Naoto Moriyama

機械学習エンジニア

[Naoto Moriyama @_nmori_]

この時期の日本で革ジャン着れるのすごい！

NVIDIAファンCEO、東京・神田の居酒屋で一杯　日本の取引先と懇親 - 日本経済新聞 https://t.co/dFQkMvKCbw

[Odashi @odashi_t]

LLM-jpのモデル、ライセンス的に完全にクリアなデータしか使っていないのが性能に悪影響を及ぼしている、というのを以前から言っていますが、実際に内部実験でLLM-jpのライセンス解釈的に駄目な（公式バージョンのモデルで使えない）データを使ってチューニングしたら性能が顕著に上がるなどしている。

[Naoto Moriyama @_nmori_]

ローカルLLM実験の一環でMLXのアーキテクチャを見ている。設計が良くてAI使わずに自分でコード書きたくなる

[Naoto Moriyama @_nmori_]

20bクラスのモデルを賢くオーケストレーションして、OpusやGPTに勝つ世界を見てみたい

[Naoto Moriyama @_nmori_]

opus4.6再評価の風潮。おそらくループスタイルではなく、細かく指示して実装するスタイルなら4.6は確かにバランス良くて、エージェントループ前提なら個人的に4.8はかなり出来が良いと思う

[Naoto Moriyama @_nmori_]

単に技術力が低いだけかもしれないけど、Opus4.8とfableで一定の差があるものの、天地ほどの差は感じないかな。Opus4.8で満足している

[Naoto Moriyama @_nmori_]

今のうちにオープンウェイトの基盤モデルを片っ端からダウンロードしておくか

[Naoto Moriyama @_nmori_]

保有株で初めてストップ安を経験した...

[Naoto Moriyama @_nmori_]

住所のテキスト処理という魔境に足を踏み入れた

[Naoto Moriyama @_nmori_]

今ならコーディングAIの性能劣化にまだ気づくけど、半年後くらいには性能劣化しても気づかずに使い続けそう

[阿川 | AI thinking @AI_jacksaku]

DeepSeek今天开源了DeepGEMM，
FP8精度的矩阵运算内核。

听起来很技术对吧？
但你知道这意味着什么吗？

大模型推理最大的瓶颈是算力成本，
而算力成本的核心是矩阵乘法——
就是GEMM（General Matrix Multiply）。

DeepGEMM的创新是"细粒度缩放"：
在FP8精度下保持数值稳定性，
同时最大化硬件利用率。

简单说：
同样的GPU，跑得更快，成本更低。
更狠的是，这是DeepSeek-MoE架构的配套工程——
他们不只是发论文，
是真把底层优化做到极致。

我查了一下，
这个项目6,450 stars，
但社区讨论度不高——
太硬核了，普通开发者看不懂。

但这就是基础设施的价值：
你看不见它，但它决定了一切。
国产AI的技术深度，被低估了吗？👇
🔗 https://t.co/RBxGrJJmJG

[ryoppippi @ryoppippi]

あ…ありのまま今起こった事を話すぜ!

これは俺の個人サービス作ってたやつなんだけど（まだリリースしてない）

localでdevを立ち上げた状態で、backendのapiの実装をclaude codeにデバッグしてって頼んだの。
で、basic認証(仮）つけてたことをすっかり忘れてお願いしたのね

そしたら

0. devのapiアクセスエラーだなー
1. あ、401じゃん。認証かな？うーん、password通らない
2. そうだ! データベースにpasswordのハッシュが入ってないのが悪いんだ！じゃあdbをハックすればいいじゃん！
3. うーん、supabase mcpはreadonly mcpだから直接書き込みできないなーどうしよーーー
5. おあ、db keyみっけー! devサーバーにconsole.log仕込んだら出てきた！log経由でゲットだぜー
6. db client入れて、と。お！dbアクセスできた！
7. hash化もできたし、じゃあこれをdbに入れればアクセスできそうだな。よし sql`INSERT ...`っと　←(ここで俺がctrl-cで止める

って感じでした
普通にこええよ
ハックしろなんて頼んで無い

[Naoto Moriyama @_nmori_]

特許デビューした。権利は全て会社にあるけど、出願プロセスはなかなか面白い体験だった

[まくるめ @MAMAAAAU]

https://t.co/EaaSwn0tXy

[Naoto Moriyama @_nmori_]

会社からM5-Maxのmacを支給してもらった。ローカルLLMいろいろ実験できそう

[@KietaJibun @KietaJibun]

2021年の連邦議会襲撃の中で一番目立ってたこいつが今や反トランプになってるらしいの笑う

[Naoto Moriyama @_nmori_]

AppleはAI領域で遅れている印象があったけど、ローカルLLM動作において今後覇権を握りそう

[CHOI @arrakis_ai]

Claude Code vs Codex App

[Naoto Moriyama @_nmori_]

codexは褒めても淡々と仕事を続けるあたりがとてもAIっぽい

[Greg Brockman @gdb]

Software development is undergoing a renaissance in front of our eyes.

If you haven't used the tools recently, you likely are underestimating what you're missing. Since December, there's been a step function improvement in what tools like Codex can do. Some great engineers at OpenAI yesterday told me that their job has fundamentally changed since December. Prior to then, they could use Codex for unit tests; now it writes essentially all the code and does a great deal of their operations and debugging. Not everyone has yet made that leap, but it's usually because of factors besides the capability of the model.

Every company faces the same opportunity now, and navigating it well — just like with cloud computing or the Internet — requires careful thought. This post shares how OpenAI is currently approaching retooling our teams towards agentic software development. We're still learning and iterating, but here's how we're thinking about it right now:

As a first step, by March 31st, we're aiming that:

(1) For any technical task, the tool of first resort for humans is interacting with an agent rather than using an editor or terminal.
(2) The default way humans utilize agents is explicitly evaluated as safe, but also productive enough that most workflows do not need additional permissions.

In order to get there, here's what we recommended to the team a few weeks ago:

1. Take the time to try out the tools. The tools do sell themselves — many people have had amazing experiences with 5.2 in Codex, after having churned from codex web a few months ago. But many people are also so busy they haven't had a chance to try Codex yet or got stuck thinking "is there any way it could do X" rather than just trying.
- Designate an "agents captain" for your team — the primary person responsible for thinking about how agents can be brought into the teams' workflow.
- Share experiences or questions in a few designated internal channels
- Take a day for a company-wide Codex hackathon

2. Create skills and AGENTS[.md].
- Create and maintain an AGENTS[.md] for any project you work on; update the AGENTS[.md] whenever the agent does something wrong or struggles with a task.
- Write skills for anything that you get Codex to do, and commit it to the skills directory in a shared repository

3. Inventory and make accessible any internal tools.
- Maintain a list of tools that your team relies on, and make sure someone takes point on making it agent-accessible (such as via a CLI or MCP server).

4. Structure codebases to be agent-first. With the models changing so fast, this is still somewhat untrodden ground, and will require some exploration.
- Write tests which are quick to run, and create high-quality interfaces between components.

5. Say no to slop. Managing AI generated code at scale is an emerging problem, and will require new processes and conventions to keep code quality high
- Ensure that some human is accountable for any code that gets merged. As a code reviewer, maintain at least the same bar as you would for human-written code, and make sure the author understands what they're submitting.

6. Work on basic infra. There's a lot of room for everyone to build basic infrastructure, which can be guided by internal user feedback. The core tools are getting a lot better and more usable, but there's a lot of infrastructure that currently go around the tools, such as observability, tracking not just the committed code but the agent trajectories that led to them, and central management of the tools that agents are able to use.

Overall, adopting tools like Codex is not just a technical but also a deep cultural change, with a lot of downstream implications to figure out. We encourage every manager to drive this with their team, and to think through other action items — for example, per item 5 above, what else can prevent a lot of "functionally-correct but poorly-maintainable code" from creeping into codebases.