Intent Alignment Under Ambiguity
Evaluating Muse Glimmer 30B and Qwen3.8 27B on a new benchmark for inferring user intent under ambiguous or incomplete instructions with local inference.
Our New Find a Cheaper Model Tool
A practical model-comparison tool for finding lower-cost alternatives with similar measured capability.
Can LLMs Preserve Judgment When They Don't Know?
A 143-prompt adversarial benchmark of hallucination and epistemic control. Meta’s Muse Glimmer sets a new high-water mark, but then scores lower when it thinks harder.
Durable Memory for Agents
Store factual knowledge in a temporal, provenance-backed claim graph rather than unstructured markdown text files.
Searching Is Not Knowing
What eight open models reveal about current-news research, summarization fidelity, and agent design.
Retrieval Is Not State
Challenge the assumption that improvements in context size, retrieval quality, model scale, and memory systems will eventually produce something equivalent to long-term memory, understanding, and reliable reasoning. …
Evaluating Local Models as VSCode Agents
Evaluating local models on repository investigation and planning tasks and comparing Qwen3.6, Gemma4, GLM-4.7-Flash, and GPT-OSS-20B, across multiple dimensions of repository-agent quality. Our results show that …
Local Web Research with SearXNG and Crawl4AI
This article describes a local research stack that combines SearXNG and Crawl4AI to provide current web search and page retrieval for local language models. It explains how the components interact, how they are …
Local Vision-Language OCR Benchmark
We benchmark open-weight vision-language models for local document OCR and semantic page reconstruction. The goal is to identify a practical production configuration that balances extraction quality and operational …
Fast LLM Judging with No-Think Mode
We explore the use of “no-think” mode for LLM judges in a local evaluation harness. No-think mode suppresses the reasoning phase, which can improve latency and compliance for strict-output tasks. Our …