标签：#AR

hi,where can i find Maven AI Evals for Engineers & PMs and End-to-End AI Engineering Bootcamp videos.They are too costly.cant afford them.Can anybody help me in finding the resoursec for them? submitted by /u/Zestyclose_Block5381 [link] [留言]

One thing I didn’t expect while trying to improve my workflow: The actual tasks aren’t what takes most of the time. It’s all the context switching around them. Things like: - jumping between tools just to complete one small step - copying data from one place to another - stopping what you’re doing to handle something repetitive - switching back and figuring out where you left off Individually it’s nothing. But over a day it adds up to constant interruptions. And it’s weirdly more draining than the work itself. I started paying attention to that instead of just the tasks, and reducing those switches made a bigger difference than trying to “optimize” the work itself. Curious if others notice the same thing or if it’s just me submitted by /u/huncho-mohammed [link] [留言]

Tech Companies Regret Firing Engineers for AI: The Quiet Rehiring Nobody's Talking About [2026] Klarna's CEO Sebastian Siemiatkowski stood on stage in 2024 and bragged that AI had replaced 700 customer service employees. The stock market loved it. LinkedIn influencers celebrated. And then, quietly, in 2025, Klarna started hiring humans again. That single reversal tells you everything about why tech companies regret firing engineers for AI. I've watched this pattern unfold across the industry, and a viral YouTube video by Pooja Dutt documenting these failures is now pulling over 10,000 views per day. The audience isn't just curious. They're vindicated. The tech industry laid off over 260,000 workers in 2023 alone, according to Layoffs.fyi , with many companies explicitly citing AI automation as justification. Now, in 2026, the bills are coming due. The companies that swung hardest at the "AI replaces engineers" thesis are the ones scrambling hardest to undo the damage. Why Did Companies Fire Engineers for AI in the First Place? The logic seemed airtight. AI can generate code faster than humans. AI can handle customer queries at scale. AI doesn't need benefits, PTO, or performance reviews. Executives saw a clean line from "AI generates output" to "we need fewer people," and they drew it with a Sharpie. I've been in enough executive planning meetings to know exactly how this plays out. Someone demos an AI tool that produces a working prototype in 20 minutes. The room gets excited. The CFO asks how many engineers they can cut. Nobody asks the harder question: what happens when that prototype needs to survive contact with production? The answer is that it breaks. Badly. Klarna is the poster child, but they're far from alone. Apple has spent two full years struggling with AI-driven improvements to Siri, despite being one of the most well-resourced engineering organizations on the planet. Even with virtually unlimited budget and talent, replacing deep engineering expertise

When we build something complex—whether it’s a skyscraper, a gourmet meal, or a piece of software—we usually start with a plan. In software development, however, it’s easy to skip that step. We often jump straight into implementation, focusing on how to write the code instead of the intent behind it. Over time, this leads to rework, confusion, and systems that don't quite match our original goals. Spec-Driven Development (SDD) is an approach that shifts the focus back to the plan. Instead of starting with code, you start with a Specification : a clear, structured description of what the software should do. You then use an AI coding agent as a high-speed collaborator to help turn that specification into working code. 🔍 What is a “Spec”? A Specification (or “Spec”) is a written contract between your intention and the final product. It isn't a 50-page manual; it's a living document that defines: What the system should do. How it should behave in different scenarios. Which constraints and rules it must follow. From Prompts to Specifications There is a massive difference between a vague prompt and a structured spec. Loose prompts often lead to inconsistent results and "hallucinations," whereas clear specifications give the AI a much better target to hit. Bad Prompt: > “Build me a login system.” Good Spec: A good spec provides the clarity an AI (or a human) needs to succeed. You don’t need a 10-page document to benefit from specs; you need clarity, not length. 🛠️ Example Spec: Login Endpoint Overview Allow users to log in using email and password. Endpoint POST /api/login Request { "email" : "user@example.com" , "password" : "string" } Behavior Success: If email and password are correct → return a token and user info. Invalid Credentials: If credentials don't match → return INVALID_CREDENTIALS . Invalid Input: If fields are empty or the email format is wrong → return INVALID_INPUT . Rules Passwords must be stored hashed (e.g., bcrypt). Token expires in 24 hours. Security:

Learn how to build a real IoT weather station using the Arduino UNO R4 WiFi and BME280 sensor, sending live temperature, humidity, and pressure data to Arduino IoT Cloud — with full code, wiring diagrams, and dashboard. What We're Building In this project, you'll build a cloud-connected weather station that measures: Temperature (°C / °F) Humidity (%) Atmospheric Pressure (hPa) All three readings will be streamed live to the Arduino IoT Cloud , where you can monitor them from anywhere in the world via a browser or the free Arduino IoT Remote app on your phone. Components Required Component Qty Notes Arduino UNO R4 WiFi 1 Built-in ESP32-S3 WiFi module BME280 Sensor Module 1 Measures temp + humidity + pressure via I²C Breadboard 1 Full or half size Jumper Wires (M-M) 4 For I²C connections USB-A to USB-C Cable 1 For power & programming Why BME280 over DHT22? The BME280 gives you three measurements (including barometric pressure) over a single I²C bus using just 2 wires, making it more capable and cleaner to wire. The DHT22 only gives temperature and humidity. Wiring the BME280 to Arduino UNO R4 WiFi The BME280 uses the I²C protocol , so it only needs 4 wires: BME280 Pin → Arduino UNO R4 WiFi Pin ────────────────────────────────────── VCC → 3.3V GND → GND SDA → A4 (I²C Data) SCL → A5 (I²C Clock) Important: The BME280 runs on 3.3V , not 5V. Connecting it to the 5V pin can damage the sensor permanently. Here's the schematic overview: ┌────────────────────────────┐ │ Arduino UNO R4 WiFi │ │ │ │ 3.3V ──────────────► VCC │ │ GND ──────────────► GND │ ← BME280 │ A4 ──────────────► SDA │ │ A5 ──────────────► SCL │ └────────────────────────────┘ ☁️ Step 1 — Set Up Arduino IoT Cloud Before writing any code, you need to configure the Arduino IoT Cloud . It's free for up to 2 devices. 1.1 Create a Free Account Go to cloud.arduino.cc and sign up or log in. 1.2 Create a New "Thing" Click Things in the left sidebar Click + Create Thing Name it WeatherStation 1.3 Add Your Device Click

"# The AI Cost Crisis: How Startups Can Survive the Tokenpocalypse\n\n## Introduction\n\nThe artificial intelligence boom has brought unprecedented innovation, but it has also ushered in a era of spiraling costs. Training state-of-the-art models now requires millions of dollars in compute resources, while simultaneously, the cryptocurrency token market shows signs of a potential collapse—a \"Tokenpocalypse.\" For AI startups, this dual crisis presents an existential threat: how to sustain innovation when both traditional funding avenues and speculative token economies are under pressure? This post explores practical strategies for AI startups to navigate this landscape, focusing on cost optimization, alternative funding, and strategic pivots that can turn crisis into opportunity.\n\n## Understanding the Cost Explosion\n\n### The Compute Crunch\n\nModern AI models, particularly large language models (LLMs) and multimodal systems, demand vast computational resources. Training a single cutting-edge model can consume exaflops of processing power, translating to cloud bills that easily exceed $10 million for a single training run. For startups without deep-pocketed backers, these costs are prohibitive.\n\n### The Token Market Volatility\n\nParallel to the AI boom, the cryptocurrency space experienced explosive growth through token launches—initial coin offerings (ICOs), decentralized finance (DeFi) tokens, and utility tokens for AI-driven projects. However, regulatory crackdowns, market saturation, and declining investor sentiment have led to a sharp downturn. Many tokens have lost significant value, and launching new tokens has become increasingly difficult, removing a once-viable funding path for AI startups.\n\n## Strategies for Survival\n\n### 1. Embrace Model Efficiency\n\nInstead of chasing ever-larger models, startups can focus on efficiency techniques that deliver comparable performance at a fraction of the cost:\n\n- Model Distillation : Train smaller \"student\

Lenovo's IdeaPad Slim 5x just might be the best laptop you can buy for $850. It's hard to find any major flaws, which is basically unheard of in a laptop at this price.

Google says Gemma 4 12B is "designed to bring agentic, multimodal intelligence directly to your laptop", further noting that the new model can be combined with Google AI Edge to "build and experiment locally, on everyday machines". This integration allows for a wide range of capabilities, from autonomous data processing to generating visual insights and even building webpages or executing tools. By Sergio De Simone

Uber is getting ready to put robotaxis on London's streets, opening an interest list for riders who want to be among the first to hail one of Wayve's autonomous vehicles when the service goes live later this year. The rollout would be a milestone in one of Uber's biggest markets and an early test of […]

Birgitta Böckeler, Distinguished Engineer at Thoughtworks, returns to discuss the rapid evolution of AI in software delivery. She touches on the evolution from vibe coding, the changing tools landscape and the more autonomous agents that, besides higher velocity, introduce higher risk. By Birgitta Böckeler

In this article, the author examines how AI is transforming phishing from a manual, targeted activity into an automated and scalable attack model. The article breaks down each stage of the phishing lifecycle, showing how AI improves reconnaissance, profiling, content generation, delivery, and interaction, while outlining layered defenses that combine controls, processes, and user awareness. By Marco Rizzi

Moms are outsourcing tedious household tasks to ChatGPT and selling courses teaching others to do the same. Where are all the dads?

ArXiv has an endorsement system for a reason. I would only offer endorsement to whom I have direct academic collaboration or mentorship with, since I'm putting my own academic reputation on the stake. This is also the standard of almost any serious academic researcher I am aware of. Now ArXiv is making effort to crack down AI slop and banning accounts uploading low-quality research papers, which is a great initiative. By definition of an "endorsement", I wish ArXiv could backtrack and at least issue warnings to their endorsers, and if this happens multiple times (let's say three), people giving out careless endorsement should also face consequences. submitted by /u/AffectionateLife5693 [link] [留言]

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Follow our WWDC 2026 live blog for all of the updates coming to iOS, macOS, watchOS, and more.

I run a web design agency and there is already way too much stuff to deal with every day. Hosting client websites, maintaining them, building new sites, replying to clients, fixing random issues, handling support, doing outreach. Once you start managing a lot of company websites it quickly becomes overwhelming. That’s why I never wanted cold calling to become my main way of getting clients. I know cold calling can work, but I personally hate doing it. It drains my energy and takes up so much time. Sitting there making calls all day was never the kind of business I wanted to build. So instead I focused on email automation. The reason it works so well for me is because I can set everything up once and let interested businesses reply instead of spending my whole day chasing people. But I also don’t do the typical outreach where agencies send generic messages saying “your website is outdated” or “you need a redesign.” I use a tool called Swokei where I upload lists of company websites and it analyzes them for actual problems like speed, SEO, mobile responsiveness, layout issues, and design problems. Then it automatically creates personalized outreach emails based on those issues. That’s what helped me stand out because the emails actually feel relevant to the business instead of sounding copied and pasted. The reply rates became way better once I stopped sending generic outreach. Now I spend most of my time building websites, working with clients, and scaling the agency instead of letting outreach take over my entire day. submitted by /u/Murky_Explanation_73 [link] [留言]

Everyone's writing specs for AI now. We hand the model a markdown file, tell it what we want, and hope it builds the right thing. It mostly works — until it doesn't. Markdown has quietly become the spec language. People reach for it as the DSL for their AI-driven workflows — headings, bullet lists, the odd table — and treat that loose structure as if it were a contract. The thing is, it isn't a DSL. It's markdown. It's prose formatting with no grammar to enforce, no structure you can execute, no shared vocabulary, and no way to tell whether the spec and the code still agree. You're leaning on a document format to do a job it was never built for, and you hit the limit the moment you want the spec to actually mean something a machine can check. Before you go down that road, I want to make a small, slightly absurd suggestion. Eat a cucumber. What I actually mean Gherkin is the plain-text language behind Cucumber , a tool that's been around for years in the behavior-driven development (BDD) world. It looks like this: Feature : User login Scenario : Successful login with valid credentials Given a registered user "ada@example.com" When she logs in with the correct password Then she should land on her dashboard And she should see a welcome message Scenario : Rejected login with wrong password Given a registered user "ada@example.com" When she logs in with an incorrect password Then she should see an "invalid credentials" error And she should remain on the login page That's it. Feature , Scenario , Given / When / Then . Structured enough that a machine can parse it, loose enough that a product manager can write it. The gap it bridges Most specs live at one of two extremes. On one end you have written specs : docs, tickets, markdown files. Readable by anyone, but inert. Nothing checks whether they're still true. They rot the moment the code moves on. On the other end you have tests : precise, executable, always honest — but written in code, illegible to half the people who a

Microsoft announced the general availability of Microsoft Discovery, its Azure-based platform for deploying autonomous AI agent teams in scientific R&D. The platform powered the development of Majorana 2, a topological quantum chip with 1,000x reliability improvement and 20-second qubit lifetimes. Microsoft now targets a scalable quantum computer by 2029, halving its original timeline. By Steef-Jan Wiggers

Not talking about which one sounds smarter. talking about which one you’d actually rely on when the answer genuinely matters to you. which one and why? submitted by /u/aiprotivity_ [link] [留言]

Today, June 8th, InfoQ celebrates 20 years. This is not a comprehensive history, but a deliberately selective look at the technologies and practices InfoQ identified early, where they sit on the adoption curve in 2026, and how that curve may evolve over the next five to ten years. By InfoQ