A ten-step, training-free walkthrough that turns a frozen OpenAI text embedding into clean classifications: a multiclass relevance score sorts events into local, national, and global buckets, and a contrastive binary score splits systemic from idiosyncratic risk. Verified on real warnings from NOSIBLE World, the geometry matches Google's gemini-2.5-flash while staying deterministic, auditable, and effectively free.
2026-06-1814 min read
The Fed's Trade Policy Uncertainty index counts keywords across seven newspapers. We rebuilt it from 14.9 million NOSIBLE World events using only embeddings and five sentences, no keywords. It matches the published benchmark at 0.87 on monthly levels and 0.82 on monthly changes, as closely as the two official versions match each other. The same method, extended to sixty sentences, rebuilds the broader Economic Policy Uncertainty index and its national-security and healthcare categories.
2026-06-1723 min read
Here's how we fine-tuned Qwen3 0.6B to beat FinBERT and match GPT-5.1 accuracy. Complete with open-source models, datasets, and training scripts. Spoiler alert: active learning is all you need.
2025-12-1227 min read
Cybernaut-1 combines our powerful hybrid-3 search algorithm with LLM-guided Monte Carlo Tree Search to deliver world class search results on difficult queries.
2025-08-262 min read
AI needs its own search engine. This is how we’re rebuilding search for AI -- and the road to Cybernaut-1, the first high-trust agentic search engine.
2025-08-2017 min read
We introduce the ensemble and distil data pattern and use it to fit an ordinary least squares linear regression that outperforms GPT-4 at financial news sentiment classification using sentence transformer embeddings as features.
2024-02-0612 min read
