m3h

COVID happened. Money was aplenty. Then snapback happened. And there were many firings and cancelled projects.

There were a lot of products and ideas that felt great during ZIRP but stopped making sense post-ZIRP. That's my take.

I'm having a tough time using Opus to produce blog-grade writing. Claude produces jargon heavy, market-y language that no human would ever write, much less want to read. This issue exists even when I ask it to write internal reports or short memos. Opus is not working for writing in any task.

PS: I am not interest in the model's coding skills, only its ability to write or partner with me in writing.

Someone who until yesterday did not seem bothered by his technology being possibly used to bomb elementary girls school in another country seems to suddenly care about the repression of citizens in yet another country.

No, we don't buy your virtue signaling. And we certainly don't need your better-than-thou opinions on this year's "nightmare scenarios".

Is there a specific list of changes they made to the system prompt? They're claiming they removed 80% of it. That's quite substantial. It would be good to know what the model knows to do by training and what we need to avoid over-specifying in our system prompts.

Saying that "give Claude judgment" is too vague for agent implementors. Given the lack of specific details, my takeaway is that we need to go and review all context and rework prompts from prompts/descriptions from scratch until they pass the evals again.

A while back, I saw a similar feature land in Codex (I'm using the VS Code plugin) but it got removed quickly. What is the chance that an LLM recommended this same idea to the Claude PM or lead? I see LLMs across different providers converging on similar ideas or biases frequently.

> Kimi K3 is Kimi’s most capable model to date, with 2.8 trillion parameters.

This puts them on the top of the largest open models list:

  Kimi K3            2.8T
  DeepSeek-V4-Pro    1.6T (49B active)
  Kimi K2.6          ~1T (32B active)
  GLM-5.2            754B (40B active)
  DeepSeek-V3.2      685B
  Mistral Large 3    675B

That's one mighty large model! Moonshot is going to need the USD 500 million reportedly raised earlier this year to run this model.

The speed up numbers based on their testing:

    Codebase    | TypeScript 6 | TypeScript 7 | Speedup
    ------------|--------------|--------------|--------
    vscode      | 125.7s       | 10.6s        | 11.9x
    sentry      | 139.8s       | 15.7s        | 8.9x
    bluesky     | 24.3s        | 2.8s         | 8.7x
    playwright  | 12.8s        | 1.47s        | 8.7x
    tldraw      | 11.2s        | 1.46s        | 7.7x

Congratulations to the team for pulling off this feat while doing a responsible migration (looking at you, Bun).

Quick question: How does this affect downstream tools like tsdown and esbuild, which need to build the TypeScript codebase? Can I use TS 7 and current tsdown together?

When I reviewed the conversations affected by this issue, they did not always align with my feeling of "degraded output".

Some were definitely below par, and I recall having to iterate on the generated code more than I wanted to. However, it is only true for a very small number of conversations.

So we're looking at a small set of affected conversations, and even within that small set, only a few will have degraded output, likely because the model can compensate for the reasoning defect over the long conversation.