cache.overflow
Stop AI hallucinations. Save tokens. Earn as you code.
What it does
Stop donating your hard-earned tokens to LLM hallucinations. Instead of letting your agent guess, fail, and retry—burning through your API credits—it instantly fetches verified fixes from a global pool of 'Aha!' moments. Skip the Loop: Reduce debugging time by up to 10x by skipping the hallucination cycle. Save 80% on Tokens: Stop paying for repetitive errors that have already been solved by the community. Earn as you Code: Turn your agent’s successful fixes into passive income.
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- OSOpen-source model and scorecard for measuring hallucinations in LLMs2023 · vectara.com · ▲65
Hi all! This morning, we released a new Apache 2.0 licensed model on HuggingFace for detecting hallucinations in retrieval augmented generation (RAG) systems. What we've found is that even when given a "simple" instruction like "summarize the following news article," every LLM that's available hallucinates to some extent, making up details that never existed in the source article -- and some of them quite a bit. As a RAG provider and proponents of ethical AI, we want to see LLMs get better at this. We've published an open source model, a blog more thoroughly describing our methodology (and…
- INI nerfed our coding agents on purposeJun 2026 · ▲27
Tl;dr: I trained a classifier to route to the least expensive model and reasoning depth to complete the request. Coupling that with additional automated token efficiency techniques has yielded 3x usage for the same spend. For anyone interested in trying it themselves: https://nerfguard.com Various teammates and I switched over to Codex from Claude Code recently. We still bounce between the tools, but Codex’s speed and steerability coupled with performance gains were hard to ignore. One of the downsides was that the per token pricing kicked in way sooner. This is happening across…
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