Beating GPT-5.6 Sol on retrieval with 100x cheaper open models
Why CEREBRO kept it
LLM efficiency comparison on-topic
The text below is an automated extraction of the article at https://neon.com/blog/how-castform-neon-beats-frontier-models-on-price-and-efficiency, stored verbatim in the public cerebro-vault repository. Copyright remains with the original publisher (neon.com).
“Most teams' best training data is just sitting in their databases. The problem is that turning raw data into something usable is hard, and letting agents read, search, and mutate data cheaply at scale requires advanced infra. Pointing Castform at Neon skips both.” Ying Hang Seah, cofounder, Castform A "good agent" needs to be strong in 2 areas: - Context: can we provide the tools to find the right data? - Model: can the model decide what to search for? Neon (Lakebase Postgres) and their new Search extensions solve the first; Castform solves the second. Evolution of agentic search In ~2022, th
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Results lack standard retrieval benchmarks and don't address corpus staleness or haystack-depth pairing—critical unknowns.
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