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local LLMs
Trends
- 1AI architects look to Unix philosophy over monolithic models●The greatest shift in production AI isn't prompt engineering—it's Unix philosophy. Instead of forcing a single monolithi
A software engineer argues that the real shift in production AI is not prompt engineering but the Unix philosophy: decomposing work into small, specialized local agent pipelines rather than asking one large language model to handle every task. He says splitting roles like reasoning, coding, and accounting into lightweight components makes architectures more resilient. The take is circulating among developers debating how to build reliable AI systems.
- 2Multi-Token Prediction Boosts RTX 3090 LLM Speed▼Originally published on my blog. Enabling MTP on this RTX 3090 raised generation throughput from... # ai # llm # program
A developer reports enabling multi-token prediction (MTP) on an RTX 3090 graphics card raised local LLM generation throughput, while questioning whether the speedup affects coding quality. The write-up, originally published on a personal blog, has drawn attention from AI and open-source software communities interested in getting more performance from consumer GPUs for running large language models locally.
Repos
- ollaya-dev/ollaya Run open decision models locally: pull and serve Laya, decider, NLI and GLiClass behind a TypeSafe-compatible API. Ollam
- antirez/ds4 DeepSeek 4 Flash and PRO local inference engine for Metal, CUDA and ROCm
- TheoLeeCJ/SemIf-OpenJev Semantic ifs from open models, on a 3090 at home. Independent; not affiliated with Jev or TypeSafe.