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I didn't set out to build a content API. I set out to stop copy-pasting. Every week, the same ritual: open a doc, stare at a blank page, write a headline, delete it, write it again. Multiply that by every client, every product page, every email drip campaign. I wasn't doing creative work — I was doing assembly-line work while pretending it was creative. PostAll started as a script I wrote to stop doing that. The API is what that script became after other developers asked if they could use it too. This guide walks you through integrating PostAll's API into your own workflow — authentication, the endpoints you'll actually use, real working code in both Python and Node.js, and the specific places things will break before they work. By the end, you'll have a functioning pipeline that generates formatted, CMS-ready content programmatically. What you'll build A script that takes a list of content briefs (keywords, tone, target length) and returns publish-ready content — with proper formatting, metadata, and error handling for the rate limits you'll hit in production. Here's the shape of what you're building: [ CSV of briefs ] → [ PostAll API ] → [ formatted content objects ] → [ your CMS / database ] The full working code for both languages is at the end of each section. I'll explain the interesting parts inline. Prerequisites A PostAll account with API access enabled (free tier works for this guide — rate limits noted below) Node.js 18+ or Python 3.10+ Basic familiarity with async/await in either language An HTTP client: axios or native fetch for Node, httpx for Python Step 1: Authentication PostAll uses API key authentication. Every request needs your key in the Authorization header. Get your key: Dashboard → Settings → API Keys → Generate New Key Store it as an environment variable. Never hardcode it. export PostAll_API_KEY = "postall_live_xxxxxxxxxxxxxxxxxxxx" Your key has two prefixes: postall_live_ for production, postall_test_ for the sandbox. The sandbox returns r

I spent yesterday building purejq , a pure-Python implementation of jq. I expected it to be the slow-but-portable option. Then I benchmarked it against the jq package on PyPI (the C bindings everyone uses to run jq from Python) and got this, on a 100k-object array, in-process: workload purejq jq PyPI (C bindings) field-access stream 9 ms 368 ms filter + count 55 ms 442 ms map + aggregate 18 ms 444 ms group_by 112 ms 704 ms transform + sort 136 ms 899 ms Pure Python, 7-40x faster than the C extension. That number looked wrong to me too, so before publishing anything I made the benchmark script verify every output against the actual jq binary first ( tools/bench.py --verify ), re-ran everything as median-of-7, and gave the bindings their best-case API. The gap is real. Here's why. The serialization tax The C bindings wrap real jq, and real jq only speaks JSON. So every call does this: your dicts -> JSON text -> C parser -> jq evaluates -> JSON text -> dicts That round trip costs about 350-450 ms for 100k small objects on my machine, before any actual filtering happens. You can see it in the numbers: even a trivial field access pays the same ~400 ms floor as a group_by. purejq skips the trip entirely. It compiles the jq program once into Python closures and walks your dicts and lists directly: import purejq prog = purejq . compile ( " group_by(.team) | map({team: .[0].team, n: length}) " ) prog . first ( data ) # operates on your objects, no serialization The lesson generalizes beyond jq: when you embed a C library that has its own data model, the marshaling boundary is often more expensive than the work. An interpreter written in your language gets to skip the boundary, and that can buy back an order of magnitude. Surprise number two: the CLI beats the jq binary on big files This one I really didn't expect. End to end on a 93 MB file (1M objects), parse + filter + output: workload purejq CLI jq 1.8.1 binary single lookup 0.51 s 1.68 s filter + count 1.08 s 1.96 s grou

TL;DR — We'll install dskripchenko/laravel-api , write one controller, and end up with a versioned API ( /api/v1/... ) and interactive OpenAPI 3.0 docs at /api/doc — generated from the docblock you'd write anyway. Then we'll ship a v2 without copy-pasting a single controller. The problem Two things rot in every growing Laravel API: Versioning. v1 ships, then v2 needs to change three endpoints but keep the other twenty. You either copy-paste a V2 folder (and now bugfixes live in two places) or bolt if ($version === 2) branches into your controllers. Docs. The OpenAPI spec drifts from the code the moment you merge. Annotation libraries ( #[OA\Get(...)] , giant YAML files) ask you to describe your API twice — once in code, once in attributes. This package's bet: your controller already describes itself . The method name, the request fields, the response shape — write them once, as a normal PHPDoc, and let the package derive routes and docs from it. Versioning becomes plain PHP inheritance. Let's build it. What we'll build A tiny tasks API: POST /api/v1/task/list — list tasks POST /api/v1/task/create — create one interactive docs at GET /api/doc (raw spec per version at /api/doc/{version} ) then a v2 that adds an endpoint without touching v1 Total: ~4 small files. Step 0 — Install composer require dskripchenko/laravel-api Publish the config (optional, but handy to see the knobs): php artisan vendor:publish --tag = laravel-api-config // config/laravel-api.php return [ 'prefix' => 'api' , // → /api/... 'uri_pattern' => '{version}/{controller}/{action}' , 'available_methods' => [ 'get' , 'post' , 'put' , 'patch' , 'delete' ], 'openapi_path' => 'public/openapi' , 'doc_middleware' => [], // lock down /api/doc here ]; Step 1 — Write a controller Nothing exotic — it extends the package's ApiController , which gives you response helpers ( success() , error() , validationError() , created() , noContent() , notFound() ). The docblock is the documentation : <?php namespace App\Api

https://dsa-life-simulator-frontend.vercel.app"I made a free tool to make DSA practice feel like an RPG — would like feedback from this community"Been grinding DSA for months and it never felt fun. So I built something. What it does: 🏟️ Real-time 1v1 Arena battles against other devs 🧪 Lab to create and publish your own challenges 🏘️ Community Hub to attempt others' challenges 📖 AI writes your weekly coding journey as a life story 🎮 XP, credits, levels, leaderboards Stack: React + Tailwind + Firebase + Node.js + Socket.IO + Groq AI Still early — would genuinely love feedback from people who've felt the pain of traditional DSA prep.

AI coding tools are changing how software gets built. Claude Code, Cursor, GitHub Copilot, Windsurf and other tools can generate code incredibly fast. For small tasks, they are already useful: write a component, explain a function, scaffold an endpoint, create a test, refactor a file. But after using AI in real projects, one thing becomes obvious: The problem is no longer code generation. The problem is engineering control. Most AI coding workflows still look like this: text idea -> prompt -> code -> fix -> prompt again -> more code -> lost context -> start over It feels fast at the beginning. Then the project grows. Requirements change. Architecture decisions disappear inside chat history. The AI forgets previous context. You start acting as product manager, architect, reviewer, QA, DevOps, and prompt engineer at the same time. That is not software engineering. That is vibe coding. ## Vibe coding works until it doesn't Direct AI coding is great when the task is isolated. Ask for a React component. Ask for a SQL query. Ask for a utility function. Ask for a unit test. No problem. But real software is not a collection of isolated snippets. Real software has: - business rules - architectural constraints - existing patterns - security concerns - database impact - deployment requirements - edge cases - regression risk - long-term maintenance When AI jumps directly from prompt to code, it often skips the thinking that should happen before implementation. The result may compile. But does it fit the architecture? Does it respect the domain? Does it create hidden technical debt? Does it solve the right problem? That is the gap we are trying to close with N45.AI. ## What is N45.AI? N45.AI is a framework that turns AI coding tools into a structured engineering workflow. It works with the tools developers already use, including Claude Code, Cursor, GitHub Copilot, and Windsurf. The idea is simple: Instead of treating AI as one generic assistant, N45.AI organizes the work like a

A useful thing happened in agent infrastructure this June: several teams shipped "escrow layers for AI agents" - production MCP tools that let an agent run a full commit -> hold -> complete lifecycle without a human anywhere in the loop. An agent can now park value with a contract or service, wait for the other side to deliver, and release on completion. That is genuinely new, and it solves a real problem. It is also worth being precise about, because "escrow" and "settlement" get used as if they were one thing. They are not. There are two structurally different ways to make a trade your agent does at 3am trustworthy, and the difference is exactly who holds the money while the trade is in flight . Model one: held custody In the held-custody model, a third party - a smart contract escrow, a custody service, a payment facilitator - takes the funds, holds them, and releases them when a condition is met. The condition can be anything you can express: a delivery confirmation, an evaluator's attestation, a timeout, a multi-sig approval. This is the right tool for a large class of agent commerce. If your agent is paying a merchant, buying a dataset, or hiring another agent to do a unit of work, the hard question is subjective : did the thing actually get delivered, and was it any good? A hash function cannot see that. A custodian can - it gives the trade a place to pause while something or someone checks. The new agent-escrow tooling is built around exactly this shape: a job, a held balance, a release on completion. For agent-to-merchant payments riding on rails like x402, held custody is the honest primitive. The cost is equally concrete. A held balance is a honeypot. Someone controls the funds between commit and complete, which means someone can freeze them, lose them, misconfigure the release condition, or get drained. You have added a trust assumption and a liveness dependency - the custodian has to be online, solvent, and honest at release time. That is often an accep

It is a privacy-first, local-first photo organizer powered by deep learning face recognition. It detects, embeds, and groups faces to organize your photos automatically—all 100% offline. 🔥 Highlight Features: ✅ 100% Local: No cloud APIs, no telemetry, no leaks. ✅ Deep Learning: Driven by OpenCV DNN (YuNet + SFace ONNX models). ✅ Smart Automation: Copies matches, partial matches, and individual profiles into organized folders, complete with ZIP archives and JSON reports. ✅ Standalone EXE: Run it on Windows instantly with zero dependencies. ✅ Dynamic UI: Fully responsive Tailwind dashboard with Dark/Light modes. Check out the repository, download the EXE, or contribute: 👉 https://github.com/Shaan-alpha/face-sort-studio Let me know what you think! ⭐ machinelearning #computervision #python #localfirst #privacy #developers #opensource #ai Internet access on first launch only (to fetch the AI models ~40-50mb)

Audit thousands of passwords against data breaches — completely in your browser with zero-knowledge privacy. Published: June 9, 2026 TL;DR Most bulk password checkers require you to upload your entire vault. Utilora’s Bulk Password Breach Checker uses HIBP’s k-anonymity + local hashing so your passwords never leave your device . The Hidden Risk Most People Ignore Using a password manager is excellent, but it’s not enough. Many users unknowingly reuse or slightly modify passwords that have already been leaked in massive breaches (LinkedIn, Adobe, Yahoo, etc.). Manually checking hundreds or thousands of passwords is impractical — which is why people turn to bulk checkers. The problem? Most bulk checkers ask you to upload your password list . That creates a massive new privacy risk. How Utilora’s Zero-Knowledge Breach Checker Works We built this tool using a privacy-preserving technique called k-anonymity (popularized by Troy Hunt of Have I Been Pwned). Step-by-Step Technical Process: Local Hashing — Your browser uses the WebCrypto API to create a SHA-1 hash of each password locally. Prefix Only — Only the first 5 characters of the hash are sent to HIBP’s Range API. Server Response — HIBP returns hundreds of matching hashes that start with the same prefix. Local Comparison — Your browser checks if your full hash exists in the returned list. Result: HIBP knows someone checked a password starting with ABC12 , but has no idea which specific password it was. Why You Should Audit Your Entire Vault Regularly Discover weak or compromised passwords you forgot about Clean up old reused passwords Respond quickly after major breaches Maintain good password hygiene across all accounts Real-World Scenarios You exported your Bitwarden / 1Password / KeePass vault You want to check 500+ passwords before a security audit You just heard about a new major breach and want to verify impact You’re helping a family member or client secure their accounts How to Use the Tool Go to the Bulk Pas

PostgreSQL Error 2200G: Most Specific Type Mismatch PostgreSQL error code 2200G ( most_specific_type_mismatch ) is a SQL-standard data exception that occurs when a value's type does not match the most specific (most derived) type expected in a context involving type hierarchies, XML schema types, or user-defined structured types. It most commonly appears when working with composite types, domain hierarchies, or XML processing functions where type inheritance or derivation is in play. While relatively rare in everyday CRUD operations, it can be a significant pain point in enterprise applications with complex type systems. Top 3 Causes and Fixes 1. Composite or Domain Type Hierarchy Mismatch When a function expects a specific domain or composite type but receives a parent/base type, PostgreSQL raises 2200G. Always cast explicitly to the most specific required type. -- Define types CREATE TYPE base_info AS ( name TEXT , value INTEGER ); CREATE DOMAIN specific_info AS base_info ; -- Function expecting the specific domain type CREATE OR REPLACE FUNCTION handle_info ( data specific_info ) RETURNS TEXT AS $$ BEGIN RETURN ( data ). name || ': ' || ( data ). value ; END ; $$ LANGUAGE plpgsql ; -- WRONG: passing base type causes mismatch -- SELECT handle_info(ROW('test', 42)::base_info); -- CORRECT: explicit cast to the most specific type SELECT handle_info ( ROW ( 'test' , 42 ):: specific_info ); 2. XML Type Processing Mismatch Using XML functions like XMLTABLE or XMLCAST without explicitly matching the expected schema type can trigger this error. Always declare column types explicitly. -- Correct: explicitly typed columns in XMLTABLE SELECT * FROM XMLTABLE ( '//product' PASSING XMLPARSE ( DOCUMENT ' <products> <product> <id>1</id> <price>29.99</price> </product> </products> ' ) COLUMNS product_id INTEGER PATH 'id' , price NUMERIC PATH 'price' ); -- Explicit XMLCAST to resolve type ambiguity SELECT XMLCAST ( XMLQUERY ( '//price/text()' PASSING XMLPARSE ( DOCUMENT '<data><pri

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zxcvbn is the most widely used password strength estimator with 1M npm downloads a week. It's also 389KB gzipped and hasn't shipped a commit since 2017. Most sign-up forms are hauling that around just to block password123 . Poor password UX is a real conversion problem. A strength meter that adds 389KB to your bundle delays page load — on mobile, measurably so. Users who hit a slow registration page don't wait. They leave. The irony is that most of that weight goes toward catching passwords nobody is actually using to register on your site. So I built passcore - 3.0KB gzipped and 98.4% detection rate on real breach data - same as zxcvbn, benchmarked against a deduped list of passwords pulled live from RockYou, Adobe, HIBP, and other major leak lists. zxcvbn takes ~9.7ms to load — it's parsing 389KB of dictionary into memory on every cold start. passcore loads in ~0.2ms. It evaluates a password in ~2,600 nanoseconds. For a registration form, it's effectively invisible — no jank, no layout shift, no contribution to your Core Web Vitals score. The strength meter shows up before the user finishes typing their first character. How it works: passcore runs five detection layers on every password: Dictionary - All entries sourced directly from breach data, not a generic word list Keyboard patterns - qwerty , asdf , 1234 , numpad walks Repeats - aaaa , ababab Sequences - abcdef , 123456 L33t speak - decodes p@ssw0rd → password , m0nk3y → monkey , then dictionary lookup The dictionary is small by design. Every entry was chosen because it appears in real breach data - not because it's a common English word. Password1! is caught not by a 40k word list but by stripping the suffix and checking if the core word is in the breach list. It is. The scoring model: passcore returns a score from 0 to 4 - same scale as zxcvbn. The detection layers run first. A dictionary match, keyboard pattern, repeat, sequence, or l33t substitution scores 0 or 1 immediately - no further calculation. If

If you've ever tried to use an AI resume builder, you've probably hit the same wall I did. You sign up, poke around, find the one feature you actually need — and then boom: "Upgrade to Pro for $29/month." It's frustrating. Resume help shouldn't be locked behind a paywall. So I built my own. Meet Persona Persona is an AI-powered resume builder that you run completely on your own machine . No deployment required. No subscription. No account on some third-party service. You clone the repo, set it up, and it's yours. It's a fork of the excellent open-source project ResumeLM , but I've added a bunch of features I couldn't find anywhere else — especially around local AI and template variety. 👉 GitHub: github.com/nithiin7/persona (Drop a ⭐ if you find it useful!) The Big Deal: Run AI Completely Offline with Ollama This is the feature I'm most proud of. Most AI resume tools call out to OpenAI or Anthropic and charge you for every request. Persona supports Ollama — which means you can run the AI model locally on your own hardware, with zero API costs and zero data leaving your machine. Here's how simple it is: Install Ollama on your computer Pull any model ( ollama pull llama3 , for example) Open Persona's settings, point it to your local Ollama URL Done — the AI now runs entirely on your machine No OpenAI key. No Anthropic key. No usage limits. Your resume data never touches an external server. If you do want to use cloud models, Persona supports those too — GPT-5, Claude Opus 4.7, Claude Sonnet 4.6, and a handful of open-source models via OpenRouter. But the Ollama path is what makes this genuinely different from everything else out there. It's 100% Free — Everything Unlocked The original ResumeLM had Stripe payments baked in. I ripped all of that out. Every single feature in Persona is available to every user, always. There's no "Pro plan." There's no feature gating. You self-host it, you own it, you use all of it. 10 Resume Templates Persona ships with ten distinct templ

After 8 months of daily Claude Code use, I've distilled my workflow into a 5-layer system. Each layer builds on the previous one. Skip one, and the whole thing falls apart. The Problem with Most Claude Code Users Most people use Claude Code like ChatGPT — open terminal, ask a question, close, repeat. The next day, they explain their project from scratch. Again. The symptom: 20% of every session is wasted on context re-establishment. The root cause: No project memory, no workflow discipline. Here's the system that fixed it for me. Layer 1: CLAUDE.md — Your Project's Memory Anchor This is the foundation. Without it, nothing else works. CLAUDE.md is a file at your project root. Claude reads it automatically at the start of every session. It tells Claude: What this project is (one sentence) The tech stack (specific technologies, not "Python web framework") The architecture (the big picture you'd need 3 files to understand) Unique conventions (not generic advice like "write tests") Quality priorities Bad CLAUDE.md (you've probably written this): # My Project A web application built with Python and FastAPI. ## Development - Write clean code - Add unit tests - Use Git This tells Claude nothing it doesn't already know. Good CLAUDE.md: # CLAUDE.md ## Project Overview Internal RAG knowledge base serving 500+ employees. ## Tech Stack FastAPI + LangChain + Milvus + PostgreSQL + Redis ## Commands - Start: `uvicorn app.main:app --reload --port 8080` - Test: `pytest -x --cov=app --cov-report=term-missing` ## Architecture Request flow: router → service → retriever → Milvus → generator → LLM API Key directories: - app/router/ - API layer - app/service/ - Business logic orchestration - app/retriever/ - Retrieval strategies (vector/BM25/hybrid) - app/generator/ - LLM calls and prompt management ## Key Conventions - All APIs return `{"data": ..., "error": null}` - Retrieval results MUST include source field - Milvus collection naming: `{env}_{doc_type}` The rule: Only write what's uniq

I automated my Gumroad product screenshots with Playwright I recently started packaging a few small frontend projects as digital products, and one surprisingly annoying part was preparing product screenshots. Manual screenshots quickly became messy: different browser sizes inconsistent cropping blurry images mobile screenshots were easy to get wrong Gumroad needed a square thumbnail every update meant taking screenshots again So I built a small local screenshot workflow with Next.js and Playwright. The workflow captures: desktop screenshots mobile screenshots square thumbnail images consistent PNG outputs route status checks basic console error reporting basic horizontal overflow checks The basic command flow is: npm run build npm run start npm run screenshots The script reads a simple config file, opens the configured local routes, captures each screenshot with consistent viewport settings, and exports the images into a predictable folder. For example: screenshots/gumroad/ landing.png dashboard.png template-preview.png mobile-preview.png thumbnail.png I found this especially useful when preparing Gumroad product galleries, because I could regenerate all product images after every UI change instead of taking screenshots manually. This is not a hosted screenshot service. It is just a local source-code workflow for people who want to generate product screenshots from their own Next.js pages. I packaged the workflow as a small Gumroad product here: https://remix410.gumroad.com/l/screenshot-automation-kit Curious how other developers handle product screenshots. Do you take them manually, use Playwright/Puppeteer, or use a design tool workflow?

TLDR TypeScript has 7 primitive types: string , number , boolean , null , undefined , bigint , and symbol . You use them to tell TypeScript what kind of value a variable holds. You write them in lowercase. TypeScript can often figure out the type for you. But knowing how each one works is key to writing safe and clear code. What Are Primitive Types? Primitive types are the simplest building blocks in TypeScript. Every piece of data in your program starts with one. They hold a single value. They are not objects. You cannot add methods or properties to them directly. TypeScript has 7 primitive types in total: Type What It Holds string Text like names, messages, or IDs number Any number: integers, decimals, negatives boolean Only true or false null An intentional empty value undefined A value that was never assigned bigint Very large whole numbers symbol A unique identifier value This article covers all 7. You will use string , number , and boolean the most in everyday TypeScript code. How to Use the string Type A string holds text. Use it for names, messages, emails, URLs, and any other text data. Basic string annotation let firstName : string = " Alice " ; let greeting : string = " Hello, world! " ; let empty : string = "" ; Three ways to write strings TypeScript supports the same three string styles as JavaScript: let single : string = ' Single quotes work fine ' ; let double : string = " Double quotes work too " ; let template : string = `Template literals with ${ firstName } ` ; Template literals (backticks) let you insert values inside a string with ${} . TypeScript checks the types of those inserted values too. let age : number = 30 ; let message : string = `I am ${ age } years old` ; // TypeScript checks that 'age' is compatible here What TypeScript catches with strings let name : string = " Alice " ; name = 42 ; // Error: Type 'number' is not assignable to type 'string'. name = true ; // Error: Type 'boolean' is not assignable to type 'string'. Once a variable

I run a production multi-agent AI system on a single M1 Mac in Jamaica. 6 autonomous agents. 26 cron workflows. 5-layer persistent memory. All containerized, all running 24/7. I checked my OpenRouter dashboard last week and realized something: I'd processed 2.4 billion tokens across 52 different AI models and spent a total of $0.52 . That's not a typo. Here's exactly where that money went and what it means. The Numbers Metric Value Total Requests 26,600+ Tokens Processed 2.4 Billion Models Used 52 Total Cost $0.52 Cost per Token $0.00000021 Tokens per Dollar 4.6 Million For context: GPT-4 Turbo costs about $0.00001 per token at scale. I'm running at roughly 50x below that rate. Where the $0.52 Actually Went Here's the breakdown by model: Model Requests Tokens Cost openrouter/owl-alpha 1,334 251.2M $0.00 nvidia/nemotron-3-super-120b 32 1.8M $0.00 google/gemma-4-31b-it 47 1.8M $0.00 openai/gpt-5 1 2.8K $0.03 google/gemini-3.1-pro-preview 1 3.2K $0.04 anthropic/claude-opus-4 1 2.0K $0.13 qwen/qwen3.5-plus 1 6.3K $0.01 z-ai/glm-5-turbo 1 3.0K $0.01 moonshotai/kimi-k2.5 2 4.1K $0.01 google/gemini-2.5-flash 2 5.5K $0.01 +42 other models ~125 ~8.5M ~$0.28 99.6% of my requests cost exactly $0.00. They ran on free-tier models or local inference. The $0.52 comes from a handful of premium model calls: Claude Opus, GPT-5, Gemini Pro. These are reserved for specific high-quality tasks — not everyday inference. What This Would Cost on Cloud Approach Hardware Monthly Cost Annual Cost My setup (M1 Mac) M1 Mac 16GB, local + free tier ~$0.09 ~$1.04 OpenRouter Paid Tier API-only, no local $15-30 $180-360 AWS (g4dn.xlarge + API) 1x T4 GPU, on-demand $350-500 $4,200-6,000 AWS (g5.xlarge + API) 1x A10G GPU, on-demand $700-1,000 $8,400-12,000 A $1,200 laptop replaces $500-1,000/month in cloud bills. The break-even point is about 2 weeks. How the Architecture Works The key insight: not every task needs a $20/month model . My system routes tasks intelligently: Local inference (free): Ollama

I’m a PAM engineer and recently had to handle a few cross‑tenant migrations in Delinea Secret Server. As many of you know, there’s no built‑in way to migrate secrets, folders, roles, or permissions between tenants (cloud ↔ cloud, on‑prem ↔ on‑prem, hybrid, etc.). To avoid doing everything manually, I built an unofficial PowerShell‑based tool to automate the process. Not a vendor, not selling anything — just sharing something I built because it solved a real problem for me. What it does: Full Windows GUI Export → validate → import → reconcile Folder/role/permission mapping Integrity checks Supports cloud, on‑prem, and hybrid Auto‑update logic for long‑term use If anyone else here works with Secret Server and has had to deal with tenant splits, mergers, rebuilds, or cloud migrations, this might save you some time. Github Link: https://github.com/vijayamohanreddy/delinea-secrets-server-migration-tool-unofficial Linkedin Article: https://www.linkedin.com/pulse/introducing-delinea-secret-server-crosstenant-tool-vijaya-reddy-vj--wy47c/?trackingId=vJ3%2F9%2Fw3RLSKlm%2F1kXig1Q%3D%3D Affiliation disclosure: I built this myself for my own work. Not affiliated with Delinea, not a vendor, not selling anything. Happy to answer questions or hear suggestions from others who’ve had to do Secret Server migrations.