Idempotency is what bites me most in practice — I've been driving these against an unreleased database I work on. The main trap is using the op_id as the idempotency key rather than a business key the client reuses on retry. When they're the same thing, the checker is trivially true and the test passes without testing anything.
No-lost-ack is conceptually the same shape with a simpler property (every acked write shows up at the end), but it breaks the same way most checkers break — if the recorder treats timeouts as success or failure instead of "unknown," real lost writes silently disappear.
Recovery after partial failure is where the AI-agent angle gets shaky honestly. Quiescence is the hard part. Agents will declare a system "recovered" while compaction is still running in the background. The skill forces a three-part check (no in-flight ops, no pending background work, replicas converged) before the invariant runs. How reliably that holds up against a specific SUT, I'm still figuring out.
GPT-o1, Claude-3.5 new,... those models are capable to write quite good code. I think it just need a workflow (make be Github Action) to make it possible.
Went thru the document: https://slatedb.io/docs/introduction/#use-cases
I can not understand why are they targeting the following use cases with this architecture.
* Stream processing
* Serverless functions
* Durable execution
* Workflow orchestration
* Durable caches
* Data lakes
ChatGPT definitely can do the work. I used Google search for git command when I was a beginner. But I met the situation of losing all the changes with a single git command. This tool may be helpful for beginners. It can explain commands and also raise red flags for risky commands.
Thanks for the feedback. I made a few updates. Would you mind having another try? The new version give you more options to tune the generation, including analyzing your social account posts (support HN, working on X and Linkedin). https://www.gitdevtool.com/social-share
A free course designed for people with some coding experience, who want to learn how to apply deep learning and machine learning to practical problems.
Our findings reveal that while unsupervised
fine-tuning offers some improvement,
RAG consistently outperforms it, both for existing
knowledge encountered during training and
entirely new knowledge. Moreover, we find that
LLMs struggle to learn new factual information
through unsupervised fine-tuning, and that exposing
them to numerous variations of the same fact
during training could alleviate this problem.
The SQL Murder Mystery is designed to be both a self-directed lesson to learn SQL concepts and commands and a fun game for experienced SQL users to solve an intriguing crime.