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I'm a 17-year-old IT student from Luxembourg. A few months ago I got tired of spending 2-3 hours a day manually browsing Upwork, Malt, and Freelancer looking for projects. So I built an automation system that does it for me — 24/7, on a Raspberry Pi 3. Here's what it does, how I built it, and every painful lesson I learned along the way. What the system does Scans Upwork, Malt, and Freelancer every 30 minutes Scores each job 0–100 with AI based on my profile Generates proposals in English, French, and German Sends the best jobs to Telegram with inline A/B buttons Tracks which proposal style gets more replies Sends daily stats and weekly market trend reports Reminds me to follow up after 3 days The stack n8n — self-hosted workflow automation (Docker on Raspberry Pi 3) Groq API (Llama 3.1-8b-instant) — AI scoring and proposal generation Supabase — PostgreSQL database for jobs, proposals, clients SerpAPI — searching job boards via Google Apify — scraping Upwork listings Telegram Bot API — alerts and bot commands Cloudflare Tunnel — HTTPS for webhooks Total running cost: ~$5/month. 7 workflows 01 - Job Discovery — runs every 30 minutes, searches 10+ sources, deduplicates via Supabase unique constraint on URL 02 - Proposal Generator — AI scores the job, generates two proposal variants (formal vs hook-first), sends to Telegram with A/B buttons 03 - Follow-up Reminders — checks Supabase every 3 days for unanswered proposals 04 - CRM via Telegram — full client management through bot commands (/jobs, /stats, /clients) 05 - Market Intelligence — daily report: how many jobs found, average score, top platforms 06 - Trend Analysis — weekly report on what skills are trending in automation 07 - Lead Generation — finds companies actively using Zapier or Make who might want to switch to n8n Lessons learned (the hard way) 1. Cyrillic text breaks JSON body nodes silently If you have Cyrillic characters in a JSON body field with newlines, n8n throws a "Bad control character" error. Kee

Part 7 of 7 · Series: Building Your AI Developer Handbook · GitHub The Scenario You're building a password reset feature. User enters email → gets a reset link → clicks link → enters new password. Standard flow. Medium complexity. Let's walk through every step using the full workflow — as if you're looking over the shoulder of someone who built this system. "Show me your workflow and I'll show you your output quality." Before You Even Type Claude loads automatically in the background: ✓ ~/.claude/CLAUDE.md loaded ← the global handbook ✓ .claude/CLAUDE.md loaded ← project rules (TypeScript, pnpm) ✓ memory/MEMORY.md scanned ← all lessons and preferences You haven't typed anything yet. Claude already knows: Feature-based folder structure State management ladder No mocking the database No AI attribution in commits No useCallback without profiler evidence "A doctor who reviews your file before you enter the room is more useful than one who asks 'so, remind me who you are?'" Step 1: /status — Confirm the Setup /status Model: claude-sonnet-4-6 Effort: normal Plugins: security-guidance ✓ Thirty seconds. Sometimes the wrong model loads due to overload fallback. Sometimes a plugin fails silently. This check costs 30 seconds and prevents a surprise 30 minutes later. "A pilot's first action after sitting in the cockpit isn't to take off. It's to check all instruments are reading correctly." Step 2: /cost — Baseline /cost → Tokens used: 2,847 | Estimated cost: $ 0.004 Note this number. You'll compare it later before the expensive code review step. A surprise spike means something went wrong. Step 3: /plan — Design Before Coding /plan Build a password reset feature: - User enters email on /forgot-password - System sends a reset link (token, expires in 1 hour) - User clicks link → /reset-password?token=xxx - User enters new password - Token validated, password updated, token invalidated Claude responds with a plan — no code yet : Proposed approach: 1. DB: Add password_reset_tokens

Anthropic's strongest model is free until June 22 — and two more shifts for builders Three things landed for builders at once: the best model got cheaper (free, actually), free inference showed up on Apple's stack, and one still photo now becomes a talking video. Two of them you can act on right now. Here's the 90-second video version if you want the quick pass first: 1. Claude Fable 5 is public — and free on your plan until June 22 Anthropic released Claude Fable 5 , the first publicly available version of its Mythos-class model. It's state-of-the-art on nearly every benchmark Anthropic tests — software engineering, knowledge work, vision, and scientific research. It's free on Pro, Max, Team, and Enterprise plans through June 22 ; after that it's 10 dollars per million input tokens and 50 per million output . In high-risk areas (cyber, bio, chem) it refuses and falls back to Claude Opus 4.8 — about 95% of Fable sessions run entirely on Fable. This dropped just days after Anthropic publicly warned that AI was getting too dangerous. Why it matters: the strongest Claude is free to try on your existing plan for a two-week window. Run your hardest real task on it now and benchmark it before June 22 — the kind of jump that's worth re-checking your evals against. 2. Apple made its Foundation Models free for small developers At WWDC 2026 , Apple gave developers in the App Store Small Business Program (apps under 2 million first-time downloads ) free access to the next generation of Apple Foundation Models running on Private Cloud Compute — removing inference cost as a barrier. The Foundation Models framework now supports image input . A single Swift API can also call third-party models like Claude and Gemini, server-side. A new Dynamic Profiles system supports multi-agent workflows, and Apple will open-source the framework later this summer. Why it matters: you can ship AI features into an app without an inference bill. Prototype on Apple's free on-device models, and route

Over the last few weeks, while learning LangGraph and agentic systems, I ended up building Co-Founder Memory . It's a stateful AI assistant with: • long-term memory • planning loops • self-correcting RAG • web search fallback • automated timeline summaries • project and preference tracking Nothing revolutionary — many ideas already exist. The goal wasn't to reinvent memory, but to understand how these systems work by actually building one. A lot of concepts only started making sense once I had to connect them together: graph-based workflows with LangGraph memory extraction and storage retrieval and validation loops routing and planning nodes maintaining context across sessions Building it taught me far more than watching tutorials ever did. Repo: https://github.com/Somay-kousis/Co-Founder-Memory I'm currently entering my 3rd year at IIITM Gwalior and looking for ML / GenAI internships . If you're building interesting things around LLMs, agents, RAG, or AI products, I'd love to connect. Always happy to chat with fellow builders as well 🚀 AI #GenerativeAI #LangGraph #RAG #LLM #MachineLearning #Internship

If you've been following the AI-assisted development space, you've heard about the Model Context Protocol (MCP). But let's be honest—most MCP server lists are either too abstract or filled with niche tools you'll never use. In 2026, the ecosystem has matured, and I've curated 10 MCP servers that deliver real, measurable improvements to your daily coding workflow. Each entry includes: What it does Why it's useful (with a concrete scenario) Example config (using the standard .mcp.json or claude_desktop_config.json ) Let's dive in. 1. GitHub MCP Server (by modelcontextprotocol) What it does: Full read/write access to GitHub repos—issues, PRs, code reviews, and releases. Why useful: Instead of switching between your IDE and GitHub, your AI assistant can create a PR, request a review, and merge after CI passes—all from a single prompt. Example config: { "mcpServers" : { "github" : { "command" : "npx" , "args" : [ "-y" , "@modelcontextprotocol/server-github" ], "env" : { "GITHUB_TOKEN" : "ghp_xxxxxxxxxxxxxxxxxxxx" } } } } Scenario: "Create a new branch, add a fix for issue #42, push, and open a draft PR with a description." 2. Filesystem MCP Server What it does: Read, write, search, and manipulate files and directories on your local machine. Why useful: Your AI can now scaffold an entire project structure, rename files in bulk, or refactor code across multiple files without manual intervention. Example config: { "mcpServers" : { "filesystem" : { "command" : "npx" , "args" : [ "-y" , "@modelcontextprotocol/server-filesystem" ], "env" : { "ALLOWED_DIRS" : "/home/user/projects" } } } } Scenario: "Create a Next.js project with this folder structure, add a components folder, and move all page files into a pages directory." 3. PostgreSQL MCP Server What it does: Connect to PostgreSQL databases, run queries, and return results. Why useful: Debugging SQL queries or exploring a production database becomes a conversation. You can ask "Show me the last 10 orders with user details" a

AI has become part of our lives, whether we like it or not, and it doesn't seem to be going away anytime soon. People seem to be using AI on many different levels, ranging from those still trying to avoid it, to people actively playing with it, trying to break it and find its limitations. The same goes for companies. There are those still barely using AI, those using it for absolutely everything, hoping it's a magical solution to their problems, and those in between. If you're more on the heavy use side, agents and instruction files are probably part of your daily discussions now. For our AI’s to work correctly they need the correct instructions, so they know how we want them to respond, how our project works, etc. We can use .md files to supply these instructions and/or context to the models. Those little markdown files are getting a huge importance in the development lifecycle. Since we can use the same file in each request we make, we can put in it the specifics of our project, as detailed as we want, so the model has as much information as possible to work with. “Garbage in, garbage out” makes sense here because, in theory, the better information the model has, the better results it can provide. Because of that, we're having to be more careful with the way we write them. Although markdown isn't something new, I don't know about you, but I haven't done much markdown writing before, so this feels like another tool to learn, like we're adding a new language on our tech stack. When I say is something else to learn, I don't mean learning only the markdown syntax, but also the correct way of writing all the instructions. A development stack now could look like: HTML, CSS and JavaScript for frontend, a language like Java, a framework like Spring or Quarkus, and SQL for the backend, and now .md files and markdown for the agents. I know I'm being very simplistic here, there are a lot more pieces of technology I didn't mention, but you got the idea, right? Besides everyth

Every QA team knows the feeling. The home screen works. Browse works. Search works. Cart works. And...

A million dollars is emotional as a dream. As math, it is boring. And that is exactly why most people never get close. Break It Down Here is the thing: $1M/year is not one big bet. It is a machine. And machines are built from boring, repeatable components. 20 clients at $50,000? That is $1M. 100 clients at $10,000? That is $1M. 12 retainers at $4,000/month? That is $576k — plus 4 sprints at $10,000 each gets you to $616k. The question is not whether the number is possible. The question is which machine can realistically produce it — from where you actually stand today. 1️⃣ The Practical Ladder Here is how the staged path actually works for an AI service business: Stage What You Are Doing Why It Matters Stage 1 Sell fixed-scope sprints Creates cash and proof Stage 2 Turn repeated sprint work into templates, SOPs, automations Reduces delivery time, increases margin Stage 3 Sell retainers around highest-demand system Predictable monthly cash Stage 4 Productize repeated workflow into software or toolkit Scalable without more hours Stage 5 Scale the thing the market already proved it wants Compound the machine Notice what is missing from Stage 1. There is no SaaS. No product. No cold paid traffic. No team. Just skill, packaged cleanly, sold to people with money and a painful problem. That is the fastest path — not the most glamorous one. 2️⃣ The Proof-of-Force Line The first mission is not $1M. The first mission is $10k/month — reliably, from sprint work. Here is what that actually looks like: 2 × $1,500 teardown/audit packages = $3,000 2 × $3,500 implementation sprints = $7,000 2 × $5,000 launch/GTM sprints = $10,000 3 × $2,000 retainers = $6,000/month That is not the finish line. It is the proof-of-force line. It proves the machine works. It funds the next iteration. It creates the case studies that make the next sprint easier to sell. Then you go from $10k/month to $25k. Then $50k. Then you make the productization decision from a position of demand — not hope. 3️⃣ The

Products are easier to build. Workflows are easier to automate. Content is easier to generate. But trust is not easier. Attention is not easier. Buyer memory is not easier. The Hard Truth Here is the thing most people are not talking about in 2026: The bottleneck is no longer the product. The bottleneck is whether the right buyer has seen your diagnosis 3 times in 2 weeks. Because that is how trust is built. Not with one perfect post. With repeated, useful presence in the right feed. Distribution is the moat. 1️⃣ Why "Staying Active" Is the Wrong Goal Most founders post to stay active. That is not a content strategy. That is anxiety dressed up as marketing. Every post should do one of 3 things: Make the buyer understand a pain they already have Make the buyer trust your diagnosis of that pain Move the buyer closer to a conversation A post about your tech stack? Probably none of those. A post that says "Your AI app is not launch-ready until auth, payments, logging, and rollback are boring" — that does all 3. 2️⃣ The Five Content Pillars That Build Pipeline Here is the system I use. 5 pillars. Everything maps to one of them: Pillar What It Signals Launch risk Why AI-built products break before production GTM systems How founders turn expertise into pipeline Workflow automation How businesses leak time and revenue Proof and case studies What changed before/after — with receipts Founder operating lessons The discipline behind building for money Every post I write maps to one of these. Not because it is tidy. Because each pillar speaks directly to a buyer who has a specific pain — and positions me as the operator who sees it clearly. 3️⃣ The Daily Format That Creates Pipeline This is the actual weekly posting structure that works: Monday — mistake post: a painful thing technical founders do wrong Tuesday — teardown post: a real example dissected publicly Wednesday — checklist: the 10-item audit your buyer needs Thursday — before/after: what changed after a sprint, with s

Something changed in software engineering, and I do not think we have fully named it yet. For years, the job was mostly about writing code directly. Then autocomplete got better. Then chat-based coding assistants arrived. Now the workflow is shifting again: we describe goals, hand off chunks of work to agents, inspect their output, tighten the tests, and decide what gets merged. That is not the same job with a faster keyboard. It is a different shape of work. I would call it agentic engineering. The engineer is becoming a director Agentic engineering does not mean the engineer disappears. If anything, it makes the engineer's judgment more visible. A coding agent can read files, make changes, run commands, open pull requests, and iterate through errors. GitHub describes Copilot agent mode as a workflow where the agent can plan, edit, run terminal commands, and keep working until a task is complete. Google describes Jules as an asynchronous coding agent that can take a task, work in a virtual machine, and produce a pull request. Anthropic's Claude Code guidance talks openly about using multiple Claude sessions in parallel, giving agents clear context, and treating them like workers that need direction. That is the shift. The engineer is no longer only the person typing every line. The engineer is also the person deciding what should be built, what constraints matter, how to verify the result, and when the agent is wrong. Prompting is too small a word for this People often describe this work as prompting, but that undersells it. A prompt can be a single instruction. Agentic engineering is more like delegation. You define the task, provide the relevant context, set the boundaries, create checks, review the work, and decide the next move. If the agent goes in the wrong direction, the failure is not always the model's fault. Sometimes the task was too vague. Sometimes the repository had no tests. Sometimes the acceptance criteria lived only in someone's head. This is why

Migrated 4 of 7 Notion automations to an MCP server in one weekend Two workflows stayed in Notion because the database UI beat any tool call MCP scope rule: one tool does one verb, never a Swiss Army function Result: 12 manual steps collapsed into 3 Claude prompts per publish I spent a weekend pulling four automations out of Notion and rebuilding them as MCP tools. Three of them got faster and one got worse before it got better. The biggest lesson was not about code. It was about deciding which jobs should never leave Notion in the first place. Why I Moved Off Notion In The First Place My Notion setup was not broken. It was just slow in a specific way. I had seven automations stitched together with Notion buttons, formula properties, and two third-party connectors. Every blog publish meant clicking through four pages, copying a title here, pasting a tag list there, and triggering a sync that took 90 seconds to confirm. Multiply that by the 18 articles I push in a normal month and the clicking adds up. The breaking point was a Tuesday where I lost 40 minutes to a connector that silently stopped firing. No error, no log, just a row that never updated. I checked the connector dashboard and it told me everything was healthy. It was not healthy. That kind of invisible failure is the worst kind because you trust it until you do not. MCP changed the math for me. An MCP server lets Claude call my own functions directly. Instead of Claude writing text and me ferrying that text into Notion by hand, Claude can call a tool that does the writing into my systems. The model becomes the operator, not just the writer. If you want the deeper context on what MCP actually is and why it matters at scale, MCP: The 97 Million Agentic Foundation goes through the bigger picture. So I made a list. Seven automations, sorted by how much human judgment each one needed. The ones at the top were pure mechanical steps: format this, push that, fetch a status. The ones at the bottom needed me to loo

Claude Fable 5 me permitiu criar um "GTA" em apenas um prompt. Prompt: "Crie um jogo, Tiny GTA 3D." A própria Anthropic afirma que o Fable 5 é seu modelo mais poderoso já lançado ao público, com avanços significativos em engenharia de software, pesquisa científica, visão computacional e execução autônoma de tarefas complexas. Em testes iniciais, empresas relataram que o modelo foi capaz de comprimir meses de trabalho de engenharia em poucos dias. Cidade 3D aberta com 64 quarteirões, prédios, parques e oceano Dirija, roube carros e fuja da polícia Sistema de procurado com 5 estrelas, viaturas e helicóptero te perseguem 42 pedestres vivos que fogem, voam e morrem 16 missões de entrega com histórias de corrupção brasileira Áudio sintetizado: motor, sirene, buzina e cantada de pneu Recorde salvo no navegador Jogue aqui: https://andredarcie.github.io/tiny-gta/

SQL is arguably the most widely used language in software engineering, yet it is often the least carefully written. Most teams enforce strict linting on their application code but leave SQL queries as a free-for-all. This guide covers the formatting rules that separate maintainable, team-friendly SQL from query spaghetti that haunts on-call rotations. Why Poorly Written SQL Is a Real Engineering Problem Unformatted SQL is not just an aesthetic issue - it is a correctness risk. Dense, run-on queries make it nearly impossible to spot accidental Cartesian products, missing GROUP BY clauses, or WHERE conditions that silently bypass indexes. By the time a performance problem surfaces in production, tracing it back to the root cause becomes a painful exercise in reading someone else's stream of consciousness. Rule 1: Keyword Capitalization SQL engines treat select and SELECT identically, but human readers do not. Always uppercase reserved keywords such as SELECT, FROM, WHERE, JOIN, GROUP BY, and ORDER BY. Keep table names, column names, and aliases lowercase. This single habit immediately creates a visual boundary between the logic structure of the query and the underlying data it operates on. Rule 2: Indentation and Clause Alignment Think of SQL clauses as layers in a data pipeline. Each major clause - SELECT, FROM, WHERE, GROUP BY, ORDER BY - should start at the left margin. Columns and filter conditions beneath them should be indented by 4 spaces (or 1 tab, as long as your team is consistent). This structure lets any reviewer skim the query top-to-bottom and understand the data flow at a glance. Rule 3: Trailing vs. Leading Commas This is a genuinely debated topic on data teams. Leading commas (placing the comma at the start of each new line) make version control diffs significantly cleaner when columns are added or removed. Trailing commas look more natural for developers coming from JavaScript or Python. Neither approach is wrong - what is wrong is mixing both styles

Visibility Comes Before Optimization in IT Operations is a practical operating principle, not a slogan. The useful version of analytics, automation, and software operations is usually quieter than the marketing version. It is less about collecting everything or automating everything, and more about making the work easier to understand, review, and improve. The practical problem Teams often try to optimize before they can see the system clearly. That creates confident changes based on partial evidence, especially in infrastructure and telecom-adjacent workflows where signals are distributed. This is where many teams lose clarity. They have tools, charts, workflows, and activity, but the connection between evidence and decision is weak. When that connection is weak, software work becomes harder to evaluate. Teams still make decisions, but they rely more on memory, opinion, or urgency than on a reviewable operating picture. A smaller operating model Start with visibility: what is running, which state changed, where the weak signal appeared, and which workflow was affected. Then connect that signal to a decision or operational review. The important detail is restraint. A useful system does not need to track every possible action or automate every possible step. It needs to preserve the signals that help operators understand the situation and act with more confidence. That usually means naming the workflow, keeping the outcome visible, preserving enough context to explain the signal, and making uncertainty explicit instead of hiding it behind a polished interface. What to review Useful analytics separates normal activity from operational risk. It should make the next investigation smaller, not create another dashboard that requires interpretation from scratch. A reviewable system is easier to trust because it can explain its own state. It shows what happened, what changed, what remains uncertain, and which decision should move next. For WebmasterID, this is the practical

The Problem I was using Claude Code, Codex, and Cursor daily but had no idea how much I was spending on tokens. Bills kept surprising me. The Solution I built AIUsage — a local-first, open-source CLI that tracks everything. Key Features Token usage tracking with daily breakdowns Cost estimation with configurable pricing Model usage ranking Multi-device sync via GitHub or S3 Desktop widget How It Works bash npm install -g @juliantanx/aiusage aiusage parse aiusage serve Why Local-First? Your data never leaves your machine. No accounts, no API keys, no cloud servers. Try It [aiusage.jtanx.com](https://aiusage.jtanx.com)

AI in 2026 is no longer best understood as a technology trend. It has become a structural layer...

AI coding agents feel sharp when a project is small. They can scan a few files, understand the shape, and make useful changes. In that phase, the project still fits inside the agent’s short-term memory. The architecture is obvious. The dangerous files are nearby. The blast radius is small. But something changes when a project reaches MVP size. The agent still sounds confident, but it starts guessing. It finds a nearby file and assumes it is the right one. It trusts stale documentation. It misses hidden callers. It forgets architecture boundaries. It edits something that was not really part of the task. I kept running into that problem while building larger projects. Source-level guardrails help. A CONTRIBUTING.md, AGENTS.md, or project instruction file can tell the agent how to behave. But those are still instructions. They are not facts. That is where the idea for CodeMeridian came from. What CodeMeridian is CodeMeridian is a local code knowledge graph for AI coding tools. It indexes a codebase into Neo4j and exposes that graph through MCP, so tools like GitHub Copilot, Claude Code, Codex-style agents, or other MCP-compatible clients can ask better questions before editing. The basic idea is: The assistant is the AI. CodeMeridian is the project map. It does not replace the coding assistant. It gives the assistant a structured way to ask about the codebase. Examples: What calls this method? What tests cover this area? What files are likely in scope for this feature? Is the graph stale before I trust it? How is this frontend component connected to backend code? Why a graph? Code is already a graph. Methods call methods. Classes implement interfaces. Tests cover production paths. Frontend components call API clients. API handlers touch services. Services use repositories. Docs mention symbols. Projects depend on other projects. A normal file search can find text. A graph can answer relationship questions. That matters because many AI coding mistakes are relationship m

Tóm tắt Nếu thay đổi gói miễn phí của Postman khiến bạn mất quyền truy cập vào workspace được chia sẻ, dữ liệu của bạn chưa chắc đã bị xóa. Việc cần làm là phục hồi càng sớm càng tốt trước khi cache cục bộ, quyền API hoặc bản sao lưu còn sót lại không còn dùng được. Bài viết này hướng dẫn các cách lấy lại collection/environment từ Postman và nhập chúng sang Apidog để giảm rủi ro bị khóa dữ liệu trong tương lai. Dùng thử Apidog ngay hôm nay Bối cảnh Sau bản cập nhật gói miễn phí Quý 1 năm 2026 của Postman, nhiều developer dùng workspace chia sẻ với đồng nghiệp phát hiện rằng họ không còn truy cập được dữ liệu nhóm. Các collection nằm trong workspace team, thay vì workspace cá nhân, đột nhiên bị khóa sau paywall. Một developer mô tả trên Reddit: “Tôi đến làm việc vào thứ Hai và toàn bộ không gian làm việc của nhóm tôi đã biến mất. Ba tháng với các bộ sưu tập, môi trường được sắp xếp gọn gàng, tất cả đều biến mất. Chỉ còn cách trả tiền thì mới có lại.” Điểm quan trọng: dữ liệu thường không bị xóa ngay. Postman lưu dữ liệu workspace phía server, còn việc bạn không nhìn thấy collection là hạn chế quyền truy cập. Vì vậy, hãy xử lý theo thứ tự dưới đây, ưu tiên các nguồn có khả năng còn dữ liệu đầy đủ nhất. 1. Kiểm tra cache trong ứng dụng Postman desktop Trước tiên, mở Postman desktop app nếu bạn đã từng dùng nó. Không mở bản web tại app.getpostman.com . Ứng dụng desktop có thể còn cache cục bộ của collection và environment bạn truy cập gần đây. Cache này thường chỉ tồn tại trong thời gian ngắn, tùy hệ thống và cơ chế invalidation của Postman, nên hãy xuất dữ liệu ngay nếu còn nhìn thấy. Các bước thực hiện: Mở Postman desktop. Kiểm tra tab History để xem các request gần đây. Kiểm tra sidebar bên trái xem collection còn hiển thị không. Nếu collection còn hiển thị, xuất ngay từng collection. Cách export collection: Nhấp chuột phải vào collection hoặc bấm menu ba chấm. Chọn Export . Chọn định dạng Collection v2.1 . Lưu file .json ra thư mục an toàn. Nếu collection vẫn hiển t

TL;DR If Postman’s 2026 Q1 free plan change blocked access to shared collections, your data may still be recoverable. Start with your Postman desktop cache, then check exports, admins, the Postman API, and logs. Once you recover the JSON files, import them into Apidog so your team has a safer workflow going forward. Try Apidog today Introduction After Postman’s 2026 Q1 free tier update, many developers found that shared workspaces were no longer accessible on the free plan. Collections that lived in team workspaces, instead of personal workspaces, became locked behind a paid plan. One developer described it on Reddit: “I came in on Monday and my whole team workspace was gone. Three months of organized collections, environments, all of it. Just gone unless we pay.” In most cases, the data is not immediately deleted. Postman stores workspace data server-side, and the issue is usually access restriction rather than deletion. That said, recovery is time-sensitive because local cache, API access, and workspace availability may not last. Use the steps below in order. 1. Check the Postman desktop app cache first Start with the Postman desktop app, not the web app. The desktop app may still have cached copies of recently opened collections and environments. Even if your server-side access is revoked, the local cache can sometimes keep enough data available to export. Steps Open the Postman desktop app. Do not use the web app at app.getpostman.com . Check the left sidebar for your collections. Open the History tab to confirm which endpoints you recently used. If collections are visible, export them immediately. To export a collection: Right-click the collection or open the three-dot menu. Select Export . Choose Collection v2.1 . Save the file locally. Repeat for every visible collection. If the collection appears but export fails, try working offline: Click your avatar in the top-right corner. Select Go Offline . Retry the export. Going offline can prevent the app from refre

A tweet landed on my timeline from Peter Steinberger — OpenClaw founder, now at OpenAI: "Here's...