Toshiaki Maki @making

Senior Principal Architect at @VMwareTanzu / @Broadcom | Ex @Pivotal | Bichon Frise 🍋🐩 ik.am Tokyo, Japan Joined April 2007

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Toshiaki Maki @making

2 days ago

Spring and Security In The Times Of AI spring.io/blog/2026/06/0…

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Toshiaki Maki @making

4 days ago

MireMockの話だから別か。

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Toshiaki Maki @making

4 days ago

> HTTP2を有効にしてると、なぜか一部のテストが落ちる h2cだと4KB以上のボディでTomcatが413エラーを返すやつだったりしませんか？ github.com/making/icelog/…

Tada #つくまなBoot @suke_masa

5 days ago

今から話す資料です！ #jjug_ccc #jjug_ccc_c #jjug_ccc_cd RestTemplate非推奨化に備えよう！RestClient入門、そしてリニューアルされたSpring Retryでリトライして、WireMockでテストする。 | tada #docswell docswell.com/s/MasatoshiTad…

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Toshiaki Maki @making

5 days ago

センジュナマコ見れた！

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llama.cpp now has an official website: llama.app Our goal is to make local AI accessible to everyone, and improving the user experience is a big part of that. On the new landing page you’ll find a single-line cross-platform installer. The installation provides a single unified `llama` entrypoint which you can use to run/serve models and interface with 3rd-party agentic applications. While oriented towards simplified user experience, the new `llama` application also provides all the advanced functionality of the existing llama.cpp tooling with which experienced users are already familiar. Also note that all GGUF models that you might have already downloaded with llama.cpp in the past will be automatically available to use without downloading again (they are stored in the common HF cache on your machine). We have many improvements in the pipeline both at the UX and at the engine level and we plan to iteratively ship new things over the coming months. One of the main focuses will be seamless integration with local-friendly 3rd-party agents (such as Pi). In the meantime, we’ll continue to listen for feedback from the community and adjust accordingly, so keep letting us know what you think and need.

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antirez @antirez

2 weeks ago

For the DGX Spark owners. This is what you get with DS4 in your hardware. I want to post this to show how with fast prefill and not very fast generation, the system remains absolutely fine to use.

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Toshiaki Maki @making

2 weeks ago

reddit.com/r/SpringBoot/c… Spring BootとNode.jsで同じマイクロサービスを作り18ヶ月プロダクション運用で比較。インフラコストはSpring Bootが半額、メモリも安定。だそうです

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Toshiaki Maki @making

2 weeks ago

例えばSpring DataでRepositoryを作った場合、実体はこういうクラスが使われるけれど、全メソッドにトランザクションがはられている。トランザクションをRepostioryかUsecase(Service)にはるかではなく、ネストさせた上でどう伝播させる(既存に参加、新規作成等)か、だと思う github.com/spring-project…

Toshiaki Maki @making

2 weeks ago

Javaだと当たり前だけど、他の言語・フレームワークだとそうじゃないのかな。両方にトランザクション貼ってネストさせて、必要に応じて伝播属性を調整すればええやん。

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Toshiaki Maki @making

2 weeks ago

@shinjishim40534 長さの話ではなく境界の話なので、もしその処理のロックが長くなるとしたら言語関係なくそうなります

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Toshiaki Maki @making

2 weeks ago

@NoobFunctor I've been using it for over 15 years and have never encountered any problems with propagation. There are many other parts that are more problematic, though.

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Toshiaki Maki @making

2 weeks ago

Toshiaki Maki @making

2 weeks ago

トランザクションの伝播について誰も言及していないのは何故？

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Toshiaki Maki @making

2 weeks ago

トランザクションの伝播について誰も言及していないのは何故？

farstep @farstep_

2 weeks ago

x.com/i/article/2056…

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Toshiaki Maki @making

2 weeks ago

FFS原則 - ソフトウェア保守のトリレンマ claude.ai/public/artifac…

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Toshiaki Maki @making

2 weeks ago

llama.cppのMTPサポートのおかげで、Qwen3.6-27B on DGX Sparkで、26 tok/s 出るようになった。以前は11 tok/s だったので、だいぶ快適になった。

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Georgi Gerganov @ggerganov

2 weeks ago

llama.cpp adds MTP for the Qwen3.6 family This is a significant milestone for the local AI ecosystem. The performance jump with these changes is massive and elevates local inference on commodity hardware further. Special thanks to Aman Gupta for leading this development! github.com/ggml-org/llama…

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Toshiaki Maki @making

3 weeks ago

RabbitMQ 4.3のDelayed Retriesは良い機能だと思う。作り込みが要らなくなる rabbitmq.com/blog/2026/04/2…

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Toshiaki Maki @making

3 weeks ago

ASUS Ascent GX10 (DGX Spark) 上でds4(DwarfStar 4)を試すメモ ik.am/entries/904

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antirez @antirez

3 weeks ago

I just pushed a big refactoring of DS4 backends with CUDA support and single direction activation steering. The Metal path should be unaffected. Note: I only support hardware I have own (or have full access to): so just M3 (no M5 NE for now), DGX Spark.

antirez @antirez

3 weeks ago

Soon in DS4: 1. CUDA support (14 t/s, 350 t/s prefill on DGX Spark), 2. Single direction steering support. 3. Huge refactoring to support Metal / CUDA / CPU in a more sensible way.