The paradox arises when humans develop SaaS, apps, workflows, etc. The question is "will AI simply do things or know things more than we can tell?"
19 karma · joined April 11, 2013
The paradox arises when humans develop SaaS, apps, workflows, etc. The question is "will AI simply do things or know things more than we can tell?"
In contrast, LLMs are still constantly evolving. Not only the models, the way of agents doing things, the tools/skills agent shall use, the whole system may be completely different next month. GEO is merely an attempt to ride the LLM hype,clumsily stitching together unrelated concepts.
For remote MCP servers, storing access_token is a very common practice. For MCP servers hosted locally, how to deal with a bunch of secret keys is a problem.
> However, artificial intelligence may soon swing the pendulum in the opposite direction. AI makes it possible to process enormous amounts of information centrally. In fact, it might make centralized systems far more efficient than diffuse systems, because machine learning works better when the machine has more information to analyze. If you disregard all privacy concerns and concentrate all the information relating to a billion people in one database, you’ll wind up with much better algorithms than if you respect individual privacy and have in your database only partial information on a million people.
For local physical store, geo-location is a naturally filter for customers as long as beaming a person from a spaceship to earth is not invented. For web, a equally effective solution is very hard to find.
The emerging of DeepSeek V3 and DeepSeek R1 somehow make the moat vulnerable with their much smaller training cost. The fact verified the claim of Google.
https://developers.google.com/search/docs/appearance/google-...
A new platform is not built on a single day. Even creators found a new platform and decided the first thing they do should be re-posting their old works.
rclone also works smoothly with CI servers.