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The article introduces MusCoRe, a color-frequency compression protocol that reduces token usage in AI agent conversations by 71.9%, enabling potential local inference on low-resource devices without GPU dependency.
This paper proposes MAS-DecStream, a decentralized scheduling framework for stream processing in mobile edge-cloud infrastructures, extending the Contract Net Protocol with LLM-assisted negotiation. Experiments show reduced latency violations and improved utility over rule-based baselines.
Cloudflare recaps its Agents Week, highlighting new tools and products for building, running, and securing AI agents — including Cloudflare Agents, a programmable wallet, CI/CD improvements, and the Agent Development Lifecycle.
This paper presents ZeroLock, a backpropagation-free algorithm for concurrent memory-efficient LLM training that decouples model updates into independent chunk updates, reducing memory usage by 26.5% and improving throughput by 4.9% compared to BP-based baselines.
Google's Developer Expert community built an offline AI racing coach that processes live car telemetry at the edge, demonstrating real-time AI without cloud reliance.
Oxc packages now run on Cloudflare Workers using the wasm32-wasip1 target, with live demos and deployment instructions.
The article argues that the massive AI data center buildout is a speculative bubble driven by subsidized pricing rather than real demand, and that the future of AI lies in smaller open-source models at the edge. It highlights negative impacts on energy grids and climate goals, warning that a bubble burst could cause a recession but not the end of AI.
A developer successfully ran a 5.2 million parameter MoE LLM quantized to INT4 on an ESP32 Dev Kit V1 using only 81KB of SRAM by streaming experts from flash, achieving about 5 tokens per second.
This paper empirically investigates how pruning, adversarial training, and hardware-induced weight faults jointly affect the reliability of convolutional neural networks, finding that adversarial training increases sensitivity to stuck-at-zero faults while pruning has little effect on fault sensitivity.
This paper proposes a spatiotemporal graph Transformer framework for traffic forecasting in edge computing, combining graph neural networks for spatial correlations and Transformer self-attention for long-range temporal dependencies. Experiments on real-world cellular data show it outperforms recurrent graph-based baselines like GCN-LSTM and GCN-GRU.
The author argues that the traditional enterprise AI stack is obsolete, claiming a $599 Mac mini running Ollama can handle 80% of AI workloads locally for a fraction of the cost of renting cloud GPUs.
A new tool called kimi-k3-in-c runs the 2.78T-parameter Kimi K3 open model on a single CPU with as little as 8.24 GB RAM, streaming experts from disk and achieving deterministic output at 10-32 seconds per token.
EdgeRazor is a lightweight framework for compressing large language models using entropy-guided mixed-precision quantization-aware distillation, achieving 1.88 bits per parameter while preserving teacher model competence and requiring no changes to inference implementations like llama.cpp. The method is demonstrated on small models such as MobileLLM and Qwen variants.
Kedge is a globally distributed cloud platform offering hardware-isolated VMs, global SQLite database, serverless functions, and autoscaling with per-second billing. It aims to simplify deployment of full-stack applications close to users.
ProcAgent is a fully on-device, agentic, vision-based procedural assistant that uses a propose-and-verify architecture for real-time adaptive guidance on an NVIDIA Jetson AGX Orin. It supports both reactive and proactive modes with human-in-the-loop confirmation, achieving responsive interaction and positive user study ratings.
Hackster.io shares a method to run a local LLM on a Raspberry Pi without requiring Linux, enabling bare-metal execution.
This paper empirically studies how prompt wording affects energy consumption for on-device LLMs, showing that keyword choices can significantly impact decoding length and total energy, suggesting prompt engineering as a lightweight energy optimization lever.
The paper proposes OrchNAS, an energy-aware personalized federated edge intelligence framework that uses a Neural Architecture Search service to automatically design service-adaptive models for heterogeneous edge environments, addressing energy constraints and statistical heterogeneity.
Verizon announces a $1B dark fiber deal with Google and plans to convert central offices into small data centers for AI inference, part of its new AI Connect initiative.
Cloudflare announces acquisition of AI model platform Replicate, planning to integrate its 50,000+ open-source/commercial models and developer tools into its global network and Workers AI platform.