Llama

Evidence in the last complete seven days

Source mentions

1

Previous seven days 2

Distinct sources

1

Previous 2

Comparable growth

Not comparable

Ranking appearances

0

Collection observations and undated records are separate from dated publication counts. Observed in 7d：0 · Undated records：0

Daily history · up to 90 days

Bars show dated source mentions. The latest UTC day may be incomplete. Zero mentions and missing collection coverage are different conditions.

Dotted markers indicate days with no record. They are not counted as zero mentions.

Source evidence

Sources in the evidence shown：HF Daily Papers 1 · V2EX 2 · Product Hunt 1 · arXiv 1 · Hacker News 2 · Hugging Face 1

1. HF Daily Papers · Published Oct 30, 2026

   Cross-Prompt Reuse of Entity-Replacement Responses

   With model weights fixed, we measure how much of an entity-replacement response transfers between English factual questions, beyond whether answers change. We add the hidden-state change from two source questions to a target question about the same entity, comparing the predicted log-probability shift between two relations' published answers, given known ans

2. V2EX · Published Oct 24, 2026

   [程序员] 分享 Vibe Coding 作品： AI Vault，多 Agent 技能管理工具+llama.cpp 本地部署工具

   技能、提示词、MCP 、模型与密钥，统一管理，一键分发到 49+ Agent 。数据始终留在本机。Llama.cpp 、KVMEM 、NInfer 部署、模型下载，功能有点多。 长这样，不知图能否正常显示： https://ibb.co/fzssKRK7 官网及文档地址： https://ergeaia.github.io/aivault-site/ 网盘： https://pan.quark.cn/s/a23f244661b4 ，提取码：s9mG B 站视频介绍：BV1pbeV68EG4 ，BV1VRHT6NERF 欢迎交流！~

   View source record →

3. Product Hunt · Published Oct 24, 2026

   Llama Windows

   One local AI server for all your Windows apps

   View source record →

4. arXiv · Published Oct 15, 2026

   Titan-Llama: Retrofitting Pre-trained LLMs with External Neural Memory Modules

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5. V2EX · Published Oct 10, 2026

   [开源软件] 折腾了一个 Go Agent：魔改 Runtime + Hybrid RAG + llama.cpp + WebGPU，分享一下实现

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6. Hacker News · Published Sep 30, 2026

   Launch HN: Magnitude (YC S25) – Self-optimizing inference engine for agents

   Hey HN, Anders and Tom here. We're building Magnitude, an inference engine for agents that optimizes itself to run as fast as possible on your hardware. It works on Mac, Linux, and Windows on any hardware and is up to 2x faster than llama.cpp. We're both software engineers and previously built an open source browser agent to 4k+ GH stars and 100k+ downloads.

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7. Hacker News · Published Sep 28, 2026

   Show HN: Nibia Fabric – Turn everyday computers into a shared AI compute cluster

   I built NIBIA Fabric, an open-source distributed LLM inference system that pools CPU and RAM across macOS, Linux, and Windows machines. It builds on llama.cpp, with capacity-aware scheduling, adaptive memory reservation, persistent tensor caching, and an OpenAI-compatible API. I’ve validated the current alpha on three physical machines across several models,

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8. Hugging Face · Published Sep 22, 2026

   Transformers now runs llama.cpp quants

   View source record →

Review priority 40 / 100

This score orders research candidates. It is not search volume, a forecast or verified commercial demand.

• Evidence

  8 / 30

  Distinct dated articles or first observations; repeat crawling adds nothing.

• Publisher diversity

  8 / 25

  Conservative owner grouping; syndicated copies count once.

• Comparable change

  0 / 20

  Comparable complete weeks only; no percentage from a zero baseline.

• Recent evidence

  15 / 15

  Latest publication or first observation, not repeated fetch time.

• Name specificity

  9.4 / 10

  Explicit entities and technical concepts outrank ambiguous acronyms.

Limitations and interpretation

• First observation is a collection milestone, not proof that a word or product is new to the world.
• Counts describe monitored public sources, not the whole web or search demand.
• First observed here is not the invention date of a name or idea.
• Candidates come from public titles, summaries and entity URLs, not verified full-text analysis.
• Owner grouping is conservative; undisclosed syndication may remain.
• Repeat ranking appearances are observations, not new articles or independent endorsements.
• An established concept or common product name retained as a baseline, not a newly invented term.
• Both weeks need adequate, comparable collection coverage.
• Sources behind this term changed or lack a comparable baseline.
• Recent evidence includes collection over three days after publication.
• Current evidence comes from one publisher or owner.