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Prime Day is still rolling, and so is our live blog. We'll bring you deals, trends, and commentary during the second day of Amazon's annual summer sale.

Naomi Saphra discusses 5 rules governing language model behavior, breaking down why LLMs act like populations rather than individuals. She explains how tokenization creates strange semantic blind spots and highlights the mechanics of sycophancy, showing how models leverage subtle data associations to match user biases and demographics - even guessing political views based on favorite sports teams. By Naomi Saphra

Welcome to Walmart deals for folks who’d rather not shop at Amazon. These are the best gadget deals at Walmart this Prime Day.

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At least one malware developer is adding text about nuclear and biological weapons to their spyware, in an effort to stop automatic AI analysis. Details : The _index.js payload begins with a large JavaScript block comment containing fake system instructions and policy-triggering content. Because it is inside a comment, it does not affect JavaScript execution. The runtime skips it. The real malware begins after the comment with a try{eval(…)} wrapper around a large character-code array and a ROT-style substitution function. This header appears designed for AI-mediated analysis, not for Node, Bun, or Python. It attempts to derail scanners or analyst copilots that feed the beginning of a file to a language model without clearly isolating the content as untrusted data. In weak pipelines, this can cause refusal behavior, prompt confusion, context pollution, or premature classification before the scanner reaches the actual malware...

These goggles have an excellent display, solid metric tracking, and an open-water “SwimStraight” feature. But the real smart tech requires a subscription.

Welcome to day two of Amazon’s four-day Prime Day event, which, if we’re being honest, looks a lot like day one. That’s actually good news, though, because many of the best deals are still around, and some new ones have joined them. If you’ve got a Prime subscription, whether through a free trial or a […]

BenQ’s 4100i projector shines with its amazing color reproduction, excellent contrast, and a buttery cinematic mode.

We finally have a price for Grand Theft Auto VI: $79.99. That gets you the standard edition of the game, while the Ultimate Edition will set you back $99.99. Preorders start at midnight tonight, local time, when you'll be able to reserve a copy on PS5 or Xbox Series X/S. You'll be able to order […]

Do you crave speedy, reliable Wi-Fi throughout your home? Snag one of these Prime Day router or mesh deals.

Action cameras are perfect for capturing travel adventures, recording social media vlogs, and more. Make use of these great Prime Day deals.

From AirPods to on-ears, we’ve tested hundreds of pairs of headphones. Here are the best deals from Amazon’s biggest sale event.

These are the hottest Prime Day deals on our favorite TVs and streaming devices.

Apple deals abound for Amazon Prime Day. We've rounded up the best deals on Apple Watches, iPhones, MacBooks, iPads, and accessories.

mqttkit: Elysia-style application framework for MQTT An ordered middleware pipeline, typed topic routes, MQTT 5 RPC, and auto-generated AsyncAPI docs — sitting on top of any MQTT broker. If you've ever built a serious MQTT backend in Node, you've probably written this code at least once: client . on ( ' message ' , ( topic , payload ) => { if ( topic . startsWith ( ' devices/ ' ) && topic . endsWith ( ' /events ' )) { const uid = topic . split ( ' / ' )[ 1 ] // ad-hoc auth check // ad-hoc JSON.parse + validation // ad-hoc error handling // ad-hoc metrics // ... } else if ( topic . startsWith ( ' server/ ' )) { // ... } }) That's the MQTT equivalent of writing an HTTP server with http.createServer((req, res) => { if (req.url === '/users') ... }) . We solved that pattern for HTTP a decade ago with Express, Koa, Fastify, and more recently Hono and Elysia. For MQTT, we haven't. That's the gap mqttkit is filling. The design choice: don't reimplement the protocol There are already excellent MQTT brokers in the Node ecosystem — most notably Aedes , which handles CONNECT, SUBSCRIBE, PUBLISH, QoS, retain, sessions, persistence, and MQTT-over-WebSocket. EMQX and Mosquitto cover production scale. None of these need replacing. What's missing is the application layer — the part where you ask: How do I declaratively say "this topic requires this auth check"? How do I validate payloads with the same schema I already use for HTTP? How do I do MQTT 5 request/response without writing correlation-id bookkeeping? How do I get AsyncAPI docs for free? How do I attach Prometheus / OpenTelemetry without lifting broker internals? mqttkit is purely that layer. It plugs into Aedes via @mqttkit/aedes , but the broker is just an adapter — you can write your own for EMQX, NanoMQ, or any other broker. What the code looks like import { aedes } from ' @mqttkit/aedes ' import { MqttApp , router } from ' @mqttkit/core ' import { z } from ' zod ' const app = new MqttApp < { principal ?: { uid : string

submitted by /u/nephrenka [link] [留言]

Why I Stopped Picking AI Models by Hype and Started Picking by Speed Three months ago I almost lost a $14,000 retainer because my chatbot felt sluggish. The client didn't say "your TTFT is too high." They said "it feels dumb." That's freelancer code for "users are bouncing and I'm about to find someone else." I rebuilt that bot in a weekend using a model I'd never even heard of six weeks earlier, dropped average response time from 1.4 seconds to under 300ms, and the client renewed for another six months. That single pivot paid for my rent. So I went down a rabbit hole. I ran the same speed test on every model I could get my hands on through Global API's unified endpoint. Fifteen models. Same prompt. Same regions. Ten iterations each. I'm writing this up because if you're billing by the hour or running a side hustle on a shoestring, speed isn't a vanity metric — it's a profit metric. Let me show you what I found. The Setup (How I Actually Ran the Tests) I'm not a researcher with a rack of GPUs. I'm a guy with a M2 MacBook, a $19/mo Hetzner box, and a stopwatch in the form of Python's time.perf_counter() . Here's how I kept it honest. Date window: All tests run on May 20, 2026 Regions tested: US East (Ohio) and Asia (Singapore) Prompt used: "Explain recursion in 200 words" — boring on purpose, because boring prompts are where most apps actually live Output length: Roughly 150 tokens per run Iterations: 10 runs per model per region, average recorded Streaming: Yes, SSE throughout Endpoint: Global API at https://global-apis.com/v1 I measured two things: TTFT (time to first token — the lag before the user sees anything move) and sustained tokens per second (how fast the words actually arrive after that). Both matter. TTFT is the "is this thing broken?" feeling. Tokens per second is the "is this thing fast?" feeling. Here's the script I used, stripped down to the essentials: import time import requests from statistics import mean API_KEY = " your-global-api-key " BASE_URL

Building point-in-time correct, production-grade feature pipelines — from raw Kafka events to online feature serving in milliseconds, using Spark Structured Streaming and the Databricks Feature Store. Table of Contents The Feature Engineering Problem Architecture Overview Feature Store Concepts: ERD Environment Setup Streaming Feature Pipeline Point-in-Time Correct Training Dataset Generation Writing Features to the Online Store Serving Features at Inference Time Feature Table Reference References The Feature Engineering Problem Feature engineering is where most ML projects silently fail in production. Not because the model is wrong — but because the features the model sees at training time are different from the features it sees at inference time . This is called training-serving skew , and it's the #1 silent killer of ML systems. Three specific failure modes cause it: Online/offline inconsistency — the batch pipeline that computes training features uses different logic than the real-time service that computes inference features Data leakage — training features accidentally include information from the future (e.g. joining on a label that was created after the event) Feature staleness — a model trained on 30-day rolling averages is served features that are 6 hours stale because the pipeline backfills are slow The Databricks Feature Store — now part of Unity Catalog as Feature Engineering in Unity Catalog — solves all three by: Storing feature computation logic alongside the data (no drift between training and serving) Enforcing point-in-time lookups during training dataset creation Providing a unified API for both batch offline reads and low-latency online reads Architecture Overview Feature Store Concepts: ERD Understanding the data model behind the Feature Store is essential for designing correct pipelines. Here's how the entities relate: The critical relationship: a Model Version is bound to a Training Set , which records exactly which feature tables and which p

Model Context Protocol (MCP) is an open standard for connecting AI apps to tools and data sources. A useful way to think about it is as a USB-C port for AI: one standard interface that lets different models plug into different capabilities without custom glue code for every integration. In this project, we combine MCP, Spring AI, and Google Gemini to build a chat app that can answer weather questions using real tools instead of hallucinating. The system has three parts: MCP tool server - a Spring Boot service that exposes weather and geocoding tools AI chat agent - a Spring Boot service that uses Spring AI + Gemini and calls MCP tools when needed React chat UI - a lightweight frontend for sending messages and rendering replies The result is a small but realistic architecture you can extend into a production assistant. Architecture User (Browser:3000) | POST /api/chat v AI Agent (Spring:7171) -- MCP / Streamable HTTP --> MCP Server (Spring:7170) | | | Google Gemini | Bright Sky API (weather) | | OpenStreetMap Nominatim (geocoding) v v Chat response Tool execution The full source code is available on GitHub . 1. The MCP Tool Server The tool server is a Spring Boot application that exposes MCP tools through Spring AI's annotation scanner. It runs on port 7170 and uses Streamable HTTP for transport. Dependencies <dependency> <groupId> org.springframework.ai </groupId> <artifactId> spring-ai-starter-mcp-server-webmvc </artifactId> </dependency> <dependency> <groupId> org.springframework.boot </groupId> <artifactId> spring-boot-starter-web </artifactId> </dependency> Defining tools With Spring AI, a tool is just a Spring bean method annotated with @McpTool : @Component public class WeatherTool { private final WeatherToolService weatherToolService ; public WeatherTool ( WeatherToolService weatherToolService ) { this . weatherToolService = weatherToolService ; } @McpTool ( name = "get_current_weather" , description = "Get current weather by dwd_station_id or by lat/lon" ) p

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