翻訳言語

GPT‑Live

OpenAIが「GPT‑Live」を発表。これはChatGPTの新機能で、ユーザーがリアルタイムに音声対話を行えるようになる。これにより、AIとの自然な会話や、より動的なインタラクションが可能となる。

背景メモ

OpenAIは2025年5月、GPT‑Live（ジーピーティー・リブ）という新製品を発表した。同社が提供する汎用AI「GPT」シリーズの最新版で、従来のテキスト生成に加え、音声・画像・動画をリアルタイムで処理できる「マルチモーダル・ネイティブ」なモデルを謳う。人間と自然な会話ができたり、動画を解析しながら質問に答えたりといった機能が売り。OpenAIとしては初めて、応答速度が人間の平均反応時間を下回るレベルの対話性能を実現したとしている。 — OpenAIはChatGPTを開発した米国AI企業。GPTシリーズは同社の主力大規模言語モデル（LLM）。 — 直前の大型モデル「GPT‑5」は2024年後半に発表されていたが、GPT‑Liveはその後継という位置づけ。 — リアルタイム性を重視した設計で、GoogleのGemini LiveやMetaの音声対話モデルに対抗する製品。 — プライバシー面やAIの安全性（人間らしすぎる会話がもたらす影響）については、現時点で詳細な評価は公表されていない。

RT Max Weinbach: I had early access to this model and I'll post more Thursday, but I think the most impressive part is this model NEVER GIVES UP If yo...
9.0
Max Weinbach says he had early access to OpenAI's new model GPT-5.6 Sol, calling it his favorite model by far. He highlights that it never gives up and will keep reasoning until it's done. OpenAI announced that GPT-5.6 Sol, along with Terra and Luna, will launch publicly on Thursday, with preview access expanding globally now.
Better Models: Worse Tools
7.5
Newer Claude models sometimes invent extra keys in tool call arguments, breaking validation in Pi's edit tool. The author suspects post-training for Claude Code's forgiving harness makes alternative schemas fail. This suggests closed RL training can degrade general tool-use reliability.
Writing an LLM from scratch, part 34b -- from bigrams to GPT-2, one component at a time (in JAX)
3.0
The author builds and trains a GPT-2 small model from scratch in JAX, starting from a basic bigram-style model and incrementally adding components like LayerNorm and Transformer blocks. Achieved a final loss of 3.418, beating their PyTorch version (3.538) and original GPT-2 small (3.499) on the same test dataset.
Flax debugging: making a hash of things
2.0
The author debugged a Flax NNX training loop where the loss was stuck at 10.82, indicating random guessing. By hashing the model parameters and comparing hashes across steps, they discovered the parameters weren't changing. The root cause was using @jax.jit instead of @nnx.jit, which is needed for proper in-place state propagation of parameter updates in NNX.
Writing an LLM from scratch, part 34a -- building a JAX training loop for an LLM training run
1.0
Building a JAX training loop for an LLM from scratch using Flax NNX and Optax. First validated the harness with a minimal "A-to-A" model (embedding then projection) before adding Transformer layers. Key challenges included fixing JAX's GPU memory default and slow data iteration by committing data to CPU memory.

GPT‑Live

背景メモ

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RT Max Weinbach: I had early access to this model and I'll post more Thursday, but I think the most impressive part is this model NEVER GIVES UP If yo...

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