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Reddit r/LocalLLaMA·

LLM planner - pick a rig for your use-case/model/budget, or pick models for your rig. 60+ builds, 50+ models, 130+ cited t/s sources, 150+ reviewer YouTube videos, idle+active watts, multi-region prices, regular updates.

LLM Planner is an interactive guide to match hardware or open-weights models. 60+ build configs, 50+ models, sourced tokens/sec, power draw, multi-region pricing, 150+ reviewer YouTube videos. Bidirectional modes: "which rig for this model/budget" or "what models run on my GPU". Data updated weekly, public GitHub repo.

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Reddit r/LocalLLaMA·

HalBench: I built a custom sycophancy and hallucination benchmark and tested 4 frontier models (Sonnet 4.6, Grok 4.3, GPT 5.4 and Gemini 3.1 Pro), looking for input on what OSS models to run next!

HalBench: open-source benchmark measuring sycophancy and hallucinations across 3,200 false-premise prompts tested on 4 models (Sonnet 4.6, Grok 4.3, GPT-5.4, Gemini 3.1 Pro). Sonnet 4.6 scores 0.565/1, Grok 4.3 0.498, GPT-5.4 0.381, Gemini 3.1 Pro 0.339. Dataset, code, and results public.

BenchmarksEvalsAI safety
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GitHub Trending·

<svg aria-hidden="true" data-component="Octicon" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-repo mr-1 tmp-mr-1 color-fg-muted"> <path d="M2 2.5A2.5 2.5 0 0 1 4.5 0h8.75a.75.75 0 0 1 .75.75v12.5a.75.75 0 0 1-.75.75h-2.5a.75.75 0 0 1 0-1.5h1.75v-2h-8a1 1 0 0 0-.714 1.7.75.75 0 1 1-1.072 1.05A2.495 2.495 0 0 1 2 11.5Zm10.5-1h-8a1 1 0 0 0-1 1v6.708A2.486 2.486 0 0 1 4.5 9h8ZM5 12.25a.25.25 0 0 1 .25-.25h3.5a.25.25 0 0 1 .25.25v3.25a.25.25 0 0 1-.4.2l-1.45-1.087a.249.249 0 0 0-.3 0L5.4 15.7a.25.25 0 0 1-.4-.2Z"></path> </svg> <span data-view-component="true" class="text-normal"> microsoft /</span> azure-devops-mcp

Microsoft releases an MCP server for Azure DevOps, enabling AI agents to access Azure DevOps capabilities directly.

MCPAI AgentsTools
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GitHub Trending·

<svg aria-hidden="true" data-component="Octicon" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-repo mr-1 tmp-mr-1 color-fg-muted"> <path d="M2 2.5A2.5 2.5 0 0 1 4.5 0h8.75a.75.75 0 0 1 .75.75v12.5a.75.75 0 0 1-.75.75h-2.5a.75.75 0 0 1 0-1.5h1.75v-2h-8a1 1 0 0 0-.714 1.7.75.75 0 1 1-1.072 1.05A2.495 2.495 0 0 1 2 11.5Zm10.5-1h-8a1 1 0 0 0-1 1v6.708A2.486 2.486 0 0 1 4.5 9h8ZM5 12.25a.25.25 0 0 1 .25-.25h3.5a.25.25 0 0 1 .25.25v3.25a.25.25 0 0 1-.4.2l-1.45-1.087a.249.249 0 0 0-.3 0L5.4 15.7a.25.25 0 0 1-.4-.2Z"></path> </svg> <span data-view-component="true" class="text-normal"> vllm-project /</span> vllm

vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.

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arXiv cs.LG·

PROWL: Prioritized Regret-Driven Optimization for World Model Learning

PROWL introduces a KL-constrained adversarial curriculum to improve robustness of action-conditioned video world models. A policy exposes high-error trajectories of a diffusion-based model while a Prioritized Adversarial Trajectory (PAT) buffer re-ranks data by prediction error and learning progress. Evaluation on MineRL demonstrates improved robustness on out-of-distribution trajectories.

ReasoningReinforcement learningPapers
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