LLMs

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Agentic AI vs Generative AI: Why one without the other hits a ceiling

 ✍️Prompt Engineering  Content type: Blog
udacity.com·

LLM-Based Code Documentation Generation and Multi-Judge Evaluation

 ✍️Prompt Engineering  Content type: Academic
arxiv.org·

AI Glossary

 ✍️Prompt Engineering  Content type: Blog
0xdf.gitlab.io·

The Order Matters: Sequential Fine-Tuning of LLaMA for Coherent Automated Essay Scoring

 🎛️Fine-tuning  Content type: Academic
arxiv.org·

Evaluating Hallucinations in Domain-Adapted Large Language Models

 Speculative Decoding  Content type: Academic
arxiv.org·

Using Probabilistic Programs to Train Inductive Reasoning in Large Language Models

 📊Bayesian Statistics  Content type: Academic
arxiv.org·

ClinicalBench: Can LLMs Beat Traditional ML Models in Clinical Prediction?

 💬Natural Language Processing  Content type: Academic
arxiv.org·

fix(gateway): fail closed for unknown model auth · openclaw/openclaw@85343ea

 ✍️Prompt Engineering  Content type: Code
github.com·

Rosetta Memory: Adaptive Memory for Cross-LLM Agents

 🎯RLHF  Content type: Academic
arxiv.org·

Impacts of Histories and Models on LLM Grading: A Study in Advanced Software Engineering Courses

 ✍️Prompt Engineering  Content type: Academic
arxiv.org·

SLUUG Talk: Demystifying Large Language Models on Linux

 🤖AI  Content type: Code
github.com··DEV

Benchmarking Empirical Privacy Protection for Adaptations of Large Language Models

 🎛️Fine-tuning  Content type: Academic
arxiv.org·

zhongkaifu/TensorSharp: A C# inference engine for running large language models (LLMs) locally using GGUF model files. TensorSharp provides a console application, a web-based chatbot interface, and Ollama/OpenAI-compatible HTTP APIs for programmatic access. It supports Windows/MacOS/Linux with full GPU capability

 Quantization  Content type: Code
github.com··Hacker News

Distilling Safe LLM Systems via Soft Prompts for On Device Settings

 🎛️Fine-tuning  Content type: Academic
arxiv.org·

Causal Semantic Alignment for LLM-based Time Series Forecasting

 🤖AI  Content type: Academic
arxiv.org·

heterodoxin/graphkv: Graph-guided KV cache compression for memory-efficient LLM inference.

 🤖AI  Content type: Code
github.com··r/LocalLLaMA

Analyzing the Correlation Between Hallucinations and Knowledge Conflicts in Large Language Models

 Speculative Decoding  Content type: Academic
arxiv.org·

john-rocky/apple-silicon-llm-bench: Neutral, reproducible benchmark for local LLMs on Apple Silicon (Mac · iPhone · iPad) — MLX, llama.cpp, CoreML, Apple Foundation Models

 Quantization  Content type: Code
github.com··Hacker News

A handy llama-server launcher with easy model and configuration customisation

 🐧Open Source  Content type: Code
github.com··r/LocalLLaMA

SaqlainXoas/llm-system-patterns: A docs-first guide to LLM system design — hybrid search, embedding pipelines, reranking, and LLM-as-judge patterns.

 ✍️Prompt Engineering  Content type: Code

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