Steven Gonsalvez

Software Engineer

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

Community take

Results lack standard retrieval benchmarks and don't address corpus staleness or haystack-depth pairing—critical unknowns.

Backlinks

Appeared in 1 briefing

Related

Shares tags: ai/llm-mechanics

Also from neon.com

Only signal from neon.com so far.