标签：#ui

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A few years ago, securely accessing my servers at home or in a small office from outside was always a headache. Dynamic IP addresses, complex port forwarding settings in the modem interface, and even the need to open rules in the network firewall always presented me with a new problem. Especially outside of corporate projects, when setting up my side products or test environments, these processes caused a loss of time. Cloudflare Tunnel offered a game-changing solution for such self-hosted services. Essentially, a cloudflared daemon (tunnel client) on your local network establishes a single outbound connection to Cloudflare, eliminating the need to open any firewall holes for incoming connections. This model provides significant advantages over traditional methods in terms of both security and ease of use. Why Were Traditional Self-Hosting Methods Challenging? Exposing a self-hosted service to the internet typically brings multiple technical challenges. First, most home or office internet connections have dynamic IP addresses, meaning your IP address changes from time to time. In this case, to access your service via a fixed domain name, you need to resort to solutions like Dynamic DNS (DDNS), which adds an extra dependency and sometimes causes delays. Second, and perhaps most importantly, is the necessity of opening port forwarding rules in your network firewalls (modem/router firewall). This means you need to direct specific ports to your internal IP address to allow incoming connections. This process both increases security risks (internet-facing ports invite brute-force attacks) and causes access problems if not configured correctly. I once experienced a serious panic during a client project when I accidentally exposed a critical internal service port to the outside while dealing with these ports. Since that day, I approach such manual interventions with more skepticism. ℹ️ Port Forwarding Risks Every port you open in your network firewall creates a potential at

When analyzing supply chain data in a production ERP, the idea of sending critical information to a publicly accessible cloud-based LLM always bothered me. Processing sensitive data like proprietary production plans, customer lists, or financial details on third-party servers was an unacceptable risk for me. In such situations, the only way to ensure data security is to run the LLM in a local environment, under our own control. This is where Ollama comes in. Ollama is a tool that allows you to easily run large language models (LLMs) on your local system. This way, you can set up your own AI assistant without an internet connection and without the risk of leaking your data, securely automating your sensitive business processes. In this post, I will step-by-step explore why Ollama is important, how to install and use it, and the data security advantages it offers. Why Ollama is Important: Does it Provide Data Security and Control? The potential of LLMs in modern software development processes and operational workflows is huge, but the privacy and security of corporate data are always a primary concern. Especially in high-security environments like a bank's internal platform or in the financial calculators of my own side product, I need to process user data without sending it externally. In these scenarios, cloud-based LLM services often create a new risk factor rather than a solution. Ollama is designed to close this critical data security gap. By running models on your local machine, it prevents your data from leaving your company network or personal computer. This is a vital advantage, especially for those working in sectors subject to data protection regulations like GDPR and KVKK. Furthermore, because it doesn't require an internet connection, you can continue to benefit from LLM capabilities even in offline environments or during network outages. Since you have full control, you can manage from start to finish which model runs with which data, how much resource t

"I'll just write a quick button style... and a text field with validation... and a card component..." Two weeks later you have a bespoke design system that only half the app uses, three slightly different buttons, and nothing shipped. If you're building with an AI coding agent, the drift problem is worse, not better. Ask Claude, Cursor, or Codex to "add a settings screen" three separate times and you'll get three different paddings, three different corner radii, and a button style that quietly forked itself somewhere around commit 40. Agents are great at writing plausible SwiftUI and bad at remembering what the rest of your app already decided. DesignFoundation is an open-source SwiftUI package that gives you a token-based theming engine, 25 components, and 6 feedback/overlay modifiers that all read from the same theme. You set it once at the app root, and everything underneath updates. That's the whole idea — and it ships with CLAUDE.md , AGENTS.md , and Cursor rules already written, so an agent working in your repo reaches for DFButton instead of inventing a new one. In this tutorial you'll go from zero to a themed, consistent SwiftUI UI — without writing a single custom button style. What We're Building By the end you'll have: A working app that uses DesignFoundation's theme system A themed form with inputs, validation states, and feedback components A custom theme built from tokens An understanding of how the style system composes Requirements: Xcode 16+, iOS 18+ / macOS 15+ target, Swift 6. Installation Via Xcode File → Add Package Dependencies Paste https://github.com/NerdSnipe-Inc/design-foundation Set the version rule to Up to Next Major from 1.0.0 Add DesignFoundation to your target Via Package.swift dependencies : [ . package ( url : "https://github.com/NerdSnipe-Inc/design-foundation" , from : "1.0.0" ) ], targets : [ . target ( name : "YourApp" , dependencies : [ "DesignFoundation" ]) ] Step 1: One Line Instead of a ThemeManager Singleton Every app that

How I Built 7 Apify Actors and Started Earning Passive Income from Web Scraping A few weeks ago I had zero Apify actors. Now I have seven, all published on the Apify Store, monetized with pay-per-event pricing, and slowly building a passive income stream. Here's exactly how I did it — the strategy, the tech stack, the mistakes, and what I'd do differently. The Strategy: Zero Competition Most new Apify developers go after hot categories. LinkedIn scrapers, Amazon product extractors, Twitter data. Makes sense — those have demand. But they also have dozens of established actors with hundreds of reviews. I took the opposite approach. Find niches with zero existing actors. This means lower total addressable market, but 100% of whatever traffic exists goes to you. No competing on price, no fighting for reviews, no SEO war against actors with years of history. How I found the niches: Browsed Apify Store categories sorted by actor count Searched for common developer pain points with no existing Apify solution Checked search volume for "[keyword] API" and "[keyword] scraper" Verified zero results on Apify Store for each candidate The winners: domain intelligence, screenshot comparison, Swedish company registry, IP geolocation, QR code generation, and link metadata extraction. The Tech Stack Every actor uses the same foundation. Apify Python SDK v3.4 handles input/output, storage, proxy, and deployment. Playwright for JavaScript-heavy sites and screenshots. aiohttp for lightweight API scraping (way faster than a full browser). Pillow for image processing. Deployment is one command: apify push The Actors {{domain-intel}} WHOIS, DNS, SSL, and tech stack in one API call. Uses socket + ssl + python-whois for data collection, no external API dependency. $0.005 per run. {{screenshot-api}} Full-page screenshots via Playwright. Handles lazy-loading, infinite scroll, and viewport sizing. $0.003 per run. {{metadata-extractor}} Open Graph, Twitter Cards, JSON-LD, and meta tags from any

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Part 2 of 2. This tutorial builds on Part 1 — DesignFoundation core . If you haven't added the base package and theme yet, start there. The core package gives you tokens and primitives. DesignFoundationPro adds what comes next — 29 blocks, 47 screens, 18 navigation shells, and 9 runnable composition examples across auth, onboarding, charts, data tables, and full product verticals. The docs claim ~87% fewer lines of code versus building from scratch. That's the bet. This matters even more if an AI coding agent is doing the building. Ask an agent for a CRM screen and a settings screen in the same session and, without guardrails, you'll get two different takes on spacing, two different sidebar behaviors, and a table that's native on Mac in one screen and a scroll view pretending to be a table in the other. Pro ships with that guardrail already in place — more on that in Where to go from here. How the two packages relate Pro sits on top of Foundation and re-exports it. Import only DesignFoundationPro and you get everything from both: YourApp your models, data, routing DesignFoundationPro 29 blocks · 47 screens · 18 shells · 9 examples DesignFoundation tokens · primitives · validation · theme engine · MIT Access: Foundation is MIT and public. Pro is a commercial add-on — repo access is granted after purchase. Licenses are lifetime (no subscription); annual updates are $39/year and entirely optional. Pricing: $149 individual · $449 team (up to 5 devs). Before buying, browse everything in DFPlayground — a free macOS app that lets you preview all 29 blocks, 47 screens, and 18 shells with live theming. Step 1: Add DesignFoundationPro Pro declares Foundation as its own dependency and re-exports it via @_exported import DesignFoundation . Add only the Pro package — Foundation comes with it automatically. The Pro repo URL is provided after purchase — contact nerdsnipe.inc@gmail.com or visit the Pro page to get access. dependencies : [ . package ( url : "https://github.com/NerdS

Every developer has been there: you need to format a JSON blob, decode some Base64, or convert a timestamp. You open your terminal, look for the right npm package, or — worse — write a quick script. I used to do this too. Then I discovered a better pattern. The Problem with Local CLI Tools Local tools have real drawbacks: Installation overhead : npm install -g some-tool for a one-time task Version rot : tool stops working after OS update No sharing : you format JSON but cant send the result to a colleague Environment drift : works on your machine, not on staging Online Tools as a Pattern Opennomos Json (reachable via opennomos.com/en/project/01KJ850Z7PNGXHXESBM68HE12Y) represents a shift: developer tools as a platform , not as utilities you install. What makes this different: Zero install — browser tab, done Cross-device — phone, laptop, any OS Shareable results — formatted output has a URL you can send to teammates Timestamp converter built in — ms, seconds, ISO 8601, bidirectional Base64 codec — no need for a separate site The Bigger Trend We are seeing the same pattern across the dev ecosystem: GitHub Codespaces (IDE in browser), Replit (runtime in browser), Vercel (deployment in browser). The next frontier is utility tools in browser . Why run jq locally when a well-designed online tool does it faster and gives you a share link? Try It Head to opennomos.com/en/project/01KJ850Z7PNGXHXESBM68HE12Y — the JSON tools are free, fast, and part of a broader contributor rewards system that makes open-source tooling sustainable. Built as part of the Nomos Build-in-Public series.

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It Started With a Simple Misunderstanding I was spelling something over a phone call. I said: "B" The other person heard: "D" So I repeated it. Still wrong. Then I remembered something I'd heard before: "B as in Bravo." Instantly... There was no confusion. That's When I Realized Some letters sound almost identical. Especially over: Phone calls Weak connections Noisy environments Different accents And repeating the same letter five times doesn't always help. Why I Built This Tool So I built something simple: 👉 https://allinonetools.net/phonetic-alphabet-converter/ A tool that instantly converts normal text into the NATO phonetic alphabet. For example: CHAT Becomes: Charlie Hotel Alpha Tango No signup. No setup. Just: Paste → Convert → Read What I Learned Before building this, I thought the phonetic alphabet was mostly for pilots or the military. Turns out it's useful for anyone who needs to spell things clearly. Like: Email addresses Usernames License keys Customer support Phone conversations The Small Problem It Solves Have you ever said: "M" And someone replied: "N?" Or: "P?" 😅 That's exactly the kind of confusion this avoids. Why It Works So Well Instead of similar-sounding letters... You use unique words. Like: A → Alpha B → Bravo C → Charlie D → Delta It's much harder to misunderstand. What Surprised Me I expected only developers or IT people to use it. But it also makes sense for: Customer support Call centers Students Remote workers Anyone spelling things over the phone What I Focused On I wanted the tool to be: Fast Simple Easy to copy Beginner-friendly Because if you're already on a call... You don't want extra steps. The Real Insight Good communication isn't always about saying more. Sometimes it's about making sure the first attempt is understood. Simple Rule I Follow Now If people keep repeating themselves... 👉 There's probably a simpler way to communicate. Final Thought The NATO phonetic alphabet has been around for decades. But after using it once... Yo

This is the story of a for-fun project, Luka Fit Index that started with me typing "ai for fun?...

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The 2026 World Cup has $3.89 billion bet on it across Polymarket. That's not retail money — that's whales. I built WhaleTrack to track exactly what those big wallets are doing. Here's the stack: Backend: Node.js server fetching live data via Bullpen CLI Frontend: Vanilla JS, real-time updates Data: Polymarket CLOB API via Bullpen Analytics: Google Analytics for traffic tracking The hardest part wasn't the code — it was getting users. Pure SEO and content distribution (Reddit, Twitter, IH). The site is live at whaletrack.app — would love feedback from devs on the UX and performance. Happy to open source parts of it if there's interest.

Originally published on tamiz.pro . Introduction Zig's innovative approach to package management through its build system represents a paradigm shift in software development. By eliminating external dependency managers, Zig offers a streamlined workflow that prioritizes determinism, performance, and simplicity. Understanding the Shift Traditional package managers often introduce complexity with version conflicts, global state management, and ecosystem fragmentation. Zig's build system, powered by the build.zig configuration file, directly handles dependency resolution, compilation, and linking. This integration removes the need for tools like Cargo (Rust) or Go Modules, creating a unified interface for project lifecycle management. Key Capabilities of Build-Driven Package Management Deterministic Dependency Resolution : Zig's build system uses checksums for dependencies, ensuring identical builds across environments. Version conflicts are mitigated via semantic versioning baked into the build logic. Zero-Configuration Compilation : With @import("std").fetch , dependencies are automatically fetched and compiled without requiring separate installation steps. Cross-Platform Consistency : The build system abstracts platform-specific details, ensuring dependencies compile correctly on Windows, Linux, and macOS without manual configuration. Minimal Runtime Overhead : No virtual environments or global state: dependencies are embedded directly into the project structure during compilation. First-Class Testing Support : Built-in test runners execute tests from dependencies alongside your code, ensuring compatibility at build time. The Impact on Developer Workflow Dependency Declaration : Developers define dependencies in build.zig using URLs or Git repositories with semantic version pins. Automated Fetching : The build system downloads dependencies to a zig-cache directory, validating checksums before use. Incremental Builds : Changed dependencies trigger recompilation only

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