OpenAI launches the Economic Research Exchange to study AI’s impact on jobs, productivity, and the economy. Applications are now open for selected research projects.

Article URL: https://www.reuters.com/business/aerospace-defense/us-troops-families-adjust-to-new-normal-iran-war-2026-06-07/ Comments URL: https://news.ycombinator.com/item?id=48439709 Points: 10 # Comments: 2

This is a submission for the June Solstice Game Jam What I Built SOLSTICE — The Longest...

We've seen Axiom Space and Prada's collaboration on the Axiom Extravehicular Mobility Unit (AxEMU) spacesuit. Now the company has revealed the Liquid Cooling and Ventilation Garment (LCVG) that astronauts will wear underneath it when Artemis IV returns humans to the Moon in 2028. The LCVG is the all-important base layer that will keep the crew […]

GitHub热门项目 | 🤱🏻 Turn any webpage into a desktop app with one command. | Stars: 50,088 | 72 stars today | 语言: Rust

I haven't written articles in quite a while and I recently decided to come back to it. At work I use AI daily, so a big part of my coding tasks are delegated to agents. I try not to do the same when it comes to writing text but I don't have a clear reason for that (or maybe I do and I don't want to admit it). I am sure people can take advantage of generating text with the help of AI but at the moment I feel like prompting would not save me any time and writing the text myself would be faster. What about you? Are you using AI to write your articles? Image credit - jessica olivella, on pexels

It's 2am. A pipe just let go behind a kitchen wall and water is coming through the ceiling into the room below. The homeowner is standing in the dark in a panic, phone in hand, Googling "emergency plumber near me." They tap the first number. It rings four times and drops to voicemail. They don't leave a message. They tap the second number. You were the first number. You were asleep. And you just lost the most profitable job you'd have booked all week to whoever picked up on the second ring. This is the part of running a plumbing business that nobody puts on a P&L. The call that matters most arrives at the exact moment no human is there to answer it. And in plumbing, unlike any other trade, that's not the exception. It's the majority of the work. Plumbing's problem is different from every other trade An HVAC shop bleeds during summer rush. A roofer loses jobs to slow follow-up over a three-week sales cycle. Plumbing is its own animal, because plumbing is emergency-driven, and emergencies don't keep business hours. Industry call-tracking data tells the story. Depending on the source, anywhere from 40% to over 70% of home-service calls land outside the standard nine-to-five window, and for emergency-driven trades like plumbing it skews to the high end of that range. Either way the takeaway is the same. A huge share of your inbound isn't coming in while someone's at the desk. It's coming in at night, on a Saturday, on Thanksgiving morning when a basement is filling with sewage. If you're staffed to answer the phone nine to five, you are structurally set up to miss a large slice of your own demand. Not because you're doing anything wrong. Because the work shows up when the lights are off. And here's what makes it brutal. The after-hours emergency call isn't just any job. It's your best one. Why the missed emergency call costs more than the tech A routine daytime service call, a dripping faucet, a running toilet, runs a couple hundred dollars and the customer is happy to

The Letter VCs Are Quietly Deleting from ARR Startups are reporting revenue they haven't earned yet. VCs know it. Investors are cheering anyway. We've seen this movie. You're evaluating an AI startup. The pitch deck shows $100 million in ARR. The growth curve is parabolic. The deck says they signed $100 million. What it doesn't say is that $70 million of that is "committed ARR": contracts signed but not yet invoiced, customers who haven't deployed yet, pilots that count toward the number if they convert. Subtract the gap and you've got $30 million in actual recognized revenue hiding under a $100 million headline. This is the ARR inflation playbook, and it's running at full speed right now. The Trick Has a Name "CARR" stands for Contracted or Committed ARR. It's a legitimate concept. In industries where revenue accrues slowly after contract signing (healthcare AI deployments, energy optimization platforms, multi-year enterprise integrations), the gap between signature and recognition can legitimately take months or years to close. Reporting CARR alongside ARR, properly labeled, is defensible. That's not what's happening. What's happening is simpler: founders strip the "C" and just call it ARR. One VC told TechCrunch the gap between CARR and actual ARR can run as high as 70% [1]. In some confirmed cases the spread is 3-5x. Another investor said flatly: "For sure they are reporting CARR as ARR" [1]. The article indicates that the investor community is not only aware, but many are actively complicit. The logic follows its own warped rationality. When one startup in a category inflates, the others have to follow to stay competitive for talent and headlines. "When one startup does it in a category, it is hard not to do it yourself just to keep up," as one investor put it [1]. Spellbook CEO Scott Stevenson, one of the few willing to call this in public, described the practice as a "huge scam," adding that major VC funds are not just watching it happen but actively supporti

Including, me. There’s no question the world has experienced significant change, over just the past 10 years. You’ve got standard concerns like AI; questions around whether tech is helping or hurting us - but I want to present another side to the story: one that I’ve got direct experience in. In a new “series” I’ll be rolling out, I’ll be deep diving into my prior experience as a software engineer in this new world where everything is a metric to be improved. Laying out the problem Not to be vague, I’ve referenced the problem in the title. Companies, and by proxy, engineers at the companies, are being driven (by shareholders), to ensure you maximise the time spent on their platforms. Perhaps, that much is apparent and obvious. But, have you stopped to think recently how vehement and aggressive these practices are getting? How many services do you use or rely on that are (subtly or not), making decisions purely just to increase the time customers spend with the software open ? I’m a big believer that software can make lives better . I’ll be quick to announce, though, that as of 2025, there’s a lot of secrecy behind the scenes around why companies are striving harder to hit engagement metrics. It’s a race to the bottom, and as we continue seeing software take up more and more of our time spent, it’s worth exploring just how bad things can get when you’re laser focused on app enegagement. Making something good, isn’t good enough I worked at Flux Finance for 3 and a half years, before unfortunately being made redundant. Flux went on, only months later, to be bought out by Networth. Not terrible; acquisition complete, and everybody’s clapping. As a refresher, Flux was an app designed to make your money journey easier, and make you more financial literate, in fun ways. The motto: “helping 400k+ Aussies win at money ” - with gamification being a major aspect. For Australians, it features: a way to check your credit score articles released regularly to learn about money new

LLM-powered Learning, Handwritten Digit Recognition, and AI Career Guidance Today's Highlights This week's top stories showcase practical AI applications: an LLM-powered tool for domain learning, a cloud-enhanced handwritten digit recognition system, and an AI-driven career guide. These projects demonstrate how AI frameworks are being applied to real-world workflows, from knowledge acquisition to personalized advice. Show HN: Lathe – Use LLMs to learn a new domain, not skip past it (Hacker News) Source: https://github.com/devenjarvis/lathe This project, Lathe, presents a novel approach to leveraging Large Language Models (LLMs) not just for quick answers, but for deep, structured learning within a new domain. Unlike traditional LLM interactions that might encourage skipping detailed research, Lathe aims to facilitate a more profound understanding by guiding users through a systematic learning process. It likely employs advanced retrieval augmentation generation (RAG) techniques, potentially combined with iterative prompting strategies and graph-based knowledge representation, to help users build a comprehensive knowledge base on a chosen topic. The framework focuses on transforming raw information into actionable insights and structured learning paths. This makes LLMs a powerful study aid, enabling domain experts or newcomers to grasp complex subjects more efficiently by providing tools for semantic search, concept mapping, and progressive knowledge acquisition, moving beyond simple question-answering into true assisted learning workflows. Comment: This is precisely what's needed for complex enterprise knowledge management – turning LLMs into an active learning partner, not just a summarizer. I'd explore how it structures knowledge graphs or progressive learning paths. Handwritten Digit Recognition System with Cloud and AI Enhancements (Dev.to Top) Source: https://dev.to/yohannesah/handwritten-digit-recognition-system-with-cloud-and-ai-enhancements-i4e This project

This is a submission for the GitHub Finish-Up-A-Thon Challenge What I Built The average student opens their notes 3 days before the exam. The above average student opens them the night before. Either way, they both end up on YouTube at 3am watching a guy explain thermodynamics with a whiteboard and too much energy. I built something better. I built Forge AI — a collaborative AI study platform that turns your lecture notes, PDFs, and YouTube links into a full study session with an AI tutor that actually talks back. Here is what it does: 📄 Upload your notes or paste a YouTube link — Forge AI reads everything and builds you a prioritised study plan in seconds. 🧠 Deep Dive into any topic — get full AI generated study notes, a mnemonic memory trick, and a built-in quiz. 🎙️ Talk to your AI tutor live — it listens, speaks back, and you can see the transcript building in real time as it talks. 👥 Create a Forge Room — invite your study group, ask the AI questions together, battle each other in a live quiz, and everyone sees everything at the same time. It started as CrammAI . A solo study tool I built under exam pressure. Well, it could generate a study plan from your uploaded files and quiz you on topics. That was it. No voice. No collaboration. No rooms. No Finesse. Just you, alone, cramming at 2am. Five months later I came back to finish it. The result is Forge AI. Demo Live app GitHub Here is what Forge AI can do: 1. Upload your materials and pick your mode Three modes based on how much time you have: 🧘 Cruise Control — 1+ week until exam 🚀 Turbo Mode — 2 days left ⚡ Zoom Mode — due tonight 2. Paste a YouTube link — it transcribes automatically ts export const apiFetchYoutubeTranscript = async ( url : string ): Promise < string > => { const videoId = extractYoutubeId ( url ); const response = await fetch ( `/api/transcript?videoId= ${ videoId } ` ); const data = await response . json (); return data . transcript ; }; No manual transcription. Paste the link, Forge AI pull

I'm starting a new project. It's the most ambitious thing I've attempted from a phone. The goal: an AI agent that controls a smartphone. It opens apps, navigates screens, taps buttons, types text, and completes multi-step tasks. All offline. All local. No cloud. This is Day 1 of a public build log. No fluff. Just what I'm building, how it works, and what breaks along the way. What I'm Building An autonomous AI agent that runs entirely on an Android phone. You give it a command in plain English: · "Open WhatsApp and message Mom I'll call later." · "Search for Kotlin jobs on Wellfound." · "Open my notes and summarize what I wrote yesterday." The agent parses the command, plans the steps, and executes them—opening apps, finding the right buttons, typing text, hitting send. No cloud. No API keys. Just a phone that acts on your behalf. The Stack Component Tool AI Brain Gemma 4 E4B (local, via Ollama) Runtime Termux (Linux on Android) Phone Control ADB + UI Automator Orchestration Python Why This Matters Most AI agents live in the cloud. They need internet, APIs, and someone else's server. A local agent that runs on a phone means: · Privacy: your data never leaves your device. · Offline: works even without internet. · Accessible: built for the device billions of people already own. The Hard Parts I Already See · The agent needs to "see" the screen to know where to tap. Text detection is doable. Image-based buttons are harder. · Multi-step tasks need verification. If one tap misses, the whole chain fails. · Android permissions. ADB requires developer mode. A user-facing version would need a workaround. What's Next · Day 2: Create the repo. Set up the project structure. Push the first working script. · Day 3: Get screen text detection working with OCR. · Day 4: Test a full 3-step task. This is Day 1. The repo goes live tomorrow. Follow along if you want to see something rare get built from scratch.

A few months back I got frustrated. I needed to calculate something — nothing crazy, just a loan payment — and every site I landed on either wanted me to sign up, showed ads on every input, or was in English only (not helpful when sharing with family abroad). So I built Calculora . It's a free calculator site. 150+ tools, 13 languages, zero data collection. Everything runs in your browser — nothing gets sent anywhere. Why 13 languages? Because most people on the internet don't speak English as a first language. I wanted it to actually work for them — not just have a translated homepage, but real localized URLs, right-to-left layout for Arabic, the whole thing. Getting that right in Next.js took more time than I expected. Each tool has a different slug per language, so the routing has to map hundreds of combinations correctly. Totally worth it though. The math rendering For tools like the equation solver and statistics calculator, I wanted the formulas to look like real math — not just text. I used KaTeX for this. It's fast, lightweight, and renders LaTeX properly in the browser. The step-by-step solutions actually show the working — discriminant, roots, the full process — rendered cleanly. Some tools I'm proud of Equation Solver — linear and quadratic, with full steps shown Statistics Calculator — mean, median, mode, IQR, std dev, exportable FIRE Calculator — retirement planning done simply Debt Snowball/Avalanche — side-by-side payoff comparison What's next More tools, better mobile experience, and filling in gaps across the language versions. If you try it, I'd genuinely love feedback — especially on anything that feels off or missing. calculora.net