One thing I wish ai would teach vibe coders is how to use semantic versioning.

Trying to infer state of project from versions in ai-assisted software is an exercise in confusion.

Thank you. Hoping to continue to improve the products in meaningful ways. Always appreciate your continued support!

yes both are included as an available option to enable/disable!

Yeah, unfortunately that’s a Google account/billing thing, not a playback thing. Fathom only fetches the video stream on your machine; it doesn’t touch your account or Premium. That “verify your country” check is much stricter than playback and wants a residential IP in the country, so datacenter VPN ranges like Proton’s can get “flagged”. Nothing i think a media client alone can get around.

Good idea, I think this could be a solid fit for Fathom. I haven’t tried TubeArchivist myself yet, but I’ll add it to my to-do list to look into. Thanks for the pointer!

Interesting idea, but that’s out of scope for this app, Fathom is just a Jellyfin/YouTube/Seerr client, with no torrent or P2P layer, so there’s nothing for I2P to integrate with.

Unfortunately, it’s Jellyfin-only. Emby and Jellyfin came from the same codebase originally, but their APIs and auth have drifted apart enough that Fathom’s Jellyfin-specific calls won’t work against Emby. No Emby support planned, the focus is Jellyfin, alongside the Seerr and YouTube pieces.

Yes, the YouTube side is fully client-side, the app resolves and fetches the stream directly, the Jellyfin server isn’t involved at all. So YouTube sees the connection of whatever machine is running Fathom, using that machine’s IP and location. Fathom doesn’t spoof anything itself, but because it’s client-side, running it at home or behind a VPN is what should decide the location YouTube sees.

here are a few pics. i have to setup some demo screenshots for github, on my to do!

yes here are a few of different areas:

Appreciate the kind words, thank you.

Happy to hear! You are very welcome!

Updated, thank you.

Thanks for pointing this out. As I’ve mentioned before, I have always been transparent about utilizing AI assistance to help develop my apps. I wasn’t aware of the requirement to include a specific tag for them, but I appreciate you letting me know. I will make sure to include [AIP] in the title of my posts going forward.

Thanks for giving it a chance. Alot of time, effort, and testing went into this self hosted app. I am a longtime Mealie user and though its an absolutely great recipe manager, there were a few more things i wanted to see in an app like this, and thus CookTrace was born. Feel free to report any bugs or feature requests on the github page.

RC2 now includes improved import functionality. I believe this will address your concern above!

should now be addressed in latest RC6.

Genuinely useful feedback, thanks for taking the time. Walking through each:

Per-exercise sharing. Real gap. The exercises table is already structured cleanly enough to dump one as JSON; adding a “share this exercise” button that produces an importable file is small. The community-repo idea is good too: a separate awesome-lifttrace-exercises repo where people can drop contributions, and the in-app importer can read from a URL. Going on the roadmap.

Custom equipment + searchable tags. The schema already supports arbitrary equipment strings; the UI just defaults to a fixed list. Adding a “+” in the equipment picker plus a “what’s available today” filter chip set on the Exercises page is genuinely useful, especially for travel and home-gym use. Adding to the roadmap as a near-term candidate.

Per-exercise timer for time-based work. Real gap. The rest timer infrastructure exists but doesn’t currently cover “do this exercise for N seconds with a countdown and a cue.” That’s a meaningful addition for anyone doing carries, planks, isometrics, hangs, or hybrid stuff. Sharing the same audio + haptic plumbing as the rest timer keeps it cheap. Roadmapped.

Per-set / per-round rest in HIIT-style circuits. Currently rest is global with per-exercise memory, which doesn’t cover the “20s between reps, 2 minutes between rounds” pattern you described. That’s a structural change to how sets express rest. Going on the list to think through together with the time-based timer above, since they share infra.

Heart-rate data. The export direction you called out is the cheap win, and you’re right that PWA makes it easy. I’ll push a CSV export of the completed workout (sets, reps, weights, RPE, timestamps, rest durations) in a near term RC. That covers anyone who wants to feed an external analysis pipeline. Live HR ingestion from a BLE chest strap is the heavier direction and goes on the roadmap.

Thanks again for your valuable feedback. Appreciate the level of detail.

Thanks for highlighting the above:

Banner overlapping the H1 (“rcises”). This one I’m fixing structurally. The illustrated SVG banners across LiftTrace (and my other 2 Trace apps) are getting removed entirely. They were the single biggest source of layout fragility across viewport sizes, plus they were doing the project no favors aesthetically. In their place: the existing compact Gradient header stays as-is, and the “Animated” option becomes a subtly animated version of the same compact gradient (slow accent-hue drift, same height, no overlapping content). Three modes total: Animated, Gradient, Off, all sharing the same compact-header geometry. That removes a whole category of bugs and trims a real chunk of bespoke SVG maintenance.

Until that is released, you can flip your view to Settings → Appearance → Page Banners → Gradient (or Off) and the overlap should go away immediately.

Add button + exercise count missing on mobile. Real bug, haven’t pinned down the CSS yet but I’ll find it. The Exercises page action bar isn’t wrapping or relocating correctly at narrow widths.

Thanks again for bringing these to my attention.

Here is what i see as some differences today between CookTrace and Tandoor:

CookTrace:

• Pantry is a real first-class catalog, not a tag list. Barcode scanner, Open Food Facts + USDA lookup, per-item stock / serving size / nutrition, plus an “8 / 10 in pantry” match pill on every recipe card. Tandoor has the food list; CookTrace treats it as a primary surface.
• AI assistant with tool use — not just a chat bubble. Trace can log a cook, plan a cook, add things to the shopping list, mark a pantry item out of stock, import a recipe URL, etc. by reading and writing your real data
• FDA-style Nutrition Facts box auto-computed per recipe from pantry-linked ingredients
• Android app with full offline mode (on-device SQLite, differential sync when you reconnect)
• NutriTrace federation can import foods from NutriTrace directly into CookTrace’s pantry.

Where Tandoor still wins today: PDF / image OCR bulk import, longer track record, larger ecosystem, iCal meal-plan feeds.

Importer’s there in CookTrace, so you can move a copy over to compare with zero risk to your existing library whenever the time shows up.

Honestly, not automagically. CookTrace’s bulk importers today handle JSON, HTML, schema.org Recipe markup, and full-backup zips from Mealie / Tandoor / Paprika. Plain text, RTF, and PDF aren’t on that list yet, and there’s no “point at a folder and import everything” mode.

What works today for the same end goal:

• Photo Import — point Trace AI at an image of any recipe (including a PDF you’ve screenshotted) and it extracts it. Fine for a handful, probably painful for hundreds.
• Trace AI chat — paste the recipe text into the assistant and ask it to create a recipe. Same idea, no image step.

A proper OCR import is a worthwhile feature. I created a feature enhancement request in the GitHub repo, so that in the near future I can set aside some time to explore how feasible it would be to implement.