标签：#productivity

If you're building any kind of personal-memory layer on top of SQLite — Claude Code conversation history, notes app, indexed knowledge base — there's a sharp edge in FTS5 that takes most people by surprise the first time they hit it. The default tokenizer ( unicode61 ) silently drops most Japanese substring queries. The fix is one line of SQL. But the failure mode is invisible enough that you can ship a personal search tool, use it for weeks, and never realize half your content is unreachable. This post walks through: The failure, reproducible in 20 lines of Python The one-line fix ( tokenize='trigram' ) and what it actually does under the hood A two-layer Git + SQLite design that uses this index in production for ~800 Claude Code conversations A separate FTS5 footgun around the - character that breaks time-blocking -style queries A free GitHub sample at the end if you want to run the same approach against your own data The failure, reproducible in 20 lines Spin up a fresh SQLite FTS5 table with the default settings and insert a single multilingual sentence: import sqlite3 conn = sqlite3 . connect ( " :memory: " ) conn . execute ( """ CREATE VIRTUAL TABLE notes USING fts5(content) """ ) conn . execute ( """ INSERT INTO notes(content) VALUES ( ' Tried time-blocking with the new 朝の運用フロー — ' ' the 9-11 slot worked but the 午後 part collapsed again. ' ) """ ) for q in [ " time " , " blocking " , " 朝の運用 " , " 午後 " ]: hits = conn . execute ( " SELECT count(*) FROM notes WHERE content MATCH ? " , ( q ,) ). fetchone ()[ 0 ] print ( f " { q !r} : { hits } hit(s) " ) Output: 'time': 1 hit(s) 'blocking': 1 hit(s) '朝の運用': 0 hit(s) '午後': 0 hit(s) Same row. Same content. English queries land, Japanese substring queries don't. That's not a bug, it's the default tokenizer behavior — and the default doesn't print a warning about it. The reason: unicode61 segments text on whitespace and unicode word-break properties. English words have spaces between them, so individual tokens are reco

If you use Cursor, Claude Code, or Windsurf daily, you've probably had this experience: You open a fresh chat, ask for a small fix, and twenty minutes later the AI has rewritten your API layer, added three new dependencies, and switched your data-fetching pattern "for consistency." The model isn't broken. It's contextless. Every new session starts from zero. It doesn't know your stack, your conventions, or the things it must never touch. So you spend the first ten minutes re-explaining — and the last hour undoing. Here's what fixed it for me. The real problem isn't prompts Most devs collect prompts. Notes app, Slack snippets, old chat threads. That helps for one-off tasks, but it doesn't solve the session problem. What you need is persistent context — rules that load automatically before you type anything. Two files do this: CLAUDE.md — read by Claude Code (and usable as project context elsewhere) .cursorrules — loaded by Cursor on every session (rename to .windsurfrules for Windsurf) Drop them in your project root. Done. What goes in a good config file A useful config is not ten lines of "use TypeScript and write clean code." That's too vague to change behavior. Mine include: Project structure — where pages, components, and API routes live Stack + versions — Next.js 14 App Router, not Pages; Zod; shadcn/ui Commands — npm run dev, npm run typecheck, npm run test Coding conventions — naming, import aliases, Server vs Client Components DO NOT section — the most important part (more on this below) Workflow notes — use @folder, prefer editing existing files, minimal diffs Here's an excerpt from the DO NOT section that saved me the most time: DO NOT — Critical Anti-Patterns Do NOT create a pages/ directory or use the Pages Router Do NOT rewrite the entire API layer — extend existing route handlers Do NOT add new npm dependencies without stating why Do NOT make drive-by refactors in unrelated files Do NOT fetch data in useEffect when Server Components can fetch directly T

Retry storms turned 1 timeout into 340 duplicate calls billed in 90 seconds Infinite tool loop ran 1,200 iterations before I noticed at 2am Partial stream cleanup stopped half-written DB writes corrupting records Trap every error class with a circuit breaker and a hard iteration cap Five Claude API errors quietly drained my account before I built guards around them. None of them threw a loud crash. They just kept billing while I slept. Here is exactly what broke, what it cost, and the traps I now run on every project. The Retry Storm That Billed 340 Times in 90 Seconds The most expensive mistake I made was naive retry logic. A single request timed out. My code caught the timeout and retried. The retry also timed out, so it retried again. Within 90 seconds I had fired 340 requests for one piece of work. The problem was that the Claude API had actually received and processed several of those requests. The timeout happened on my side waiting for the response, not on Anthropic's side. So I was paying for completed work I never saw, then paying again for the retry. My first version of the retry looked harmless. A while loop, a counter set to 5, a sleep of one second between attempts. The flaw was that the sleep was constant and the counter reset on every new job. Under load, jobs stacked, and each one spawned its own retry chain. That is how 1 timeout became 340 calls. The fix was exponential backoff with a hard ceiling and a request ID. I now generate a unique idempotency-style key per logical job and refuse to issue a second call for the same key until the first fully resolves or hard-fails. Backoff starts at 2 seconds and doubles up to 32 seconds, then gives up after 5 total attempts. attempt = 0 delay = 2 while attempt < 5 : try : return call_claude ( job_key ) except Timeout : attempt += 1 sleep ( delay + random_jitter ()) delay = min ( delay * 2 , 32 ) raise GiveUp ( job_key ) The jitter matters more than it looks. Without it, ten failed jobs all retry at the exact

I said "maybe a couple days" on a call last Tuesday. By Wednesday morning it was in a Jira ticket as "2 days." By Thursday afternoon somebody was checking in to see if we were tracking against the two day commitment. Nobody did anything wrong. The person who wrote it down was capturing what I said. The person checking in was doing their job. I was the one who said the words. The system worked exactly as designed. The system is the problem. Something Ive learned is that theres no such thing as a rough number in meetings today with all of the AI note takers... The moment you say a number out loud, it stops being a feeling and starts being a quote. The hedge in front of it doesnt survive the transcription. "Maybe" disappears. "Couple" gets rounded to a specific integer. "Give or take" is the first thing that hits the cutting room floor. What lands in the document is the number, naked, with no caveats and no error bars. Everyone in the meeting heard what you heard. They heard the hedge. They watched you wave your hands. They understood, in the moment, that you werent committing. But the document doesnt remember any of that. The document just remembers the number. And the document outlives the conversation, which is where all the nuance lived. Ive watched myself do this for years and I still get caught by it. Someone asks how long something will take. I want to be helpful. I want to seem confident. I want to keep the meeting moving. So I say a number. The number is approximately right, or at least I think it is, but I havent actually thought about it the way you would think about it if you were going to commit to it. By saying it out loud, Ive committed to it. The fix, if theres one, is to refuse the number. Not rudely. Just clearly. "I need to look at it before I give you a real number. I can have one for you by Friday." This works about half the time. The other half, somebody in the room is going to ask you for a ballpark anyway, and youre going to give them one, and t

If you work with Brazilian companies — as an accountant, credit analyst, or anyone processing PJ clients at scale — here's a practical automation approach using free public data. What you can verify automatically For any CNPJ, public data gives you: Situação cadastral : ATIVA, BAIXADA, INAPTA, SUSPENSA — critical for invoice validation Razão social : legal name for contract matching CNAE : is this company allowed to do what they claim? QSA : who are the actual partners/directors? Data abertura : how old is the company? The data 65M+ CNPJs from Receita Federal, indexed and searchable at Jurídico Online . Free. Also available as a Python package: pip install juridico-online from juridico_online import empresa_url , buscar_url # Get company page URL for a CNPJ url = empresa_url ( " 00.000.000/0001-91 " ) print ( url ) # https://juridicoonline.com.br/empresa/00000000000191 # Search by company or partner name search = buscar_url ( " Magazine Luiza " ) print ( search ) Checks worth automating 1. Situação ATIVA before accepting any invoice INAPTA or BAIXADA companies cannot legally issue NF-e. 2. CNAE vs service being billed A company with CNAE "comércio de alimentos" billing for software development is a red flag. 3. Company age vs contract value A 3-month-old company offering a R$500k contract deserves extra scrutiny. 4. Shared partners across suppliers If two suppliers share directors, that's a conflict of interest. Search partner names at juridicoonline.com.br to see all companies they control. Integration patterns ERP/AP : validate CNPJ status before releasing payment Onboarding : auto-fill razão social when client enters CNPJ Batch audit : cross-check your vendor list quarterly Monitoring : alert if a key supplier's CNPJ changes status The data is public, free, and updated regularly. No excuse to check manually at scale.

Been doing a lot of competitive research for clients lately — checking hashtag volumes, tracking top posts in a niche, that kind of thing. Kept switching between Claude Code and browser tabs to cross-reference stuff manually. Got annoying fast. Found hikerapi-mcp, a Model Context Protocol server that exposes 100+ Instagram endpoints as tools directly inside Claude Code. Figured I'd try it. Setup was straightforward. The one thing I did differently was keeping the API key out of config files entirely — passed it as an environment variable instead. Smaller attack surface if I accidentally commit something. Also filtered down the tool groups with HIKERAPI_TAGS because 100+ tools showing up in context is chaos. I only need hashtag search and competitor profile data, so I scoped it to just those. "env": { "HIKERAPI_KEY": "${HIKERAPI_KEY}", "HIKERAPI_TAGS": "User Profile,Post Details,Search,Hashtags,Stories" } One thing that tripped me up for a solid 20 minutes: HikerAPI runs on a prepaid model (credits in rubles). If your balance is zero, you get HTTP 402, not 401. I kept thinking my key was invalid and regenerated it twice before I figured out I just needed to top up. Once that was sorted, it actually works well. Now I can ask things like "what are the top 10 posts for #socialmediamarketing this week" or pull a competitor's recent content directly in the same session where I'm building the campaign strategy. Cuts out a lot of context switching. Repo if you want to check it out: github.com/subzeroid/hikerapi-mcp Wrote up the full setup with config details here if useful: https://dev.to/simrp360/querying-instagram-from-claude-code-wiring-up-hikerapis-mcp-server-57jf Anyone else using MCP servers for social data research? Curious what other setups people are running.

Most people pick a model the wrong way around. They look at the leaderboard, see Opus on top, and reach for it by default. Sonnet if they want to save money. Haiku almost never, because the name says "small." That habit costs you. For a lot of what you actually build, Haiku is the right call, and you're paying three to five times more for capability the task never uses. This post is about how to choose, and why Haiku should be your default more often than it is. The short version: don't start from "what's the best model." Start from "what does this task need." Most tasks don't need much. Comparison Here is the current lineup, with the numbers that matter when you're choosing. Haiku 4.5 Sonnet 4.6 Opus 4.8 Model ID claude-haiku-4-5 claude-sonnet-4-6 claude-opus-4-8 Input price (per 1M tokens) $1 $3 $5 Output price (per 1M tokens) $5 $15 $25 Context window 200K 1M 1M Max output 64K 64K 128K Best at speed, volume balance hardest reasoning Two things jump out. First, price . Haiku input is a fifth of Opus and a third of Sonnet. Output is the same ratio. If you send a million tokens through Opus for $25 and the same work would have been fine on Haiku, you spent $20 for nothing. And that gap is per request, so it compounds. A feature that runs ten thousand times a day on Opus instead of Haiku is not a rounding error. It is the difference between a feature that ships and one that gets cut for cost. Second, the context window . This is where Haiku gives something up: 200K tokens instead of 1M. That is the real tradeoff, and it points straight at when to use it. We'll come back to that. The mental model Stop ranking models. Rank tasks . Ask three questions about the task in front of you: Does it need real reasoning, or is it bounded? A task is bounded when a competent junior could do it from a clear spec without much judgment: pull these fields out, sort this into one of five buckets, rewrite this in a different tone, answer this from the text I gave you. A task needs reason

When we build something complex—whether it’s a skyscraper, a gourmet meal, or a piece of software—we usually start with a plan. In software development, however, it’s easy to skip that step. We often jump straight into implementation, focusing on how to write the code instead of the intent behind it. Over time, this leads to rework, confusion, and systems that don't quite match our original goals. Spec-Driven Development (SDD) is an approach that shifts the focus back to the plan. Instead of starting with code, you start with a Specification : a clear, structured description of what the software should do. You then use an AI coding agent as a high-speed collaborator to help turn that specification into working code. 🔍 What is a “Spec”? A Specification (or “Spec”) is a written contract between your intention and the final product. It isn't a 50-page manual; it's a living document that defines: What the system should do. How it should behave in different scenarios. Which constraints and rules it must follow. From Prompts to Specifications There is a massive difference between a vague prompt and a structured spec. Loose prompts often lead to inconsistent results and "hallucinations," whereas clear specifications give the AI a much better target to hit. Bad Prompt: > “Build me a login system.” Good Spec: A good spec provides the clarity an AI (or a human) needs to succeed. You don’t need a 10-page document to benefit from specs; you need clarity, not length. 🛠️ Example Spec: Login Endpoint Overview Allow users to log in using email and password. Endpoint POST /api/login Request { "email" : "user@example.com" , "password" : "string" } Behavior Success: If email and password are correct → return a token and user info. Invalid Credentials: If credentials don't match → return INVALID_CREDENTIALS . Invalid Input: If fields are empty or the email format is wrong → return INVALID_INPUT . Rules Passwords must be stored hashed (e.g., bcrypt). Token expires in 24 hours. Security:

Hello, I'm Maneshwar. I'm building git-lrc, a Micro AI code reviewer that runs on every commit. It is...

While vibecoding, you sometimes need some background music. But music can also be a massive...

TL;DR: I built PackagePal — paste in any package from any language, pick your target language, and AI instantly finds the equivalent. No more Googling "what's the Node.js version of Python's requests ?" The Problem That Drove Me Crazy You know that moment when you're migrating a project — or just jumping between ecosystems — and you hit a wall trying to find the right package? I do. Every time. # You're used to this in Python import requests response = requests . get ( " https://api.example.com/data " ) And you move to Node.js and think: "Okay, what do I use here? axios? node-fetch? got? undici?" So you Google it. You find a Stack Overflow thread from 2019. Half the answers recommend packages that are now deprecated. You open 6 tabs. 20 minutes later you're still not sure which one is the current best choice. This wasn't a once-in-a-while thing for me. It happened constantly — switching between Python, JavaScript, Go, and Ruby on different projects. I was wasting real hours on a problem that felt completely solvable. So I built PackagePal . What PackagePal Does PackagePal uses AI to understand what a package actually does — its purpose, not just its name — and finds the best equivalent in whatever language you're moving to. The key insight: this isn't a lookup table. A simple mapping of requests → axios misses context. What if you're using requests for its session management? Or its retry logic? PackagePal surfaces options and explains why each one is a good match. Example searches people use it for: Python's pandas → JavaScript Ruby's devise → Node.js Go's cobra → Python JavaScript's lodash → Go Just type the package, pick the target language, and get results in seconds. 👉 Try it: packagepal.dev How I Built It Tech Stack 🤖 AI: Gemini Pro — handles the semantic understanding of what a package does and why an alternative matches ⚛️ Frontend: React + TypeScript ⚙️ Backend: Node.js + TypeScript on Google Cloud ⚡ Caching: Redis — so repeat searches (e.g., "requests → No

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...

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

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

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