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Posts by S. Ota

Claude Code settings for Opus 4.6

Claude Code settings for Opus 4.6

Opus 4.7 feels a bit too intrusive for my taste.

3 days ago 0 0 0 0
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NuPhy Air 65 V3 1.The Air65 V3 is available in three layout versions: ANSI, JlS and ISO ANSI - US EnglishShipping NowJlS and ISO (German,French,British)[Only available in Nova White]Shipping Now 2.The default configu...

Standard 65% > quirky 60%.

nuphy.com/products/nup...

3 weeks ago 0 0 0 0
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Qwen 3.5 Small Model Series just dropped on
@hf.co 🔥

huggingface.co/collections/...

✨ 0.8B/2B/4B/9B
✨ Apache2.0
✨ 262K→1M token context

1 month ago 83 18 1 8
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日本語性能を強化したオープンなLLM「GPT-OSS Swallow」と「Qwen3 Swallow」リリース | gihyo.jp Swallow LLM Projectは2026年2月20日、OpenAI GPT-OSSおよびAlibaba Qwen3の日本語能力と思考力を強化した推論型言語モデル「GPT-OSS Swallow」と「Qwen3 Swallow」をリリースした。

ニュース「日本語性能を強化したオープンなLLM「GPT-OSS Swallow」と「Qwen3 Swallow」リリース」公開
gihyo.jp/article/2026...

1 month ago 6 2 0 0
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日本語能力を強化したAI「GPT-OSS Swallow」と「Qwen3 Swallow」を東京科学大の研究チームが公開 2026年2月20日、東京科学大学情報理工学院の岡崎研究室と横田研究室、国立研究開発法人産業技術総合研究所(産総研:AIST)の研究チームが、OpenAI GPT-OSSの日本語能力と思考力を強化した推論型大規模言語モデルの「GPT-OSS Swallow」と、Alibaba Qwen3の日本語能力と思考力を強化した推論型大規模言語モデルの「Qwen3 Swallow」を発表しました。

日本語能力を強化したAI「GPT-OSS Swallow」と「Qwen3 Swallow」を東京科学大の研究チームが公開
gigazine.net/news/20260224-gptoss-qwe...

1 month ago 10 8 0 1
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Cornix LP Split Ergonomic Keyboard | Wireless Low-Profile Cornix LP is a low-profile split ergonomic keyboard designed to reduce wrist and shoulder strain, featuring adjustable tenting, CNC aluminum case, and dual-mode wireless connectivity.

"While our earlier split keyboard, Cornix, helped many users relieve discomfort caused by traditional one-piece keyboards, we learned that highly specialized layouts are not for everyone.
Jiffy 75 bridges that gap—combining ergonomic benefits with a familiar 75% layout that most users already love."

2 months ago 0 1 0 0
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KAP_Retro Lights R2 Keycaps Inspired by the iconic Light Mode of Apple's MacOS, KAP_Retro Lights R2 bring a touch of nostalgia with their charming dot icons. Upgrade your keyboard now!

"Designed by Jr.Mars
Inspired by the sleek and elegant Light Mode of Apple's MacOS system, bring a touch of classic nostalgia to your keyboard."

keyreative.store/products/kap...

5 months ago 1 0 0 0

How to burn firmware to the Swagkeys Eave65

1. Install dfu-util
2. Plug in the USB-C cable
3. Press the reset button on the back of the PCB
4. Run `dfu-util -l`. It should show 3 targets (alt=0, 1, 2)
5. Run `dfu-util -d 1eaf:0003 -a 2 -D eave65.bin` (alt=2)
6. Unplug and plug the USB-C cable

6 months ago 0 0 0 0
While the ``deep reasoning'' paradigm has spurred significant advances in verifiable domains like mathematics, its application to open-ended, creative generation remains a critical challenge. 

The two dominant methods for instilling reasoning -- reinforcement learning (RL) and instruction distillation -- falter in this area; RL struggles with the absence of clear reward signals and high-quality reward models, while distillation is prohibitively expensive and capped by the teacher model's capabilities. 

To overcome these limitations, we introduce REverse-Engineered Reasoning (REER), a new paradigm that fundamentally shifts the approach. 

Instead of building a reasoning process ``forwards'' through trial-and-error or imitation, REER works ``backwards'' from known-good solutions to computationally discover the latent, step-by-step deep reasoning process that could have produced them. 

Using this scalable, gradient-free approach, we curate and open-source DeepWriting-20K, a large-scale dataset of 20,000 deep reasoning trajectories for open-ended tasks. 

Our model, DeepWriter-8B, trained on this data, not only surpasses strong open-source baselines but also achieves performance competitive with, and at times superior to, leading proprietary models like GPT-4o and Claude 3.5.

While the ``deep reasoning'' paradigm has spurred significant advances in verifiable domains like mathematics, its application to open-ended, creative generation remains a critical challenge. The two dominant methods for instilling reasoning -- reinforcement learning (RL) and instruction distillation -- falter in this area; RL struggles with the absence of clear reward signals and high-quality reward models, while distillation is prohibitively expensive and capped by the teacher model's capabilities. To overcome these limitations, we introduce REverse-Engineered Reasoning (REER), a new paradigm that fundamentally shifts the approach. Instead of building a reasoning process ``forwards'' through trial-and-error or imitation, REER works ``backwards'' from known-good solutions to computationally discover the latent, step-by-step deep reasoning process that could have produced them. Using this scalable, gradient-free approach, we curate and open-source DeepWriting-20K, a large-scale dataset of 20,000 deep reasoning trajectories for open-ended tasks. Our model, DeepWriter-8B, trained on this data, not only surpasses strong open-source baselines but also achieves performance competitive with, and at times superior to, leading proprietary models like GPT-4o and Claude 3.5.

[30/30] 198 Likes, 6 Comments, 3 Posts
2509.06160, cs․AI | cs․CL, 07 Sep 2025

🆕Reverse-Engineered Reasoning for Open-Ended Generation

Haozhe Wang, Haoran Que, Qixin Xu, Minghao Liu, Wangchunshu Zhou, Jiazhan Feng, Wanjun Zhong, Wei Ye, Tong Yang, Wenhao Huang, Ge Zhang, Fangzhen Lin

7 months ago 1 1 1 0
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Chat - add fontSize and fontFamily settings by lszomoru · Pull Request #263299 · microsoft/vscode

"chat.fontSize" and "chat.fontFamily" for GitHub Copilot Chat.

github.com/microsoft/vs...

7 months ago 0 0 0 0

GRPO for gpt-oss-20b with verl and sglang

github.com/volcengine/v...

7 months ago 0 0 0 0
Slurm Workload Manager - Rosetta Stone of Workload Managers

A useful table to convert Slurm scripts to ABCI (PBS) and TSUBAME (SGE/AGE).

"This table lists the most common command, environment variables, and job specification options used by the major workload management systems: PBS/Torque, Slurm, LSF, SGE and LoadLeveler."

slurm.schedmd.com/rosetta.html

7 months ago 0 0 0 0
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gpt-oss: How to Run & Fine-tune | Unsloth Documentation Run & fine-tune OpenAI's new open-source models!

"Unsloth gpt-oss fine-tuning is 1.5x faster, uses 70% less VRAM, and supports 10x longer context lengths. gpt-oss-20b LoRA training fits on a 14GB VRAM, and gpt-oss-120b works on 65GB VRAM."

docs.unsloth.ai/basics/gpt-o...

8 months ago 0 0 0 0
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GitHub - huggingface/gpt-oss-recipes: Collection of scripts and notebooks for OpenAI's latest GPT OSS models Collection of scripts and notebooks for OpenAI's latest GPT OSS models - huggingface/gpt-oss-recipes

"Collection of scripts demonstrating different optimization and fine-tuning techniques for OpenAI's GPT-OSS models (20B and 120B parameters).

...

For full-parameter training on one node of 8 GPUs, ..."

github.com/huggingface/...

8 months ago 1 0 0 0
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Agar Mini Agar mini The Agar Mini represents the latest evolution in the esteemed Agar series, distilling its signature design language into a new compact form factor. This model preserves the elegant, curved a...

"The Agar Mini is available in two distinct versions ...

Wired Edition: Powered by QMK firmware, ... is fully compatible with VIA, VIAL, ...

Dual-Mode Wireless Edition: Built on ZMK firmware, ... is fully customizable via the zmk.studio editor."

kbdfans.com/products/aga...

8 months ago 1 0 0 0
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Gemini CLI First there was Claude Code in February, then OpenAI Codex (CLI) in April, and now Gemini CLI in June. All three of the largest AI labs now have their own …

My notes on Gemini CLI, including poking around in their system prompt which I've extracted into a more readable rendered Gist simonwillison.net/2025/Jun/25/...

9 months ago 81 11 5 1
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Gemini CLI: your open-source AI agent Free and open source, Gemini CLI brings Gemini directly into developers’ terminals — with unmatched access for individuals.

The "secret project" I've been working on at my job has gone public (and open source) today! Check it out!

9 months ago 4 1 0 0

Since I had already used the Gemini API, I had to unset the GEMINI_API_KEY in order to authenticate with my Google account.

GEMINI_API_KEY="" gemini

9 months ago 0 0 0 0
Skyfeed  settings for the Claude Code | Gemini CLI feed.

Skyfeed settings for the Claude Code | Gemini CLI feed.

I expanded the Claude code feed to include the Gemini CLI.

bsky.app/profile/did:...

9 months ago 3 0 0 0

bsky.app/profile/did:...

This feed will be moved to

bsky.app/profile/did:...

9 months ago 0 0 0 0
RegEx patterns for Custom Keyboard feed.

RegEx patterns for Custom Keyboard feed.

RegEx patterns for Custom Keyboard feed.

9 months ago 0 0 0 0

Posts related to custom keyboard, DIY keyboard, mechanical keyboard, key switch, keycap, etc.
自作キーボード, キースイッチ, キーキャップなどを含むポスト.

bsky.app/profile/did:...

9 months ago 0 0 1 0

Posts related to `Claude Code`.
Claude Code を含むポスト。

bsky.app/profile/did:...

9 months ago 0 0 0 0
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GitHub - jupyterlab/jupyter-ai: A generative AI extension for JupyterLab A generative AI extension for JupyterLab. Contribute to jupyterlab/jupyter-ai development by creating an account on GitHub.

Jupyter Notebookと生成AIの組み合わせ、あると思います。チャット用のサイドウィンドウを表示したり、%%aiでNotebookの中から生成AIに問い合わせできたり。各種AI利用の他、Ollamaなどにも対応してるのでローカルLLMも利用可。

10 months ago 2 1 0 0

Thanks! I fixed the feeds.

11 months ago 0 0 0 0
Reinforcement learning with verifiable rewards (RLVR) has shown promise in enhancing the reasoning capabilities of large language models by learning directly from outcome-based rewards.  Recent RLVR works that operate under the zero setting avoid supervision in labeling the reasoning process, but still depend on manually curated collections of questions and answers for training.  The scarcity of high-quality, human-produced examples raises concerns about the long-term scalability of relying on human supervision, a challenge already evident in the domain of language model pretraining.  Furthermore, in a hypothetical future where AI surpasses human intelligence, tasks provided by humans may offer limited learning potential for a superintelligent system.  To address these concerns, we propose a new RLVR paradigm called Absolute Zero, in which a single model learns to propose tasks that maximize its own learning progress and improves reasoning by solving them, without relying on any external data.  Under this paradigm, we introduce the Absolute Zero Reasoner (AZR), a system that self-evolves its training curriculum and reasoning ability by using a code executor to both validate proposed code reasoning tasks and verify answers, serving as an unified source of verifiable reward to guide open-ended yet grounded learning.  Despite being trained entirely without external data, AZR achieves overall SOTA performance on coding and mathematical reasoning tasks, outperforming existing zero-setting models that rely on tens of thousands of in-domain human-curated examples.  Furthermore, we demonstrate that AZR can be effectively applied across different model scales and is compatible with various model classes.

Reinforcement learning with verifiable rewards (RLVR) has shown promise in enhancing the reasoning capabilities of large language models by learning directly from outcome-based rewards. Recent RLVR works that operate under the zero setting avoid supervision in labeling the reasoning process, but still depend on manually curated collections of questions and answers for training. The scarcity of high-quality, human-produced examples raises concerns about the long-term scalability of relying on human supervision, a challenge already evident in the domain of language model pretraining. Furthermore, in a hypothetical future where AI surpasses human intelligence, tasks provided by humans may offer limited learning potential for a superintelligent system. To address these concerns, we propose a new RLVR paradigm called Absolute Zero, in which a single model learns to propose tasks that maximize its own learning progress and improves reasoning by solving them, without relying on any external data. Under this paradigm, we introduce the Absolute Zero Reasoner (AZR), a system that self-evolves its training curriculum and reasoning ability by using a code executor to both validate proposed code reasoning tasks and verify answers, serving as an unified source of verifiable reward to guide open-ended yet grounded learning. Despite being trained entirely without external data, AZR achieves overall SOTA performance on coding and mathematical reasoning tasks, outperforming existing zero-setting models that rely on tens of thousands of in-domain human-curated examples. Furthermore, we demonstrate that AZR can be effectively applied across different model scales and is compatible with various model classes.

[5/30] 396 Likes, 96 Comments, 4 Posts
2505.03335, cs․LG | cs․AI | cs․CL, 06 May 2025

🆕Absolute Zero: Reinforced Self-play Reasoning with Zero Data

Andrew Zhao, Yiran Wu, Yang Yue, Tong Wu, Quentin Xu, Yang Yue, Matthieu Lin, Shenzhi Wang, Qingyun Wu, Zilong Zheng, Gao Huang

11 months ago 1 1 1 0
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GitHub - evan-liu/karabiner.ts: Write Karabiner-Elements configuration in TypeScript Write Karabiner-Elements configuration in TypeScript - evan-liu/karabiner.ts

"Write Karabiner-Elements configuration in TypeScript. ... Easier-to-understand TypeScript/JavaScript syntax, Strong-typed abstractions and key aliases with IDE support, Structured config files instead of one big file"

github.com/evan-liu/kar...

1 year ago 2 0 0 0
Screenshot of layers and keymap.

Screenshot of layers and keymap.

Karabiner-Elements でマルチレイヤのキーマップを作った。karabiner.ts というライブラリを使ったらレイヤーが簡単に実装できた!

layer('japanese_eisuu', '英数 + ijkl').manipulators([
map('i').to('↑'),
map('j').to('←'),
map('k').to('↓'),
map('l').to('→'),
]),

それと久しぶりに Deno を使ってみたが、こういう簡単なプログラムならかなり楽。

github.com/susumuota/ka...

1 year ago 2 0 2 0

"By serving as an intermediary in user interactions, it can autonomously generate context-aware responses, prefill required information, and facilitate seamless communication with external systems, significantly reducing cognitive load and interaction friction."

1 year ago 0 0 0 0
Results of SWE-bench Verified at 2025-04-07.

Results of SWE-bench Verified at 2025-04-07.

SWE-bench Verified 56.0% は 2024-12 頃のモデルとコンパラブル。

www.swebench.com#verified

1 year ago 0 0 0 0