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Every business that has ever typed a web address into a browser owes a small debt to a company most people have never heard of. On March 15, 1985, a computer maker called Symbolics registered Symbolics.com and, in doing so, became the first ever holder of a .com domain name. More than forty years later that address is still registered and still resolves - making it the oldest .com domain on the internet. Who was Symbolics? Symbolics Inc. was a Massachusetts company that built specialized computers called Lisp machines - workstations designed from the silicon up to run the Lisp programming language, then the darling of artificial intelligence research. These were serious, expensive machines aimed at labs and universities, and the company sat right at the cutting edge of 1980s computing. So it was fitting, if a little accidental, that they were first in line when commercial domains became available. The domain name system itself was brand new. DNS had only been introduced in 1983 to replace the unwieldy HOSTS.TXT file that every machine on the early internet had to keep in sync. The now-familiar top-level domains - .com , .org , .net , .edu , .gov - were defined in 1984. When registration opened, .com was meant for commercial entities, and Symbolics grabbed theirs before anyone else did. A slow start for the web's most valuable real estate What is striking today is how little demand there was. In the whole of 1985, only a handful of .com domains were registered - names like BBN, Think, and a few other technology companies trickled in over the following months. There was no gold rush, because there was no web yet. Tim Berners-Lee would not propose the World Wide Web until 1989, and the first website would not appear until 1991. A domain name in 1985 was a technical convenience for reaching a machine, not a brand or a piece of property. That makes Symbolics.com a kind of time capsule. It was registered before the web, before browsers, before e-commerce, and before anyon

submitted by /u/Critical-Ratio-3190 [link] [留言]

Unpopulate opinion: Fewer PRs done with proper prompting, review, and refinement usually win long term. 3 thoughtful PRs a day > 40 poorly thought ones no matter how many AI agents reviewed them.

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Welcome to My Generative AI Learning Journey Artificial Intelligence is changing the way we work, learn, build software, and solve problems. Every day, new AI tools, models, and technologies are being released, making it difficult to know where to begin. Instead of randomly watching videos or reading articles, I've decided to follow a structured learning path—and I'm inviting you to join me. This blog marks the beginning of a long-term Generative AI learning series. Whether you're a student, software developer, freelancer, entrepreneur, or simply curious about AI, this roadmap will help you understand what we'll learn together over the coming weeks and months. The goal isn't just to understand AI theory. It's to build practical skills that can be used in real-world projects and professional development. Why Learn Generative AI in 2026? Generative AI is no longer a futuristic concept. It is already transforming industries such as: Software Development Healthcare Education Finance Marketing Customer Support E-commerce Human Resources Design and Creativity Companies are actively seeking professionals who can build AI-powered applications, automate workflows, and integrate AI into existing systems. Learning Generative AI today means preparing for the next generation of technology. What You Can Expect from This Series This series is designed for beginners but will gradually move toward advanced concepts. Each article will build upon the previous one, making the learning process simple and structured. We'll focus on: Understanding AI concepts Learning industry terminology Exploring popular AI models Writing effective prompts Building AI applications Working with APIs Using open-source models Creating AI-powered software Deploying AI projects By the end of this journey, you'll have both theoretical knowledge and practical development experience. Complete Learning Roadmap Phase 1: AI Fundamentals We'll begin by building a strong foundation. Topics include: What is Generativ

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In this eMag, we try to establish agentic AI architecture as a new type of software architecture that will likely dominate the industry for years to come. The articles, written by industry experts, cover various elements and aspects of agentic AI architecture. We aim to present the latest trends and developments shaping the new type of architecture as it enters the mainstream. By InfoQ

For the past two years, the AI world has been obsessed with finding the perfect prompt or building better UI wrappers around LLMs. But while everyone was distracted by the models themselves, a silent revolution happened at the architecture layer. It is called Agentic AI , and it is being entirely reshaped by a new standard: Model Context Protocol (MCP) . If you are building AI agents in 2026 and you aren't using MCP, you are already falling behind. Here is why this changes everything. The Problem: The Custom Tooling Nightmare Up until recently, building an autonomous AI agent was incredibly fragmented. If you wanted your agent to read a GitHub repository, query a Postgres database, and send a Slack message, you had to write custom tool-calling logic for every single integration. Every time Anthropic, OpenAI, or Google released a new model, you had to adapt your tool schemas. It was a brittle, non-standardized nightmare. Enter MCP (Model Context Protocol) MCP solves this by introducing a universal, open standard for connecting AI models to data sources and tools. Think of it like a USB-C cable for AI. Instead of writing custom API wrappers for your agent, you simply build or download an MCP Server . An MCP Server is a standalone program that exposes specific capabilities (like "Search the web" or "Read a local file"). Any agent, regardless of the underlying LLM, can connect to that server and instantly understand how to use its tools. Why This Changes Agentic AI Forever Plug-and-Play Ecosystem: We are seeing the birth of an "App Store" for AI tools. Developers are open-sourcing MCP servers for absolutely everything: Jira, GitHub, AWS, local file systems, and more. True Autonomy: Because the protocol standardizes how context is passed, agents can autonomously discover what tools a server has, read the instructions, and chain them together without human intervention. Security and Isolation: You can run an MCP server in a secure, sandboxed environment (like a Docker con

If you are still using a vanilla installation of VS Code, you are leaving massive amounts of productivity on the table. We all know the standard extensions: Prettier, ESLint, GitLens. But what about the tools that actually change how you write code? Here are 7 hidden VS Code extensions that feel almost illegal to use because of how much time they save. 1. Error Lens Stop hovering over red squiggly lines. Error Lens highlights the entire line and prints the error message inline, right next to your code. You instantly know what is wrong without moving your mouse. Once you install this, you will never be able to code without it again. 2. Console Ninja Tired of switching back and forth between your browser console and your editor? Console Ninja prints console.log output and runtime errors directly in your editor, right next to the line of code that triggered it. It is like magic. 3. Turbo Console Log Highlight a variable, press Ctrl+Alt+L , and this extension automatically inserts a perfectly formatted console.log statement with the variable name and its value. It saves you hundreds of keystrokes a day. 4. Mintlify Doc Writer Writing documentation sucks. Mintlify uses AI to instantly generate beautiful, accurate JSDoc/Python docstrings for your functions. Just highlight the function and hit a button. 5. CSS Peek If you work with large HTML or React files, CSS Peek allows you to hover over a class name and instantly see (and edit) the CSS attached to it in a floating window. No more hunting through massive .css files. 6. Code Spell Checker There is nothing worse than pushing a PR and having a senior developer point out a typo in a variable name. This extension highlights spelling errors in your code, keeping your codebase looking professional. 7. WakaTime Do you actually know how much time you spend coding? WakaTime generates beautiful dashboards showing exactly which languages, projects, and files you spent your time on each week. It is incredible for tracking your own

There is a ghost haunting the tech industry right now, and nobody wants to talk about it: The Junior Developer role is disappearing. With tools like GitHub Copilot, ChatGPT, and advanced coding agents becoming standard issue in every IDE, senior developers are suddenly 10x more productive. They no longer need a junior developer to write boilerplate code, write unit tests, or scaffold out basic UI components. The AI does it instantly. So, if you are a junior developer, or aspiring to break into tech, how do you survive? 1. Stop Memorizing Syntax, Start Thinking Architecturally AI is incredible at writing syntax, but it is terrible at system design. If your only skill is writing a for loop in React, you are competing with an AI that works for $20/month. Instead, focus on understanding how systems fit together. Learn about cloud architecture, database indexing, and distributed systems. The AI can write the function, but you have to know where that function lives and how it scales. 2. Become a "Domain Expert" Developer AI doesn't understand the nuanced business logic of the healthcare industry, or the strict compliance regulations of fintech. If you combine coding skills with deep industry knowledge, you become irreplaceable. 3. Embrace the Tools (Be the Orchestrator) Don't fight the AI. Master it. The developers who thrive in the next decade will be the ones who treat AI agents like a team of junior developers reporting to them. Learn how to craft the perfect prompts, how to use Retrieval-Augmented Generation (RAG), and how to orchestrate multiple LLMs to build complex applications. The barrier to entry for writing code has dropped to zero. But the barrier to entry for building valuable software remains exactly the same. Are you terrified of AI replacing you, or are you using it to level up?

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The trust problem nobody scopes correctly When companies talk about trust in AI, they almost always mean trust in the model. Is the output accurate? Is it hallucinating? Can we rely on what it says? Those are valid questions but they're the wrong starting point. The trust that actually determines whether AI gets adopted or quietly abandoned inside an organization isn't about the model. It's about the system surrounding it. The four questions that determine Every team evaluating AI in a production workflow eventually runs into the same four questions. Not about model quality. About operational control. Can we understand the outputs? Not just "does the answer look right" but can someone on the team explain why this output was produced and whether it's appropriate for this specific context. An AI that generates correct-looking code or recommendations that nobody can verify is a system that runs on hope. Hope doesn't survive the first incident. Can we validate the decisions? When the AI recommends an action or generates an output that feeds into a business process, is there a way to check it against the actual requirement? Or does the team just trust the output because questioning it is harder than accepting it? The second one is more common than anyone admits. Can we intervene when needed? When something goes wrong, how fast can a human step in? Is there a kill switch? Is there a fallback path? Or does the AI output flow directly into downstream systems with no circuit breaker? The teams that skip this question are the ones that discover the answer during an incident. Can we trace what happened afterward? When an AI-generated decision produces a bad outcome, can you reconstruct the chain? What input went in, what output came out, what context was available, what wasn't? Without traceability, post-mortems hit a dead end, and the same failure happens again. Why opaque systems don't survive real operations There's a tempting argument that opacity is fine as long as the sy

Laravel middleware can be perfectly written and still behave unexpectedly. You may notice authentication running too late, permission checks failing, tenant initialization not working, logging middleware missing important data, or custom middleware executing in an order you didn't expect. In many cases, the middleware code itself is not the problem. The real issue is middleware execution order. Understanding how Laravel executes middleware is critical when building secure and scalable applications because every request passes through multiple layers before reaching your controller. Common Symptoms You may encounter problems such as: Authenticated users being treated as guests Permission middleware failing unexpectedly Tenant information not being available Request logging missing user details Rate limiting triggering before authentication Redirect loops after login Middleware appearing to be ignored completely These issues are often caused by middleware running in the wrong sequence. How Laravel Processes a Request A typical Laravel request follows this flow: Browser ↓ Global Middleware ↓ Middleware Group (Web/API) ↓ Route Middleware ↓ Controller ↓ Response ↓ Browser Each middleware layer can inspect, modify, allow, or block the request before it reaches your application logic. Because of this, execution order matters. Example Problem #1 Suppose you have two middleware: Authenticate User Log User Activity Your logging middleware expects an authenticated user. $user = auth()->user(); However, the log always shows null. Why? Because the logging middleware executes before authentication. The solution is ensuring authentication middleware runs first so user information is available when logging occurs. Example Problem #2 Multi-tenant applications often initialize tenant information through middleware. TenantMiddleware If another middleware accesses the database before tenant initialization, queries may use the wrong database connection. This can lead to: Incorrect data

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Actually it happened in June of 2025, but the process has completed recently, though. After Apple had announced the support of OCI-compatible containers in the June '25 it took a year to complete development and implement full support of continers. Apple had published 1.0 version of own container manager ( https://github.com/apple/container ). And Microsoft had announced native support of containerization without Docker in Windows 11 ( https://devblogs.microsoft.com/commandline/wsl-container-is-now-available-for-public-preview/ ). Now Linux is a part of any major platform: Windows, MacOS, BSD and Linux itself. Knowledge of Linux is now part of learning any of these systems, at least for developers. And now you can rely on Linux based containers running everywhere. What it is if not a win!? What's also interesting. Linux can run other Linux distros and with this Alpine Linux could become the most popular version of Linux in the World It's the biggest win for the whole open-source software and I believe it should get into history books of technological progress submitted by /u/BankApprehensive7612 [link] [留言]

Spent the week deep-diving into my dev-publish tool, focusing on durability and orchestrator resilience. 21 commits across two repos, with a massive cleanup of the publishing logic and some much-needed architecture documentation. TL;DR There is a specific kind of satisfaction that comes from taking a tool you use every day and finally giving it the "production-grade" treatment it deserves. This week was exactly that. I spent most of my time in the guts of dev-publish , moving past the "it works on my machine" phase and into "it works even if the world is on fire" territory. With 21 commits and over 11,000 lines of code churn, I focused on making the publishing orchestrator resilient and the state durable. What I Built The star of the show this week was dev-publish . If you’ve ever tried to automate cross-platform technical writing, you know that the edge cases are where the real pain lives. I pushed 16 commits here, touching about 45 files. The diff was pretty wild: +6,926 additions and -4,289 deletions. That net positive tells part of the story, but the deletions represent me ripping out brittle logic that just wasn't cutting it. Hardening the Orchestrator The biggest win was a massive fix to make the publish state durable and the orchestrator resilient. In the previous iteration, if a network request to an API (like Dev.to) failed halfway through a multi-platform push, the state was... let's just say "vague." I spent a lot of time in src ensuring that the orchestrator can now pick up where it left off. I also documented the published-flag semantics and re-run resilience in the README. It sounds like a small thing, but knowing that a re-run won't accidentally double-post your article is a huge weight off my mind. I also spent some time on the "boring but important" stuff. I normalized how tags are handled to make them safer across different platforms and implemented a much stricter resolution for cover images. If a local image is required but missing, the tool now

One question I get surprisingly often is: "How do you manage thousands of AI prompts without losing track of them?" The answer is simple. I don't treat prompts as conversations. I treat them as reusable software assets. Over the years, I've created prompt libraries across multiple AI projects, books, research initiatives, and client work. That means managing well over 10,000 prompts covering everything from Python development and AI agents to content generation and workflow automation. If you're still storing prompts in random ChatGPT conversations, you're making life much harder than it needs to be. Here's the system that works for me. Stop Thinking of Prompts as Temporary Most people write a prompt, get an answer, and move on. That's fine for casual use. But builders rarely solve the same problem only once. If you find yourself writing: API documentation SQL queries FastAPI endpoints Docker configurations Code reviews Git commit messages ...you're probably solving recurring problems. Recurring problems deserve reusable prompts. My Folder Structure Instead of organizing prompts by AI tool, I organize them by purpose. For example: AI-Prompts/ │ ├── Python/ │ ├── FastAPI │ ├── Django │ ├── Flask │ └── Automation │ ├── JavaScript/ │ ├── React │ ├── Node.js │ └── TypeScript │ ├── DevOps/ │ ├── Docker │ ├── Kubernetes │ └── GitHub Actions │ ├── AI/ │ ├── RAG │ ├── Agents │ ├── MCP │ └── Prompt Engineering │ └── Documentation/ This mirrors how software projects are organized. Finding a prompt takes seconds. Every Prompt Has Metadata A prompt isn't just text. It's documentation. Each prompt in my library includes: Category: Purpose: Model: Input: Expected Output: Version: Last Updated: For example: Category: FastAPI Purpose: Generate CRUD endpoints Model: GPT-4o Expected Output: Production-ready FastAPI code Six months later, I know exactly why that prompt exists. I Version My Prompts Developers version code. Why not prompts? For example: FastAPI_CRUD_v1.md FastAPI_CRUD_v