标签：#ama

Amazon has launched a new feature for its Echo and Echo Kids smart speakers called Sleep Studio that's designed to make the daily transition to bedtime more enticing for kids and less stressful for parents and caregivers. The feature uses a combination of bedtime stories, relaxing sounds, and guided meditations along with scheduling and customization […]

Pinterest is adding support for Amazon Storefronts, allowing creators to earn affiliate commissions more easily while showcasing their product recommendations in one place.

On Tuesday, the Seattle City Council will vote on whether to enact a one-year moratorium on new data centers - just two months after several companies proposed building five large-scale centers in the city. Among the moratorium's fiercest supporters are current employees from the city's biggest tech giant, Amazon, who joined others to testify in […]

The waiver "serves the public interest by promoting a second large satellite broadband constellation."

Amazon is expanding its print-on-demand features to AI-generated designs created using Alexa for Shopping for products like T-shirts, water bottles, and hoodies. Shoppers can use text prompts to generate images that are then printed on to blanks for sale on Amazon. They can then share the link to the design so other people can buy […]

A new feature in the Amazon Shopping app allows users to generate designs with Alexa, then print them on products like T-shirts, hoodies, and tumblers.

Coding agents — Codex CLI, Claude Code, Cursor, and Pi — are productivity multipliers. But they all assume you are happy sending your code to someone else's servers. For many of us that is a deal-breaker: proprietary codebases, client NDAs, compliance requirements, or just the principle of owning your own compute. This guide shows how to swap out every cloud API with a local Ollama server running qwen3-coder:30b . Same tools, same workflows, no data leaving your network. Why Run AI Locally? The case is simple: Zero data exfiltration. Your code never leaves your machine or LAN. No per-token cost. Run 10,000 completions or 10 — the electricity bill does not care. Works offline. Airplane mode, restricted network, flaky VPN — irrelevant. No rate limits. No 429s at 2 am when you are in flow. The honest tradeoff: frontier models (Claude Opus 4, GPT-5) still outperform local models on complex multi-step reasoning and very large context tasks. For the 80% of day-to-day coding work — autocomplete, refactors, test generation, documentation — a well-chosen local model is more than good enough. Hardware Requirements I run this on an Apple M4 Pro with 48 GB unified memory . Apple Silicon's unified memory architecture is exceptionally well-suited to LLM inference: the GPU and CPU share the same memory pool, so a 22 GB model fits comfortably alongside a full development environment. Minimum viable setup: RAM What fits 16 GB 7–8B parameter models (qwen3:8b, llama3.2:8b) 32 GB 14–20B models (qwen3:14b, gpt-oss:20b) 48 GB 30–35B models (qwen3-coder:30b, qwen3.6:35b) 64 GB+ 70B models (deepseek-r1:70b, llama3.3:70b) On Intel/AMD systems with discrete GPUs the math is different: VRAM is the bottleneck, and models that don't fit entirely in VRAM fall back to slow CPU offloading. Choosing a Model For 48 GB unified memory, these are the models worth knowing about: Model Size on disk Active params Strengths qwen3-coder:30b ~22 GB 3.3B (MoE) Coding, 256K context, HumanEval SOTA qwen3.6:35b

The Problem With Choosing a Local Model Everyone has an opinion on which local LLM is best. "Use Llama — it's the most popular." "Mistral 7B has the best quality." "Phi-3 Mini is small and efficient." None of these claims come with numbers. Specifically: your numbers, on your hardware, for your workload. I built a benchmarking system to change that. Three models, 30 prompts, full latency distribution, memory profiling per inference call, and a JSON validation layer to measure structured output reliability. Here's what I found — and why the results matter for anyone deploying local models in production. The Setup Three models tested: llama3.2:3b — 3B parameters, Q4_K_M quantization, 2 GB download phi3:mini — 3.8B parameters, Q4_K_M, 2.3 GB download mistral:7b — 7B parameters, Q4_K_M, 4.1 GB download Hardware: CPU only, no GPU acceleration. This is the worst-case baseline — the scenario that exposes real latency and memory numbers. 30 test prompts across 5 categories: Short factual (10): "What is the capital of France?" Reasoning (8): "Explain why the sky appears blue." Code generation (5): "Write a Python function to reverse a string." Structured output (5): "List 3 frameworks in JSON format with name and use_case." Multi-step (2): Complex chained reasoning tasks. Architecture POST /query → Pydantic validation → Ollama HTTP API → JSON Validator → QueryResponse POST /benchmark → Load test_prompts.json → For each prompt: psutil memory before → Ollama → psutil memory after → NumPy: P50/P95/P99 latency, avg TPS, peak/avg memory → BenchmarkResult JSON The benchmark runs prompts sequentially, not in parallel. Parallel would contaminate the per-prompt memory measurements. Results Llama 3.2 3B (Q4_K_M) avg_tokens_per_second : 42.3 p50_latency_ms : 1203 p95_latency_ms : 3847 p99_latency_ms : 5120 peak_memory_mb : 6953 avg_memory_mb : 6842 total_test_duration_s : 87.4 Interpretation: P50 at 1.2 seconds is excellent. P95 at 3.8 seconds misses a 3-second SLA — the outliers are m

I recently rebuilt my homelab from scratch. The goal was simple: one machine, everything containerised, zero exposed ports, GPU-accelerated local AI, and a fully automated backup setup. No cloud subscriptions for the tools I use every day. This is the full technical breakdown — what I'm running, how it's wired together, and the hard-won fixes that cost me hours so you don't have to repeat them. What I'm Running Eight services, 26 containers, one machine: Service Purpose Portainer Docker management UI Uptime Kuma Service monitoring (7 monitors) NocoDB Self-hosted Airtable — CRM & leads n8n Workflow automation Open WebUI Local AI chat interface Ollama Local LLM inference (GPU) AFF!NE Collaborative docs & whiteboards Plane Project management (roadmaps, sprints) Duplicati Encrypted daily backups Cloudflare Tunnel Zero Trust secure access — no open router ports All external-facing services sit behind Cloudflare Zero Trust with email OTP. No passwords to manage, no VPN clients — Cloudflare handles authentication at the edge. Architecture ┌──────────────────────────────────┐ │ Cloudflare Edge (Zero Trust) │ │ *.yourdomain.com — email OTP │ └──────────────┬───────────────────┘ │ HTTPS ┌──────────────▼───────────────────┐ │ Ubuntu Machine │ │ │ │ cloudflared (outbound tunnel) │ │ │ │ │ ┌─────▼────────────────────┐ │ │ │ homelab-net (bridge) │ │ │ │ │ │ │ │ portainer uptime-kuma │ │ │ │ nocodb n8n │ │ │ │ open-webui affine │ │ │ │ plane-* duplicati │ │ │ │ ollama (GPU passthrough) │ │ │ └───────────────────────────┘ │ └───────────────────────────────────┘ Everything runs on a shared Docker bridge network ( homelab-net ). The cloudflared container maintains an outbound-only encrypted tunnel — no inbound ports open on the router at all. Ollama runs in Docker with NVIDIA GPU passthrough. The AI model inference happens on the GPU, leaving CPU headroom for all other services. Prerequisites Ubuntu 24.04 LTS Docker Engine + Compose v2 NVIDIA GPU with driver 535+ NVIDIA Container Too

Amazon's AI-upgraded Proteus robot can now be controlled using plain language.

Your membership gets you more than free two-day shipping. Here’s what you may be missing ahead of Amazon Prime Day.

Amazon has announced a new version of its fully autonomous warehouse robot, Proteus, that will can interact using language instead of code. The expanded capabilities come as part of a growing pivot toward automation as the e-commerce giant replaces its human workers with robots. Amazon says the AI-powered upgrade means its human employees can assign […]

AWS disclosed that Resilient Network Graphs, a flat network architecture based on quasi-random graph theory, is now the default for most new data center builds. The design replaces fat-tree hierarchies with direct ToR-to-ToR mesh connections using passive optical ShuffleBoxes, cutting routers by 69%, boosting throughput by 33%, and reducing network power consumption by 40%. By Steef-Jan Wiggers

Activists say it’s the first time Big Tech employees have publicly called for regulations governing data center projects.

Amazon's updated search bar will now show you AI-generated images of products as you describe them. For now, the in-app feature only surfaces AI images of clothing and home goods, allowing you to tap on the image that best matches what you're looking for and search for similar-looking items. In a blog post, Amazon positions […]

Amazon will use visual search and AI to show AI generated product images that match your search queries. The retailer says it will help guide users to products.

This is the technical, reproducible version of a fix I shipped on my own homelab. If you want the narrative version, that's on Medium. This one is the recipe: the measurements, the math, the Modelfile, and the exact prompt I gave Claude Code to generate it. Copy-paste friendly. Repo for the dashboard used throughout: https://github.com/SikamikanikoBG/homelab-monitor TL;DR One 24GB RTX 3090, two GPU services: WhisperX large-v3 (STT, 7.7GB peak) and a Devstral Small 24B email-triage LLM (Q4_K_M, ~18.3GB). 18.3 + 7.7 = 26GB → CUDA OOM whenever they overlapped. The LLM was loaded with a 40k context window but the triage job never needed more than ~5–8k tokens. Capped num_ctx to 8192 → KV cache drops from ~6.1GB to ~1.25GB → model footprint ~18.3GB → ~14.2GB . 14.2 + 7.7 = 21.9GB → both resident, zero OOM, no quality loss. The setup Host : openSUSE, Xeon (56 threads), 125GB RAM, 1x RTX 3090 (24GB) GPU svc : WhisperX large-v3 (speech-to-text) GPU svc : Ollama -> devstral-small-2 (24B, Q4_K_M) for background email triage Both services run all the time. The OOM only happened when I dictated to my assistant (WhisperX) while the triage loop was active. Step 1 — Make the contention measurable nvidia-smi shows instantaneous VRAM. It can't show you which service spiked or when two of them overlapped — and an intermittent OOM is a timing problem. You need per-service VRAM history. I use my own dashboard (homelab-monitor) for this. The relevant view is "AI Models", which attributes VRAM per model server and per loaded model, over a time range, with OOM markers and a capacity ceiling line. What the history showed at the overlap window: Service Peak VRAM Devstral 24B (triage) ~18.3 GB WhisperX large-v3 7.7 GB Total ~26 GB on a 24 GB card If you want to reproduce the measurement, the dashboard runs as a single container: git clone https://github.com/SikamikanikoBG/homelab-monitor cd homelab-monitor docker compose up -d --build # open http://<host>:9800 -> AI Models / GPU views (NVIDI

Hollow Knight joins the Amazon Luna game catalog this month.

Lawsuit: Ring cameras scan guests and passersby and use AI to identify faces.

The class action lawsuit, filed in Seattle by Virginia resident Charles Sigwalt, claims that Ring's Familiar Faces feature stores images of passersby without consent.