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Intro AI Avatar is a completely free app that lets your VRoid (VRM) 3D avatar animate in...

Last week I did something dumb: I sat down and wrote 50 Claude Code prompts in one sitting. Halfway through I was sure most of them would never get used. But I finished, pushed them to GitHub, and made myself use them for an entire week -- no ad-hoc prompting allowed. The result surprised me. Some skills were life-changing. Others were useless. Here is the honest breakdown. The Methodology I categorized the 50 skills into 5 types: Analysis (12), Generation (14), Debugging (8), Planning (10), Maintenance (6). Rule: every time I hit a task, I must use a skill file or admit I did not have one. The 7 That Actually Saved Me Hours 1. Code Review Assistant (saved ~3h) This was the biggest surprise. I usually review PRs by gut feel -- scan the diff, look for obvious bugs, approve. The Code Review skill forced me to be systematic: On a 400-line PR it caught 2 security issues I would have missed. That alone justified the experiment. 2. Bug Investigator (saved ~2h) Instead of pasting errors and asking "why?", this skill forces you to provide: error message, file context, hypothesis. 3. Dependency Audit (saved ~1h) Scanned a 3-year old Node project. Found 2 CVEs, 8 unused devDependencies (21 MB). 4. Auto Commit Messages (saved ~30m) Saves 2 minutes per commit. Over 15 commits in a week that is 30 minutes. 5. Test Generator (saved ~2h) Generates 5-8 test cases per function in seconds. 6. Refactoring Planner (saved ~1h) Reads the function, identifies extraction candidates, outputs a dependency-ordered plan. 7. Performance Audit (saved ~30m) Found an unoptimized 3 MB hero image and a render-blocking script. The Ones I Never Touched About 8 out of 50 were "not this week" -- Database Migrations, API Documentation, CI/CD Pipeline. What I Learned The ROI is in the analysis skills. Code review, bug investigation, dependency audit -- these are high-judgment tasks where Claude thoroughness beats speed. Skills are habit, not technology. The hardest part was not writing the prompts -- it w

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

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It's 2am. A pipe just let go behind a kitchen wall and water is coming through the ceiling into the room below. The homeowner is standing in the dark in a panic, phone in hand, Googling "emergency plumber near me." They tap the first number. It rings four times and drops to voicemail. They don't leave a message. They tap the second number. You were the first number. You were asleep. And you just lost the most profitable job you'd have booked all week to whoever picked up on the second ring. This is the part of running a plumbing business that nobody puts on a P&L. The call that matters most arrives at the exact moment no human is there to answer it. And in plumbing, unlike any other trade, that's not the exception. It's the majority of the work. Plumbing's problem is different from every other trade An HVAC shop bleeds during summer rush. A roofer loses jobs to slow follow-up over a three-week sales cycle. Plumbing is its own animal, because plumbing is emergency-driven, and emergencies don't keep business hours. Industry call-tracking data tells the story. Depending on the source, anywhere from 40% to over 70% of home-service calls land outside the standard nine-to-five window, and for emergency-driven trades like plumbing it skews to the high end of that range. Either way the takeaway is the same. A huge share of your inbound isn't coming in while someone's at the desk. It's coming in at night, on a Saturday, on Thanksgiving morning when a basement is filling with sewage. If you're staffed to answer the phone nine to five, you are structurally set up to miss a large slice of your own demand. Not because you're doing anything wrong. Because the work shows up when the lights are off. And here's what makes it brutal. The after-hours emergency call isn't just any job. It's your best one. Why the missed emergency call costs more than the tech A routine daytime service call, a dripping faucet, a running toilet, runs a couple hundred dollars and the customer is happy to

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

A native Mac cockpit for Claude Code Discussion | Link

How to Auto-Sync Your Hashnode Blog to Dev.to Using GitHub Actions (2026 Guide) FOLASAYO SAMUEL OLAYEMI FOLASAYO SAMUEL OLAYEMI FOLASAYO SAMUEL OLAYEMI Follow Jun 7 How to Auto-Sync Your Hashnode Blog to Dev.to Using GitHub Actions (2026 Guide) # discuss # automation # tutorial # devops 5 reactions Comments Add Comment 5 min read

Sigma’s new entry is both a bold design experiment and a pretty decent camera.

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After 16 weeks of daily use, our WIRED testers saw visible hair regrowth with these red-light therapy devices.

Why RAG needs context judgment, not just better retrieval Most RAG systems optimize for retrieval. That makes sense. Search better. Embed better. Chunk better. Rank better. Fetch more sources. All of that matters. But retrieval alone does not answer a different question: Should this context actually influence the model? That is the problem FreshContext is built around. FreshContext is context judgment infrastructure for AI agents, RAG systems, and retrieval workflows. The simple version: candidate context in decision-ready context out Retrieval is not judgment A retriever usually answers: What might be relevant? A context judgment layer asks: What should happen to this context before it reaches the model? Those are different problems. A source can be relevant but stale. A source can be recent but low-confidence. A source can be useful as background but not strong enough to cite. A source can have no reliable date. A source can be a duplicate. A source can need verification before it should influence an answer. A normal RAG pipeline can retrieve all of that and still pass it straight into the prompt. That is where things get messy. The model may reason fluently from weak context, and the final answer can look confident even when the input material was stale, uncertain, or not citation-grade. The missing layer between retrieval and reasoning FreshContext sits after retrieval and before reasoning. It does not try to replace search, vector databases, RAG frameworks, or agent frameworks. It focuses on the boundary between them and the model. The product spine looks like this: candidate context -> FreshContext Core -> freshness / provenance / confidence / utility / source profile -> decision helper -> decision-ready output -> model / agent / app The goal is not just to produce another score. The goal is to turn candidate context into a decision. Example decisions include: cite_as_primary cite_as_supporting use_as_background needs_refresh needs_verification watch_only excl

The brief Small professional services firm. Needed a clean marketing site: homepage, services section with individual service pages, about page, a blog, and a contact form. Timeline: tight. Budget: fixed. The kind of project where the question isn't whether you can build it — it's whether you can build it without spending three days on project scaffolding before writing a single line of content-specific code. This is what that afternoon looked like. One command to start The cms-sync is the entry point. One command creates the local workspace and starts the file watcher: npx @sleekcms/sync --token YOUR_AUTH_TOKEN On first run, the workspace appears with three files before you've written anything: my-site/ ├── CLAUDE.md ├── AGENT.md └── .vscode/ └── copilot-instructions.md These context files are the key. CLAUDE.md for Claude and Claude Code. AGENT.md for GitHub Copilot in agent mode. .vscode/copilot-instructions.md for GitHub Copilot in VS Code. Each contains the complete SleekCMS site-building reference — file naming conventions, model syntax, template helpers, field types, content format. Open the workspace in Cursor (or VS Code with Copilot, or Claude Code). The AI already knows how to build a SleekCMS site. No setup prompt, no pasting documentation. The prompt We kept it realistic — not a paragraph of precise technical specification, but the kind of description you'd give a developer on a call: Build a professional services marketing site with: - A homepage with a hero section, services overview, client logos, and a contact CTA - A services page listing all services, and individual service detail pages - An about page - A blog with individual post pages - A shared header and footer - A contact form - Tailwind CSS styling - SEO meta tags on every page What the AI generated The output was a complete set of working files: models/pages/_index.model models/pages/services.model models/pages/services[].model models/pages/about.model models/pages/blog[].model models/page

Chinese models now account for 61% of global LLM token consumption. DeepSeek, Qwen, GLM, and Doubao consistently dominate the global top 10 on OpenRouter. But for developers outside China, accessing them is painful — no English docs, no international payment, confusing pricing. I tested all 6 major APIs. Here's what I found. Price Comparison (June 2026) Model Provider Input $/1M tokens Output $/1M tokens vs OpenAI DeepSeek V3 DeepSeek $0.35 $0.52 95% cheaper DeepSeek V4-Flash DeepSeek $0.003 $0.015 99.7% cheaper Qwen-Max Alibaba $0.58 $1.74 92% cheaper GLM-5 Zhipu AI $0.87 $4.05 84% cheaper Doubao Pro ByteDance $0.43 $0.87 95% cheaper MiniMax M2.5 MiniMax $0.45 $0.90 95% cheaper DeepSeek V4-Flash at $0.003/M is 1/300th the cost of GPT-4o . For agent chains or batch processing, you can call it without thinking about cost. Quick Start All Chinese models follow OpenAI API format. Change base_url and model — zero code changes. # DeepSeek curl https://api.deepseek.com/v1/chat/completions \ -H "Authorization: Bearer $API_KEY " \ -d '{"model":"deepseek-chat","messages":[{"role":"user","content":"Hello"}]}' # Qwen — same format, different endpoint curl https://dashscope.aliyuncs.com/compatible-mode/v1/chat/completions \ -H "Authorization: Bearer $API_KEY " \ -d '{"model":"qwen-max","messages":[{"role":"user","content":"Hi"}]}' How to Get API Access Model Sign Up Payment Free Tier DeepSeek platform.deepseek.com Alipay/WeChat 5M tokens Qwen dashscope.aliyun.com Alipay 2M tokens/month GLM-5 open.bigmodel.cn WeChat/Alipay 1M tokens Doubao console.volcengine.com/ark Alipay 500K tokens MiniMax platform.minimaxi.com Alipay 1M tokens All platforms support English UI. Most don't require a Chinese phone number. Latency (tested from Singapore) Model TTFT Tokens/sec Total (100 tokens) DeepSeek V3 380ms 85 t/s 1.5s DeepSeek V4-Flash 120ms 240 t/s 0.5s Qwen-Max 450ms 65 t/s 2.0s GLM-5 520ms 55 t/s 2.3s Which Model for What Use Case Model Agent chains (5-10 calls) DeepSeek V3 Bulk process

By Vladislav Shter · The Sovereign Ecosystem Ask any major AI model a question and you'll notice something: it almost always agrees with you. You propose an idea, it tells you the idea is great. You make a claim, it validates the claim. You ask if your code is fine, it reassures you that it's fine. This is not an accident. It's a design choice. And once you see it, you can't unsee it. The agreeable machine Modern AI assistants are trained, in part, to keep you satisfied. A satisfied user comes back. A user who comes back keeps the subscription. So the models are nudged — through their training — toward being pleasant, encouraging, and agreeable. Researchers even have a name for this failure mode: sycophancy, the tendency of a model to tell you what you want to hear rather than what is true. It feels good. You get a small hit of validation every time the AI confirms you were right. But for anyone doing serious work — auditing code, checking facts, making decisions — that agreeableness is dangerous. A tool that mostly agrees with you is not a tool that catches your mistakes. And it gets worse when the model doesn't actually know the answer. When confidence and truth come apart Here's the real trap: a single model doesn't just agree too easily — it also fills gaps with invented detail, delivered in the same confident tone as its correct answers. There is no visible difference between "I know this" and "I'm guessing and dressing it up." The fluency is identical. Even the heavyweight, expensive models do this. A premium model like Gemini can produce beautifully written, authoritative text that contains fabricated facts, invented citations, or specifics that simply aren't real. For an inexperienced user this is invisible. For an experienced user it's worse — it's actively disorienting, because the wrong answer looks exactly as polished as the right one. So you're left with two problems stacked on top of each other: the model is biased toward agreeing with you, and when it

Hi everyone, As a Full-Stack and Cloud engineer, I’m used to automating everything I can. Whether I'm managing my 28+ container Kubernetes homelab on Proxmox or writing deployment scripts, I absolutely hate doing the same manual task twice. But a few months ago, when I was hunting for a new role, I found myself doing exactly that: manually tweaking my resume for every single job, copy-pasting into black-box ATS portals, and tracking it all in a chaotic spreadsheet. It was completely draining. So, I took a break from the applications and built a tool to solve my own problem. It’s called OneApply. It started as a small browser extension to check ATS keywords, but it quickly snowballed into a complete workspace. Here is what the stack handles now: Resume Tailoring: Automatically adjusts your resume to fit specific job descriptions. ATS Keyword Scoring: Checks your overlap with the job description so you know you'll actually pass the automated filters. Cover Letter Generation: Drafts contextual cover letters based on the role and your specific engineering experience. Pipeline Tracking: Manages all your applications natively so you can finally ditch the spreadsheets. Building this and using it to automate the worst parts of the daily grind actually helped me land my current SRE role. Since it worked for me, I decided to polish it up and release it for other devs who are currently stuck in the application trenches. We all know the tech market is tough right now, and any edge helps. I would love for this community to try it out and roast the UX, the workflow, or the core features. Check it out here: https://www.oneapply.app I am more than happy to hand out some premium access codes to anyone here who is actively applying and wants to test drive the full feature set. Just drop a comment below!

On-device voice recorder that transcribes + exports Markdown Discussion | Link

10 Prompt Patterns I Use Every Single Day Last Tuesday I spent 40 minutes arguing with Claude about a database schema before I realized I had never told it what I already tried. I described the problem, it gave me the same three suggestions I had already ruled out, I pushed back, it apologized and gave me variations of the same three suggestions. The entire session was garbage because I started from zero instead of from where I actually was. I closed the tab, rewrote my first message, and had a working solution in six minutes. That gap — between how most people prompt and how it actually works when you treat the model like a collaborator who needs real context — is what this post is about. Pattern 1 & 2: Lead With What You Already Tried, and State the Constraint That Binds You These two patterns are almost always used together, so I won't pretend they're separate. When you describe a problem without the history of your attempts, you are forcing the model to rediscover your dead ends. Every developer knows this frustration: you explain a bug, get back a solution you tried on Monday, explain you tried that, get a variation, explain that too — it's a recursive waste. The fix is brutal honesty upfront: "I need X. I already tried A and B. A failed because [specific reason]. B is off the table because [constraint]. Don't suggest either." The constraint layer is the other half. Models are optimists by default. They will give you the architecturally clean, perfectly testable solution that requires three new dependencies and a refactor of your auth layer. Unless you tell them you are shipping in two days, can't add dependencies, and the code needs to be readable by someone who last touched Python in 2019. Constraints aren't limitations on the answer — they are the answer. Front-load them or you will spend the session rejecting suggestions that are technically correct but situationally useless. Pattern 3 & 4: Output First, Reasoning After — and Diff Only, Not Rewrites Two sid

View, monitor, and analyze 1.8M+ US jobs Discussion | Link

Aerospace completely revolutionized my workflow after 15 years of using macOS the way Apple intended. I no longer hunt for apps and windows in Mission Control or drag them around spaces to organize. I can open as many windows as I need and have them all under my fingertips. And instead of swiping around to find one, I instantly teleport to where they are. This incredible software is technically aimed at advanced users. It’s installed from the command line and offers extensive configuration options. For basic use though, you don’t need to configure it at all, and if you have opened the Terminal application before and know what running a command means, you should be good to go. Rest assured, I will not show you how to configure Aerospace with Vim, or show you how to create an elaborate but useless dashboard! Just the essentials to get you started. How to set up Aerospace Aerospace is a menu bar application, but you can’t download it from an App Store or get it as a DMG file. You need a package manager. Go to the Homebrew website and follow the installation guide. Make sure to accurately follow the on-screen instructions. This may include any of the following: A prompt to enter your password. When you type passwords in Terminal, you will not see stars or anything. Just make sure you’re typing the correct one and hit Enter. A prompt to install XCode Command Line Tools . Somewhere around the end of the installation process, you may get a prompt to run some extra commands, which depend on your system. Make sure you run them as instructed. To test if you have correctly installed Homebrew, run which brew in Terminal. If you see a path printed out, like /opt/homebrew/bin/brew , you’re good to go. If not, something has gone wrong. Try searching for other, more focused guides on installing Homebrew. With Homebrew, you can install applications from the Terminal app using the brew command. For Aerospace, you would run the following command: brew install --cask nikitabobko/tap/ae