ef core is great for simple queries and modification of your data while using the changetracker.
You can use AsNoTracking/Projection to perform similar like dapper for queries.
When using command query seperation you can also use dapper for queries and ef core for commands.
I think we weren't paying for support and it was standard Business Support they were pitching. At the time we were having pretty fundamental problems with Azure Single Server Postgres which was really just a terribly engineered solution which they admitted had some nasty issues (there was some bug that would cause the storage IO threads to deadlock causing Postgres to crash)
in many cases: no service health alerts, no status page updates and no confirmations from the support team in tickets.
still we can confirm these issues from different customers accross europe. Mostly the issues are regional dependent.
Where do these alerts supposedly come from? I started having issues around 4PM (GMT), couldn't access portal, and couldn't make AKV requests from the CLI, and initially asked our Ops guys but with no info and a vague "There may be issues with Portal" on their status page, that was me done for the day.
This is the single most frustrating thing about these incidents. As you're harmstrung on what you can do or how you can react until Microsoft officially acknowledges a problem. Took nearly 90mins both today and when it happened on 9th October.
Constrained generation guarantees syntax. It does not guarantee semantic correctness tho. Imagine you want a json object with "hp" and "damage". If you use a grammar, the model will be forced to output a json object with those two values. But it's not guaranteed to get sensible values.
With a 2nd pass you basically "condition" it on the text right above, hoping to get better semantic understanding.
I'm pretty sure the grammar is generated from the Json schema, it doesn't just constrain json syntax, it constraints on the schema (including enums and such). The schema is also given to the model (at least in openai) you can put instructions in the json schema as well that will be taken into account.
Perhaps I worded that poorly. What I mean by semantic correctness is that the model could output nonsensical values for some things. Say in a game, "normal" health is ~100hp and the model creates a wizard with 50hp but then a mouse with 10000hp. So you're guaranteed to get a parsable json object (syntactically correct) but what the values are in that json is not guaranteed to make sense in the given context.
I find it does pretty well given a reasonable prompt and (especially) well-named keys/JSON structure. So if you had boss.mouse.hp you would get higher HP than random_enemies.mouse.hp, or better: enemies.level_1.mouse.hp.
what is a unhealthy request? is searching for a user which was _not found_ by the server unhealthy? was the request successful? thats where different opinions exist.
Sure, there's some nuance to it that depends on your application, but it's the server's responsibility to do so, not the client's. The status code exists for this reason and the standard also classifies status codes under client error and server error so that clients can determine whether a server is unhealthy simply by looking at the status code.
The idea is to replicate the weights of the network in the electronics. Somehow like our brains work? This way an analog input signal could lead to a neural network processed output signal without the digital emulation on an gpu. As this is very much simplified, the question is if this could work for modern llms?
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