faxmeyourcode

19 日前議論

I feel like another comparison worth looking at is purely cost.

Capability per dollar is something I care about:

    Opus API    $5/$25
    Sonnet API  $5/$15
    Haiku API   $1/$5

    GLM 5.2 API $1.4/$4.4

So you're really getting near opus level capability for the price of haiku.

faxmeyourcode

24 日前議論

A town I grew up in went absolutely crazy with roundabouts over the last 10 years or so. They built them everywhere. Most are pretty straightforward and they simplify traffic a ton but there were some bad design choices made.

Take this similarly confusing double-roundabout for example https://maps.app.goo.gl/VTSrSWsBGnsYyzKU6

There are accidents here almost every week and when an out of towner comes off the interstate to get some gas at Sam's Club (where I used to work) I had to try and explain to them how to get back onto the interstate...

I might try to clone this project idea, it was fun to play!

They also proposed a peanut style roundabout with a *train track going through it* that thankfully has not been built yet https://media.conwayarkansas.gov/media/documents/project/85/...

faxmeyourcode

25 日前議論

I work a lot with databases, but only a couple times have I really messed with time series stuff. I've enjoyed reading your list of tricks and it's gotten me interested in playing around with writing my own toy database to just experiment with some of these ideas.

faxmeyourcode

先月議論

Somebody else in the thread brought up the benefit of snapshotting a database at a point in time stores not only the state of execution but also the code, etc. That is a unique benefit I'd be interested in exploring over storing your orchestration outside of the database.

Not trying to dismiss the project - it looks like a lot of hard work has gone in and somebody has a use for it. I just come from an airflow style external orchestrator frame of mind that manages durability state in postgres but keeps the control flow out. Sorry if I came off as a bit snarky

faxmeyourcode

先月議論

I aggree - I'm not understanding the value of the project either if you look at the example here https://github.com/microsoft/pg_durable/blob/main/examples/i...

It's an interesting technical achievement I guess, but it's very bizarre to try and read this

    SELECT df.start(
        @> (
            ($$SELECT ... FROM demo.invoices WHERE status = 'pending'$$ |=> 'inv')
            ~> df.if_rows('inv',
                $$UPDATE ... SET status = 'processing'$$
                ~> (df.http(...) |=> 'resp')
                ~> df.if($$SELECT $r.ok$$,
                    -- classify, branch, wait for signal ...
                ),
                df.sleep(5)
            )
        ),
        'invoice-approval-pipeline'
    );

faxmeyourcode

先月議論

This feels like the wrong solution to an age old problem solved by the DAG schedulers like Apache Airflow for a while now.

Why would I want to store my control flow in the database and not in code? It feels strange.

Not trying to dismiss the project, I'm just not getting it yet I think.

faxmeyourcode

先月議論

I had a hunch that opus 4.7 hedged more than other models - and it turns out it's true

    model                 total_claims  hedged_count  hedged_pct
    claude-opus-4-7       1000          451           45.1
    sonar-pro             1000          391           39.1
    gpt-5.4               1000          277           27.7
    gemini-3-retrieval    1000          129           12.9
    gemini-3-pro          1000          60            6.0

datasette query here

https://lite.datasette.io/?csv=https%3A%2F%2Fstatic.simonwil...

faxmeyourcode

2 か月前議論

An HP 50g was my calculator of choice, and the whole RPN style really rubbed off on me. Plus it had more advanced symbolic algebra capabilities than a ti83 equivalent. I enjoyed learning common lisp, scheme, racket, etc through high school and college and still am fond of them today because of this calculator.

faxmeyourcode

3 か月前議論

How does it compare to mythos?

faxmeyourcode

4 か月前議論

Wow, I didn't realize some RFID could reach 15 feet out - that's good to know. I naively thought you essentially had to be touching the surface of the tag.

faxmeyourcode

4 か月前議論

If you treat LLMs as generic transformers, you can fine tune with a ton of examples of input output pairs. For messy input data with lots of examples already built, this is ideal.

At my day job we have experimented with fine tuned transformers for our receipt processing workflow. We take images of receipts, run them through OCR (this step might not even be necessary, but we do it at scale already anyways), and then take the OCR output text blobs and "transform" them into structured receipts with retailer, details like zip code, transaction timestamps, line items, sales taxes, sales, etc.

I trained a small LLM (mistral-7b) via SFT with 1000 (maybe 10,000? I don't remember) examples from receipts in our database from 2019. When I tested the model on receipts from 2020 it hit something like 98% accuracy.

The key that made this work so well is that we had a ton of data (potentially billions of example input/output pairs) and we could easily evaluate the correctness by unpacking the json output and comparing with our source tables.

Note that this isn't running in production, it was an experiment. There are edge cases I didn't consider, and there's a lot more to it in terms of accurately evaling, when to re-train, dealing with net new receipt types, retailers, new languages (we're doing global expansion RN so it's top of mind), general diversity of edge cases in your training data, etc.

faxmeyourcode

4 か月前議論

Especially for super constrained applications. I don't care if the language model that I use for my extremely specific business domain can solve PhD math or remember the works of Shakespeare. I'd trade all of that for pure task specific accuracy.

faxmeyourcode

4 か月前議論

Labeling or categorization tasks like this are the bread and butter of small fine tuned models. Especially if you need outputs in a specific json format or whatever.

I did an experiment where I did very simple SFT on Mistral 7b and it was extremely good at converting receipt images into structured json outputs and I only used 1,000 examples. The difficulty is trying to get a diverse enough set of examples, evaling, etc.

If you have great data with simple input output pairs, you should really give it a shot.

faxmeyourcode

4 か月前議論

> Filip also told me that he asked Claude to continue on the even case after the odd case had been resolved. “But there after a while it seemed to get stuck. In the end, it was not even able to write and run explore programs correctly anymore, very weird. So I stopped the search.”

Interesting snippet towards the end. I wonder if they were using claude.ai or claude code. Sounds like they ran out of context and entered the "dumb zone."

faxmeyourcode

4 か月前議論

Yea, I agree. The dataset is < 100MB... so duckdb can very easily handle this on an old macbook air.

https://duckdb.org/2025/05/19/the-lost-decade-of-small-data

faxmeyourcode

5 か月前議論

Neel, this is really cool. How long have you been working on this, and where did you guys get inspiration from? Did you work on vlms earlier or something like that? Just curious.

Also, thanks for choosing a technical blog post for presenting this information.

faxmeyourcode

5 か月前議論

I love the idea behind MyVisualRoutine as a father with a disabled kiddo, thanks for sharing.

The app is beautiful - much better than I could build - what tech is it using if you don't mind me asking? Is it flutter, react native, something else? Just want to get better at mobile dev.

faxmeyourcode

5 か月前議論

Love this. An example of complete and total dominion over the machine. Great quote here too lol

> Prometheus stole fire from the gods and gave it to man. For this he was chained to a rock and tortured for eternity.

faxmeyourcode

5 か月前議論

Everybody is different, I simply cannot stand the sight of chatgpt styled writing. Give me paragraphs.

faxmeyourcode

5 か月前議論

I've lobbied to replace our internal tool with a django admin panel. I prototyped it and it showed that it would reduce our code by > 15k lines.

Any internal webapps I need to build like this will 100% be set up with django in the future due to this. I don't need it to be pretty, I just want the UI, database migrations, users, roles, groups, etc for free

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CVE-2025-43330: breaking out of a sandbox using font files

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