はまち

[はまち @hama_jp]

@GOROman ？

[はまち @hama_jp]

@R_are_L_ ほんとそう。フライトシムとか発禁レベルなのかなぁ

[池田信夫 @ikedanob]

アメリカのAIに10兆円以上投資するソフトバンクに、なんで役所が3800億円の補助金を出すのか。

[Google Research @GoogleResearch]

Introducing TabFM, a foundation model designed specifically for tabular data classification & regression. This approach allows generation of high-quality predictions on previously unseen tables in a single forward pass.

Learn more and try out the model →https://t.co/OTbVQ8oUQs

[はまち @hama_jp]

ただ推論するだけだったらわざわざ買わなくてもいいのにね。

[うみゆき@AI研究 @umiyuki_ai]

その通り。ここまで頑張って（280万円）マトモに使えない。だからローカルLLMなんかに夢見んなって100回言ってる。それでもローカルLLMを愛好するシネベンチマニアみたいなAIオタク向けにツイートする時もある。ワケ分かってない人がそれ見て「ローカルLLMならタダでAI使い放題や！」とか誤解を招く。一生この悪循環が続く

[おざけん @ozaken_AI]

https://t.co/jlBP6PLL0W

[はまち @hama_jp]

@nikkei 船頭が多すぎる気もする

[はまち @hama_jp]

@47news_official すごっ!

[hata @hatappo]

今日上司に WebMCP を教えてもらったんだけど名前も知らなかった。

色んなオンラインの手続きとか、各社がこのインターフェイスを持てば楽になりそうな未来でいいなあ。

https://t.co/Bi6ZZQnZN7

[はまち @hama_jp]

@ebiebi_pg その構文知ってそうな人に、言えなくて困るよねー

[DAIR.AI @dair_ai]

Cool new paper from NVIDIA.

Looks like agentic coding is moving into hardware design.

HORIZON treats hardware design as repository-level code evolution. A Markdown harness becomes a project pack with domain knowledge, an executable evaluator, an acceptance predicate, and a git policy.

The agent then evolves an isolated worktree.

That is a strong pattern because hardware design needs executable checks. The verifier harness becomes the real interface between the agent and the design task.

The paper reports 100% benchmark completion across several hardware design suites, which makes this one worth tracking even if you do not work on EDA.

Paper: https://t.co/zoUSIPhYGt

Learn to build effective AI agents in our academy: https://t.co/LRnpZN7L4c

[はまち @hama_jp]

@mikumo_hk ほんとこれ

[Dan Kornas @DanKornas]

Transformers are easier to learn when you can poke the model directly.

Transformer Explainer is an interactive visualization tool for learning how Transformer-based text-generation models like GPT work.

It helps you connect the architecture to real behavior by running a live GPT-2 model in the browser, letting you enter your own text, and showing how internal components work together to predict the next tokens.

Key features:

• Live GPT-2 in the browser – experiment without setting up a separate model server first
• Custom text input – try your own prompts and watch how the model handles them
• Internal component views – observe the operations that work together inside the Transformer
• Next-token prediction focus – connect each visual step to the model’s token predictions
• Local development path – clone the repo, install dependencies, and run it with npm for deeper inspection

It’s open-source (MIT license).

Link in the reply 👇

[はまち @hama_jp]

@gsgenjitsu 手厳しい

[はまち @hama_jp]

@shinjukuacc ありがたい

[elvis @omarsar0]

If you use LLM-as-judge, this one is worth reading.

(bookmark it)

It's actually one of the most effective ways to use LLM-as-a-Judge for evals.

Holistic judge scores hide both their reasoning and their ceiling effects.

BINEVAL decomposes each evaluation criterion into atomic yes-or-no questions, answers each independently per output, then aggregates the verdicts into calibrated multi-dimensional scores.

Every question-level verdict is inspectable, so you can diagnose exactly why an output scored low, and the same verdicts feed straight back as targeted prompt-improvement signal.

Across SummEval, Topical-Chat, and QAGS, it matches or beats UniEval and G-Eval, training-free, with especially strong results on factual consistency.

Paper: https://t.co/oar6BZcasm

Learn to build effective AI agents in our academy: https://t.co/1e8RZKs4uX

[はまち @hama_jp]

@hangstuck 言い方よな

[はまち @hama_jp]

@riskhedge110 胆力がないサラリーマン経営者だから

[はまち @hama_jp]

@daitojimari 長時間待たせておいて、その間の電話代もこちら持ちというのも腹が立つ

[Piyo@個人開発してるリーマン @shizuku837335]

フーリエ変換と言えば、
ラプラス変換ですね。

両者の違いが分かるように整理をしました。
#フーリエ変換 #ラプラス変換 #波動 #スペクトル #理系 #大学生