HyperspaceDB v3.1.4 ships Turbo 4-bit Lloyd-Max quantization, 1-bit ADC cascades, and Mem0 support
A developer demonstrates running a 180.9M-parameter LLM and agent inference fully offline on an ESP32-P4 microcontroller, showcasing the feasibility of on-device AI on low-power hardware. The project is shared via GitHub and discussed on Hacker News.
A developer demonstrates running a 180.9M-parameter LLM and agent inference fully offline on an ESP32-P4 microcontroller, showcasing the feasibility of on-device AI on low-power hardware. The project is shared via GitHub and discussed on Hacker News.
A developer released Vestige, a queryless memory layer for multi-agent coding fleets, on GitHub. The tool aims to improve context sharing among AI agents without explicit queries.
A developer released Vestige, a queryless memory layer for multi-agent coding fleets, on GitHub. The tool aims to improve context sharing among AI agents without explicit queries.
A new paper proposes Persistent State Machines, a hardware-software co-design that implements LLM attention using INT4 in-memory cells. This approach aims to reduce memory movement and energy consumption for attention mechanisms, potentially improving inference efficiency.
A new paper proposes Persistent State Machines, a hardware-software co-design that implements LLM attention using INT4 in-memory cells. This approach aims to reduce memory movement and energy consumption for attention mechanisms, potentially improving inference efficiency.
A new arXiv paper introduces 'Speculate with Memory,' a method that equips speculative execution for LLM agents with three online memory systems—contrastive transition table, episodic memory, and confusion tracker—to learn from past trajectories and improve prediction quality over time, enabling lossless acceleration.
A new arXiv paper introduces 'Speculate with Memory,' a method that equips speculative execution for LLM agents with three online memory systems—contrastive transition table, episodic memory, and confusion tracker—to learn from past trajectories and improve prediction quality over time, enabling lossless acceleration.
A research post introduces ThetaMem, a method using signed multiplicative key lifts for fixed-state sequence memory. Results are preliminary, based on a single seed, and mixed, indicating potential but not yet conclusive performance.
A research post introduces ThetaMem, a method using signed multiplicative key lifts for fixed-state sequence memory. Results are preliminary, based on a single seed, and mixed, indicating potential but not yet conclusive performance.