I just shipped 3pio, a drop-in test runner that context-optimizes your test output. It uses your existing test runner and tests so zero changes to your codebase or tooling to use it.
IME it results in much less context clutter from your test output.
Mark Brown of Game Maker's Toolkit fame runs a yearly game jam centered around a specific theme [1], which would line up well with this initiative. The quality of submissions is really, really impressive.
You see a similar loosening of the term in other fields e.g. open source journalism. Although that seems to be more about crowdsourcing than transparency or usage rights.
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.
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?
Hey Samson, thanks for sharing your experience and I'm glad you persevered to diagnose and treat your condition. I think many people would have given up.
I'm curious, did you track metrics past AHI and how rested you felt the next day?
Really interesting. I've always wondered about accuracy issues with this type of tool, i.e. are incorrect or misleading explanations obvious? What are the downstream consequences?
Similar issues with people-sourced explanations I suppose!
I feel the same way! In fact, I wrote the first version of ClojureDocs (on rails) because people kept complaining on the mailing list about the official docs. It seemed like the best way to move community focus on to bigger and better things.
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.
IME it results in much less context clutter from your test output.
https://github.com/zk/3pio