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Backstage won the developer portal war and most teams running it still failed. Spotify's open-source framework, now a CNCF project, holds about 89% of the IDP market, runs at more than 3,400 organizations, and serves over two million developers outside Spotify. That is a decisive victory by any market-share metric. Then you find the number nobody puts on a keynote slide: the average internal adoption rate of a self-hosted Backstage instance sits around 10%. Most teams stand up a portal, demo it to leadership, and watch it quietly fail to become the thing engineers open every morning. So the slogan making the rounds in platform circles this year, "DIY is dead," is aimed at the exact tool that dominates the category. That sounds like a contradiction. It isn't. It's a math problem about where a small platform team's hours actually go. The 89% that doesn't mean what you think Framework market share and developer adoption are two different scoreboards, and Backstage is winning one while losing the other. Winning the framework war means a lot of organizations chose Backstage as the thing to build on. It says nothing about whether developers inside those organizations use the resulting portal. The honest read of the data is grim: teams "burn out on maintenance before delivering features developers actually want," and 56% of Backstage adopters name upgrades as their single biggest pain point. Not catalog design, not plugin gaps. Upgrades. The treadmill of keeping a fast-moving framework current is the thing that eats the year. I've watched this pattern up close on infra teams that had every reason to succeed. The portal goes live, gets a round of applause, and then the catalog data goes stale because nobody owns the integrations, the scorecards never get wired to real ownership data, and three months later the platform engineers are spending their sprints chasing a plugin that broke on the latest bump. The portal became the project instead of the platform. That is the failu

One of the first cloud concepts many people encounter while learning AWS is EC2 . The name sounds technical. The documentation is extensive. And the number of configuration options can make it feel like something fundamentally different from a regular computer. But while trying to understand cloud computing, I found myself repeatedly coming back to a simple thought: At the end of the day, an EC2 instance is just another computer. That realization helped me understand cloud infrastructure much more clearly. The Intimidation Factor When people first open the AWS console, they encounter terms such as: EC2 VPC Security Groups Elastic IPs Auto Scaling It is easy to feel that cloud computing is an entirely different world. But before diving into those concepts, it helps to ask a simpler question: What is an EC2 instance actually providing? Starting with the Name EC2 stands for: Elastic Compute Cloud The important word here is: Compute AWS is essentially renting computing resources. When you launch an EC2 instance, AWS allocates: CPU Memory (RAM) Storage Networking to a virtual machine that you can access. In other words: You are renting a computer that lives inside AWS's infrastructure. Comparing It to a Personal Computer Consider a typical laptop. It contains: A processor RAM Storage An operating system Network connectivity Now consider an EC2 instance. It also contains: Virtual CPUs RAM Storage An operating system Network connectivity The location is different. The concepts are the same. The Main Difference: Ownership The biggest difference is not technical. It is operational. With a personal computer: You own the hardware. The machine sits near you. You maintain it. With EC2: AWS owns the hardware. The machine runs in a data center. AWS manages the physical infrastructure. You only manage the virtual machine running on top of it. Why Linux Knowledge Transfers This was one of the most interesting observations during my learning. If an EC2 instance runs Linux, many of th

A few weeks after switching from Windows to Linux, I noticed something interesting. The hardware had not changed. The processor was the same. The RAM was the same. The SSD was the same. And yet, the laptop felt noticeably faster. Not necessarily because applications were completing tasks dramatically quicker, but because the entire system felt more responsive. Keyboard input felt immediate. Windows opened faster. Terminal commands appeared instantly. The desktop experience felt smoother. This raised a question: How can the same hardware feel different simply because the operating system changed? While I'm still learning, this is the mental model I've built so far. The Hardware Didn't Change Consider a laptop with: AMD Ryzen processor 16 GB DDR5 RAM NVMe SSD Modern integrated graphics When switching operating systems, none of these components change. The CPU does not suddenly become faster. The RAM does not magically increase. The SSD remains identical. From a hardware perspective: ```text id="u3m9xd" Before → Same Hardware After → Same Hardware So the difference must come from somewhere else. --- ## An Operating System Is Not Just a User Interface Many people think of an operating system primarily as the desktop they see. But an operating system does far more than display windows and icons. It manages: * Memory * CPU scheduling * Processes * Storage * Networking * Device drivers * Background services In other words: > The operating system decides how hardware resources are used. Two operating systems can therefore create very different experiences using the same hardware. --- ## Perceived Performance vs Raw Performance One thing I have learned is that performance is not always about benchmarks. A system can have excellent benchmark scores and still feel sluggish. Why? Because users experience responsiveness, not benchmark numbers. Examples include: * How quickly a window opens * How fast a menu appears * How responsive typing feels * How quickly applications launch

One of the first networking concepts that confused me was this: Why does a computer need both a MAC address and an IP address? At first glance, they seem to solve the same problem. Both appear to identify a device. Both show up in networking tools. Both appear in packet captures. So why do we need two different addresses? While exploring Linux networking tools and Wireshark, the distinction finally started making sense. This article summarizes the mental model that helped me understand the difference. Looking Inside the Machine Before discussing addresses, it helps to understand where they come from. If you open a typical laptop, you will usually find components such as: Battery RAM Storage Processor Cooling system Network interfaces One of those network interfaces is typically: A Wi-Fi card An Ethernet controller These components are responsible for network communication. They are the parts of the machine that actually send and receive data across a network. Every Network Interface Has an Identity A network interface needs a way to identify itself. This is where the MAC address comes in. A MAC address is associated with a network interface card (NIC). Example: ```text id="q3d9nm" 2C:9C:58:8B:2D:7B Think of it as the identity of the network interface itself. Not the operating system. Not the browser. Not the application. The network hardware. --- ## What Is a MAC Address? MAC stands for: **Media Access Control** A MAC address operates at the **Data Link Layer** of the OSI model. Its primary purpose is to help devices communicate within a local network. Examples include: * Laptop to router * Router to switch * Switch to printer In other words: > MAC addresses help devices find each other on the same local network. --- ## What Is an IP Address? An IP address serves a different purpose. Example: ```text id="g8x4tc" 192.168.1.20 or ```text id="v6u7mz" 2405:201:8000::1 IP addresses operate at the **Network Layer**. Their job is to identify where a device exists within a

So I’ve been using Ai as an art tutor I give it my own art and I review it on how’d I’d look colored a certain way, and how best to detail and shade, as well as a sorta 2d model I can have rotated and view at different angles to get a feel for the shapes and such this is how Ai should be used to teach and improve not to outright replace, it’s like Siri submitted by /u/Intelligent-Fig-1755 [link] [留言]

Hi folks, Deciding between these two Mac options has been a challenge for me, so pls help. I know mac is not even necessary for this but just help me to decide between these two options. For the reference, Im a swe student and looking forward to go deep into ml and data science in the near future… EDIT: mac book pro m5 ( base chip) that I’m referring here. submitted by /u/Both-Hovercraft3161 [link] [留言]

I was just thinking about the difference between an LLMs capacity for theory of mind and a human's capacity for theory of mind, and I realize it gets at the heart of what differentiates an LLM from human, and that's the method of how we gather information. LLMs are based on objective data, e.g. text, numbers, pixels, etc. Whereas we as humans, use subjective information, e.g., feelings, sensations, experiences; as well as objective data. Within cognitive science, this would be described as affective empathy vs cognitive empathy. Or in other words, LLMs simply possess a cognitive theory of mind, whereas we have both a cognitive *and* affective theory of mind. The problem I have with figures like Hinton, who claim that AI is already conscious, is that his whole framework is based on the idea that consciousness (subjective experience) is just an artifact of computation (an illusion), and therefore there is no recognition of subjective measure - that reality is only defined by what we can measure objectively (with fixed metrics). I think what this fails to recognize is that in pursuit of reproducible results, which requires fixed metrics, we've thrown out a whole set of other measurements, which is subjective (variable). submitted by /u/flasticpeet [link] [留言]

Interactive diagrams with AI, and your code always in sync Discussion | Link

A lot of supply-chain attacks have taken place in the last year. Altough I don't think NeoVim itself has been mentioned so far, I was concerned about my setup, especially the one on my office laptop. I think this is a good opportunity to learn how to write plugins ourselves, but I also know that writing everything on my own is not ideal. At this rate, might as well write my own kernel and operating system because sudo pacman -Syu also carries supply-chain risks. What are the ways which you are dealing with this? submitted by /u/Full-Ad4541 [link] [留言]

A vision for the future of AI, focusing on access, safety, and shared prosperity as OpenAI works to ensure AGI benefits everyone.

Quiet Defaults, DNSSEC Cracks, and Agents in the Data Plane I read the AWS Nitro V6 TCP timeout change twice before I believed it. Default went from 432,000 seconds to 350 seconds. Five days to six minutes. On the newest instance family. Quietly, in release notes most people won't read until something breaks. That sort of set the tone for May. No flagship launch to anchor the month around. What there was a lot of: defaults moving in places vendor press releases don't celebrate. Post-quantum crypto pushing into campus boot chains. Every cloud vendor shipping some flavor of agentic-networking pattern. The .de TLD briefly breaking because of DNSSEC. None of it announced loudly. All of it the kind of thing that breaks production at 2am if you weren't paying attention. What Moved This Month Three things, fast. Post-quantum crypto left the VPN tunnel. Cisco's full-stack PQC for campus and branch is the next chapter after April's PQ IPsec story — boot, firmware signing, supply chain attestation, and transport-layer crypto all moving together. If your campus has mixed-vintage gear (which is basically everyone), this is multi-year partial coverage with no clean switchover. Agentic networking became a real category. Cloudflare's Town Lake / Skipper writeup and Claude Managed Agents , Palo Alto's Portkey-based unified AI Gateway , and AWS's Bedrock AgentCore connectivity patterns all dropped this month. The right question stopped being "can my agent reach the model" and became "what IAM blast radius does this agent have if it gets prompt-injected." DNSSEC had a rough month. The .de TLD broke briefly, the DNSSEC root key was rolled, and Cloudflare also debugged a QUIC CUBIC death spiral that was hiding in plain sight. The Internet's core had a louder month than usual, and not in a good way. 1. Agentic AI Is Now Actually A Networking Problem An agent in production isn't a fancy chatbot. It's a thing that calls APIs, reads logs, accesses SaaS data, and sometimes writes back to sy

🌐 Read this post in Bahasa Indonesia here . 📝 A note on this article This post is based on my personal study notes on version control and Git collaboration. To make these notes more readable and useful — for myself and for others — I worked with AI to help expand and structure them into a proper blog format. The ideas, learning journey, and understanding are mine; the AI helped with the writing and presentation. Learning Git doesn't have to be intimidating. In this article, I'll break down the essential concepts of version control and collaboration — using simple analogies that anyone can understand. What Is Git? Git is a version control system . Think of it as a save system for your code — like save points in a video game. Every time you save (commit), Git remembers the state of your project at that moment. If something goes wrong, you can always go back. Repository: Your Project's Warehouse A repository (or "repo") is the folder that Git watches. There are two types: Local repository : lives on your computer. Your personal workspace. Remote repository : lives on a server (GitHub, GitLab, Bitbucket). The "official" shared copy your team can access. They stay connected through a Remote URL, so you can push your local changes up and pull others' changes down. git init # Start tracking a folder git remote add origin <url> # Connect to a remote repo git push origin main # Send commits to remote git pull origin main # Get latest from remote Commit: Your Project's Save Point A commit is a snapshot of your project at a specific moment. Each commit has: A message describing what changed A unique ID (hash) A timestamp git add . # Stage all changes git commit -m "Add homepage layout" # Save a snapshot git log --oneline # View commit history Write meaningful commit messages. Future you will thank present you. Checkout, Reset, Revert: Traveling Through Time These three commands all interact with your commit history — but in very different ways: git checkout — Visit the Past (T

🌐 Baca artikel ini dalam Bahasa Inggris di sini . 📝 Catatan tentang artikel ini Artikel ini dibuat berdasarkan catatan belajar pribadi saya tentang version control dan Git kolaborasi. Untuk membuat catatan tersebut lebih mudah dibaca dan bermanfaat — bagi saya dan orang lain — saya menggunakan bantuan AI untuk mengembangkan dan menyusunnya menjadi artikel blog. Ide, perjalanan belajar, dan pemahamannya adalah milik saya; AI membantu di bagian penulisan dan penyajiannya. Belajar Git itu tidak harus membingungkan. Di artikel ini, saya akan menjelaskan konsep-konsep penting dalam version control dan kolaborasi menggunakan bahasa yang sederhana — bahkan dengan analogi yang bisa dipahami anak kecil sekalipun. Apa Itu Git? Git adalah version control system — sistem yang merekam setiap perubahan yang kamu lakukan pada file-file proyekmu. Bayangkan Git seperti fitur save point di video game. Setiap kali kamu menyimpan (commit), Git mengambil "foto" dari kondisi proyekmu saat itu. Kalau ada yang salah, kamu bisa kembali ke foto sebelumnya. Repository: Gudang Proyekmu Repository (atau "repo") adalah folder yang diawasi oleh Git. Ada dua jenisnya: Local repository : ada di komputermu sendiri. Ruang kerja pribadimu. Remote repository : ada di server (GitHub, GitLab, Bitbucket). Salinan "resmi" yang bisa diakses seluruh tim. Keduanya terhubung lewat Remote URL, sehingga kamu bisa mengirim perubahan ke remote ( push ) atau mengambil perubahan terbaru dari sana ( pull ). git init # Mulai memantau sebuah folder git remote add origin <url> # Hubungkan ke remote repo git push origin main # Kirim commit ke remote git pull origin main # Ambil update terbaru dari remote Analogi: Local repo adalah buku sketsamu di rumah. Remote repo adalah papan pengumuman kelas — semua orang bisa melihat dan mengaksesnya. Commit: Save Point Proyekmu Commit adalah snapshot dari kondisi proyekmu pada satu titik waktu. Setiap commit berisi: Pesan yang menjelaskan apa yang berubah ID unik (hash) Timestamp g

Fundraising, GTM, and bounties for startups Discussion | Link

This is a submission for the GitHub Finish-Up-A-Thon Challenge What I Built I built Academia, a modern, delivery-only Learning Management System (LMS) designed for creators who want a premium, distraction-free environment for their students. With Academia, educators can spin up their own custom academy, build out rich curriculums with video and text lessons, and securely invite students via email magic-links. Once a student accepts an invite, they are dropped into an isolated, hyper-clean student portal that tracks their course progress in real-time. This project started two years ago during a hackathon. I had this really great idea, but I was still very much a beginner. Between the tight timeframe, lack of sleep, and my slow coding speed, I just couldn't pull through to finish the project in time to submit. It sat abandoned in my repositories, gathering dust. Finishing it means finally delivering on the exact vision I had in my head two years ago, but with the skills and architecture of today. Demo Live Site Repository The Comeback Story I always told myself I’d finish this project "after the dust settles," but I never got around to it. When I saw this challenge, I knew it was time. Just looking at the 2-year-old repository gave me an instant headache. The packages were entirely outdated, Vercel was throwing massive build errors, and running npm run dev was painfully slow. Basically, everything was lagging. I seriously considered just starting a fresh repo from scratch. I figured I would just look at the shabby design I created two years ago and try to manually copy-paste the "important" code over, because I knew updating the existing mess would be an absolute nightmare. But instead, I decided to lean heavily on GitHub Copilot Auto (so I could automatically get the best models for the job) to see if we could salvage and modernize the original codebase. Here is the story of my before-and-after journey, and how Copilot helped me finally finish what I started. My Expe

After running AI coding agents in production for a while, one thing became clear: the failures aren't in the code the model writes. They're at the seams — git, CI, auth, the network. The boundaries with the outside world. The model itself is genuinely capable. It writes functions, writes tests, refactors. What breaks is everything around the work: pushing the result, waiting on CI, merging the PR, refreshing a token, calling another service. And the failures are often the kind a human would avoid without thinking. Here are five incidents we hit and fixed in Codens' Purple (the orchestration core) over the last few weeks. All real, with production task IDs and dates. Every fix is merged. There's a shared design lesson at the end that ties them together. Incident 1: a half-resolved merge nearly flooded a PR with 12,000 lines This was the scary one. A Purple task on opsguide-back opened a PR. I looked inside: +12,162 lines / 149 files changed, with literal <<<<<<< markers in 2 of them . The commit graph: e567ce67 (merge commit, "chore: Fix HYBRID_SEARCH...") ├ parent[0] = 0b069e5d (develop tip, +1468 commits over main) └ parent[1] = 2940de35 (the actual feature commit) What happened: in the fix step, the AI decided to git merge develop to backport some test fixes. The merge conflicted. The AI resolved it partially and drove git commit through anyway with markers still in the tree. What got pushed: develop's entire divergence plus unresolved conflict markers. If anyone had clicked merge, main would have been polluted by 1468 commits of develop drift in one shot. A human wouldn't do this. They wouldn't merge develop into a main-targeted PR in the first place, and if it conflicted they wouldn't commit until it was fully resolved. But the AI, optimizing locally to get one test passing, does it without hesitation. Fix: stop it at push time, in two layers A single git pre-push hook. This is where the AI's git push actually goes, so this is where the guard belongs. #!/bin/bas