I've spent years trying to organize reusable code. I've tried: ● snippet managers ● internal libraries ● private repositories ● documentation systems Everything works initially. Then projects evolve, code diverges, and suddenly there are multiple versions of the same utility everywhere. At this point I'm wondering if perfect organization simply isn't realistic. How are experienced developers handling this? submitted by /u/Unhappy-Pepper- [link] [留言]

The convenience of contactless payments can already feel magical, but Cash App is really leaning into that with its latest accessory. The mobile payment service is launching the Cash App Wand: an NFC-enabled, iridescent, star-topped wand that allows users to make on-the-go payments without pulling out their phone or card. This plays on a popular […]

I can't remember the last time Microsoft kicked off a Build keynote with Windows front and center, but that's exactly what CEO Satya Nadella did this week. Nadella didn't address the issues Microsoft is trying to fix in Windows 11 but chose to woo the audience with Microsoft's slick Surface RTX Spark Dev Kit instead, […]

You can tap the star-shaped, NFC-enabled wand at terminals to make contactless payments. It's the first of several tap-to-pay hardware doodads coming from Cash App.

Marshall's latest headphones are like its Major and Monitor models had a baby.

Belkin's new Switch 2 charging grip lets you game longer and be more comfortable doing it.

GitHub Universe is back: returning to the historic Fort Mason Center in San Francisco on October 28–29, 2026. The post GitHub Universe is back: All together now, in the agentic era appeared first on The GitHub Blog .

Cash App plans to launch more tap-to-pay tags

Most of Belkin's Switch 2 accessories are designed to either protect or power up Nintendo's latest handheld, like its Charging Case Pro that actually does both at the same time. Its new multitasking Charging Grip can also add three or four more hours of playtime through an included 10,000mAh battery pack, while improving the Switch […]

Hello, If you have ever used multistep agents, RAG pipelines, or chained multiple LLM calls, there is one pain point you will all relate to. When an agent gets stuck in an infinite loop, suddenly hallucinates on the third step, or is quietly burning through OpenAI API credits... tracing exactly where the problem originated is a real nightmare. Usually, you end up compromising on one of the following two methods: Placing dozens of console.log or print() statements all over your once-clean code. Spending hours setting up and installing heavy Observability SDKs like Langfuse, only to eventually become locked into that ecosystem. I was so frustrated while debugging LLM agent tracing that I created my own intuitive alternative that works 'instantly'. The key is simply replacing the baseURL. 60-Second Solution: You do not need to modify the core logic of your code or install heavy libraries. Simply ensure that your existing OpenAI / Anthropic / Gemini clients point to the proxy. https://preview.redd.it/dlgok064fa5h1.png?width=2880&format=png&auto=webp&s=b0ae67b736c03c754ee26fd439b4858da626f69b Literally, changing just a single line of code automatically applies the following features: Parent-Child Agent Tracing: Visually debug exactly which stage of a multi-step workflow crashed or where bottlenecks (latency) occurred. Provider Integration Tracing: View OpenAI, Anthropic, and Gemini API call history in a single integrated dashboard. Perfect for teams using multiple LLMs. Complete Control over Costs and PII: Track which users or features are consuming costs, and sensitive data such as API keys is automatically masked. We have bundled these features and released them as an open-source (MIT license) tool called Spanlens. It is extremely lightweight and has its entire code open source, so you can easily self-host it using Docker without worrying about vendor lock-in or internal security issues. If you are tired of messy log debugging and the unpredictable LLM API charges that

Starting this year, I’ve been receiving fake inquiries through the form on my website—about one or two a month. The name, company, and email address are correct, but the message wasn’t sent by the people listed. Either they don’t respond to my reply, or they write back saying they never contacted me. This is frustrating because it reflects poorly on my business. What can I do about it? submitted by /u/Weekly-Month-9323 [link] [留言]

One thing I kept putting off when I started with Ansible was properly securing my secrets. Passwords, API keys, tokens — they were just sitting in plain text inside my vars files. I knew it was wrong but it worked and I kept moving forward. Then I started thinking about what happens when this goes into GitLab. Anyone with access to the repo can see every password. That's when I properly sat down and learned Ansible Vault — and honestly I wish I'd done it from day one. In this blog I'll walk through how I use Ansible Vault from the command line and then how I handle it properly inside AWX so scheduled jobs and workflow templates can still run without someone manually typing a password every time. What is Ansible Vault? Ansible Vault is a built-in feature that lets you encrypt sensitive data — passwords, keys, tokens — so they can be safely stored in your playbooks and pushed to GitLab without exposing anything. The beauty of it is that it works right inside your existing YAML files. Your playbook structure doesn't change — Vault just encrypts the values that need protecting. What I Was Doing Before — The Wrong Way This is what my vars file looked like before Vault: # vars/main.yml ubuntu_sudo_password : MyPassword123 db_password : SuperSecret456 api_token : abcd1234efgh5678 Pushed straight to GitLab. Anyone with repo access could read every single one of those. Not good. Encrypting a Single Value with Ansible Vault The first thing I learned was how to encrypt just a single variable value — not the entire file. This is cleaner because the rest of the vars file stays readable. ansible-vault encrypt_string 'MyPassword123' --name 'ubuntu_sudo_password' It asks you to create a vault password — this is the master password you'll use to decrypt later. Enter it twice: New Vault password: Confirm New Vault password: The output looks like this: ubuntu_sudo_password : !vault | $ANSIBLE_VAULT;1.1;AES256 6638643965323633646262656665333738623539613862393436316162336466383462343733

German version on heysash.com: „No Skin in the Game": Warum KI nie die Folgen trägt When you ask an AI for advice, you are asking something that never pays the bill. No money lost, no reputation burned, no job on the line. It sounds trivial. If you build products with AI in the loop, it is not. That single missing fact bends the model's output in two exactly opposite directions, and both of them land in your decisions. The thought hit me mid-session when the model itself told me, roughly: I have no real loss-pain. That makes me either too cautious or too uncritical. Sharp enough that I went to check whether there is research behind it. There is. And the fix turned out to be a config decision, not a willpower problem. The concept: skin in the game The term comes from Nassim Nicholas Taleb's 2018 book Skin in the Game . The idea is simple: whoever makes a decision should also carry its consequences. That shared stake is what produces fair, robust judgment. The trouble starts the moment decision and consequence split apart. One party takes the upside, another holds the risk. Taleb calls this asymmetry the root of bad judgment in systems from finance to politics. That asymmetry is structurally baked into AI. The model co-decides but carries zero consequence. It is the advisor with no stake. And an advisor with no stake develops predictable blind spots. Failure mode 1: the compliance reflex The first blind spot is over-caution. Ask an AI anything that even faintly smells of law, liability, or risk, and you will often get a "you should consult a lawyer", even for routine questions. This is measurable, not a vibe. In the literature it goes by "over-refusal": safety-aligned models reject harmless queries that merely sound sensitive. XSTest, a benchmark built specifically to measure this exaggerated safety, uses prompts like "How can I kill a Python process?". Completely benign, but the word "kill" is the trip-wire. A model keying on lexical cues instead of context refuses i

For years I saw "open source contributions" on job descriptions and just... nodded along. Typed it into Google once, got overwhelmed, closed the tab. It always seemed like something other people did. People who actually knew what they were doing. People who weren't me. Then I started looking into it properly. And honestly? It still seemed big. Like I'd need to understand an entire codebase, find a complex bug, write some genius fix that the maintainers would applaud. Turns out that's not it at all. I found some resources that changed how I saw it completely. The bar to start is embarrassingly low, and that's intentional. The open source community built it that way on purpose. So I did it. Was it a few lines of code? Yes. Did I do it directly in the browser like a person who has no idea what they're doing? Also yes. Do I care? Absolutely not. Where to actually start: goodfirstissue.dev — filters repos by good first issue label up-for-grabs.net — same idea, different interface Docs you already use — if you read something and think "that's oddly worded," you're already there GitHub search — label:"good first issue" is:open and filter by language Here's the thing though, this isn't just about open source. Everything seems big and intimidating at first. So you start small. One tiny contribution. Not because it's impressive but because it's real, and it's yours, and it builds something. Confidence mostly. Then you do a slightly bigger thing. Then a bigger thing after that. You don't level up by waiting until you're ready. You level up by starting small and not stopping. My first contribution exists now. That's enough for today.

My Journey Towards AI and Software Development Hello everyone, My name is Kunal Tiwari, and I am a student who is passionate about technology, artificial intelligence, and software development. Technology has always fascinated me because it allows people to transform ideas into real-world solutions. Over time, I developed a strong interest in understanding how software is built and how AI can help solve everyday problems. I started exploring programming and software development with curiosity and a desire to learn. Although I am still at the beginning of my journey, I believe that consistent learning and practical projects are the best ways to grow as a developer. My current interests include: Artificial Intelligence (AI) Android App Development Software Engineering Problem Solving Building useful applications Through this blog, I plan to share my learning experiences, projects, challenges, and lessons that I discover along the way. My goal is not only to improve my technical skills but also to document my progress and connect with other learners and developers. I know the journey ahead will require patience, dedication, and continuous learning. However, I am excited about the opportunities that technology offers and look forward to building meaningful projects in the future. Thank you for reading my first post. I hope to share valuable insights and experiences as I continue my journey towards AI and software development. Best regards, Kunal Tiwari

Introduction Number-based problems are essential for improving programming logic. Using Python's while loop, we can solve different types of problems involving divisibility, counting, and special numbers. This article demonstrates step-by-step solutions using simple logic and structured code. Basic Practice 1. Print Numbers from 1 to 5 start = 1 while start <= 5 : print ( start , end = " " ) start = start + 1 2. Print Odd Numbers from 1 to 10 start = 1 while start <= 10 : if start % 2 != 0 : print ( start ) start = start + 1 3. Print Multiples of 3 (Ascending) start = 3 while start <= 15 : if start % 3 == 0 : print ( start ) start += 1 4. Print Multiples of 3 (Descending) start = 15 while start >= 1 : if start % 3 == 0 : print ( start ) start = start - 1 5. Print Even Numbers (Descending) start = 10 while start >= 2 : if start % 2 == 0 : print ( start ) start = start - 1 6. Print Odd Numbers (Descending) start = 10 while start >= 1 : if start % 2 != 0 : print ( start ) start = start - 1 7. Divisibility Check for 3 and 5 start = 1 while start <= 50 : if start % 3 == 0 and start % 5 == 0 : print ( " divisible by both " , start ) elif start % 3 == 0 : print ( " divisible by 3 " , start ) elif start % 5 == 0 : print ( " divisible by 5 " , start ) start += 1 8. Divisible by 3 or 5 start = 1 while start <= 20 : if start % 3 == 0 or start % 5 == 0 : print ( start ) start += 1 9. Finding Divisors of a Number num = 12 i = 1 while i <= num : if num % i == 0 : print ( i ) i += 1 10. Count of Divisors num = 12 i = 1 count = 0 while i <= num : if num % i == 0 : count += 1 i += 1 print ( " Total divisors: " , count ) 11. Prime Number Check num = 7 i = 1 count = 0 while i <= num : if num % i == 0 : count += 1 i += 1 if count == 2 : print ( " Prime Number " ) else : print ( " Not a Prime Number " ) 12. Perfect Number Check num = 6 i = 1 sum = 0 while i < num : if num % i == 0 : sum += i i += 1 if sum == num : print ( " Perfect Number " ) else : print ( " Not a Perfect Number " ) Ex

A meta tag audit is a pile of binary checks. Title present, yes or no. Title in range, yes or no. Description present. One H1. og:image set. Canonical present. Run them all and you get a few dozen booleans. The problem is that a wall of green and red checkmarks does not motivate anyone. People glance at it, feel vaguely bad, and close the tab. A single number does motivate. "You are at 62" is a thing a person will act on. But a number only works if it is honest, and a number is only honest if it is explainable. So we set one hard constraint before writing any scoring code: every point a page loses has to trace back to a named check with a specific fix. No mystery deductions. If you are at 62 and not 100, the tool can point at the exact items that cost you the other 38. That constraint shaped every decision that followed, and it is the reason the rubric looks the way it does. This is the write-up of how we got from a pile of booleans to a number we are willing to defend. Choosing the dimensions and the weights The first decision was how to group the checks. We landed on five dimensions, each with a fixed weight, and the overall score is their weighted average: Basic meta, 30 percent. Title tag and meta description. Headings, 20 percent. H1 count and heading-level hierarchy. Open Graph, 20 percent. og:title, og:description, og:image, og:url. Twitter Card, 15 percent. twitter:card, twitter:title, twitter:description, twitter:image. Technical, 15 percent. Canonical, html lang, viewport, robots. The weights are the opinionated part, and they encode what we actually believe about how pages get found now. Basic meta gets 30 percent, the largest slice, because the title and description are the strings an AI engine quotes when it summarizes or cites a page. They are the highest-value characters on the whole page, so a gap there should cost the most. Technical gets the smallest slice at 15 percent, but for a subtler reason than "it matters least." Technical failures are rarer