Efforts to make chatbots feel more human are splitting between prompt engineering with identities and fine-tuned models. The goal is shorter, less polished and more personable responses without large training overhead.
Fyxer uses OpenAI models, fine-tuning, memory, and real user feedback to organize inboxes and draft emails in each user's voice. The story highlights a shift in value: the differentiator is not the model but the calibration loop on real data. For tho...
A three-step method shows how to get 'human' chatbots without fine-tuning: biography, voice, operational instructions. For on-premise deployments the signal cuts both ways: less pressure on training, but more fragility and audit risk. Cost shifts fro...
A viral Reddit post dismantles the myth of 'humanlike' models: getting a chatbot with a personality does not require fine-tuning, just a well-built system prompt with biography, dialogue examples, and instructions. For those managing on-premise LLMs,...
A rank-256 LoRA fine-tuning on a Qwen3.8-27B base attempts to remove the digital-assistant tone: shorter, less polished, more human replies. Dataset of 125,217 anonymized messages. Open questions remain on IFEval and coding; repository and demo avail...
