It is not, it doesn't matter if a brain's neurons or a prediction algorithm managed to get the solution if it has done so in a (sort of) reliable way.

https://en.wikipedia.org/wiki/Duck_test

Errors are made by both machines and humans, so that's not a differenting factor.

What is clear is that the human element will be more valued on art as time goes on.

Still, if LLMs are capable of writing working code, art and solving math problems, they qre definitely capable of doing suboptimal routing (with some algorithmic aid)

The LLM would learn the heiristics (example: data lines first, power lines later, etc.) and would request the autorouter to do that routing, then take the image output and request a different part (depth-first). If later no routing is posible with that configuration, after some retries it could try another way.

It wouldn't solve complex boards, and engineers are always needed for short comings (and even if not, for research), but I wouldn't say this is something different than poetry, music or pixel art, LLMs can imitate although most of the times in a uncanny way.

The big AI labs won't do that unless they are forced to, as they want you to spend more money on the big, expensive, frontier models (so they can live up to their valuation), so it's more likely that you will see this on smaller open weights models.

Comments URL: https://news.ycombinator.com/item?id=48596902

Points: 3

# Comments: 0

Companies have to pay monthly for the harness app (codex, claude code) and the tokens are priced separately based on standard API pricing.

Then they have a price table that is the official public API prices for the models, but converted to their AI credits.

Currently, I'm averaging 15 credits (0.15 $) per request for some QnA questions with claude sonnet 4.6. It should be similar with gpt 5.4 (same output token price, similar input token price).

I think with the current pricing you can do light work with normal models (10-20 code completions/day), but vibe coding, heavy usage or using bigger models is not viable anymore.

It's hard to measure since I did not average my token expenditure per day.

Unfortunately, nobody saved it on the wayback machine (I just did with today's page), but you can search "github copilot pricing multiplier" and still see on google images the cost increase x6 or x9 on some models.

I am on an Enterprise plan so it's different for me: I pay the subscription and I get credits, but the token pricing is essentially the same as the public API pricing.

I understand they needed to change it, but I don't like how they try to erase the old prices from the record.

Comments URL: https://news.ycombinator.com/item?id=48355640

Points: 4

# Comments: 3

The library intentionally exposes only the functionality common across providers to avoid provider-specific parameters.

Libraries like LangChain provide many integrations but often rely on many abstractions, heavy use of kwargs, and complex code that can be difficult to customize.

Features: - Sync and async APIs - LLM calls: invoke and stream (temperature, reasoning level) - Response metadata: answer, token usage, stop reason - RAG documents: retrieval, reranking, embeddings - Chat history: conversation store - Common error handling across providers - Providers: OpenAI, Anthropic, Google, AWS

Retry logic is left to the user (see README). Agent functionality is not supported yet.

Comments URL: https://news.ycombinator.com/item?id=47419930

Points: 1

# Comments: 0