Article URL: https://authorsguild.org/news/ag-v-openai-top-execs-knew-mass-book-piracy-was-illegal/ Comments URL: https://news.ycombinator.com/item?id=49863864 Points: 50 # Comments: 8
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A computer science student at Politeknik Negeri Jakarta built Klyra-64M, a 64-million-parameter language model trained entirely from random weights rather than fine-tuning an existing model. The project was motivated by curiosity about whether a very small language model — well under 100 million parameters — could still learn language, follow instructions, and perform basic reasoning. Using the open-source MiniMind-3 Dense architecture as a base, the model was pretrained on approximately one billion tokens from the Ultra-FineWeb dataset, achieving a validation perplexity of 10.643. The student then continued training with an additional two billion tokens to broaden the model's knowledge before moving on to instruction tuning and reinforcement learning experiments. The base checkpoint, Klyra-64M-Base, has been publicly released on Hugging Face for further research.
A developer has created a Chinese-language iPhone app called 他爱我吗 that uses a Core ML conversion of the Laya multilingual model to evaluate relationship questions entirely on-device. Users describe a situation and the app compares candidate answers—such as affection, no affection, or uncertainty—returning relative scores rather than definitive predictions. Chinese speech input is transcribed locally using SenseVoice, and all interaction history stays on the device to protect personal data. The developer is explicit that output scores reflect the model's relative preference among given options and are not evidence of any person's actual feelings. Several evaluation questions remain open, including whether answer ordering or phrasing changes results, and the app is not being released as an open-source project.
A developer on DEV Community has outlined a method to convert Qwen 3.5, a compact 4-billion-parameter open-weights language model, into a rapid text classifier using LoRA fine-tuning. The approach targets common production tasks such as helpdesk ticket routing, user intent detection, and policy validation, where low latency matters more than generative ability. Training uses roughly 38,000 examples drawn from datasets like MultiNLI, BoolQ, and Banking77, supplemented with synthetic data, keeping costs surprisingly low. The model is prompted to return a single token answer from a fixed set of options, reducing output ambiguity and generation time. Serving is handled via vLLM, enabling response times in the hundreds of milliseconds once the endpoint is warm.
A solo founder has built and operates BlueVeta, a marketing automation startup, entirely through a team of coordinated Claude Code AI sessions, each assigned ownership of a specific business domain such as billing, image generation, and copywriting. The sessions communicate across domains, maintain shared documentation as institutional memory, and are required to report findings to the founder before anything is built, deployed, or spent. The founder, who is not a developer, describes the setup as analogous to managing a distributed team where every member is an AI session. BlueVeta allows users to upload a single product photo and receive a full multi-platform marketing campaign within minutes, with roughly 75% of the work handled by Claude. The platform currently has paying customers via Stripe, and the founder credits the multi-session coordination model with catching errors — including a near-mislabeling of real customer photos as test data — before they caused harm.
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