maelstrom VS openai-python

Maelstrom [1], a workbench for learning distributed systems from the creator of Jepsen, includes a simple (model-checked) implementation of Raft and an excellent tutorial on implementing it.

Raft is a simple algorithm, but as others have noted, the original paper includes many correctness details often brushed over in toy implementations. Furthermore, the fallibility of real-world hardware (handling memory/disk corruption and grey failures), the requirements of real-world systems with tight latency SLAs, and a need for things like flexible quorum/dynamic cluster membership make implementing it for production a long and daunting task. The commit history of etcd and hashicorp/raft, likely the two most battle-tested open source implementations of raft that still surface correctness bugs on the regular tell you all you need to know.

The tigerbeetle team talks in detail about the real-world aspects of distributed systems on imperfect hardware/non-abstracted system models, and why they chose viewstamp replication, which predates Paxos but looks more like Raft.

[1]: https://github.com/jepsen-io/maelstrom/

[2]: https://github.com/tigerbeetle/tigerbeetle/blob/main/docs/DE...

I've tried to run the echo example and I'me getting some problems. I assume it works as the same example in the maelstrom's getting started ready (https://github.com/jepsen-io/maelstrom/blob/main/doc/01-getting-ready/index.md).

Love it. Thanks for putting this together! The actual challenges here [0].

Though I'm curious: are these different from the chapters in the Maelstrom documentation [1]? There seems to be a bit of overlap anyway.

[0] https://fly.io/dist-sys/

[1] https://github.com/jepsen-io/maelstrom#documentation

The ongoing model anchoring/grounding issue likely affects all GPT-4 checkpoints/variants, but is most prominent with the latest "gpt-4-turbo-2024-04-09" variant due to its most recent cutoff date, might imply deeper issues with the current model architecture, or at least how it's been updated:

https://github.com/openai/openai-python/issues/1310

See also the original thread on OpenAI's developer forums (linked on the GitHub issue) with confirmations from others.

A test code snippet is included in the GitHub issue to A/B test the problem yourself with your own questions if need be.

- Iterate on large codebases quickly: we use Rust for maximum performance

GritQL has already been used on thousands of repositories for complex migrations[1] but we're excited to collaborate more with the open source community.

[1] Ex. https://github.com/openai/openai-python/discussions/742

Explore OpenAI and learn how to easily integrate it into your existing application with the OpenAI Python module.

You tried to access openai.Completion, but this is no longer supported in openai>=1.0.0 - see the README at https://github.com/openai/openai-python for the API. You can run `openai migrate` to automatically upgrade your codebase to use the 1.0.0 interface. Alternatively, you can pin your installation to the old version, e.g. `pip install openai==0.28` A detailed migration guide is available here: https://github.com/openai/openai-python/discussions/742

Relatedly, I checked and OpenAI deleted all references to their ChatML spec from their GitHub repo.

This is what it said in an earlier commit: https://github.com/openai/openai-python/blob/2942bf4bb635b1e...

Make sure to install the OpenAI library first. Since I'm using Python, I need

Source: https://github.com/openai/openai-python/discussions/742