{
  "version": "https://jsonfeed.org/version/1.1",
  "title": "Open Source LLMs Signals \u2014 VQV.me",
  "home_page_url": "https://vqv.me/t/open-source-llms/",
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  "description": "Latest meaningful AI signals tracked under Open Source LLMs by VQV.me.",
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      "url": "https://vqv.me/s/dtH1k/",
      "external_url": "http://arxiv.org/abs/2607.26987v1",
      "title": "Study on Developer Experience with Code Tours Generated by Open-Weight LLMs",
      "summary": "This study examines how developers interact with code tours\u2014interactive onboarding tools\u2014automatically generated and evaluated by open-weight large language models (LLMs) when debugging unfamiliar codebases. It focuses on developer experience and trust calibration, areas not previously explored in...",
      "date_published": "2026-07-29T14:45:05+00:00",
      "date_modified": "2026-07-29T14:45:05+00:00",
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        "OPEN SOURCE",
        "Open Source LLMs",
        "SOURCE-BACKED",
        "TECHNICAL"
      ],
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        "source_name": "arXiv",
        "source_domain": "arxiv.org",
        "topic": "Open Source LLMs",
        "topic_slug": "open-source-llms",
        "technical_label": "SOURCE-BACKED",
        "signal_strength": 95
      }
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    {
      "id": "vqv:signal:tIgFu",
      "url": "https://vqv.me/s/tIgFu/",
      "external_url": "http://arxiv.org/abs/2607.26286v1",
      "title": "Study on Prompt Scope and Demonstration Selection in LLM Machine Translation",
      "summary": "This paper examines how large language models perform machine translation when prompted with varying scopes, such as translating into multiple related languages or using example-based conditioning. It highlights the impact of prompt design and demonstration similarity on translation outcomes.",
      "date_published": "2026-07-28T21:26:36+00:00",
      "date_modified": "2026-07-28T21:26:36+00:00",
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        "Open Source LLMs",
        "SOURCE-BACKED",
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        "source_name": "arXiv",
        "source_domain": "arxiv.org",
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        "signal_strength": 95
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}