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This article was originally published on e-shikumi-labo . Hello, I'm Shin from e-Shikumi-Labo. This is Part 3 of "Systematized Thinking," where we use AI to build our own tools and independently maintain them. Last time, I talked about creating a system to automatically output Markdown (.md) files to Google Drive simultaneously with appending to a spreadsheet. With list management in a spreadsheet and a comfortable viewing environment in Obsidian established, it was getting very close to completion as a tool. However, as I continued to use it practically, new challenges emerged on the operational front. This time, I will share the risks I faced while transitioning from a "manual button" to "full automation," and the process of evolving into safe code. 1. I Want to Eliminate the "Hassle of Pressing a Button" During the prototype stage, the system was designed so that logs were saved by pressing a button placed on the screen. However, as long as a human operates it manually, there are inevitably limitations. If you are concentrating on the conversation, you might forget to press the save button and close the screen. If the conversation gets long, you might miss past utterances that are no longer displayed on the screen. "If I have the screen open and am conversing, I want it to automatically save in the background without bothering human hands." Thinking this, I asked the AI to write the code for full automation. 2. The Code the AI Produced: "Patrolling the Screen Every 1.5 Seconds" When I consulted the AI, it immediately presented code for full automation. The mechanism was, "Start a timer every 1.5 seconds, check the entire screen in the background, and send any new utterances." When I actually tried it, the logs accumulated automatically as soon as I conversed without pressing the button, and at first glance, it looked like exceptionally well-done full automation. However, I felt something was slightly off regarding this "monitoring on a 1.5-second cycle." 3. The B

A suite of developer tools you run with the curl you already have: UUIDs, hashes, JSON End-points, JWTs, JSON, QR, File Transfers and more. Nothing to install, and no signup required. "curl curlhub.sh" to see list of commands or visit https://curlhub.sh to view all tools full details and soon contribute. root@web01:~# curl curlhub.sh curlhub.sh — CLI-first developer utilities Zero-setup tools you curl straight from the terminal. Generators /uuid RFC 4122 v4 UUID. /pass High-entropy password / API key / token. CLI: server-side CSPRNG over TLS; web: client-side. Never logged. ( https ) Utilities /qr ANSI/UTF-8 QR code from ?data. Nothing stored server-side; use HTTPS or pipe stdin for sensitive payloads ( a ?data = value appears in request URLs ) . Encoding /b64 Base64 encode / decode. (https) /hash MD5 / SHA-1 / SHA-256 / SHA-512 of input. (https) Developer /json Validate + prettify + colorize JSON. (https) /jwt Decode & pretty-print JWT header/payload. No verify; token never logged. ( https ) /status Explain an HTTP status code + troubleshooting. /cron Translate a cron expression to English + next run times. /ua Parse the User-Agent you sent (browser / OS / engine / device). /headers Echo the request headers you sent (+ the edge view). Your own request, not a remote audit. /hook Webhook inspector: mint a temp endpoint, inspect incoming HTTP. (https) Text & Logs /md Render Markdown to colorized ANSI (safe, bounded parser). (https) /p Pastebin: pipe text/logs to a short URL (plaintext in terminal, highlighted on web). (https) Network /cidr Subnet / CIDR math: range, netmask, broadcast, host count. /ip Your public IP (thin). Geo/ASN live at worldip.io. /whois Domain registration / registrar / dates (public OSINT; rate-limited + cached ) . Security /ssl Decode a PEM certificate or CSR you paste (expiry, issuer, SANs, key). No outbound connection. (https) File Transfer /u Ephemeral file drop (<=100 MB). ANSI QR; auto-purge after 1 download or TTL. ( https ) Docs /man Com

security #api #domain #subdomaintakeover #defcon #whois #rapidapi #threatintel DEF CON 32 made one thing clear: open-source security chips and hardware keys are having a moment. But while badges get the spotlight, most real-world attacks still start with something far less glamorous — a forgotten DNS record, a dangling CNAME, or a missing DMARC policy. Subdomain takeover remains one of the most reliable paths from "benign misconfiguration" to "account compromise." If your organization owns dozens or hundreds of domains, manual checks do not scale. This is where an API-first domain intelligence tool becomes essential. In this post, we'll use the Domain WHOIS API to automate: WHOIS/RDAP lookups and domain-age checks DNS record enumeration and SSL certificate inspection Subdomain discovery and takeover-risk scoring Email-security validation (SPF, DMARC, DKIM, DNSSEC, MTA-STS) Historical snapshots via /history Why subdomain takeover still matters A subdomain takeover happens when a DNS record points to a third-party service — GitHub Pages, Heroku, AWS S3, Vercel, etc. — that is no longer registered under your account. An attacker can claim the dangling endpoint and suddenly serve content under your brand's domain. Bug bounty programs consistently rank subdomain takeovers as high-severity findings because they enable phishing, session hijacking, and reputation abuse. The root cause is usually an orphaned CNAME that nobody is monitoring. The fix is continuous monitoring. Instead of running dig , whois , and openssl by hand, we can consolidate everything into a single API call. What the Domain WHOIS API returns The API combines several data sources into one response: Capability Use case WHOIS via RDAP Ownership, registrar, creation/expiration dates DNS records A, AAAA, CNAME, MX, NS, TXT records SSL certificate Issuer, expiry, SANs, validity Subdomain discovery Asset inventory and shadow-IT detection Takeover risk Dangling CNAME/A-record scoring Email security SPF, DMARC,

If your script suddenly started printing this: >>> import yfinance as yf >>> df = yf . download ( " NG=F " , period = " 1mo " ) 1 Failed download : [ ' NG=F ' ]: YFPricesMissingError ( ' possibly delisted; no price data found ' ) …you didn't break anything. NG=F (the natural gas futures ticker on Yahoo Finance) periodically stops returning data for everyone, and futures tickers get hit harder than stocks. This post covers why it happens and the three fixes that actually work, ordered from "quick patch" to "never deal with this again." 1. What the error actually means yfinance is not an official API . It's a (great) community library that scrapes Yahoo Finance's internal endpoints — the same ones Yahoo's own website uses. Yahoo doesn't document them, doesn't promise they'll keep working, and changes them whenever it suits their frontend. When Yahoo changes something — an endpoint, a rate limit, a response format — yfinance breaks until its maintainers reverse-engineer the change. Futures symbols like NG=F and GC=F are the most fragile: they've had recurring gaps and failures reported over the years, for example #2620 (missing recent data for NG=F/GC=F) , #2635 (whole missing days in futures history) and the evergreen #865 "Futures only work sometimes" . So: "possibly delisted" almost never means delisted. It means "the scrape came back empty." 2. Fix #1 — the quick patches (works today, breaks tomorrow) Three things fix most transient failures: Upgrade first. The maintainers usually patch Yahoo changes within days: pip install -U yfinance Retry with backoff. Failures are often intermittent rate-limiting, not hard breaks: import time import yfinance as yf def download_with_retry ( ticker , retries = 3 , wait = 5 , ** kwargs ): for attempt in range ( 1 , retries + 1 ): df = yf . download ( ticker , progress = False , ** kwargs ) if not df . empty : return df print ( f " attempt { attempt } came back empty, retrying in { wait } s… " ) time . sleep ( wait * attempt ) rai

If you're an African developer, SaaS founder, freelancer, or online business, one of the biggest challenges isn't finding customers. It's getting paid by them. Most articles about African payment APIs focus on moving money out of Africa. But what if your customers are the ones sending money to you? Whether you're billing international clients, collecting subscription payments, or accepting payments from marketplace users, you need a collection method that's easy for customers and simple to reconcile on your end. The Afriex Business API offers three different ways to collect payments, each designed for a different use case. Depending on who your customers are and how they prefer to pay, you can collect funds through dedicated virtual accounts, shared pool accounts, or stablecoin wallets. In this guide, you'll learn how each collection method works, when to use it, and how to integrate it into your application. The Three Collection Methods Although all three collection methods ultimately deposit funds into your Afriex Business wallet, they differ in how customers send money and how you identify who made each payment. Method Best for How the payer sends Dedicated virtual account Known customers, repeat payments Bank transfer to a unique account number Pool account Quick collection, one-off payments Bank transfer with a reference Crypto wallet Customers holding USDT or USDC Crypto transfer to a wallet address Choosing the right method depends on your product and your payment flow. If you already know your customers and expect them to pay repeatedly, dedicated virtual accounts provide the smoothest experience. If you want to launch quickly without creating individual accounts for every customer, pool accounts are a great fit. And if your users prefer paying with stablecoins, crypto wallets make that process straightforward. Method 1: Dedicated Virtual Accounts Dedicated virtual accounts are the easiest way to reconcile bank transfers from repeat customers. Instead of ask

The API Gateway Has Grown Up When API gateways first entered the enterprise architecture conversation, the value proposition was straightforward: put a reverse proxy in front of your APIs, enforce authentication, and add basic rate limiting. Problem solved. That framing was adequate for 2012. It's dangerously incomplete for 2026. Today's API gateway sits at the intersection of integration, security, observability, and increasingly AI — and the organizations that still treat it as a simple routing layer are leaving significant capability on the table while accepting operational risk they don't have to carry. The modern API gateway is an integration hub in its own right, and understanding its full capabilities is essential to building a resilient, scalable API strategy. What Traditional API Gateways Got Right (and Wrong) The first generation of API management platforms — Layer 7, Apigee, legacy enterprise API managers, early Kong — nailed the fundamentals. Authentication enforcement, basic transformations, developer portals with API keys, rudimentary analytics. For the REST API era, this was genuinely valuable. But these platforms had structural limitations that became more painful as API ecosystems scaled: Static configuration : Policy changes required deployment cycles, not dynamic updates Monolithic architecture : The gateway itself became a single point of failure and a scaling bottleneck Reactive observability : Dashboards showed what happened; they didn't predict or prevent problems Protocol silos : REST gateways couldn't route gRPC, GraphQL, or WebSocket traffic without additional infrastructure No integration context : The gateway was blind to the systems it was protecting — it enforced policies without understanding the business logic behind the APIs The Modern API Gateway: A Capability Map Authentication and Authorization — Now Much More Than Token Validation Modern gateways don't just validate that a token exists and hasn't expired. They implement the full

Key takeaways A request is judged on many layers at once — IP reputation, TLS fingerprint, HTTP/2 shape, headers, and how the browser is driven — and failing any one is enough for a 403. Your laptop passes because every layer is consistent with a real home browser; a server changes one (usually the IP) and the inconsistency is the tell. A 403 with no challenge page almost always means you were blocked at the network layer (IP/ASN reputation or TLS/JA3 fingerprint) before any HTML was served — not a credentials or rate-limit bug, so 'add a User-Agent' or 'slow down' won't fix it. The fix order that actually works: get off datacenter IPs (residential/ISP proxies), match a real browser's TLS fingerprint, and spin up a real-browser stealth setup only for the pages that truly need JavaScript — escalate, don't lead with a browser. A proxy only changes your IP; a Linux VPS still leaks a Linux-shaped TLS/JS fingerprint, so 'residential IP + datacenter everything-else' is a contradiction a real home machine never makes — which is why a proxied server can get blocked harder than your laptop. Your scraper runs perfectly on your laptop. You deploy it to a VPS or a CI runner, change nothing in the code, and suddenly every request comes back 403 . It feels like a bug — the code is identical — but it usually isn't. Anti-bot systems judge a request on many signals at once, and moving from your home machine to a datacenter flips several of them at the same time. This post breaks down exactly which signals change, how to tell which one is blocking you, and how to fix it — for authorized access to public data (we'll keep that framing honest throughout; nothing here is about defeating a protection). A request is judged in two stages It helps to know that detection happens in two stages: Stage 1 — before any HTML is served. IP reputation, your TLS handshake, your HTTP/2 settings, and header order are all inspected on the connection itself, passively and cheaply, before your request is e

If your training dashboard starts as one HTTP request and grows into athletes, activities, wellness, workouts, gear, and performance data, a hand-written fetch wrapper becomes expensive to maintain. Every new endpoint adds another URL, another response shape, and another place to get authentication or retry behavior wrong. This tutorial shows a small, reproducible path with intervals-icu , an open-source TypeScript client for the Intervals.icu API . The goal is not to build a complete training application. It is to establish a typed client, choose the right authentication boundary, call one service, and understand what changes when you move from version 1 to version 2 of the library. TL;DR Install the stable npm package, create an IntervalsClient with an API key or OAuth access token, and use service accessors such as client.athletes or client.activities. Version 2 uses typed service methods, retries selected transient failures, and defaults requests to the authenticated athlete. Prerequisites You need: Node.js 18 or newer. npm. An Intervals.icu account with an API key, or an OAuth access token for an application acting for other users. A TypeScript project that can run ESM modules. The published package is intervals-icu version 2.2.1, and its package metadata declares Node.js >=18.0.0. The repository is public and licensed under MIT. The examples below target that stable package version, not an unreleased default-branch change. Install the stable client Create a small project and pin the package version used in this tutorial: mkdir intervals-demo cd intervals-demo npm init -y npm install intervals-icu@2.2.1 npm install -D typescript tsx The package publishes both ESM and CommonJS entry points and exposes TypeScript declarations from its package root. Add a script so a .ts file can run without a separate build step: { "type" : "module" , "scripts" : { "start" : "tsx src/index.ts" } } Create the smallest useful client Create src/index.ts. Keep the credential outside

As software developers, we often spend most of our time building APIs, databases, authentication systems, and web applications. That's certainly been my focus recently, especially working with Go, JWT authentication, and backend services. Last week, however, I had the opportunity to participate in the KijaniSpace Hackathon , held at Zone01 Kisumu , and it introduced me to an entirely different side of software development. Our challenge was to build solutions using: Geographic Information Systems (GIS) The Copernicus API IoT devices where applicable It was an opportunity to see how software can interact with our physical world. What is GIS? GIS (Geographic Information Systems) is a technology used to collect, analyze, visualize, and manage data that has a geographic location. Imagine not just storing information like: Temperature Population Vegetation Buildings Roads ...but also knowing exactly where that information exists on Earth. That location data allows developers to build intelligent systems capable of answering questions like: Which farms are experiencing drought? Which roads are likely to flood? Which areas are losing forest cover? Where should new infrastructure be built? GIS transforms ordinary data into meaningful geographic insights. Discovering the Copernicus Program Before this hackathon, I had heard very little about Copernicus. Copernicus is the European Union's Earth Observation Programme. It provides free satellite imagery and environmental data collected by the Sentinel satellite missions. Through its APIs, developers can access information about: Land cover Vegetation health Weather patterns Water bodies Air quality Climate changes Disaster monitoring What amazed me most is that much of this data is openly available for developers to build impactful applications. Where IoT Fits In Some teams also explored Internet of Things (IoT) solutions. IoT devices can collect real-world information through sensors measuring: Soil moisture Temperature Humidi

Rate limiting is one of those features every production API eventually needs. Whether you're building a public REST API, a WebSocket service, or an authentication endpoint, you'll eventually face problems like: Credential stuffing Brute-force attacks API abuse Bots scraping your endpoints Unexpected traffic spikes Most applications solve this with a simple request counter. But after building several APIs with Django, FastAPI, and Flask, I realized that production traffic requires much more than "X requests per minute." That observation led me to build drogue , an open-source Python library for rate limiting and traffic protection. The Problem Traditional rate limiting is straightforward: Allow 100 requests per minute. This works well for many cases, but real-world applications quickly expose its limitations. For example: A distributed attack can remain below the per-IP limit. A bot can rotate through proxies. WebSocket connections often require different handling than HTTP requests. Different endpoints need different protection strategies. I wanted a system that could go beyond simple request counting. Design Goals From the beginning, I focused on a few principles. 1. Clean framework integration I didn't want endpoint functions filled with framework-specific plumbing. Instead, the library should feel like a natural extension of the framework. from fastapi import FastAPI from drogue.adapters.fastapi import DrogueLimiter app = FastAPI () limiter = DrogueLimiter ( app , default_limits = [ " 100/minute " ]) @app.get ( " /users " ) @limiter.limit ( " 10/minute " ) async def users (): return { " status " : " ok " } No additional request objects. No complicated middleware configuration. Minimal boilerplate. Multiple Rate Limiting Algorithms Different applications require different algorithms. Instead of supporting only one approach, drogue includes multiple options: Token Bucket Sliding Window Fixed Window Each has different trade-offs between accuracy, burst handling, and

"Log in with Google" — without Google ever seeing the other site's password. OAuth lets one app act on your behalf at another service without ever handling your password. Instead of credentials, apps get a scoped, revocable token. The authorization-code flow Redirect. The app sends you to the provider with the scopes it wants. Consent. You authenticate with the provider and approve (or deny) those scopes. Code. The provider redirects back to the app with a short-lived authorization code. Token exchange. The app's server swaps the code (plus its secret) for an access token. Use & refresh. The app calls APIs with the token, refreshing it as needed. Why it's safer than sharing a password Scoped. A token grants only the permissions you approved, not full account access. Revocable. You can revoke one app without changing your password. PKCE. Public clients add a proof step so an intercepted code alone is useless. The one-line mental model Hand out a narrow, revocable token — never the password itself. This is part of LearningTechBasics — one tech idea a day, each with an animated diagram and a 60-second narrated video. 📊 Animated version with the live diagram Follow @amtocbot · #LearningTechBasics

Hands-On tests with 'UserHarbor' and IBM Bob: A Modular Approach to Python User Authentication and Permissions Introduction When evaluating open-source libraries for core application infrastructure - such as authentication, session management, and fine-grained Role-Based Access Control (RBAC) - getting hands-on with a complete reference application is invaluable. This is especially true for libraries aiming to be framework-agnostic, promising flexibility but requiring more explicit wiring. Recently, I wanted to explore UserHarbor ( github.com/userharbor/userharbor ), a lightweight Python user-management library designed without direct coupling to any web framework or database toolkit. Rather than manually bootstrapping a new project, setting up the SQLite database, and writing boilerplate code to explore every edge of the library, I used IBM Bob , to scaffold and implement an end-to-end reference demonstration integrated with FastAPI , SQLAlchemy for persistence, and SMTP (or a local console fallback) for transactional emails. The goal was to rapidly test UserHarbor's entire feature lifecycle - registration, email verification, session tokens, optional authentication, RBAC guards, password resets, and account deletion, which I personally find really useful. These capacities could be implemented in many applications and ease the phase of user registration, email validation etc… This post details the architecture built, highlights the key implementation logic, and illustrates how easily a decoupled core can be integrated into a modern web stack. UserHabor (from official GitHub repostory) Image from official project's repository Project status: UserHarbor is currently in an early stage of development. The API may change frequently. The library is not ready for production use yet . UserHarbor is a framework-agnostic Python library for user account management. Its goal is to provide a simple, stable, and framework-independent interface for common user-related operations:

A scraping job usually fails in boring ways: the browser hangs, a selector starts returning empty strings, a login expires, or the target site returns a captcha halfway through the run. The awkward part is that many platforms still bill you for the work done before the failure. If you run enough jobs, that difference shows up both in your invoice and in the amount of defensive code you need around the scraper. Billing by compute time changes how you build A lot of scraping platforms charge for runtime. Apify, for example, uses compute units: memory multiplied by time. A browser-heavy actor running for ten minutes with 2 GB of RAM consumes roughly a third of a compute unit before any actor-specific result fees. That model is reasonable from the provider side. Chromium processes are expensive. Proxies cost money. Retries use resources. But as the caller, you care about a different unit: did I get the rows I needed? The hard part is that runtime billing makes cost hard to know before execution. A job that normally takes 30 seconds might take 8 minutes when a site slows down. A job that returns malformed data can still count as successful from the platform's point of view. A job that fails after rendering 200 pages still consumed browser time. If your pipeline runs once a day, that may be fine. If it runs continuously, you probably want a local cost model that tracks outcomes, not just requests. type ScrapeRun = { jobId : string ; target : string ; startedAt : string ; finishedAt ?: string ; status : " queued " | " running " | " succeeded " | " failed " ; rowsExpected ?: number ; rowsReceived ?: number ; billedUnits ?: number ; }; function isUsefulResult ( run : ScrapeRun ) { if ( run . status !== " succeeded " ) return false ; if ( run . rowsExpected && ( run . rowsReceived ?? 0 ) < run . rowsExpected * 0.9 ) { return false ; } return ( run . rowsReceived ?? 0 ) > 0 ; } function costPerUsefulRow ( run : ScrapeRun ) { if ( ! isUsefulResult ( run )) return Infinity ; ret

If you just want the recommendation: call a plain REST image generation endpoint from your Node.js backend, keep the prompt-in / image-out path as dumb as you can stand, and add a chat model on top only when you actually need policy checks or structured prompts. For a first text-to-image feature inside a SaaS app, that is the entire architecture worth building. I've shipped that feature twice. Both times the generation call was the boring part. What ate the calendar was everything around it: deciding whether the output was safe to show a paying customer, reading the licence terms closely enough to know we could put generated art in a customer's exported PDF, storing the result somewhere that wasn't the provider's temporary URL, and — the part I got wrong, which I'll come back to — making retries safe. I run a one-person company, so I optimise for the number of moving parts I have to keep in my head at 2am, and a text-to-image feature that pulls in three new vendors is a feature I'll quietly regret. Your priorities may be different if you have an infra team. What should I look for in a text-to-image API for a SaaS app? Four things, in the order they'll actually hurt you. Model availability in your regions comes first. If you sell into both the US and the EU, check that the model you pick is served in both, because "we support Europe" sometimes means the marketing site and not the inference region. Ask for it in writing if the answer matters to your DPA. Commercial use terms come second, and they're the ones nobody reads until legal asks. Most of the big image models now permit commercial use of outputs, but the details differ on who owns the output, whether you can train on it, and what happens with likenesses and trademarks. Read the actual terms page for the model, not the aggregator's summary of it — aggregators route to several vendors and the upstream licence is what governs your PDF. Then pricing shape. Per-image billing is easy to model in a spreadsheet; per-s

When an application starts using more than one language model provider, the hard part is rarely the first API call. The hard part is everything that follows: separate credentials, different request shapes, provider-specific errors, billing dashboards, and model migrations scattered across the codebase. A useful way to reduce that surface area is to keep one OpenAI-compatible client contract and move provider choice into configuration. This guide shows the smallest working setup with Routara , plus the production checks I recommend before sending real traffic. 1. Keep the SDK, change the endpoint If your project already uses the OpenAI Python SDK, the client initialization is the only part that needs to change: import os from openai import OpenAI client = OpenAI ( api_key = os . environ [ " ROUTARA_API_KEY " ], base_url = " https://api.routara.ai/v1 " , ) response = client . chat . completions . create ( model = " deepseek-chat " , messages = [ { " role " : " user " , " content " : " Explain idempotency in two sentences. " } ], ) print ( response . choices [ 0 ]. message . content ) Store the key in an environment variable. Do not put it in browser code, a public repository, screenshots, or support messages. The same pattern works in Node.js: import OpenAI from " openai " ; const client = new OpenAI ({ apiKey : process . env . ROUTARA_API_KEY , baseURL : " https://api.routara.ai/v1 " , }); const result = await client . chat . completions . create ({ model : " deepseek-chat " , messages : [{ role : " user " , content : " Return one short test sentence. " }], }); console . log ( result . choices [ 0 ]. message . content ); 2. Treat model IDs as configuration Do not spread model names throughout the application. Put them in environment variables or a typed configuration object: model_id = os . environ . get ( " ROUTARA_MODEL " , " deepseek-chat " ) That makes model evaluation and rollback much safer. Routara's live model catalog is the source of truth for current availa

NASA has not made substantial investments in thermal protection research for decades.

Spain is putting a hash chain behind every invoice, and almost everything written about it so far has been written for accountants. This is the version for whoever has to ship it. The deadlines are January 1, 2027 for companies and July 1, 2027 for sole traders. If you read something last year that said 2026, that was true until RD-ley 15/2025 moved the whole calendar back twelve months. Software vendors have been on the hook since July 2025, which is a detail worth holding on to if you sell a product that issues invoices for other people. At BeeL., we sell an API for this, so read the rest with that in mind. The requirement Each invoice your software issues has to produce a registro de facturación de alta: a record containing a defined set of fields, hashed with SHA-256, where the hash of each record folds in the hash of the one before it. One chain per issuing tax ID, growing forever, never edited. Cancelling an invoice is not a delete. It's a second record type, a registro de anulación, which goes into the same chain. Same for corrections, which come in two flavours depending on whether you're amending a difference or replacing the original document. The printed invoice carries a QR code with verification data, plus the string VERI*FACTU if you're in submitting mode. Then you either push each record to the tax agency as it happens, or you keep everything locally under stricter signing and retention rules and hand it over when asked. Written down like that, it reads like an afternoon of work. A hash function, a previous_hash column, an HTTP call. Where the estimate falls apart The chain is strictly sequential, so two workers issuing invoices for the same tax ID at the same time are racing for the same link. You need a lock per issuer, or a queue, or both, and either way concurrent issuance stops being free. Retries are worse than they look. A failed submission that you retry carelessly either duplicates a record or breaks the chain, and a broken chain isn't someth

You’ve probably seen that little prompt that says “Sign in with Face ID” or “Use a passkey” instead...

The same parser, four times Over the last year I kept running into the same shape of problem: A docs site generator that wanted Markdown chapters turned into navigation JSON. A RAG ingestion script where each Markdown file needed to become a list of text chunks plus its frontmatter metadata. An n8n flow that took Markdown emails and extracted only the tasklists. A static-site backend that accepted user Markdown and needed to validate structure before persisting. Each one is small on its own. But every time I reached for a different library — remark here, gray-matter there, marked once, a hand-rolled regex once too many — and every time one of them broke on the same edge cases: Nested GFM tasklists where the checked state was silently lost YAML frontmatter that included quoted booleans (parsed as strings, not booleans) Tables whose headers contained spaces (regex parsers treated them as one key) Code blocks containing Markdown — re-parsed as Markdown instead of fenced code So I built one endpoint that does it once, properly. What it returns POST /v1/parse takes a Markdown body ( text/markdown ) or a JSON envelope ( application/json ) and returns one stable JSON shape: { "success" : true , "data" : { "title" : "Project Alpha" , "frontmatter" : { "title" : "Project Alpha" , "status" : "shipping" }, "headings" : [ { "level" : 1 , "text" : "Project Alpha" , "id" : "project-alpha" } ], "sections" : [ { "heading" : { ... }, "children" : [ ... ], "content" : [ ... ] } ], "lists" : [ { "ordered" : false , "items" : [ "ship MVP" , "write README" ] } ], "tasklists" : [ { "items" : [ { "text" : "ship MVP" , "checked" : true } ] } ], "tables" : [ { "headers" : [ "Module" , "Status" ], "rows" : [{ "Module" : "API" , "Status" : "Done" }] } ], "codeBlocks" :[ { "lang" : "js" , "value" : "..." } ], "links" : [ { "text" : "..." , "url" : "https://..." } ], "paragraphs" :[ "..." ], "ast" : null } } The sections tree is the part I care most about. It's not just a flat list of headings

How to pull large volumes of data out of any enterprise SaaS platform — safely, repeatably, and without a single write permission. Every enterprise runs on SaaS platforms — marketing automation, CRM, HR systems, finance tools. And every data team eventually gets the same request: "Can we get that data into our lake?" The naive answer is to grab an admin's credentials, hit the API, and start downloading. It works — right up until the admin leaves the company, the password rotates, someone accidentally writes data back into the source system, or the security team asks who exactly has been exporting customer records at 2 AM. This post describes a framework I've used to expose SaaS platform data to a data lake the right way. It's platform-agnostic: the same pattern works for almost any modern SaaS tool that offers a REST API. The framework has four pillars: A least-privilege, read-only API role A dedicated, non-human service account OAuth 2.0 client-credentials authentication An asynchronous bulk-export job pattern Let's walk through each. Pillar 1: A Read-Only API Role Before touching any code, create a dedicated permission role inside the source platform — and give it only read permissions, only on the API surface. Most enterprise SaaS platforms separate permissions into two planes: UI permissions — what a human can click on in the web interface API permissions — what a token can do programmatically Your extraction role should have zero UI permissions and only the Read-Only API permissions for the objects you need: records, activities, memberships, whatever your platform calls them. Why this matters: Blast radius. If the credentials ever leak, the worst an attacker can do is read the same data you were already reading. They cannot delete records, trigger campaigns, or modify configuration. Auditability. When the security team reviews the role, "read-only, API-only" is a one-line conversation. Future-proofing. Ticking all the read-only permissions (rather than the two