zero-shot
zero-shot at Beyond Market Intelligence is a file of 3 stories. The newest of them: “When you have labels, classic models outperform the AI hype”, “AI That Reasons in Motion, Depth, and Words Expands Beyond Text”, and “Discover how open-weight TTS brings expressive narration to your local machine.”. Classic models beat the hype when you have labels. Chain-of-thought reasoning has been a breakthrough, but it's still just words. 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 zero-shot story on Beyond Market Intelligence, newest first.
When you have labels, classic models outperform the AI hype
Classic models beat the hype when you have labels. We tested embedding inputs with a cheap model, then fitting a simple sklearn model on top using your own labels. The result? A 37% error reduction against Jev, at a fraction of the cost. It's not glamorous, but it works. For deeper context on smarter decision-making, see our guide on "How Your Agent's Tools Can Make Smarter Decisions."
AI That Reasons in Motion, Depth, and Words Expands Beyond Text
Chain-of-thought reasoning has been a breakthrough, but it's still just words. What happens when an AI learns to think in motion, depth, and imagined scenes? That's the leap with Ψ-0.6. It reasons through streams of thought, text, camera pose, physical movement, and decides which mode fits the task. The results speak for themselves: it outperforms leading open-weight models on spatial reasoning and transfers from simulated rooms to real robot tabletops.
Discover how open-weight TTS brings expressive narration to your local machine.
TontaubeV1 takes a character-level approach to text-to-speech, and that decision is worth pausing on. Most modern TTS models lean on the backbone tokenizer, but the team behind this release found that forcing character-by-character tokenization kept the model more stable and made the mapping from text to sound more direct. It is a thoughtful response to a real problem, especially for long-form narration where rare token combinations can trip up generation. The chunking and position scheme is just as deliberate.