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grep -l @dwarfstar /news/*.json | wc -l → 12

DwarfStar

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// recent coverage 12 mentions

00:00
2026-08-19
lucumr.pocoo.org
artificial-intelligence

What Is Reasoning

A new paper and online discussions have revealed methods to extract hidden reasoning traces from closed-weight AI models, prompting an investigation into how these traces work. Reasoning traces are si…

05:53
2026-07-20
pub.towardsai.net
artificial-intelligence

Building AI Agents in Rust - part 10

In the tenth installment of a series on building AI agents in Rust, the author explores driving a local model directly via stdin/stdout instead of HTTP, using Salvatore Sanfilippo's DwarfStar inferenc…

23:20
2026-07-17
github.com
artificial-intelligence

Show HN: Qwen3.6-35B-A3B on a 16 GB M1 Pro with SSD-streamed MoE

A fork of the DwarfStar inference engine, andreaborio/ds4, aims to run large Mixture-of-Experts models like Qwen3.6-35B-A3B on 16–64 GB Apple Silicon Macs by using adaptive SSD streaming and Metal res…

11:39
2026-07-13
antirez.com
artificial-intelligence

Control the ideas, not the code

Redis co-creator Salvatore Sanfilippo (antirez) argues that programmers should focus on controlling the ideas behind software rather than reading every line of code, as AI-generated code makes line-by…

10:23
2026-07-10
twitter.com
large-language-models

GPT 5.6 Sol better than Fable?

GPT 5.6 Sol improved tensor parallel prefill numbers while preserving numerical correctness in DwarfStar after multiple rounds of Fable optimizations, signaling potential superiority over Fable.…

05:06
2026-07-08
twitter.com
large-language-models

Run GLM 5.2 on 2 MacBooks with 128gb on RDMA with DS by antirez

Antirez achieved running GLM 5.2 and DeepSeek v4 Flash with Tensor Parallelism across two M5Max 128GB MacBooks via RDMA, enabling models that previously could not fit on any affordable machine to run …

11:53
2026-05-28
youtube.com
artificial-intelligence

Distributed Inference in DwarfStar [video]

DwarfStar has released a video demonstrating distributed inference, a technique that splits AI model computations across multiple devices. The approach aims to reduce latency and hardware requirements…

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