Javaで実装したCommon Lispサブセット。インタプリタに加えて.lispからJVMバイトコードとWASMバイトコードにコンパイル可能 github.com/making/rontolisp これ自体GraalVMでWASMにコンパイルしてブラウザで試せる making.github.io/rontolisp/ selfhostのreplもwasmにコンパイルできる github.com/making/rontolisp#…

spring.io/blog/2026/06/10/sp… spring.io/blog/2026/06/10/sp… spring.io/blog/2026/06/10/sp… Spring Boot 4.1.0, 4.0.7, 3.5.15がリリースされました。 spring.io/security を見ると(AIによる)脆弱性Fixが60件以上取り込まれているので、今すぐアップデート!

Springプロジェクトの脆弱性、いっぱい出ています spring.io/security Bootリリースは今週中

先週の土曜日に見られて良かった

Spring and Security In The Times Of AI spring.io/blog/2026/06/01/sp…

センジュナマコ見れた！

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.

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.

reddit.com/r/SpringBoot/comm… 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

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-projects/s…

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。

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？

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

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。

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.cp…

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/2026/04/23…

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

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.