grounding
Beyond Market Intelligence keeps grounding in one place: 3 stories so far. The section currently leads with “Running local LLMs from Python with Apple's fm CLI”, “When AI draws one path while describing another, CO₂Ju keeps them consistent”, and “Blinded Tests Reveal When Long Context Beats RAG on Quality”. Running local LLMs from Python with Apple's fm CLI is a clever experiment, but a Sate Padang vision test and a Malaysia capital question reveal why free, private models still need grounding before they leave prototyping. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work… The list below is every grounding story on Beyond Market Intelligence, newest first.
Running local LLMs from Python with Apple's fm CLI
Running local LLMs from Python with Apple's fm CLI is a clever experiment, but a Sate Padang vision test and a Malaysia capital question reveal why free, private models still need grounding before they leave prototyping. The promise of on-device inference is real; the accuracy is not yet trustworthy without validation. For more on why agentic projects often stumble before they scale, see our related piece on why most fail before they start.
When AI draws one path while describing another, CO₂Ju keeps them consistent
A model describes the correct maze solution while drawing a different path. That inconsistency is the problem CO₂Jump solves. This sampler from Google, DeepMind, and Stony Brook uses text confidence and cross-modal attention to keep text and image generation aligned during sampling. Low-confidence tokens can be masked and regenerated, allowing earlier decisions to be revised. No additional training is required. Across 8-512 steps, CO₂Jump improved monotonically on both editing quality and grounding. For those exploring image editing, our related piece on *ChatGPT 2.
Blinded Tests Reveal When Long Context Beats RAG on Quality
A 127,000-token prompt can beat a top-5 RAG pipeline on the same 12 questions, graded blind for correctness, completeness, and grounding. That's the claim this controlled comparison puts to the test, and it's a useful one. Most teams assume retrieval is the only way to manage cost and latency, but Kimi K3's 1M token context window challenges that reflex. The tradeoffs are real, and the data here gives you a clearer lens on when full-context wins.