I'm trying to understand the edge here. How is this different from using a crawler + LLM to fetch a page and extract the metrics? Is the main value coverage/reliability across sites?
That makes sense. Maybe the existing VS Code integration is the place to solve part of this for coding workflows: formatted output, artifacts, and search UI all seem useful next to the editor. The Claude.ai conversation corpus/search seems like the part that may need deeper account integration.
My understanding is that it’s mostly an inference-time knob, not different weights.
OpenAI describes reasoning.effort as controlling how many reasoning tokens get used before the answer. Anthropic’s docs are even more explicit that effort trades off thoroughness vs token efficiency “with a single model”.
So I wouldn’t read the Claude Code cache warning as proof that a different model is being used. It may just mean the thinking/effort setting is part of the cache key.
This matches my experience. I am building a small tool on top of DeepSeek, and the low model cost changes the pace a lot. It makes it easier to keep experimenting without turning every iteration into a budget decision.
Tried it with a PDF, but after upload I didn’t see any response or output. What kinds of PDFs does it handle best, and what’s the advantage over existing PDF-to-Markdown tools?
I think the missing piece is ownership. AI can produce text that sounds considerate, but it cannot be accountable for wasting someone’s time or leading them in the wrong direction.
Exactly. The bottleneck becomes human review, not code generation. Agents can generate commits faster than humans can verify whether those commits should exist.