标签：#Automation

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!

Claude LLM Execution Harnesses, RAG Rerank, & Browser-based Edge AI Today's Highlights This week's top stories delve into advanced LLM orchestration with Anthropic's execution harnesses, highlight rerankers as a critical RAG pipeline upgrade, and explore practical browser-based AI for sign language recognition without cloud dependencies. Anthropic Explains How Claude Builds Its Own Execution Harnesses (InfoQ) Source: https://www.infoq.com/news/2026/06/claude-code-harnesses/?utm_campaign=infoq_content&utm_source=infoq&utm_medium=feed&utm_term=global This InfoQ article provides a deep dive into Anthropic's sophisticated orchestration system designed for managing multi-step processes with large language models (LLMs) like Claude. It details how the AI company constructs "execution harnesses" that enable Claude to chain together various operations, handle complex tasks, and recover from errors, going beyond simple prompt-response interactions. The system effectively functions as an internal agentic framework, showcasing advanced patterns for LLM workflow automation and robust production deployment. Understanding these internal mechanisms offers valuable insights for developers and architects aiming to build more resilient and capable AI agents that can tackle intricate, real-world workflows, from dynamic task planning to adaptive execution. It highlights the importance of modularity, self-correction, and tool integration in scaling LLM applications for enterprise use, providing a blueprint for building sophisticated AI agent orchestration layers. Comment: This is a fantastic look behind the curtain at how a leading LLM provider tackles agent orchestration at scale. It underscores that robust LLM applications require sophisticated workflow management, not just better models. RAG Rerank: the Highest-Leverage Upgrade to Your Retrieval Pipeline (Dev.to Top) Source: https://dev.to/dev48v/rag-rerank-the-highest-leverage-upgrade-to-your-retrieval-pipeline-7o5 This Dev.to artic

After months of manual trading on Polymarket, I got tired of missing fast momentum moves on BTC, ETH,...

That's the reality of what I've been testing - whether you can actually run a micro SaaS from a phone. Not as a gimmick, but as a real workflow. The key is prompting discipline. When I want a changelog section added to my delivery page, I'm not just asking. I'm structuring the task: queue it up, do QA after each step, create the build, update the OTA link, ping me on Telegram, then move to the next one. If something breaks, take notes and continue - I'll deal with it later. The AI handles the repetitive loop. I handle the decisions. Most of my dev ops now fits in a chat thread. Is this the future of solo building? Maybe. Or maybe it's just a useful edge case for when your laptop is in for repair and you have a deadline. Either way, it's worth knowing what's actually possible.

When to choose ansible roles over playbooks depends on the need for reusable structure, clear separation of concerns, and scalable maintenance across many environments. In a deployment that touches 1,200 servers, the early design decision determines whether the codebase remains maintainable or devolves into ad‑hoc tasks that require weeks of debugging. 📑 Table of Contents 📦 Modularity — Why Structure Matters 🧩 Reusability — When Scaling Demands Roles 🔧 Example: Deploying a Database Across Multiple Environments ⚙️ Dependency Management — How Requirements Influence Choice 🔗 Role Dependency Example 📁 File Layout — Organizing Artifacts for Maintenance 📊 Performance & Execution — Impact on Runtime 🔍 Comparison – Roles vs. Playbooks 🟩 Final Thoughts ❓ Frequently Asked Questions When should I still use a flat playbook? Can I mix roles and tasks in the same playbook? How do I test a role without affecting production? 📚 References & Further Reading 📦 Modularity — Why Structure Matters Roles enforce a predictable directory hierarchy that isolates tasks, variables, handlers, and files. What this does: # roles/webserver/tasks/main.yml - name: Install Nginx apt: name: nginx state: present - name: Deploy configuration template: src: nginx.conf.j2 dest: /etc/nginx/nginx.conf mode: '0644' notify: Restart Nginx # roles/webserver/handlers/main.yml - name: Restart Nginx service: name: nginx state: restarted tasks/main.yml: defines the ordered steps the role performs. handlers/main.yml: runs only when notified, preventing unnecessary restarts. The directory roles/webserver groups all related artifacts, making the role portable. Because the role encapsulates its logic, a playbook can invoke webserver without repeating internal steps. This eliminates duplication and aligns with the DRY principle. Key point: Enforced structure turns a loose collection of tasks into a self‑contained unit that can be shared across multiple playbooks. 🧩 Reusability — When Scaling Demands Roles Roles enable r

Overview Today's focus was on automating article publishing to CSDN and WeChat MP (微信公众号) using Playwright, after CSDN deprecated its public Open API. Key achievements include: injecting Markdown content into CSDN's dynamic editor, handling title input quirks, implementing QR code login for WeChat MP, updating the Dev.to API publisher, and consolidating platform configs into a single YAML file. We also fixed session log capture after a Claude Code update changed the log file path. Problems and Solutions 1. CSDN Open API Deprecation → Browser Automation Background : In early 2026, CSDN silently shut down its public Open API. All endpoints returned 404/403. We needed a fallback to keep publishing to China's largest developer platform. Solution : Use Playwright to simulate a real user login and article creation. The approach: Launch a headless Chromium browser. Navigate to CSDN's login page. Perform one-time manual login via QR code. Serialize cookies to csdn_cookies.json . On subsequent runs, load the cookies and skip login. Go to the editor, inject Markdown content via DOM manipulation, fill the title, and click publish. Code snippet : import asyncio from playwright.async_api import async_playwright async def publish_to_csdn ( title : str , content_md : str ): async with async_playwright () as p : browser = await p . chromium . launch ( headless = True ) context = await browser . new_context ( storage_state = " csdn_cookies.json " if exists else None ) page = await context . new_page () await page . goto ( " https://mp.csdn.net/mp_blog/creation/editor " ) # Inject content await page . evaluate ( f ''' () => {{ const editor = document.querySelector( ' .editor-content ' ); if (editor) {{ editor.innerHTML = ` { escaped_content } `; editor.dispatchEvent(new Event( ' input ' , {{ bubbles: true }})); }} }} ''' ) # Fill title await page . fill ( ' #title-input ' , title ) await page . click ( ' button:has-text( " 发布 " ) ' ) await page . wait_for_url ( " **/mp_blog/manage/ar

Optimizing RAG Pipelines, Migrating AI Agents, and LLM-Powered Troubleshooting Today's Highlights This week's highlights cover advanced strategies for building and maintaining robust AI systems, from fine-tuning RAG pipelines to orchestrating agent migrations. We also explore practical, real-world LLM application in IT operations. A Cognitive Benchmark for Code-RAG Retrieval: Part 2 — Why Model Rankings Depend on the Pipeline (Dev.to Top) Source: https://dev.to/miftakhov/a-cognitive-benchmark-for-code-rag-retrieval-part-2-why-model-rankings-depend-on-the-pipeline-12a4 This article delves into the critical but often overlooked aspect of RAG (Retrieval Augmented Generation) performance: how the entire pipeline, not just the underlying LLM, dictates retrieval efficacy, especially in code-RAG scenarios. It introduces a cognitive benchmark for code retrieval, moving beyond simple keyword matching to evaluate how well a RAG system understands developer intent when querying unfamiliar codebases. The core insight is that model rankings are highly dependent on the complete RAG pipeline design, including chunking strategies, embedding models, and retrieval algorithms, rather than solely on the base LLM's capabilities. For developers building code-centric RAG applications, this implies a need for holistic pipeline optimization. The article emphasizes that focusing on individual components in isolation may lead to suboptimal results. It encourages a structured approach to benchmarking that reflects real-world developer queries and challenges, such as understanding system behavior rather than just file names. This technical perspective is crucial for anyone looking to deploy robust and performant RAG systems for code generation, search augmentation, or automated code understanding. Comment: This is a crucial read for anyone moving beyond basic RAG demos. It highlights that success in production RAG systems, particularly for code, is all about the pipeline engineering , not just

TL;DR: Two tools cut Claude Code token usage at two different layers. RTK is a shell proxy that compresses command output before it ever reaches the context window. context-mode is a Claude Code plugin that does heavy tool work in a sandbox and hands back only the answer. They stack cleanly on top of each other, and a single skill installs both. This article explains how each one works and how to wire them in. Two commands into a session, my context window was already a third full, and I hadn't written a line of code yet. A pnpm install had dumped its entire dependency tree, a git log paid out two hundred commits, then a stack trace landed in full. None of that was work I'd asked for - it just sat there in the context window eating tokens on every turn. Most of the token budget goes on that boring output - the installs, the logs, the traces - which piles up and gets re-read on every single turn, never on the clever reasoning you actually wanted. Two tools attack that pile from two directions. Here's how they work, and how to install both in one command. This is the last article in the series, and it builds on the skill pattern from the third. You can pass this article URL straight to Claude Code and follow along. Where the tokens actually go Picture the context window as a desk. Everything Claude needs stays on the desk so it can glance at it: your prompts, its replies and the output of every command it ran. The desk has a size limit, and once something is on it, it gets re-read on every turn until it falls off the edge. Two kinds of clutter land there: Command output that arrives bloated. A dependency install, a long log, a verbose test run. It enters once and costs tokens on every turn after. The accumulated pile itself. Even reasonably sized outputs add up across a long session until the desk is buried. The two tools map onto those two problems. RTK trims the output before it ever reaches the desk. context-mode keeps the heaviest work off the desk altogether. Lay

You push v1.2.3 and expect a predictable sequence: tests pass → version is resolved → GitHub Release is created . In practice, teams usually pick one of two painful options: One giant workflow — every stage in a single YAML file. It works until you need reuse, workflow_call , or different triggers per stage. workflow_run chains — workflow A triggers workflow B. Passing outputs between runs is awkward, and renaming a workflow breaks the chain silently. There is a middle path: keep small, focused stage workflows (the ones you already have), declare order and wiring in one pipeline file , and use a single orchestrator step on tag push. This tutorial uses pipeline-compose-run — available on the GitHub Marketplace — and a copy-paste example you can drop into any repo. Full example (copy .github/ ): examples/run-tag-release What we are building On git push origin v* : release.yml ← one job, one action step └─ pipeline.yml ← declares order + wiring ├─ ci.yml ├─ stage-version-sync.yml → exports version └─ stage-release-publish.yml ← receives version No generated workflow to commit. No manual workflow_run graph. Step 1 — Entry workflow Create .github/workflows/release.yml : name : Release on : push : tags : [ " v*" ] permissions : contents : write actions : write jobs : run-pipeline : runs-on : ubuntu-latest steps : - uses : actions/checkout@v6 - uses : aeswibon/pipeline-compose-run@v0.3.0 with : pipeline_file : .github/pipelines/pipeline.yml github_token : ${{ github.token }} The actions: write permission is required because the action dispatches your stage workflows via workflow_dispatch . Step 2 — Pipeline file (order only) Create .github/pipelines/pipeline.yml : name : pipeline version : 1 stages : - id : ci workflow : .github/workflows/ci.yml - id : version-sync workflow : .github/workflows/stage-version-sync.yml needs : - ci outputs : - version - id : release-publish workflow : .github/workflows/stage-release-publish.yml needs : - version-sync inputs : version : ${{ co

You built an outreach agent, it sent 80 follow-ups this week, and you have no idea what happened to any of them. Did the prospect open the message? Click the demo link? Is the silence a "no" or a spam-folder problem? Without engagement signals, your agent is firing into the void and your follow-up logic is guesswork. The fix has two parts: turn tracking on when you send, and subscribe to the webhooks that report what recipients do. Tracking starts at send time, not after Opens, clicks, and replies are only reported for messages sent with tracking enabled — you can't retroactively track a message that's already out. On the Send Message request, pass a tracking_options object with three booleans plus an optional label that gets echoed back in every notification: curl --request POST \ --url 'https://api.us.nylas.com/v3/grants/<NYLAS_GRANT_ID>/messages/send' \ --header 'Content-Type: application/json' \ --header 'Authorization: Bearer <NYLAS_API_KEY>' \ --data-raw '{ "subject": "Quick follow-up on your trial", "body": "Thanks for trying us out. Reply or <a href=\"https://example.com/demo\">book a demo</a> when ready.", "to": [{ "name": "Kim Townsend", "email": "kim@example.com" }], "tracking_options": { "opens": true, "links": true, "thread_replies": true, "label": "trial-followup-q2" } }' The label is the piece agents should lean on: stamp it with your campaign ID or contact ID and every later notification carries it, so your handler matches events back to outreach state without storing a message-ID mapping. One caveat before you test: message tracking needs a production application — trial accounts get "Tracking options are not allowed for trial accounts" back. Three triggers, one endpoint Engagement events arrive over webhooks. Subscribe one HTTPS endpoint to all three triggers — message.opened , message.link_clicked , and thread.replied : curl --request POST \ --url 'https://api.us.nylas.com/v3/webhooks/' \ --header 'Content-Type: application/json' \ --header 'Autho

AI Agents Level Up Workflows: Terraform MCP, WebMCP, Pinecone Integrations Today's Highlights This week showcases significant advancements in AI agent orchestration and workflow automation, with new tools enabling AI to manage infrastructure, interact with the web, and leverage enterprise data. These developments highlight the growing maturity of applied AI frameworks for real-world production use cases. Terraform MCP Server Enables AI Assistants to Interact with Terraform Infrastructure (InfoQ) Source: https://www.infoq.com/news/2026/06/terraform-mcp-server-ga/?utm_campaign=infoq_content&utm_source=infoq&utm_medium=feed&utm_term=global The Terraform MCP (Machine Code Platform) Server has recently achieved general availability, marking a significant step forward in the integration of AI assistants with infrastructure-as-code paradigms. This new server allows AI agents to directly interpret and execute operations on Terraform-provisioned infrastructure, providing a robust and standardized interface for AI-driven automation. Instead of relying on complex scripting or indirect API calls, AI assistants can now receive natural language instructions, translate them into appropriate Terraform commands, and manage resources like virtual machines, networks, and databases across various cloud providers. This capability introduces unprecedented potential for advanced workflow automation within DevOps environments. Teams can leverage AI for tasks ranging from autonomous resource provisioning based on demand surges to intelligent incident response that dynamically scales or reconfigures infrastructure. The MCP Server acts as a crucial middleware, ensuring secure and controlled interaction between intelligent agents and critical infrastructure, thereby reducing manual operational burdens and enhancing system resilience through automated, intelligent responses. This direct interaction paves the way for a new era of self-managing, AI-orchestrated cloud environments. Comment: This i

The Moment I Realised UI Testing Wasn't Enough Three years into my QA career, I thought I was doing well. I had a solid Selenium suite running. Regression coverage was green. Stakeholders were happy. Then a production incident happened. A payment API was returning incorrect amounts under a specific condition. The UI looked perfect — amounts displayed correctly after rounding. But the raw API response? Off by a significant margin. My entire test suite missed it. Every single test. Because I was only testing what users saw . Not what the system was actually doing . That incident changed how I approached QA forever. 👇 Why API Testing Is the Most Underrated Skill in QA Let me be direct about something. Most QA engineers treat API testing as a secondary skill. Something you do with Postman when a developer asks you to verify an endpoint. A quick sanity check before moving on. That's the wrong mental model entirely. Here's the truth after 7.5 years: The API layer is where your product actually lives. The UI is a presentation layer. It shows users a version of the truth. But the API? That's the truth itself. Data contracts, business logic, validation rules, error handling — all of it lives at the API layer. If you're only testing the UI, you're testing the packaging. Not the product. My API Testing Journey — Tool by Tool Let me walk you through exactly how my API testing practice evolved, and what each tool actually taught me. Stage 1 — Postman: Learning to Think in Requests Postman was my entry point. And it's still the tool I reach for first when exploring a new API. But most people use Postman wrong. They treat it like a manual testing tool — fire a request, check the response, move on. That's wasting 80% of what Postman can do. Here's how I actually use it: Collections + Environments = your real power combo // Environment variables — not hardcoded values {{ base_url }} /api/ v1 / users / {{ user_id }} // Switch between dev/staging/prod by changing one environment // No

Local AI Coding Agents, Secure Production Deployment, and Angular-Specific AI Skills Today's Highlights This week's top stories highlight practical ways to deploy and secure AI agents, from setting up local coding assistants on macOS to sandboxing untrusted agent code in Azure, alongside new resources to improve AI-generated code quality for Angular. How to setup a local coding agent on macOS (Hacker News) Source: https://ikyle.me/blog/2026/how-to-setup-a-local-coding-agent-on-macos This guide provides a step-by-step tutorial on deploying and configuring an AI coding agent directly on a macOS system. The process typically involves setting up a local Large Language Model (LLM) or connecting to a local inference engine, integrating it with an orchestration framework, and configuring it to interact with local development tools and environments. The emphasis is on enabling developers to have a private, customizable AI assistant for code generation, debugging, and project scaffolding without relying on external cloud services. This local setup is crucial for privacy-conscious developers and for those who want to fine-tune agent behavior for specific internal codebases. The article likely covers prerequisites such as Python environments, relevant libraries, API key management for local models (if applicable), and how to set up the agent to execute code within a sandboxed environment on the machine. It offers a practical pathway for developers to experiment with AI agents in their daily coding workflows, providing immediate utility and control over the AI's operations and data handling. Comment: This is a great hands-on guide for anyone wanting to run AI coding agents locally, which is essential for privacy and custom development workflows. Run Untrusted AI Agent Code Safely with Azure Container Apps Sandboxes (InfoQ) Source: https://www.infoq.com/news/2026/06/untrusted-ai-agents-sandboxes/ Microsoft has announced the public preview of Azure Container Apps Sandboxes, a new