Insights on AI, model training, and building with ALTAI.
A guided explanation of the OpenSimula pieces used in examples/simula.
A guided explanation of the AfterImage pieces used in examples/caselaw_rag/generate.py.
A Python library and CLI for synthetic conversational datasets — grounded, diverse, and observable. Today we are releasing AfterImage as open source.
Open-source language models have made incredible progress in reasoning and instruction following — yet they still struggle with one crucial skill: evaluation.
At Altai, we are frequently asked one question — why bother fine-tuning when you can just use RAG? Here is the answer.
Businesses are eager to harness AI — but the biggest, most expensive models aren't always the right answer. Here's why smaller, domain-specific models win.
Enterprises are eager to leverage LLMs but adoption is hard. Here's why we built Altai — and what we're doing differently.
The rapid progress in NLP is mainly due to large neural network models — but size comes at a cost. Here's our approach to compressing bge-m3 with synthetic data.
That's why we built llm-food — a FastAPI-based service that converts documents and URLs into clean, LLM-friendly Markdown with batch processing support.
Training a custom image classifier used to mean weeks of setup. AfterImage changes that — bring your data, get your model.
According to Gartner, over 85% of AI projects never make it to production. After talking to hundreds of enterprise teams, the reasons are almost always the same.
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