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Research#llm📝 BlogAnalyzed: Jan 3, 2026 06:04

Lightweight Local LLM Comparison on Mac mini with Ollama

Published:Jan 2, 2026 16:47
1 min read
Zenn LLM

Analysis

The article details a comparison of lightweight local language models (LLMs) running on a Mac mini with 16GB of RAM using Ollama. The motivation stems from previous experiences with heavier models causing excessive swapping. The focus is on identifying text-based LLMs (2B-3B parameters) that can run efficiently without swapping, allowing for practical use.
Reference

“The initial conclusion was that Llama 3.2 Vision (11B) was impractical on a 16GB Mac mini due to swapping. The article then pivots to testing lighter text-based models (2B-3B) before proceeding with image analysis.”

PermalinkZenn LLM
Research Paper#Quantum Computing/Networking🔬 ResearchAnalyzed: Jan 3, 2026 16:18

Quantum Network Simulator

Published:Dec 28, 2025 14:04
1 min read
ArXiv

Analysis

This paper introduces a discrete-event simulator, MQNS, designed for evaluating entanglement routing in quantum networks. The significance lies in its ability to rapidly assess performance under dynamic and heterogeneous conditions, supporting various configurations like purification and swapping. This allows for fair comparisons across different routing paradigms and facilitates future emulation efforts, which is crucial for the development of quantum communication.
Reference

“MQNS supports runtime-configurable purification, swapping, memory management, and routing, within a unified qubit lifecycle and integrated link-architecture models.”

PermalinkArXiv
Research#llm📝 BlogAnalyzed: Dec 24, 2025 20:26

Exploring Img2Img Settings Reveals Possibilities Before Changing Models

Published:Dec 12, 2025 15:00
1 min read
Zenn SD

Analysis

This article highlights a common pitfall in Stable Diffusion image generation: focusing solely on model and LoRA changes while neglecting fundamental Img2Img settings. The author shares their experience of struggling to create a specific image format (a wide banner from a chibi character) and realizing that adjusting Img2Img parameters offered more control and better results than simply swapping models. This emphasizes the importance of understanding and experimenting with these settings to optimize image generation before resorting to drastic model changes. It's a valuable reminder to explore the full potential of existing tools before seeking external solutions.
Reference

“"I was spending time only on changing models, changing LoRAs, and tweaking prompts."”

PermalinkZenn SD
Research#LLM🔬 ResearchAnalyzed: Jan 10, 2026 11:44

PD-Swap: Efficient LLM Inference on Edge FPGAs via Dynamic Partial Reconfiguration

Published:Dec 12, 2025 13:35
1 min read
ArXiv

Analysis

This research paper introduces PD-Swap, a novel approach for optimizing Large Language Model (LLM) inference on edge FPGAs. The technique focuses on dynamic partial reconfiguration to improve efficiency.
Reference

“PD-Swap utilizes Dynamic Partial Reconfiguration”

PermalinkArXiv
Research#Video Editing🔬 ResearchAnalyzed: Jan 10, 2026 12:24

DirectSwap: Mask-Free Video Head Swapping with Expression Consistency

Published:Dec 10, 2025 08:31
1 min read
ArXiv

Analysis

This research from ArXiv focuses on improving video head swapping by eliminating the need for masks and ensuring expression consistency. The paper's contribution likely lies in the novel training method and benchmarking framework for this challenging task.
Reference

“DirectSwap introduces mask-free cross-identity training for expression-consistent video head swapping.”

PermalinkArXiv
Research#Face Swap🔬 ResearchAnalyzed: Jan 10, 2026 12:43

High-Fidelity Face Swapping: Achieving Cinematic Realism in Video

Published:Dec 8, 2025 19:00
1 min read
ArXiv

Analysis

This research from ArXiv focuses on improving the realism of face swapping in videos, a crucial area for visual effects and content creation. The paper likely details technical advancements aimed at mitigating artifacts and improving the visual fidelity of the generated content.
Reference

“The research originates from ArXiv, indicating a focus on academic or pre-print findings.”

PermalinkArXiv