This is my simple utility app to transcribe lists into plain text.

I use it for my workout log and for fast ChatGPT queries.

It should be more accurate than others as it is using Microsoft AI's 1.5 transcribe tool which is one of the most accurate and fastest models.

It is very simple; it just aims at reducing the number of buttons to press to activate the mic, transcribe accurately and split the text into new lines.

You could probably do the same with LLM apps, just with a few more clicks.

You can "Add to Home Screen" on iOS for fast use from your phone.

Thanks, Enzo

Comments URL: https://news.ycombinator.com/item?id=49090242

Points: 1

# Comments: 0

This is a macOS app for offline transcribing.

You can use the new Apple Speech APIs or Whisper.

It has a batch queue interface, so you can watch many input sources, including external drives.

It is completely sandboxed in the App Store, so it is isolated from your system and can only read the files you explicitly give it access to.

It stores its transcription data in plain files, so they can be read without the app on any machine.

Hope you find it useful. Thanks, Enzo.

Comments URL: https://news.ycombinator.com/item?id=49034845

Points: 2

# Comments: 1

This is an experiment of creating a simple progressive web app (can be installed as a mobile app) to test Microsoft's new MAI 1.5 transcription model.

The standout features of the MAI model are accuracy and speed.

The app converts your speech into plain text lists.

No sign up required.

Comments URL: https://news.ycombinator.com/item?id=48810763

Points: 2

# Comments: 1

This is a very simple utility. My aim is to reduce the number of taps needed on iPhone to transcribe my speech into a plaintext list on my clipboard.

I use it for my workouts, but it works for any kind of list.

On iOS, you can go to the Safari share menu and select “Add to Home Screen” so it can be launched quickly.

You can also set Microphone access to “Always Allow” to remove the dialog on every launch.

It is currently using the Microsoft MAI 1.5 transcription model, which seems fast and accurate.

Thanks, Enzo

Comments URL: https://news.ycombinator.com/item?id=48799347

Points: 3

# Comments: 0

As soon as you compile to WASM you no longer have the C FFI and the ability to call the OS systems interfaces for files, network and others.

It is extra work to move something to WASM vs just compiling it and running it in a sandbox.

A football is the default “toy” given to young lads, and you can play anywhere with anyone.

So much so there are custom “no ball games” signs in many neighbourhoods due to noise averse residents getting annoyed.

There are also many supporting elders who try to improve and guide skill development.

It is using Gemini Flash 3 at the moment.

There is a lot to learn in nutrition. The glycemic load metric is quite revealing for pizza vs pasta (slow digesting carbs are supposed to be better). Al-dente cooked pasta is also slower digesting than well cooked pasta.

Another interesting thing is how each plant food has unique molecules that can be health promoting in humans. That was one aspect I wanted to reveal/compare for the foods I ate.

Dr Weil / Perfect Health Diet / Marks Daily Apple are three sources I like to check for information on nutrition.

https://kg.enzom.dev/

You specify your foods in grams with plaintext (no pictures).

I never liked the "take a picture to measure calories" approach, as you could have 10 table spoons of olive oil which would drastically change the calories but would not show in a picture.

I think it is the only tool in the JS ecosystem that has not broken after a few years.

This is my molecule tracking web app.

It tracks calories like traditional apps, but goes into much higher detail on micronutrients (vitamins, minerals) and amino acids.

It is powered by Google Gemini, so you can just type a rough list of foods for each meal and it will infer the exact foods you meant. No more scanning barcodes!

I personally built this because the biochemical nature of foods interests me, and it is much faster to use this tool than looking up every food manually.

I hope you will find it useful, and let me know of any feature requests.

Thanks, Enzo

Comments URL: https://news.ycombinator.com/item?id=47300692

Points: 2

# Comments: 0

This is a food log web app powered by Google Gemini.

You can use it from any of your devices.

Thanks, Enzo

Comments URL: https://news.ycombinator.com/item?id=47270700

Points: 3

# Comments: 0

It's pretty simple:

Type in the foods you consumed in your meal and get a table and charts of nutrients in return!

The trial has limited tokens, but email me if you would like an extended trial.

Feedback welcome, thanks!

Comments URL: https://news.ycombinator.com/item?id=47227302

Points: 2

# Comments: 0

The code_challenge == sha256(code_verifier). You will share the code_challenge at the start of the flow.

The LLM is only for converting the JS based example code into your language X and HTTP framework Y (instead of giving example code for every combination of X and Y).

The standard implementation is in a single file `http_server.ts`, which is around 200 lines of well commented code, with important logic commented (around 5 lines). The example code can be run locally with a few commands.

The repo also contains a sequence diagram [1], a description of the HTTP handlers needed [2], and a live demo [3] where you can see the request/responses.

Thanks for your feedback I have made this clearer in the readme.

- [1] https://github.com/emadda/passkeybot/tree/master?tab=readme-...

- [2] https://github.com/emadda/passkeybot/tree/master?tab=readme-...

- [3] https://demo.enzom.dev/

Comments URL: https://news.ycombinator.com/item?id=46357451

Points: 173

# Comments: 146

Comments URL: https://news.ycombinator.com/item?id=46345476

Points: 3

# Comments: 0

Take a look:

https://passkeybot.com