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heyzk

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http://heyzk.com zk AT heyzk DOT com

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Hey all, I found the post about Don Knuth and ChatGPT [1] very interesting so I hacked together this project over the weekend. Optimystic periodically re-runs Knuth's questions against the latest GPT model (additionally I've asked GPT to score the updated answers as PASS/FAIL).

Initially I thought it would just be a fun thing, but I've realized there could be some value to the larger LLM community. Also I think it would be interesting to apply this format to experts in other fields.

Appreciate any feedback.

[1] https://news.ycombinator.com/item?id=36012360

The flux docs indicate that you're using a custom-trained LLM and in another comment it was mentioned you're using LangChain (to integrate non-deterministic tooling perhaps). Just curious, but are you using some combination of your own model, GPT3/4, and calculators / datasheet readers / etc?

Which, I assume, led to one of my favorite idioms: there are only three numbers in computing: 0, 1, and n.

OpenTelemetry 5 years ago

I'm curious, who's your target customer? Should small teams be looking early on or is this more of a mid-sized company thing?

Honestly I didn't look at json1.

It was built on a single table that held the entity-attribute-value tuple along with some additional metadata like type information, whether or not the attribute was a pointer to another entity, and the cardinality of that relationship (one or many).

Relationships were walked via self joins and the eav columns were all indexed.

It was a scheduling, work tracking and invoicing app for service providers that have spotty-at-best network connections, think long periods out of cell service but still need to do complex data entry and querying.

EAV is often an anti-pattern when a schema could be defined

Super interesting, I wasn't aware that EAV is an anti-pattern in that case. Is it an efficiency thing?

For clarity, my design wasn't schemaless, values (can) have defined datatypes and relationships are first-class. I meant that I found adding to or modifying the schema was less cumbersome and error prone than traditional SQL schema additions or changes. I feel like SQL schema management is more suited to server-based dbs where you have tight control over the db lifecycle, which you don't when it lives on a bunch of mobile devices.

Totally agree with the ease of sync and conflict resolution, another strong pro.

Love to hear more about your approach! Also feel free to reach out (email in bio) if you'd like to compare notes some time.

I had a really great experience building an EAV store with Datalog as the query interface on top of SQLite for embedding in native mobile apps.

Pros: querying complex data hierarchies was easy, and was able to skip the pain typically associated with managing a SQL schema.

I noticed that these days roughly 30-40% of the code I write deals with integrity checks, anomalies and anomaly handling.

Love to hear more about this.

My approach has been to convert errors into data and have behavior that deals with conveying these errors in different ways, but it's always felt too complex for the task. I've just got a lot of stuff dealing with handling errors in the different environments (jvm / browser / nodejs / rn) and across async and non-async code.

If you're looking to embed maps in a web page I've had good experiences with using both Mapbox and Google Maps' js libraries directly from cljs. On the geospatial side JSTS (https://github.com/bjornharrtell/jsts) has been super helpful.

Like you, I was looking for a cljs wrapper for these things, but ended up not needing them in the end. The js interop ended up being pretty straightforward.

Happy to chat further, email in bio.