Comments URL: https://news.ycombinator.com/item?id=47427017

Points: 269

# Comments: 83

I also found the HN discussion at the time informative: https://news.ycombinator.com/item?id=6457801

Comments URL: https://news.ycombinator.com/item?id=47262554

Points: 1

# Comments: 1

It also speaks to the world that we live in these days - I'm having a hard time separating satire from reality.

https://www.hetzner.com/dedicated-rootserver/ax162-r/configu...

Before today, we used to be able to order an AX162-R for €207 and add 128 GB of RAM for €46. Starting today, the same calculator provides €207 for an AX162-R (*) and €264 for the 128 GB RAM add-on. Sadly, HN doesn't let me upload screenshots.

(*) The price change for AX162-R machines is effective starting April 1st.

* Cloud (VMs): 38%

* Bare metal: 15%

* Memory add-on for bare metal: 575% (effective immediately)

It feels like memory add-on is intentionally set high to discourage customers from adding more memory.

AX102 (128 GB RAM) costs €124, AX162 (256 GB RAM) costs €244, but the 128 GB memory add-on alone costs €264. If we ignore the setup fee, it’s more cost-effective to provision additional servers instead of adding RAM to bare metal instances.

Here's the link to cloud and bare metal pricing changes: https://docs.hetzner.com/general/infrastructure-and-availabi...

I understand that MySQL follows a specific pluggable storage architecture. I also understand that the direct equivalent in PG appears to be table access methods (TAM). However, you don't need to use TAM to build this - I'd argue FDWs are much more suitable.

Also, I think this design assumes that you'd swap PG's storage engine and replicate data to DuckDB through logical replication. The explanation then notes deficiencies in PG's logical replication.

I don't think this is the only possible design. pg_lake provides a solid open source implementation on how else you could build this solution, if you're familiar with PG: https://github.com/Snowflake-Labs/pg_lake

All up, I feel this explanation is written from a MySQL-first perspective. "We built this valuable solution for MySQL. We're very familiar with MySQL's internals and we don't think those internals hold for PostgreSQL."

I agree with the solution's value and how it integrates with MySQL. I just think someone knowledgeable about PostgreSQL would have built things in a different way.

In their website, the benchmarks say “Multilingual (Chinese), Multilingual (East-asian), Multilingual (Eastern europe), Multilingual (English), Multilingual (Western europe), Forms, Handwritten, etc.” However, there’s no reference to the benchmark data.

Comments URL: https://news.ycombinator.com/item?id=46309821

Points: 9

# Comments: 3

Comments URL: https://news.ycombinator.com/item?id=46204949

Points: 4

# Comments: 0

When people ask me what’s missing in the Postgres market, I used to tell them “open source Snowflake.”

Crunchy’s Postgres extension is by far the most ahead solution in the market.

Huge congrats to Snowflake and the Crunchy team on open sourcing this.

Is it because remote storage in the cloud always introduces some variance & the benchmark just picks that up?

For reference, anarazel had a presentation at pgconf.eu yesterday about AIO. anarazel mentioned that remote cloud storage always introduced variance making the benchmark results hard to interpret. His solution was to introduce synthetic latency on local NVMes for benchmarks.

https://getomni.ai/blog/ocr-benchmark (Feb 2025)

Please note that LLMs progressed at a rapid pace since Feb. We see much better results with the Qwen3-VL family, particularly Qwen3-VL-235B-A22B-Instruct for our use-case.

Comments URL: https://news.ycombinator.com/item?id=45562830

Points: 5

# Comments: 1

Comments URL: https://news.ycombinator.com/item?id=45359585

Points: 6

# Comments: 0

I agree with you. I feel the challenge is that using AI coding tools is still an art, and not a science. That's why we see many qualitative studies that sometimes conflict with each other.

In this case, we found the following interesting. That's why we nudged Shikhar to blog about his experience and put a disclaimer at the top.

* Our codebase is in Ruby and follows a design pattern uncommon industry * We don't have a horse in this game * I haven't seen an evaluation that evaluates coding tools in (a) coding, (b) testing, and (c) debugging dimension

Just in case you have $3-4M lying around somewhere for some high quality inference. :)

SGLang quotes a 2.5-3.4x speedup as compared to the H100s. They also note that more optimizations are coming, but they haven't yet published a part 2 on the blog post.

For others, I think the picture is different. When we ran benchmarks on DeepSeek-R1 on 8x H200 SXM using vLLM, we got up to 12K total tok/s (concurrency 200, input:output ratio of 6:1). If you're spiking up 100-200K tok/s, you need a lot of GPUs for that. Then, the GPUs sit idle most of the time.

I'll read the blog post in more detail, but I don't think the following assumptions hold outside of AI labs.

* 100% utilization (no spikes, balanced usage between day/night or weekdays) * Input processing is free (~$0.001 per million tokens) * DeepSeek fits into H100 cards in a way that network isn't the bottleneck

It's also disappointing that MIT requires you to fill out a form (and wait for) access to the report. I read four separate stories based on the report, and they all provide a different perspective.

Here's the original pdf before MIT started gating it: https://web.archive.org/web/20250818145714/https://nanda.med...

I think it hasn’t received much attention because the frontier shifted to reasoning and multi-modal AI models. In accuracy benchmarks, all the top models are reasoning ones:

https://artificialanalysis.ai/

If someone took Kimi k2 and trained a reasoning model with it, I’d be curious how that model performs.