41–50 of 89 posts
I wonder what is the business model. It seems like the tool to solve the problem that won't last longer than couple of months and is something that e.g. claude code can and probably will tackle themselves soon.
Earlier quoted context omitted.
That's why give the chance to the model to call expand() in case if it needs more context. We know it's counterintuitive, so we will add the benchmarks to the repo soon. Given our observations, the performance depends on the task and the model itself, most visible on long-running tasks
How does the model know it needs more context?
Funny enough, Anthropic just went GA with 1m context claude that has supposedly solved the lost-in-the-middle problem.
Just for anyone else who hadn't seen the announcement yet, this Anthropic 1M context is now the same price as the previous 256K context - not the beta where Anthropic charged extra for the 1M window: https://x.com/claudeai/status/2032509548297343196 As for retrieval, the post shows Opus 4.6 at 78.3% needle retrieval success in 1M window (compared with 91.9% in 256K), and Sonnet 4.6 at 65.1% needle retrieval in 1M (co…
I wonder what is the business model. It seems like the tool to solve the problem that won't last longer than couple of months and is something that e.g. claude code can and probably will tackle themselves soon.
I wonder what is the business model. It seems like the tool to solve the problem that won't last longer than couple of months and is something that e.g. claude code can and probably will tackle themselves soon.
Why would the problem ever go away? It's compression technologys have existed virtually since the beginning of computing, and one could argue human brains do their own version of compression during sleep.
Earlier quoted context omitted.
Just for anyone else who hadn't seen the announcement yet, this Anthropic 1M context is now the same price as the previous 256K context - not the beta where Anthropic charged extra for the 1M window: https://x.com/claudeai/status/2032509548297343196 As for retrieval, the post shows Opus 4.6 at 78.3% needle retrieval success in 1M window (compared with 91.9% in 256K), and Sonnet 4.6 at 65.1% needle retrieval in 1M (co…
Aren't these numbers really bad? > 80% needle retrieval means every fifth memory is akin to a hallucination.
But even if I've misunderstood how attention works, the numbers are relative. GPT 5.4 at 1M only achieves 36% needle retrieval. Gemini 3.1 & GPT 5.4 are only getting 80% at even the 128K point, but I think people would still say those models are highly useful.