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One canvas for every image asset you need Discussion | Link

As an Engineering Manager in a Platform team, I manage 10 engineers. I'm hiring more. I run weekly 1:1s, facilitate technical decision meetings, screen candidates, moderate retrospectives, and still need to keep up with the delivery of a platform spanning dozens of AWS accounts. Besides the lack of time to focus on technical problems, the technical part is not even the real challenge. The less obvious problem becoming an Engineering Manager is: the skills you need as an engineering manager are fundamentally different from those that made you a great engineer , and there's no compiler or unit test to tell you when you're doing them wrong. The feedback loop is absent or very slow (and when you realise that, your team has already gone silent or become dependent on you because you are the main input and the main bottleneck). Skills That Don't Come From Code As a senior or staff engineer, you develop communication skills gradually. You present ideas, challenge others respectfully, summarise outcomes, and identify owners. You participate in technical deep dives and put candidates at ease while probing technical depth. These are valuable skills, and a good IC develops them over the years. But unless you start behaving like a brilliant jerk , they're secondary - your technical depth is still what defines you. But as an EM, the game changes. You're not "the smartest person in the room" anymore, and increasingly, you shouldn't be. You still have a broad context from all those alignment meetings and roadmap syncs, but you lose contact with the codebase week by week. If your organisation has principals or staff engineers, you're not even close technically anymore. Your job is to give direction, create space for others to solve problems, and facilitate decisions, not to be the one with the answer. This is hard. Especially when you used to be the one with the answer. The urge to jump in doesn't disappear just because your title changed. And interviewing? Facilitation? Giving feed

I built ResizeKB — a free image and PDF resizer built specifically for Indian users. 25+ tools. Zero server uploads. Pure HTML, CSS, JavaScript. Here's how and why. The Problem Every Indian applying for government jobs, exams, or bank accounts hits the same wall — portals with strict KB limits rejecting documents. UPSC wants photo under 300KB. SSC wants under 50KB. Banks need Aadhaar PDF under 500KB. Every portal is different. Every rejection wastes someone's time and opportunity. Most people have no idea how to resize to an exact KB. They either give up or use random tools that upload their Aadhaar and PAN card to unknown servers. I built a tool that solves this in one click — with zero server upload. The Tech Stack No framework. No backend. No database. Canvas API for image processing pdf-lib for PDF compression Vanilla JavaScript only Cloudflare Pages for hosting — free, global CDN, auto deploys from GitHub Total infrastructure cost: ₹1,162 per year for the domain. Everything else free. Image Compression — The Binary Search Algorithm The core challenge is compressing to an exact KB target without over-compressing. Most tools use a fixed quality setting like 60% which destroys image quality. The right approach is binary search on JPEG quality: javascriptasync function compressToTargetSize(file, targetKB) { const targetBytes = targetKB * 1024; let low = 0.1; let high = 1.0; let result = null; const img = await loadImage(file); const canvas = document.createElement('canvas'); canvas.width = img.width; canvas.height = img.height; const ctx = canvas.getContext('2d'); ctx.drawImage(img, 0, 0); while (high - low > 0.01) { const mid = (low + high) / 2; const blob = await canvasToBlob(canvas, 'image/jpeg', mid); if (blob.size <= targetBytes) { result = blob; low = mid; } else { high = mid; } } return result; } This finds the highest quality setting that still hits your target KB. Result is the sharpest possible image at that file size — never over-compressed. PDF Compress

The way developers write software has genuinely changed. Not incrementally — fundamentally. A year...

personalized AI food journal + nutrition coach Discussion | Link

The Problem Wasn't Writing It was starting. Sometimes I wanted: A social media caption A motivational quote A writing prompt A meaningful message But my mind would go completely blank. Not because I had nothing to say. Because: Coming up with the right words at the right moment is surprisingly difficult. We've All Done This Open a new tab. Search: "Motivational quotes" "Success quotes" "Life quotes" "Funny quotes" Scroll for 10 minutes. Copy one. Close the tab. Why I Built This Tool So I built something simple: 👉 https://allinonetools.net/quote-generator-tool/ A tool that instantly generates quotes across different categories. Whether you need: Motivation Success Life Leadership Creativity Social media inspiration You can generate quotes in seconds. No signup. No setup. Just: Click → Generate → Use What I Realized People don't always look for quotes because they need content. Often they're looking for: A different perspective. A good quote can do something interesting. It can say in one sentence what takes us paragraphs to explain. The Surprising Part The most popular quotes are rarely complicated. They're simple. Short. Easy to remember. Yet somehow they stick with us for years. Why Quotes Still Matter In a world full of endless content: Attention is limited Time is limited Patience is limited A strong quote delivers an idea instantly. That's powerful. The Problem With Searching Manually Most quote websites feel: Cluttered Slow Full of ads Hard to browse And sometimes you spend more time searching than actually reading. What I Focused On I wanted the experience to feel: Fast Clean Inspiring Fun to explore Because finding inspiration shouldn't require effort. What Surprised Me After building it: Some people used it for: Social posts Presentations Daily motivation Writing inspiration But one thing surprised me most. People kept generating quote after quote. Not because they needed one. Because they enjoyed discovering them. The Real Insight Sometimes tools aren't abo

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

Fundraising, GTM, and bounties for startups Discussion | Link

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.

Lauf’s sleek new entry feels closer to a traditional mountain bike than anything I’ve ridden before.

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