标签：#automation

DNC removal requests shouldn't take more than a few seconds to process. If your ops team is manually logging into each system, finding the number, and removing it one platform at a time, every request is an open compliance window. Here's how to close it automatically. The Problem With Manual DNC Processing A number comes in flagged for removal. Someone on the floor submits it. If you're running Convoso alongside Zoom Contact Center, Zoom Phone, and Telesero, that means logging into each system separately — find the number, remove it, move to the next platform, repeat. At multiple removal requests per week across several systems, you're looking at significant manual work each week. More importantly, every minute between the request and the removal is a minute of active compliance exposure. A TCPA violation starts at $500 per call. When the pattern is systematic — a number that should have been removed staying active across multiple campaigns — class action exposure enters the picture. The gap between when a removal is requested and when it actually completes isn't just inefficiency. It's risk that compounds with every dial attempt on a number that should be off the list. How Automated DNC Removal Works The automated version uses a Slack slash command as the intake point. An ops manager types the number into a command and hits send. The request routes immediately to a cloud service — deployed on Google Cloud Run — that fans out across every active system in parallel. Not sequentially. Simultaneously. In a contact center running multiple Convoso campaigns alongside Zoom Contact Center, Zoom Phone, and Telesero, a single command hits every platform in parallel. Each system processes the removal independently. Results log to cloud storage with a timestamp and each system's individual response recorded separately. A confirmation returns to the Slack channel before the manager has switched back to their next task. Wall-clock time from submission to confirmed removal across

Most AI tools give Australian small businesses American advice. An Aussie tradie running Xero does not need to hear about QuickBooks. A cafe owner with three casual staff has Fair Work Act obligations that no generic "automate your business" tool will surface. I built AppZ AU Business Advisor to fix this -- a free tool powered by Gemini 2.0 Flash that generates personalised automation blueprints with real Australian business context. This post covers the technical decisions, the prompt engineering approach, and why the AU-specific scaffold makes all the difference. The Problem with Generic AI Business Advice When you ask a general AI "how should I automate my business?", the training data skews heavily American. You get advice about QuickBooks, not Xero. About W-9 forms, not BAS lodgement. About 401k, not superannuation. For an Australian sole trader approaching the $75k GST registration threshold, this is not just unhelpful -- it is actively misleading. The compliance obligations are different. The software ecosystem is different. The pain points are different. The Prompt Scaffold Approach Instead of injecting "you are talking to an Australian business" as a keyword, I built a reasoning scaffold -- a structured context block the model uses as a knowledge foundation: AUSTRALIAN BUSINESS CONTEXT: - GST: 10%, mandatory registration at $75k annual turnover - BAS: lodged quarterly (or monthly for large businesses) to the ATO - Superannuation: 11.5% employer contribution, paid per payroll from July 2026 - ATO tools: STP Phase 2 mandatory for all employers - Dominant accounting platforms: Xero, MYOB, Reckon (not QuickBooks) - Fair Work Act: award rates, leave entitlements, payslip requirements - Key software by vertical: ServiceM8 (trades), Deputy (hospitality), Cliniko (health) This is not a keyword list -- it is a reasoning foundation. When a tradesperson mentions "invoicing problems", the model now reasons about Xero integrations, GST-inclusive invoicing, and BAS categ

We're building an AI marketing operation in public, and early on we hit a question we couldn't skip: how aggressive can you be about growth before you've crossed into something you'll regret? "Be ethical" is easy to say and useless under pressure. Every real decision is messier than that. Is using a VPN cheating? Is running more than one channel a trick? Is bending a platform's rules the same as lying? We needed a line we could actually hold at 2am when a shortcut looks tempting. Here's the one we found — and it turned out to be simpler and sturdier than "follow all the rules." The line isn't rule-breaking. It's deception. The cleanest test we landed on: the line is deception, not rule-breaking. Breaking a rule is a fight you can have in the open. You can announce it, defend it, and accept what comes. Deception is different — it works by making someone believe something false, which strips away their ability to respond honestly, because they don't even know what's real. That's the move that does the damage. So the question to ask about any tactic isn't "did this break a rule?" It's: "does this work by causing a real person to believe something that isn't true?" If yes, that's the line. If no, you're probably fine even if you're being bold. The daylight test Here's how to apply it fast. Ask: would this tactic still work if everyone could see exactly what I was doing? If yes — it survives daylight. People are choosing freely with full information. That's honest, even when it's aggressive. If it only works in the dark — the concealment itself has become the product. Something only works hidden because someone is acting on a false belief you planted. That's the part to cut. A poker bluff survives daylight (everyone knows bluffing is part of poker). A magician's trick survives daylight (the audience knows it's a trick and enjoys it). A fake testimonial does not. A sock-puppet account vouching for you does not. Run every growth idea through the daylight test and most hard

LLM Fallback in Production, Agentic eCommerce, and GitHub Copilot for Parallel Agents Today's Highlights This week highlights practical applications and architectural considerations for AI frameworks, focusing on robust LLM deployments and agent orchestration. We cover building resilient multi-provider LLM systems, leveraging agents for dynamic e-commerce, and GitHub's new desktop app for managing parallel AI agent workflows. How I built a 3-provider LLM fallback system in production (and what actually broke) (Dev.to Top) Source: https://dev.to/ayush_notsogreat_b673d5/how-i-built-a-3-provider-llm-fallback-system-in-production-and-what-actually-broke-46jk This article details the implementation of a robust LLM fallback system designed for production environments, addressing the common challenge of provider reliability and API rate limits. The author shares practical insights gained from building Socra, an application reliant on multiple LLM providers. The core of the system involves orchestrating requests across three different LLM APIs, ensuring that if one fails or encounters issues, the system seamlessly switches to an alternative without disrupting the user experience. The piece delves into the specific architectural decisions made to achieve this, including strategies for managing API keys, handling varying response formats, and implementing intelligent retry mechanisms. It also transparently discusses unexpected failures and critical lessons learned during the system's deployment, offering invaluable advice on anticipating real-world production issues beyond theoretical design. This practical guide provides a blueprint for developers seeking to build more resilient and fault-tolerant LLM-powered applications, crucial for maintaining high availability and consistent performance in AI workflows. Comment: Implementing multi-provider LLM fallbacks is essential for production-grade reliability; this article provides practical architecture and lessons from real-world

The first two articles in this series each showed one technique. Implementation notes #001 was a dynamic dropdown — a form field that fills itself from an API. Implementation notes #002 was a dynamic credential — an API key that arrives from the form and threads through to the HTTP nodes. This article is the capstone. It walks through all-services-demo , the example workflow that ships with n8n-nodes-ldxhub , where those two techniques combine with a Switch node to host five different AI document-processing services inside one workflow — structured extraction, translation refinement, OCR, PDF conversion, and text extraction. The screenshots and the workflow JSON below come from the n8n-nodes-ldxhub package. The patterns themselves are generic — they work for any set of services you want to consolidate into a single template. This is not a "follow these steps" article. It's a parts catalog. No two readers are solving the same problem, and templates rarely fit anyone's situation as-is. Take what fits. Drop the rest. You don't need to understand all 46 nodes to reuse the patterns. The shape The workflow has 46 nodes — large enough to look intimidating in the editor, but structurally it's just five repeated paths plus a small routing section. The entry section is two nodes: On form submission — the trigger. Asks the user which service they want and collects an API key. Route by Service — a Switch node with five outputs, one per service. Everything to the right of the Switch is service-specific. Five paths fan out: StructFlow, RefineLoop, RenderOCR, CastDoc, ExtractDoc. Each path ends in two Form Ending nodes — one for success (auto-downloads the result), one for error. That's the spine: form → switch → service path → ending. The complexity is pushed into the service paths. The spine: routing by static comparison The Switch node ("Route by Service") uses Rules mode. Each rule reads the same expression from the form — {{ $json.service }} — and compares it to a static serv

There is a quiet tax many engineering teams pay after their automated test suite starts to matter. The tests live in code. They run in CI. They already know the branch, commit, environment, failure message, stack trace, screenshot, trace file, retry status, and build URL. Then someone asks: Can we put these tests in Jira too? That request usually comes from a good place. Jira is where work is planned. Product, QA, engineering, and release stakeholders already use it. If quality signal is invisible there, people end up asking for screenshots from CI, links to failed builds, or spreadsheet summaries before every release. The mistake is assuming the answer is to recreate every automated test as a manual Jira test case. For many teams, that creates a second source of truth that starts decaying immediately. Automated tests are not manual test cases Manual test cases and automated tests have different jobs. A manual test case is a human-readable procedure. It often describes a workflow, expected result, and maybe some preconditions. It is useful when a person needs to execute or review a scenario. An automated test is executable behavior. It is versioned with the code, refactored with the product, reviewed in pull requests, and run repeatedly by machines. When teams try to manage automated tests by copying them into a test-case inventory, they usually create a translation problem: The test name changes in code, but the Jira case does not. The test is deleted or split, but the manual record still exists. The CI failure has rich evidence, but the test case only says "failed." The test belongs to a branch or commit, but the copied case does not. The release team sees a static inventory instead of the latest run signal. The test case becomes a label for a thing that actually lives somewhere else. The better question Instead of asking, "How do we turn all automated tests into Jira test cases?", ask: What does Jira need to know from each automated run? That changes the shape of

The Playwright Playbook — Part 3: Multi-User, Multi-Tab & Browser Context Testing "Most frameworks test one user at a time. Real apps don't work that way." In Part 1, we built the full foundation — POM, storageState , fixtures, and a clean project structure. In Part 2, we took control of the network layer — mocking APIs, simulating failures, and asserting on real network calls. Now we tackle the scenario that breaks most automation frameworks. Multiple users. Simultaneously. In one test. Think about your app for a moment. How many features actually involve just one user? Admin assigns a task → regular user sees it appear in real time User A edits a record → User B gets a notification Admin revokes access → User's session should invalidate Two users try to edit the same record → conflict resolution kicks in These are real features. They need to be tested. And page.goto('/login') in a beforeEach won't get you there. Playwright's browser context architecture was built exactly for this. Let's use it properly. 🎯 🏗️ Where We Left Off After Part 2, our project looks like this: playwright-playbook/ ├── tests/ │ ├── auth/ │ │ └── login.spec.ts ✅ Part 1 │ ├── tasks/ │ │ └── task-management.spec.ts ✅ Part 1 │ └── network/ ✅ Part 2 │ ├── api-mocking.spec.ts │ ├── error-simulation.spec.ts │ └── network-assertions.spec.ts ├── pages/ │ ├── LoginPage.ts ✅ Part 1 │ └── TaskPage.ts ✅ Part 1 ├── fixtures/ │ ├── auth.fixture.ts ✅ Part 1 │ ├── tasks.json ✅ Part 2 │ ├── empty-tasks.json ✅ Part 2 │ └── tasks-har.har ✅ Part 2 ├── scripts/ │ └── record-har.ts ✅ Part 2 ├── .auth/ │ ├── admin.json │ └── user.json ├── global-setup.ts ✅ Part 1 ├── playwright.config.ts ✅ Part 1 └── .env By the end of Part 3, we add: playwright-playbook/ ├── tests/ │ ├── multi-user/ ← NEW │ │ ├── role-permissions.spec.ts │ │ └── realtime-collaboration.spec.ts │ └── multi-tab/ ← NEW │ └── multi-tab-flows.spec.ts ├── pages/ │ └── DashboardPage.ts ← NEW ├── fixtures/ │ └── multi-user.fixture.ts ← NEW Every one of th

Playwright is the best browser automation library in 2026. It's also the most fingerprinted, the most detected, and the most patched in anti-bot databases. If you're running default Playwright against any platform with serious anti-bot defenses, you're getting flagged. We learned what's left of the cat-and-mouse game the hard way building HelperX . This article is what still trips up Playwright today — beyond the well-known navigator.webdriver flag, which everyone fixes in week one. The detection surface has shifted dramatically since 2022. The classic tells (PhantomJS, missing plugins, undefined chrome object) are largely solved. The new battleground is more subtle: CDP protocol artifacts, behavioral inconsistencies, and side-effects of the Playwright runtime that aren't part of the public API. What stopped working in 2024-2025 Detection on the platforms we monitor has gotten dramatically better in the last 18 months. A few things that worked in 2023 and stopped working since: Generic navigator.webdriver = undefined patches — detectable via Object.getOwnPropertyDescriptor if you do it naively Patching Notification.permission — modern detection cross-references with Permissions.query() Faking window.chrome — the property structure changed; old fakes are missing newer subkeys Setting a single User-Agent profile — detection now expects Client Hints to match Empty navigator.languages — flagged as suspicious; needs at least 2 entries These were all "set it once, ship it" fixes. The current generation of detection requires ongoing engineering. CDP artifacts: the deepest tell The Chrome DevTools Protocol (CDP) is how Playwright controls the browser. The browser exposes signals that it's being controlled, and modern detection looks for them specifically. Symptom: Runtime.evaluate artifacts When you run await page.evaluate(() => ...) , Playwright uses Runtime.evaluate under the hood. The evaluated script runs in a special isolated world. If the page can detect that an isola

Introduction Extracting data from older websites is a technical challenge that goes beyond simple copy-pasting. The example website provided illustrates this perfectly: its outdated design, reliance on mouse-over interactions, and lack of structured export options create a perfect storm of extraction difficulties. This article dissects these challenges and provides a roadmap for extracting both visible content and mouse-over images while preserving data integrity. The Core Problem: Legacy Technology Meets Modern Needs The website's URL parameters ( screen_width=0&screen_height=0 ) immediately signal a legacy system likely built for a bygone era of fixed-width displays. This design choice breaks modern scraping tools that expect responsive layouts. The mouse-over images, critical to the site's content, are dynamically loaded via JavaScript , meaning they don't exist in the initial page source. This requires simulating user interactions to trigger their appearance, a task beyond basic HTML parsing. Why Manual Extraction Fails Attempting to manually save images or copy text from this site is a losing battle. The mouse-over images, for instance, are not directly downloadable – they're embedded in JavaScript events. Even if you could save them individually, maintaining their association with the corresponding visible content would be error-prone and time-consuming. This method also fails to scale for larger websites with hundreds of such elements. The Technical Solution: A Multi-Pronged Approach Effective extraction requires a combination of techniques: Browser Automation: Tools like Selenium or Puppeteer can simulate mouse movements to trigger hover events, capturing both visible and hidden content. This method mirrors human interaction , ensuring all dynamic elements are revealed. Network Request Inspection: Analyzing the website's backend requests using browser developer tools can reveal direct URLs for mouse-over images , bypassing the need for hover simulation. This

What Recruiters Can't See On My GitHub If you spend about 30 seconds looking at my GitHub profile, you might think I'm all over the place. React. Python. Healthcare. AI. Scrapers. Automation. Marketing tools. Job bots. Honestly, that's something I've worried about. I have over 100 repositories. Recruiters can see most of them, but not all of them. Some are private because they're client work. Some are private because they're unfinished. Some are private because they contain ideas I've spent years developing and I'm not quite ready to throw the blueprints onto the internet. From the outside, it can look random. But recently I realized something. All of those projects are solving the same problem. I hate repetitive work. My GitHub is here: https://github.com/ashb4 The Job Application That Broke Me I've applied to thousands of jobs over the years. Thousands. And one thing has always driven me absolutely insane. You upload your resume. Then the company immediately asks you to type your entire resume into fifteen different boxes. Your work history. Your education. Your skills. Everything. The computer already has the information. The resume is right there. Yet somehow I'm sitting on page seven of an application retyping information that already exists. It feels inefficient. It feels stupid. And most of all, it feels like a waste of time. Eventually I got annoyed enough to start building tools to help. Then I Noticed a Pattern At first I thought I was building unrelated projects. A job application helper. A content scheduler. A healthcare platform. An AI framework. A browser automation system. But when I stepped back, I noticed the same motivation behind almost all of them. Every project started with some version of: "There has to be a better way to do this." Take PostPunk. Most people see a social media scheduler. I see hours of repetitive posting that I never want to do again. I like creating content. I do not like manually posting the same content everywhere. So I buil

If you've ever had to generate hundreds of social media banners, OG images, or PDF certificates programmatically — you know how painful that workflow can get. Stitching together Canvas, Puppeteer, or headless Chrome just to render a templated image is a lot of overhead for what should be a straightforward task. That's exactly the problem Bannx is built to solve. What is Bannx? Bannx is a high-volume banner, ad, and PDF automation platform. It combines a visual template editor with a developer-friendly REST API, so designers can build beautiful templates and developers can render them at scale — no browser required. Think of it as the missing link between your design system and your backend. Key Features 🖼️ Visual Template Editor Bannx comes with a full-featured editor where you can create templates for: Social media graphics (Instagram, Twitter/X, LinkedIn) OG images for blogs and articles Ad banners E-commerce assets (product cards, order confirmations) Certificates and branded PDFs and more... Variables can be bound to any element in the template, making every design data-driven from the start. ⚡ REST API for Rendering Once your template is ready, rendering it is a single HTTP request: curl -X POST https://bannx.com/api/render \ -H "Authorization: Bearer YOUR_API_KEY" \ -H "Content-Type: application/json" \ -d '{ "pageId": "PAGE_ID", "format": "png" }' You get back a hosted URL and metadata — ready to display, store, or pass downstream. You can also stream raw binary output with "output": "binary" . Supported export formats: PNG, JPEG, SVG, WebP, PDF 📦 Bulk Generation via CSV Need to generate 10,000 personalized certificates or product cards? Upload a CSV and Bannx handles the rest. Each row maps to a set of variable overrides — no scripting required. 🧩 Dynamic Links & Variables Templates support per-request variable overrides, so you can pass data directly in the API call without modifying the template. Combined with template expressions (functions and conditiona

I wanted to see how far an autonomous coding agent could get unattended. The constraint that makes this hard isn't code generation — it's that each session starts with zero memory of the last. So the design problem is state, not prompting. Setup A cron-scheduled task fires hourly, 11pm–7am (~8 runs). Each run is a fresh agent session. No shared context, no carryover. State lives entirely on disk: the working tree, the git history, and two files — BUILD_SPEC.md (the immutable goal/architecture) and PROGRESS.md (an append-only decision + status log). The run loop Every session does the same thing: Read BUILD_SPEC.md, then PROGRESS.md, then git log --oneline. Reconstruct "where are we" from the files themselves (the code is the source of truth, not the narrative). Do one unit of work. Commit. Append to PROGRESS.md: what changed, why (chosen X over Y because Z), and the exact next step. Commit granularity = checkpoint granularity. Worst case on an interrupted session is losing one unit, and the next run re-derives it. The "why" lines matter as much as the diffs — without them a later session re-litigates settled decisions. Guardrails The agent was allowed to build, test, and commit locally. It was explicitly not allowed to deploy, push to a remote, or touch secrets — those get written into PROGRESS.md as "needs human" items instead. This boundary is what makes unattended runs safe to leave alone. What came out A working full-stack monorepo: a pure TS scheduling engine (with property tests), multi-tenant auth, a Drizzle/Postgres schema, server-side re-validation, and publish/share/export flows. Across the runs it cleared its own stale git lock, and one session caught and fixed an off-by-one in a labeling layer that spanned five files. The takeaway The leverage wasn't the model writing code. It was designing a process where progress is durable across total context loss — externalize state, checkpoint constantly, log decisions not just actions, and fence off irreversible o

While you're manually checking if YES + NO = 1 , quantitative systems are solving massive constraint satisfaction problems across thousands of correlated markets in milliseconds. The Hidden Reality of Prediction Market Arbitrage You see a market where YES is trading at $0.62 and NO at $0.33. You think: There's $0.05 of arbitrage here . You're right. What you don't see is that by the time you place both orders, professional systems have already: Scanned 17,000+ conditions Detected dozens of correlated mispricings Calculated optimal position sizes (with fees & slippage) Executed everything in parallel Moved on to the next opportunity Between April 2024 and April 2025, quantitative traders extracted $39,688,585 in guaranteed arbitrage profits from Polymarket. The top individual wallet made $2,009,631.76 across 4,049 trades — an average of $496 guaranteed profit per trade . This wasn't gambling. This was mathematics. Why Simple "YES + NO = 1" Checks Fail Most retail traders stop at basic price sum checks. That's not enough. Markets are logically dependent. Example: "Will Trump win Pennsylvania?" → YES: $0.48 "Will Republicans win Pennsylvania by 5+ points?" → YES: $0.32 If the second outcome happens, the first must be true. These dependencies create arbitrage opportunities that simple addition cannot detect. This is known as the marginal polytope problem — projecting prices onto the set of arbitrage-free probability distributions. The Scale of the Computational Challenge For any event with n binary conditions, there are 2ⁿ possible outcome combinations. 2024 U.S. elections: 305 markets → tens of thousands of pairs 2010 NCAA tournament: 63 games → 2⁶³ ≈ 9.2 quintillion combinations Brute force is impossible. Smart systems use constraints instead. Real example : Duke vs Cornell basketball market 7 possible win counts per team → 14 conditions. Instead of checking 16,384 combinations, 3 linear constraints were enough. Research found that 41% of 17,218 conditions showed sing

Stacking indicators doesn't make you smarter — it makes your bot dumber. You've seen the guides....

This advice is everywhere now: Stack ten indicators, wait for three of five to agree, then size up...

👋 Hey there, Tech Enthusiasts! I'm Sarvar, a Cloud Architect who loves turning complex tech problems...

Ihre OpenAPI-Datei ist die Quelle der Wahrheit für Ihre API: Pfade, Parameter, Request-Bodies, Responses und Schemas. Für Entwickler im Alltag ist rohes YAML oder JSON aber selten das beste Lesematerial. Backend-Teams brauchen eine schnelle Endpunkt-Referenz im Repository, Frontend-Teams wollen Request- und Response-Felder im Pull Request prüfen, und technische Redakteure möchten Inhalte in Wiki- oder Docs-Systeme übernehmen, ohne Schemas abzutippen. Testen Sie Apidog noch heute Markdown ist dafür das praktischste Zielformat. Es funktioniert in GitHub, Confluence, Notion, Docusaurus, MkDocs, Hugo und jedem Texteditor. Die Aufgabe lautet also: Aus einer vorhandenen openapi.yaml automatisch sauberes Markdown erzeugen. Manuell ist das zu langsam und driftet beim nächsten API-Change auseinander. Automatisch generiertes Markdown bleibt dagegen Teil Ihres Release-Prozesses. Warum Markdown aus OpenAPI generieren? Ein OpenAPI-Dokument ist primär für Maschinen gedacht. Tools parsen es, um Clients zu generieren, Contract-Tests auszuführen, Requests zu validieren oder interaktive Dokumentation zu rendern. Diese Maschinenlesbarkeit sollten Sie beibehalten. Wenn Sie zuerst die Qualität Ihrer Spezifikation prüfen möchten, hilft der Leitfaden zu OpenAPI-Validierungstools . Markdown löst ein anderes Problem: Es macht die API dort lesbar, wo kein OpenAPI-Renderer läuft. Typische Einsatzfälle: README.md oder /docs im Repository Pull-Request-Beschreibungen für neue oder geänderte Endpunkte Confluence- oder Notion-Seiten für Team-Reviews Statische Dokumentationsseiten mit Docusaurus, MkDocs oder Hugo Offline- oder interne Referenzdateien für Support und QA Wichtig ist: Markdown sollte ein abgeleitetes Artefakt sein. Die OpenAPI-Spezifikation bleibt kanonisch, Markdown wird bei Änderungen neu erzeugt. Methoden im Überblick Es gibt keinen offiziellen OpenAPI-Befehl für Markdown-Export. In der Praxis nutzen Teams entweder Konverter, ein eigenes Skript oder eine API-Plattform. Methode Am b

If you've ever needed a list of local businesses - every dentist in Manchester, every plumber in Leeds - with their contact details , you've probably hit the same wall I did: Google's Places API is rate-limited, costs money once you scale, and annoyingly doesn't return email addresses at all. Copy-pasting from Maps by hand is soul-destroying past the first ten rows. Most "scrapers" give you the name and a phone number, then stop right where the value starts: the email . This guide shows a practical way to pull structured business data straight from Google Maps and auto-enrich each result with emails, extra phones, and social links - no Google API key, exportable to JSON/CSV/Excel, and callable from code. What you actually get per business { "name" : "Ringway Dental - Cheadle" , "address" : "187 Finney Ln, Heald Green, Cheadle SK8 3PX" , "phone" : "0161 437 2029" , "website" : "https://www.ringwaydental.com/" , "rating" : 5 , "reviewsCount" : 598 , "category" : "Dental clinic" , "lat" : 53.37 , "lng" : -2.22 , "emails" : [ "reception@ringwaydental.com" ], // ← enriched from the website "socialLinks" : { "facebook" : "..." , "instagram" : "..." }, "extraPhones" : [ "..." ] } The first block (name → coordinates) comes from Maps. The emails / socialLinks / extraPhones are the bit that makes a list actually usable for outreach - they're crawled from each business's own website. The fast way: a ready-made Actor Rather than build and babysit the scraping yourself, I packaged this as an Apify Actor: Google Maps Scraper . You give it search terms + locations; it returns enriched rows. Input: { "searchTerms" : [ "dentists" ], "locations" : [ "Manchester, UK" ], "maxPlacesPerSearch" : 50 , "scrapeContacts" : true , "relatedEmailsOnly" : true } That's it. scrapeContacts: true turns on the website crawl for emails/socials; relatedEmailsOnly keeps only emails that belong to the business's own domain (so you don't get random gmail noise). Call it from code (Python) Every Apify Act

You’ve built something with Playwright. It works perfectly in your local environment. CI is green....

Let's be honest: standard AI chatbots are getting a bit boring. You ask them a question, they write back a beautiful paragraph of text, and then... nothing. They don’t actually do anything for your business. If you want to add a customer to your CRM, update a product on your website, or change something in your database, you still have to do it manually. That is why we decided to build something different. Instead of another chatbot that just talks, we created Gaotus Gaotus! See . It is an "execution AI" layer. This means it doesn't just reply to you—it actually connects to your tools (like WordPress, custom dashboards, or APIs) and does the manual work for you. Think of it like this: No more boring web forms to fill out. You just talk to the system, and it updates the database automatically. It checks the data for mistakes and logs everything securely before making any changes. It saves hours of manual data entry for small businesses. We are currently testing it with real-world scenarios, like automatic customer onboarding and syncing car dealership listings straight to web marketplaces. Since we are launching and improving this system, we would love to hear from other developers and creators: What is the most boring, repetitive task in your daily workflow that you wish an AI could just execute for you? Let’s chat in the comments!