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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

Launch week for a side project means the same chore every time: export the logo, open Figma, create artboards for LinkedIn (400×400 and 4200×700 cover), Google Business Profile (1200×1200 JPEG with a minimum file size or Google rejects it), YouTube (2560×1440 with a safe zone nobody remembers), Open Graph (1200×630), Instagram (circle crop), Discord, TikTok… Design tools are great at making brand assets. They are slow at batch-exporting exact platform specs — especially when encoding rules differ (PNG vs JPEG, max KB, min KB for GBP, center-right vs YouTube-safe positioning). So we built a small free tool: one SVG or PNG in → 21 platform files out. What it actually does Upload a logo. Pick categories (or take everything): 8 profile icons — mark-only, padded for circular crops (LinkedIn, X, GBP, Facebook, Instagram, YouTube, TikTok, Discord) 2 profile lockups — logo + wordmark for LinkedIn company and GBP 7 banners/covers — including LinkedIn 4200×700, YouTube safe-area channel art, Facebook cover under 100 KB target, Open Graph 1200×630 4 post sizes — Instagram square/portrait/story, Pinterest pin Outputs are PNG or JPEG per platform rules. White background. ZIP download. Why not Canva? Canva is excellent for templates and marketing creatives. For “I already have a logo, give me every official size,” you duplicate frames and export manually — often behind a paid tier for brand kits and bulk workflows. This tool does one job, free, no account: 👉 https://varnox.io/tools Longer write-up with platform gotchas (GBP min JPEG size, YouTube safe area, etc.): 👉 https://varnox.io/blog/free-social-media-logo-size-generator Privacy (because it’s your logo) SVG uploads sanitized before render Generated files expire after 30 minutes — nothing permanent stored No signup We use the same catalog internally when onboarding clients onto GBP + LinkedIn + OG tags. Shipping it publicly was easier than answering “what size is our Facebook cover again?” for the fifth time. Stack note (for

Three years ago we had our first real outage. Six hours of downtime. Thousands of angry users. Multiple executives on the call. Here's what we did right, what we did wrong, and what we'd do differently. What we did right 1. Communicated immediately. The moment we knew we had a problem, we updated the status page and emailed our biggest customers personally. Not when we had answers. When we had a question. 2. Had a single incident commander. One person making calls. Not a committee. When the CEO tried to direct technical work, the IC politely rerouted and told her where her help was actually needed (talking to customers). 3. Took care of our people. During hour 4, I ordered food. During hour 5, I forced the primary engineer off the call for 20 minutes to walk outside. Long incidents destroy people. You have to feed them and force them to rest. 4. Wrote it down as we went. We had a shared doc with a live timeline. When the post-mortem came, we had every decision captured. What we did wrong 1. Tried to fix the root cause during the incident. For the first 2 hours, we were digging into why the database was struggling. We should have been mitigating (rolling back) first. 2. Let too many people 'help.' By hour 3, we had 12 engineers in the call. Half of them were useless. The IC should have kicked people out sooner. 3. Gave optimistic estimates. 'We'll be back in 30 minutes.' We were not back in 30 minutes. That miscommunication was worse than saying 'unknown.' 4. Didn't prepare the executive communication. The CEO had to answer customer questions in real time with no script. We should have drafted talking points for her after hour 1. What we'd do differently Mitigate first, investigate second. Always. Cap the number of active engineers at 4 during an incident. Others go on standby. Default to 'unknown' for estimates. Only give a number when we're sure. Assign someone explicitly to 'executive liaison.' Their job is to keep the C-suite informed without interrupting the tec

We run 10+ web products. For years our SEO tooling was a $100+/month subscription we mostly used for one job: technical audits. The keyword and backlink dashboards sat untouched while the bill renewed every month. At some point the math stopped making sense, so we rebuilt our stack around what we actually use. Here's the honest breakdown — what we kept free, what we replaced, and why. What a technical audit actually needs to check Before picking tools, it helps to know what you're auditing. The technical layer is finite and well-defined: Crawlability — robots.txt, broken internal links, redirect chains Indexability — noindex tags, canonical correctness, soft 404s, orphan pages Sitemaps — only canonical, indexable, 200-status URLs On-page — titles, meta descriptions, one H1, valid structured data International — hreflang reciprocity (if you're multilingual) Performance — Core Web Vitals (LCP, CLS, INP) That's it. None of it requires a keyword database or a backlink index — the two things you're really paying $100+/mo for in the big suites. The free tools that cover most of it Google Search Console is non-negotiable and free. The Pages report is the source of truth for what's indexed and why the rest isn't. PageSpeed Insights (free) gives you real Core Web Vitals. Screaming Frog's free tier crawls up to 500 URLs, which is plenty for small sites. For a long time that was our whole stack: GSC + PageSpeed + free Screaming Frog. If your site is under 500 pages, you may not need anything else. Where it broke down for us The free Screaming Frog cap (500 URLs) is the wall. Several of our sites are bigger, and re-auditing them meant either the paid Screaming Frog licence (annual) or going back to a monthly suite. Both felt wrong for something we run after every deploy. So we built our own crawler for the technical layer — and then, honestly, turned it into a product because other people we talked to had the same problem. It does the whole checklist above across the entire sit

If you run Aviatrix on Azure, there is a slow-moving infrastructure change happening underneath your gateways right now that is worth paying attention to. Microsoft started rolling out a new generation of network hardware on May 26, 2026, called MANA (Microsoft Azure Network Adapter). For most Azure workloads, the change is invisible. For network virtual appliances (NVAs) like Aviatrix gateways, it is not, and Aviatrix has issued a field notice ( FN-2026-AZ-001 ) telling customers to take action. What is MANA and Why is Microsoft Rolling it Out? For roughly a decade, Azure VMs with Accelerated Networking enabled have used Mellanox-based NICs exposed to the guest as mlx4/5 SR-IOV adapters . SR-IOV (Single Root I/O Virtualization) lets the VM talk to the network card hardware directly, bypassing the hypervisor's virtual switch. This is what gives Accelerated Networking its low-latency, high-throughput characteristics. Microsoft has been quietly building its own in-house networking silicon. MANA is the result: a Microsoft-designed network adapter that replaces the Mellanox hardware Azure has been using on the host side. From an Azure customer's perspective, MANA preserves Accelerated Networking semantics, but the device the guest OS sees is different. The driver is different. The interface name is different. And that is where Aviatrix gateways run into trouble. Why Aviatrix Gateways Are Affected Aviatrix gateways are not generic VMs. They run a custom data plane that binds tightly to the underlying NIC for performance reasons. Specifically, the gateway image expects the Mellanox driver to be present and operational. On MANA hardware, that driver is no longer in play, and the gateway image does not yet include a MANA-aware driver. Per the field notice, the symptom is intermittent performance degradation rather than an outright outage. That makes it harder to detect: throughput drops, latency spikes, or session resets that look like noise can be the early signs of a gate

I got fired from my first job, took down a database server with a badly written query, and was...

If someone asked one of the know AI chats for feedback on body, would it be honest or be supportive only submitted by /u/thowing_away48494578 [link] [留言]

We're likely to see more price increases as the big AI companies plan to go public.

Head's true genius—and that of his character, Giles—lay in quietly filling in the gaps in every scene

A native Mac cockpit for Claude Code Discussion | Link

submitted by /u/ThereWas [link] [留言]

Been experimenting with AI-generated UGC. This whole clip — the face, the voice, the walk — is generated (I used omnigems.ai). No camera, no actor. What surprised me is the "tells" are mostly gone now if you keep the lighting candid (no studio polish), add real skin texture, and let there be natural micro-motion. Studio-perfect is what reads as fake; messy/handheld reads as real. Posting because I'm curious where this community draws the line: is AI UGC fair game for ads, or does \ undisclosed** AI cross into sketchy territory? Happy to share the exact workflow if it's useful to anyone. submitted by /u/New_Measurement_6962 [link] [留言]

The 2026 edition of Summer Game Fest just wrapped up, and it was surprisingly hectic. The nearly week-long event came at a challenging time for the games industry, and for the most part the big keynotes were used as a chance to show some strength through major announcements, while largely ignoring pesky details like hardware […]

A2A has become like a standard for enabling agent to agent communication, we could use the a2a-sdk for running and configuring the a2a server and its features such as agent card, agent skills, agent executor, request handler etc. However we are going to go with a simplified approach here with strands where the agent card will be fetched automatically. Let's get started! Server Initialize a uv project for the a2a server and switch to that directory. uv init ~/strands-a2a-server cd ~/strands-a2a-server Add the required packages. uv add python-dotenv == 1.2.2 strands-agents[a2a] == 1.42.0 Change the code in main.py to look like below. $ cat main . py from dotenv import load_dotenv from strands import Agent from strands.multiagent.a2a import A2AServer load_dotenv () def main (): agent = Agent ( callback_handler = None , description = " A sample strands agent " , model = " us.amazon.nova-micro-v1:0 " , ) a2a_server = A2AServer ( agent = agent ) a2a_server . serve () if __name__ == " __main__ " : main () I like the simplicity here, as you see above, it's quite simple to start a basic a2a server from with in strands, with just a couple of lines of code, we didn't have to install the a2a-sdk separately. Run the code, to start the a2a server. $ uv run main.py INFO: Started server process [18006] INFO: Waiting for application startup. INFO: Application startup complete. INFO: Uvicorn running on http://127.0.0.1:9000 (Press CTRL+C to quit) Client Let's now do the client part on a separate terminal. Initialize the project and switch the directory. uv init ~/strands-a2a-client cd ~/strands-a2a-client Modify main.py code to look as follows. import asyncio from strands.agent.a2a_agent import A2AAgent async def main (): agent = A2AAgent ( endpoint = " http://localhost:9000 " ) agent_card = await agent . get_agent_card () print ( " Invoking remote agent with agent card: " ) for key , value in agent_card : print ( key , " : " , value ) print ( ' - ' * 20 ) while True : prompt = input

I finished building my first portfolio. I want everyone to give their honest opinion on it. No need to hold back. waliimran.vercel.app Thanks you!

Whenever I heard about harmonics, I thought they were only related to large substations, transmission systems, and industrial facilities. I assumed harmonics were something utility engineers dealt with and not something connected to everyday devices. Phone chargers can create harmonics. Laptop chargers can create harmonics. LED lights can create harmonics. Even a UPS sitting under a desk can create harmonics. Today, modern power systems use many power electronic devices such as EV chargers, solar inverters, battery energy storage systems (BESS), UPS systems, data centers, and Variable Frequency Drives (VFDs). While these technologies bring many benefits, they can also introduce harmonic distortion. The more power electronic devices we connect to the grid, the more important harmonic analysis becomes. In this article, I will explain what harmonics are, what causes them, how they affect power quality, how they can be analyzed using PSCAD, and why they are becoming more important in modern power systems. Before we talk about harmonics, let's first understand electrical loads, because this is where harmonics usually begin. What Is an Electrical Load? An electrical load is any device that uses electrical energy to perform useful work. For example, think about a typical evening at home. You turn on a ceiling fan, LED light, laptop, air conditioner, and phone charger. All of these devices use electricity, so they are called electrical loads. Examples of electrical loads include motors, heaters, fans, computers, air conditioners, lighting systems, and EV chargers. However, not all electrical loads use electricity in the same way. Some draw current smoothly, while others draw current in short pulses. This small difference is actually where the story of harmonics begins. Linear vs Non-Linear Loads To understand harmonics, we first need to understand the difference between linear and non-linear loads. Although both types of loads consume electricity, they draw current from the