max_streese

Assuming writing code by hand is slower (which I agree with), why can't it still have merit compared to code written via llm harness?

To people stating these high commit numbers: What is your average changeset size? I have found that having agent do large changes (few hundred lines or more) results in a lot of friction for me and it feels like at some point I leave a happy path where instead of moving quickly I get dragged down.

One thing I am wondering: How much logic (in the programming sense) is there in this product and how much of it is „just“ a lean wrapper around the LLM?

Hi HN, I am wondering if with the recent push for CLIs rather than MCPs people have heard of or are using Restish for creating their own CLIs for APIs?

The thing lets you create a single CLI for any number of APIs following OpenAPI v3 or JSON Schema. I've tested if with our self-hosted Grafana and it does work, though I am not sure if it is as good as the Grafana MCP.

Anyway, it seems perfectly positioned for the CLI over MCP push, while sitting at 1.2k GitHub stars which is tiny by AI standards. So I am wondering if there is more nuance in CLI over MCP here or if other solutions exist?

(Note: I am not in any way affiliated with the project, I just think its neat and interesting)

And if we do adapt we might still get replaced because less of us will be able to do more. Or we wont because of Jevons Paradox. Linux maintainers on the other hand can code (with and without AI) what I could not (with or without AI). So in a way becoming a more knowledgeable, more skilled programmer is the way? In any case, too much speculation about the future.

Two questions:

(1) Are there any plans to make this compatible with the ducklake specification? Meaning: Instead of using Iceberg in the background, you would use ducklake with its SQL tables? My knowledge is very limited but to me, besides leveraging duckdb, another big point of ducklake is that it's using SQL for the catalog stuff instead of a confusing mixture of files, thereby offering a bunch of advantages like not having to care about number of snapshots and better concurrent writes.

(2) Might it be possible that pg_duckdb will achieve the same thing in some time or do things not work like that?

GDP?

How are you folks related to Anguilla? Couldn’t really find anything AI specific about OpenObserve so I am guessing the domain is for other reasons?

The article itself states this too (though without the undertone). To me this does not undermine the research though. Why would it?

Not to comment on Musk's ability but the other car manufacturers on this list (1) do exist?

(1) https://companiesmarketcap.com/automakers/most-profitable-au...

Is it just me or do others find the Electron bashing a little over the top as well?

I mean VS Code, Discord, Slack and Obsidian are all in very widespread use and work perfectly fine for me.

Are there alternatives to Electron that require less resources? Tauri seems to be proof that there are.

But I think there is real value in large communities and backing. Electron seems to work perfectly fine to me for everyday use as is evident by the applications mentioned above. I would personally choose Electron over Tauri even for greenfield projects, simply because Electron seems to power applications that are in much more widespread use.

Is it just me or do prices for a lot of UI/design resources seem high compared to prices of resources on other parts of IT? 100 $ for a 218 page PDF seem quite steep to me.

Awesome, thanks so much!

I use roam myself. Mind sharing the alternative(s) you use and find most useful? Would be interested in learning what roamlikes others prefer and why.

Could someone explain to me what the difference between a computer algebra system like Symbolics.jl and a theorem prover like Coq is?

Is that more in the nuances or is there a fundamental difference between these two (referring to the terms and not their specific implementations in Symbolics.jl and Coq respectively)?

Or is this question unreasonable to ask in the first place?

Yes I guess my point is when using Kafka in combination with Kafka Streams and you produce things partitioned in a way that you need them for consumption then you do not need to do any shuffling in the instance where you want to join because data is already partitioned correctly.

Hi not sure if I am just completely off here but I am wondering how this relates or compares to processing things with Kafka and Kafka Streams?

If I am reading things correctly with Kafka the workflow equivalent to what's written in the article would be to have your producer produce via hash-based-round-robin (the default partitioning algorithm) based on the key you are interested in into some topic and then your consumer would just read it and your data would already be sorted for the given keys (because within a partition Kafka has sorting guarantees) and also be co-partitioned correctly if you need to read some other topic in with the same number of partitions and the same logical keys produced via the same algorithm. No?

I wonder what people think about Ammonite (https://ammonite.io/)?

It's not Lisp but Scala so may not be the authors language of choice however it can be used as a Shell: https://ammonite.io/#Ammonite-Shell

I am personally using it and compared to a classical shell like Bash it's really nice for more structured data related tasks (exploring some API, checking some data, creating a bunch of PRs at once, ...).

It also makes use of Scala's adjustable syntax and functional concepts so you basically get shell piping but in a strongly typed fashion (e.g. `ls.! |? (_.ext == "txt") | (os.read)` would produce a list of strings representing the contents of all files ending in .txt of the current directory).

Also I am curious what people think of PowerShell which as of PowerShell Core is usable on all platforms as well.

Thanks for providing this. It's funny to read the speculations when you have read the actual root cause :D

Well I guess the thing is left unanswered for now is why the quota management reduced the capacity for Google's IMS in the first place.

Maybe we will know someday :)

Ah thanks, really exited for (1)!

Though the explanation went a bit over my head just one (two) follow up question(s): How did you end up with WASM for the inline lambdas? Did you have some discussion on alternatives like Lua? I am curious about insights on choosing scripting engines/implementations, hence why I am asking.

A different question for this could also be if alternatives to V8 where considered I guess as I believe there are quite a few pure WASM engine implementations out there (unless I did not get some feature that requires you to use V8 and rules out pure WASM engines).

Hi I like that there is a competitor to Kafka in this space and also the build in capability to do transformations. I got a few questions though which I could not find in your docs:

(1) Over at the Apache Arrow FAQ I read that the overhead of serialization in analytical frameworks can be around 80 to 90 % of total compute costs (r_1). While having no concrete numbers on this, from using Kafka together with Kafka Streams I can at least confirm that the overhead of serialization is (very) significant. My question therefore is: Does your WASM engine avoid (de-)serialization between your storage/stream layer and the engine and if not are there plans for this?

(2) Are supported WASM transformations stateless (i.e. single message) only or can they be stateful (i.e. window-ing and stream-stream/table join functionality)

(3) I could not find any reference to the WASM inline lambdas at all in the docs actually, am I missing something?

r_1: https://arrow.apache.org/faq/