simedw

It would be neat if it had a headless mode.

https://simedw.com personal site, mostly posts regarding various experiments

First of all, big kudos for not missing a single day. When I used flashcards in the past, missing even a couple of days led to an avalanche of cards to review.

Since you’ve been so consistent and are using your own software, have you experimented with different resurfacing rates? Did you notice a material difference in recall?

Thanks for the questions. Very fair concerns. Take all of this with a fairly large pinch of salt; this is still an experiment.

1. How does it know which words I already know? It doesn’t automatically. You provide that set. For example, if you’ve completed HSK 1, you can paste the HSK 1 word list into LangSeed and mark those as "known". From there, new explanations are constrained to that vocabulary. You can also paste in real text and mark the easy words as known, though that’s a bit more manual.

2. How much might I misunderstand word meanings? Depends on how advanced the vocab is and how large your known-word set is. I think of this as building intuition rather than giving dictionary-precise definitions. As you see words in more contexts, that intuition sharpens. This is just my experience from testing it over the last couple of weeks.

3. How inaccurate are the explanations? I tested it on Swedish (my native language). There are occasional awkward or slightly odd phrasings, but it’s rarely outright wrong.

This is a simplified version: Journey to the West in Easy Chinese by Jeff Pepper and Xiao Hui Wang. Otherwise, I would definitely have waited a bit before biting off something like this.

Surprisingly easy. If the language has a lot of conjugations (e.g., polite past verb forms), running each word through Snowball first makes the process a bit easier.

That's a really cool concept. Naively replacing words might work, but sometimes the context is needed. Maybe a model like gemini 2.5 flash lite would be fast enough but still maintain better context awareness?

Thanks for sharing; you clearly spent a lot of time making this easy to digest. I especially like the tokens-to-embedding visualisation.

I recently had some trouble converting a HF transformer I trained with PyTorch to Core ML. I just couldn’t get the KV cache to work, which made it unusably slow after 50 tokens…

Thanks! I think getting comfortable with characters fairly early is important, as it helps shift your mindset into the right place. That said, I don’t think this project really works until you’re comfortable with at least ~60 characters.

I noticed that I get far fewer refusals when I set my VPN to the USA.

The model is only available in AI Studio when I set my VPN to the USA (I’m located in the UK).

That's true, it's also why I didn't benchmark against any other model provider.

It has been tuned so heavily on this specific format that even a tiny change, like switching the order in the `box_2d` format from `(ymin, xmin, ymax, xmax)` to `(xmin, ymin, xmax, ymax)` causes performance to tank.

That's really interesting, thanks for sharing!

Are you using that approach in production for grounding when PDFs don't include embedded text, like in the case of scanned documents? I did some experiments for that use case, and it wasn't really reaching the bar I was hoping for.

Thank you! Better training data is often the key to solving these issues, though it can be a costly solution.

In some cases, running a model like SAM 2 on a loose bounding box can help refine the results. I usually add about 10% padding in each direction to the bounding box, just in case the original was too tight. Then if you don't actually need to mask you just convert it back to a bounding box.

It was actually a bit worse than that the LLM never got the full recipe due to some truncation logic I had added. So it regurgitated the recipe from training, and apparently, it couldn't do both that and convert units at the same time with the lite model (it worked for just flash).

I should have caught that, and there are probably other bugs too waiting to be found. That said, it's still a great recipe.

Fantastic catch! It led me down a rabbit hole, and I finally found the root cause.

The recipe site was so long that it got truncated before being sent to the LLM. Then, based on the first 8000 characters, Gemini hallucinated the rest of the recipe, it was definitely in its training set.

I have fixed it and pushed a new version of the project. Thanks again, it really highlights how we can never fully trust models.

You are right, it's Gemini 2.5 Flash Lite

If the goal is to have a more consistent layout on each visit, I think we could save the last page's markdown and send it to the model as a one-shot example...

Just had a look and three is quite a lot going into Firefox's reader mode.

https://github.com/mozilla/readability

We’ll probably have to add some custom code to log in, get an auth token, and then browse with it. Not sure if LinkedIn would like that, but I certainly would.

That's a cool project.

I think most of it comes down to Flash-Lite being really fast, and the fact that I'm only outputting markdown, which is fairly easy and streams well.

Thank you.

I was thinking of showing multiple tabs/views at the same time, but only from the same source.

Maybe we could have one tab with the original content optimised for cli viewing, and another tab just doing fact checking (can ground it with google search or brave). Would be a fun experiment.

Very interesting breakdown, OP have you deep dived in pgvectorscale as well?

After transcribing the speech into text, it seems to first translate it into English before send it over the GPT-3 (davinci). I wonder if that will lead to a bunch of mistakes, can't GPT-3 handle Mandarin directly?

I did some experiments and using the same prompt but without translating it seem to lead to better results.

Useful tool!

Do you not need to escape the ampersand in the second example? otherwise bash will run it in the background and skip the `filters=t2,m4` part.

Pretty cool but the LinkedIn Card gives me the following PHP exception: Catchable fatal error: Object of class stdClass could not be converted to string in /home/stephenou/socialbusinesscard.me/card_linkedin.php on line 49