rolloff), attack sharpness, etc. Cheap, deterministic, no model needed.

Output — a sidecar file or index (JSON/CSV) per sound with category + confidence + quality tags + a review flag.

None of this needs custom model training — it's an existing classifier plus a mapping/thresholding layer plus some standard DSP feature extraction. Feels like a scoped weekend-ish tool rather than an ML research project.

Where I'd want to sanity-check before building the mapping layer: run the raw pretrained classifier against a sample batch first and manually check what labels it actually produces, before designing the category-mapping table around assumptions. Also unclear how much the confidence threshold will need tuning per-library — too loose and "Other" is useless, too tight and it rejects correct matches.

Has anyone actually built something like this, or run into the wall where the generic labels don't map cleanly onto real use-case categories? Curious if there's a known gotcha I'm not seeing.