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achompas

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DeepSeek R1: Don't Put All Your Eggs in One LLM Basket

notdiamond.ai
10 points·by achompas·पिछला वर्ष·0 comments

1.5M Human Preference Arena Rankings on LLM Responses

notdiamond.ai
7 points·by achompas·2 वर्ष पहले·0 comments

CrowdStrike's re:Invent 2024 marketing is...a very long line

twitter.com
4 points·by achompas·2 वर्ष पहले·0 comments

Ultra low-latency routing between RAG agents

docs.notdiamond.ai
3 points·by achompas·2 वर्ष पहले·0 comments

Accurately predict when to send queries to an LLM vs. a human expert

docs.notdiamond.ai
4 points·by achompas·2 वर्ष पहले·0 comments

AI Model Rankings on Human Preference from Not Diamond's Arena Mode

notdiamond.ai
4 points·by achompas·2 वर्ष पहले·0 comments

AI-router-chat – An AI chat app with LLM model routing

github.com
12 points·by achompas·2 वर्ष पहले·3 comments

comments

achompas
·4 माह पहले·discuss
The onus isn’t on me. It’s on anyone contradicting findings by most benchmarks, because most of them show a clear advantage for Opus and GPT over OSS models.
achompas
·4 माह पहले·discuss
The problem isn’t the SDK but the API usage.

Users will say this-or-that about choice etc etc. It’s about subsidized tokens. Otherwise th users (and OpenCode) would have stopped pushing the workarounds months ago.
achompas
·4 माह पहले·discuss
I’m sorry but you’re demonstrably incorrect.

Listen, I want more open weight models in the world. They create entrepreneurial opportunities and support use cases which the foundation labs don’t want to support.

But open weight models are consistently three to six months behind on performance compared to closed models, as confirmed by both benchmarks and personal use. They’re closer on coding and much further away on non-coding tasks.

There are theories as to why these models lag, which I won’t get into. But anyone claiming open-weight models are close to closed-weight models is ignoring significant evidence to the contrary.
achompas
·4 माह पहले·discuss
This is both true and immaterial to Microsoft’s net profits.
achompas
·4 माह पहले·discuss
Yep, well said and great, sharp explanation.

I think we can attribute a bunch of consternation here to drift between assumed and actual licensing terms.

The actual licensing terms for Claude Code expressly prohibit use of the product outside of the Claude Code harness. If you want Opus outside of CC, the API is available for your use anytime.

Some percentage of the community seems to assume their Claude Code subscription licenses allow free usage of CC across any product surface - including competing products like OpenCode. While this is a great way to save on API costs, the assumption is incorrect. In fact, it is *so* incorrect that Anthropic has encoded their licensing terms into their Terms of Service, and a result can take legal action against any violating parties.

We can have separate discussions about Anthropic’s use of the Common Crawl in pre-training, or whether foundation labs adhere to robots.txt conventions. But those don’t directly impact Anthropic’s right to bring litigation.

——

Outside of that I think angry users have their own stated preferences v revealed preferences here. They claim they want Opus on their terms, and Anthropic’s actions infringe on their user rights.

Angry folks: Opus is right there! You just need an API key! The reality is you want Opus in your devtools of choice at discounted rates. You could at least be honest about your consternation
achompas
·4 माह पहले·discuss
Yes, it is true that companies often litigate against customers who violate their Terms of Service. The TOS is put into place to protect the company’s interests from user abuse.

Paying customers of Claude Code don’t receive a free-use license for any desired application. They’re paying to use Claude Code. Anthropic can take steps to litigate usage outside of those terms, even if customers find that fact really annoying.
achompas
·2 वर्ष पहले·discuss
LMSYS consistently provides a high standard for open, collaborative exploration of LLMs. Cool to see them explore LLM routing - this feels like a fertile area for problemsolving.
achompas
·2 वर्ष पहले·discuss
My experience this is common for specialty knowledge the founders might not possess. They know enough to know they cannot assess, say, AI/ML or infrastructure, and seek interview support from an advisor.
achompas
·5 वर्ष पहले·discuss
I took Adam’s point here to be that an Airflow DAG author primarily concerns themselves with the configuration of those objects, since the underlying components (Celery worker, Python execution process or K8s pod; data warehouse; RPC) have been abstracted in the form of Operators.
achompas
·9 वर्ष पहले·discuss
I loved working through this years ago. Clear and succinct.