What safety or application-level bugs could arise if developers assume Snapshot Isolation but Amazon RDS for PostgreSQL is actually providing only Parallel Snapshot Isolation, especially in multi-AZ configurations using the read replica endpoint?
Consider a "git push"-like flow: begin a transaction, read the current state, check that it matches the expected, write the new state, commit (with a new state hash). In some unfortunate situations, you'll have a commit hash that doesn't match any valid state.
And the mere fact that it's hard to reason about these things means that it's hard to avoid problems. Hence, the easiest solution is likely "it may be possible to recover Snapshot Isolation by only using the writer endpoint", for anything where write is anyhow conditional on a read.
Although I'm surprised the "only using the writer endpoint" method wasn't tested, especially in availability loss situations.
How does the extension hook into GitHub's DOM to inject the comment toolbar, and does it support dynamically loaded elements like in PR reviews with infinite scroll?
Chrome extensions let you execute JS on certain sites, so we can querySelect all target textareas and insert the toolbar anywhere. And yes! That includes dynamically loaded elements like the PR review window. We can even apply light/dark themes that match GitHub's ;)
How does oas_rails dynamically generate OpenAPI 3.1 documentation from my Rails routes and controllers—does it rely on specific annotations or conventions?
If you want to add a custom tool for your agent to use, you can use the “add custom tool” in the agent block and just define the JSON schema and the code for the tool call.
If you want a custom integration, you can either request it, or if you are running locally we have really thorough instructions on how you can add a tool/block in the contributing guide for the repo. You can use this to extend the platform for yourself or add integrations to the main repo. Hope that helps!
Have you experimented with weighting the self-evaluations based on specific criteria (e.g., correctness, clarity, creativity), or using external validators to guide the AI’s final choice? Curious how much tuning the evaluation step impacts overall performance.
How does the library handle hallucination or off-topic drift during user simulation, especially when simulating frustration or goal completion? Are there mechanisms to detect and constrain unrealistic turns during generation?
What metrics and Kubernetes runtime data does Neurox collect to provide its AI workload monitoring dashboards, and how customizable are these dashboards for different user roles like developers or finance auditors?
We collect a handful of metrics, but coming from our previous lives in DevOps, we only collect just what's needed to avoid unnecessary metrics bloat.
The main 3 are:
- GPU runtime stats from NVIDIA smi
- Running pods from Kube state
- Node data & events from Kube state
We have several screens with similar information intended for different roles. For example, the Workloads screen is mainly for researchers to monitor their workloads from creation to completion. The Reports screen shows mainly cost data grouped by team/project, etc.
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