Browse Comments — Clean (de-noised)
Close reading of the corpus at each pipeline stage: raw → clean → relevant → coded.
25
comments matched
· page 2 of 2
Yeah, ok. People confuse familiarity with understanding. That’s becoming very dangerous in the AI era.
The part that changes people’s behavior is how normal the interaction feels. Someone starts using AI for quick answers, then slowly starts typing things they would never put into a work email or a shared document.
I remember one time someone told me that they talked with a chatbot like they were a close friend. She even gave "him" a name and told "him" a lot of things you would only tell to people close to you. I told her that everything she says to it is recorded in some company database and the company staff can read all her innermost thougths. I find all this rather creepy.
Jerry Del Rio Hopefully it will be more affordable in the future to run your own model.
Csaba Zsolnai Its already there. For example, Qwen-3.6-35B-A3B can run on Ollama with a standard mid-level gaming GPU and mimicking quality found at the GTP-3.5-Turbo level while pushing out over 20 tokens a second. Qwen-3.5-397B-A17B, a bigger and much stronger AI engine is better than GPT-4 @ FP8, in my opinion and even better at FP16 I would imagine, but that would take over 1TB to run it at FP16 while leaving enough room for a sufficient context window. The costs to run this would require 100s of thousands of dollars, 240V hookups, and NVIDIA's industrial grade GPUs that are hard to come by today. ⠀ This is achievable on the LibreChat platform which is designed to integrate with Ollama, vLLM, open-source LLM providers, and proprietary models. The challenge is that the systems are moving fast, constantly changing and faster than anything I've seen in my 20+ years with open-source software. A new Wild West is underway. Those that begin to team up with AI open-source engineers and rig developers will own their destiny. ⠀