标签：#Automation

RAG, Agents, & Code Security: Applied AI Frameworks in Production Today's Highlights This week's highlights feature a practical guide to building RAG-powered knowledge bots with Laravel and pgvector, alongside major strides in AI agent orchestration with a new resource discovery specification. Additionally, we examine AWS's new agentic code security service, demonstrating the application of AI frameworks to critical enterprise workflows. RAG in Laravel: Embeddings and pgvector for a Knowledge-Base Bot (Dev.to Top) Source: https://dev.to/adityakdevin/rag-in-laravel-embeddings-and-pgvector-for-a-knowledge-base-bot-3l2g This post delves into implementing Retrieval Augmented Generation (RAG) within a Laravel application to create a more informed knowledge-base chatbot. It directly addresses the common challenge where large language models (LLMs) lack domain-specific knowledge by leveraging embeddings and pgvector to incorporate proprietary data. The article builds upon a previous discussion on streaming AI responses, now focusing on integrating custom information for enhanced chatbot accuracy and relevance. The technical approach involves generating vector embeddings for a knowledge base's documents, storing these representations in a PostgreSQL database using the pgvector extension, and then performing similarity searches. When a user query is received, its embedding is generated, used to retrieve the most semantically relevant documents from the knowledge base, and these documents are then fed as context to the LLM. This process dramatically improves the chatbot's ability to answer questions based on specific, internal data rather than relying solely on its general training. This practical guide demonstrates a tangible workflow for overcoming LLM hallucinations and improving contextual understanding in custom applications. It highlights how robust RAG architecture, even within a PHP framework like Laravel, can be achieved using battle-tested database technologies and

I built Commit Cron , a small GitHub Actions experiment that creates one automated commit every day. The project updates a text file with the latest execution time, commits the change using the github-actions[bot] account, and pushes it back to the repository. View the project on GitHub: Commit Cron How It Works The workflow runs every day at 10:00 AM Asia/Manila time. on : schedule : - cron : " 0 10 * * *" timezone : " Asia/Manila" workflow_dispatch : The workflow_dispatch trigger also lets me run the workflow manually from the GitHub Actions tab. The workflow checks out the repository, creates the bot directory when needed, and updates bot/last-run.txt : mkdir -p bot printf "Last automatic update: %s \n " \ " $( TZ = Asia/Manila date '+%Y-%m-%d %H:%M:%S %:z (Asia/Manila)' ) " \ > bot/last-run.txt The file contains a timestamp similar to: Last automatic update: 2026-07-17 10:03:24 +08:00 (Asia/Manila) After updating the file, the workflow configures the GitHub Actions bot identity and creates the commit: git config user.name "github-actions[bot]" git config user.email \ "41898282+github-actions[bot]@users.noreply.github.com" git add bot/last-run.txt git commit -m "chore: daily automated update" Before pushing, it pulls the latest branch changes with rebase: git pull --rebase origin " ${ GITHUB_REF_NAME } " git push origin "HEAD: ${ GITHUB_REF_NAME } " This helps prevent the push from failing when another commit is added while the workflow is running. Repository Structure . ├── .github/ │ └── workflows/ │ └── daily-commit.yml ├── bot/ │ └── last-run.txt ├── LICENSE └── README.md Why I Built It Commit Cron is a small demonstration of: Scheduled GitHub Actions workflows Manual workflow triggers Automated file updates Bot-generated Git commits Repository write permissions using GITHUB_TOKEN The workflow uses: permissions : contents : write This allows the built-in GitHub token to push the generated commit. Important Note The automated commits only confirm that the work

An agent moving through a multi-step task needs two things it doesn't automatically have: a reliable way to know when the task is actually finished, and a reliable way to recognize when its current approach isn't working. Without both, the agent has no internal alarm bell. It just keeps acting — and if the same action keeps producing the same unhelpful result, nothing tells it to stop, change course, or ask for help. This isn't a minor implementation detail. It's a structural gap in how most agent loops are built: observe, decide, act, observe again. That loop has no natural exit condition unless one is explicitly designed in. Pattern One: The Retry Loop : The retry loop is the simpler of the two failure modes. The agent takes an action, it fails, and the agent tries the exact same action again — sometimes with trivial variation — expecting a different outcome. A few reasons this happens: Misread failures : The agent doesn't correctly interpret why the action failed, so it can't adjust its approach. It just repeats the attempt. No failure memory : Without a persistent record of "I already tried this and it didn't work," the agent has nothing to check against before trying again. Overconfidence in the plan : If the agent's internal reasoning treats the original plan as correct, it may conclude the execution was the problem, not the plan — and simply re-execute. The result is a kind of insanity loop: identical input, identical output, repeated until a turn limit, budget cap, or timeout finally intervenes from the outside. Pattern Two: Oscillation : Oscillation is subtler and, in some ways, more dangerous, because it can look like activity rather than failure. The agent doesn't repeat the same action — it alternates between two (or more) states, undoing its own progress each cycle. A classic example : An agent editing a file makes a change, then in a later step "fixes" that change back to something close to the original, believing it's correcting an error. The next cyc

* When a project first starts, CI/CD pipelines are usually simple. * Build the application. Run a few tests. Deploy somewhere. Done. Then six months pass. Another test gets added. Then another deployment step. A security scan. Performance tests. Notifications. More environments. Before long, a pipeline that once took five minutes now takes forty-five. I've seen this happen more than once, and it's rarely because Azure DevOps is the problem. It's usually because nobody ever stops to ask one simple question: Does this step still belong here? Everything Ends Up in the Same Pipeline One of the most common mistakes I see is trying to make a single pipeline do everything. Every Pull Request ends up running: Every unit test Every API test Hundreds of UI tests Security scans Deployment steps Report generation The result? Developers wait longer for feedback, releases become slower, and people eventually start ignoring failed pipelines because they happen too often. Fast Feedback Wins Not every test needs to run on every commit. A better approach is to think about the purpose of each pipeline. For a Pull Request, I want answers quickly. That usually means: Build the application Run unit tests Run a small smoke test suite Stop if something important fails Everything else can happen later. Long-running regression tests, cross-browser testing and other expensive checks are often better suited to scheduled or nightly pipelines. Pipelines Should Evolve A pipeline isn't something you build once and forget about. Every few months it's worth reviewing it. Ask yourself: Which step takes the longest? Which tests fail most often? Are there any tasks nobody remembers adding? Are we getting useful feedback, or just more output? Removing unnecessary work is just as valuable as adding new automation. Final Thoughts Azure DevOps is an incredibly powerful platform, but even the best tools become frustrating if they're overloaded with unnecessary work. The goal isn't to build the biggest pipel

Building an AI agent with n8n is the fastest, cheapest way to turn a large language model into a useful worker — if you stay within its sweet spot. The honest truth, informed by the custom agents we ship, is that n8n carries a well-scoped agent further than most people expect. An LLM node, a few tool/webhook nodes and a trigger are all you need. This guide walks you through that exact workflow and, just as importantly, names the precise moment n8n stops cutting it and a custom build must take over. What You Need Before You Start You'll need a running n8n instance (self-hosted or cloud) and API keys for the services you want to integrate. Grab a Gemini or OpenAI key from their respective developer consoles — n8n's official AI agent builder documentation lists the full compatibility. The quick-start template also gives you a one-click import to see an agent's skeleton immediately. How to Build an AI Agent with n8n: The Core Workflow The core is a chain of nodes: a trigger wakes the agent, an LLM node reasons, and tool/webhook nodes take action. That's the entire pattern. Here's how to assemble it. Set the trigger Drag a Webhook node onto the canvas if you want the agent called via HTTP, or a Schedule node to run it periodically. For our example, we'll use a webhook that receives a customer question. Add the LLM node Attach an OpenAI Chat Model (or Gemini) node. In the node's parameters, craft a system prompt that scopes the agent. For a support bot, something like: You are a helpful support agent for our SaaS product. Use the tools provided to answer questions. If you don't know, say you need human help. This prompt is the boundary of the agent's autonomy. Keep it specific — vagueness leads to hallucinations. Attach tool and webhook nodes Here's where n8n shines. Drag a Function node to run custom JavaScript (e.g., querying a database) or a HTTP Request node to call an external API. Wire them as "tools" by connecting them to the LLM node's tool output. In the LLM node

If you've been working with cognitive architectures that rely on structured memory injection, you likely know the pain of corrupted or incomplete embedding spaces. The latest update to hermes-memory-installer directly addresses a brittle failure mode: missing embeddings in the gbrain module. This fix introduces an automatic, targeted repair mechanism that detects and rebuilds only the affected subset of embeddings, rather than triggering a full reinstall. Here’s what changed, why it matters, and how to benefit from it. The Problem: Silent Degradation in gbrain In a typical setup, hermes-memory-installer populates the gbrain—a specialized long-term memory store—with precomputed embeddings for core concepts, episodic traces, and procedural patterns. These embeddings are the numeric backbone that allows the agent to query, retrieve, and associate memories efficiently. However, under certain conditions—partial upgrades, concurrent memory imports, or incomplete network transfers—the gbrain’s embedding table ended up with holes. Specific embeddings for targeted contexts were simply missing. The agent would still boot, but retrieval quality degraded silently: queries returned null vectors or fell back to generic responses, breaking fine-grained recall. Users reported that their agents "forgot" recent conversations or failed to recognize learned skills, yet no obvious error was raised. Previously, the only remedy was a full reinstall of the memory installer, which wiped and rebuilt the entire gbrain. That was slow, wasteful, and could erase customized embeddings that were working correctly. The Fix: Targeted Auto-Repair The new update ( v2.1.0 onwards) adds a dedicated repair pass during the installation and upgrade routine. Instead of scanning the entire gbrain, the installer now maintains a lightweight manifest of expected embedding keys for each memory context. During setup, it checks the actual embedding store against this manifest. If any keys are missing, it triggers

How to Automate SEO Content Publishing Without Breaking Your Workflow Managing SEO content at scale is one of those problems that looks simple until you're staring at a spreadsheet of 200 articles in various stages of draft, review, and scheduled publication — and you still have to manually paste metadata into WordPress, set canonical tags, and remember which pieces need internal links updated. Automating SEO content publishing means connecting your content pipeline — from keyword targeting through final scheduling — into a repeatable system where the manual handoffs disappear. The short version: you use a combination of a CMS with robust API access, a content workflow tool or spreadsheet-to-publish bridge (like Zapier, Make, or a custom script), and structured content templates with pre-filled SEO fields, so that a piece of content moves from approved draft to live URL without someone doing ten small tasks by hand. The rest of this tutorial is about how that actually works, where it breaks, and what's not worth automating. What You Actually Need Before You Start Automating Most guides jump straight to tools. That skips the part that determines whether automation saves you time or just makes your errors faster. Before any automation runs, your content process needs to be defined well enough to describe in writing. Can you list every step from "keyword approved" to "post is live" right now, including who does what? If that list doesn't exist yet, building automation on top of undefined process is how you end up with 40 posts published with missing meta descriptions and no one knowing why. The other thing people underestimate: your CMS needs to support programmatic publishing. WordPress with REST API enabled, Webflow's CMS API, Contentful, Ghost — these all work. A legacy CMS that requires someone to log in and click publish is a wall, not a speed bump. If your platform doesn't have an API or a native integration path, you're looking at a rebuild before automation is

You have a project with 20 dependencies. Half of them are outdated. Running ncu -u or pip install --upgrade upgrades all of them at once — and when something breaks, you have no idea which package caused it. So you don't upgrade. The deps rot. Security patches pile up. loopgrade fixes this. It upgrades one dependency at a time, runs your test suite after each upgrade, commits if it passes, reverts if it fails, and moves on to the next one. When it's done, it gives you a full report of what was upgraded, what failed, and why. GitHub: https://github.com/Sagar-S-R/loopgrade PyPI: https://pypi.org/project/loopgrade/ Open for Contributors #Python #LangGraph #opensource

Most test automation tools look good during a demo. You record a login flow, add an assertion, run it in Chrome, and get a green result. Everyone is impressed. Then the real application gets involved. There are dynamic elements, delayed API responses, test accounts, verification emails, downloaded files, several deployment environments, and a checkout flow that behaves differently on Safari. A few months later, the original test suite has grown from 10 tests to 300. Some failures are product bugs. Others are test problems. A few only happen in CI. Nobody is completely sure which is which. That is when you discover whether you selected a test automation tool or merely a good demo. Creating tests is rarely the main problem When teams compare automation tools, they often begin with questions such as: How quickly can we record a test? Can AI generate the steps? Does it support plain-English instructions? Can a manual tester use it? Does it integrate with our CI pipeline? These are reasonable questions, but they mostly describe the beginning of an automation project. The harder questions appear later: Who updates the tests after a redesign? How do we investigate failures? Can another person understand a test created six months ago? What happens when the original automation engineer leaves? Can we test workflows that involve email, APIs, files, or mobile devices? How much infrastructure do we have to manage? Does the cost increase every time we run the regression suite? The first test tells you whether the tool works. The hundredth test tells you whether the approach works. Maintenance should be part of the evaluation A stable automated test is not a test that never changes. Applications are supposed to change. Buttons move. Components are replaced. Authentication flows evolve. APIs return different data. Product teams redesign entire sections of the interface. The objective is not to prevent tests from changing. It is to make those changes inexpensive and understandable.

Knowledge-and-Memory-Management v0.0.2 is out, delivering a clean release that prioritizes portability and modularity. This version shifts from hardcoded personal paths to $AGENT_HOME , making your knowledge pipelines reproducible across environments. If you’re building autonomous systems that need to ingest web content, video transcripts, or articles, this is the update you’ve been waiting for. The core design separates collection from memory management. The knowledge_collector module handles ingestion, while memory_manager handles storage, retrieval, and decay. The $AGENT_HOME environment variable anchors all runtime paths—no more hardcoded /home/user strings. Set it once, and your agents can carry their knowledge base anywhere. Knowledge Collection: Web, Video, Articles The collector supports three primary sources: Web : Scrapes and parses HTML, extracting body text and metadata. Handles rate limiting and retry logic. Video : Takes a YouTube URL, downloads captions (if available) or generates transcripts via Whisper integration. Articles : Parses RSS feeds or direct PDF links, chunking content by sections. All sources normalize into a KnowledgeEntry dict: {source, timestamp, content, embeddings} . The collector writes raw entries to $AGENT_HOME/knowledge/raw/ and passes them to the memory manager for processing. Memory Management with $AGENT_HOME The memory manager is where the clean release shines. Previous versions used os.path.expanduser("~/knowledge") , which broke across systems. v0.0.2 requires $AGENT_HOME to be set, then constructs all paths relative to it: $AGENT_HOME/memory/ stores persistent memories. $AGENT_HOME/knowledge/ holds raw and processed collections. $AGENT_HOME/config/ contains source definitions and memory decay rules. This design lets you ship a single agent.env file with AGENT_HOME=/opt/myagent or %AGENT_HOME%\data —no platform-specific configuration. The memory manager indexes entries by semantic embeddings (via a pluggable model provider

Smalltalk has excellent live environments, but managing the different VMs, images, and installers for Pharo, Cuis, Squeak, Glamorous Toolkit, GNU Smalltalk, etc. is painful. I made st — a lightweight shell-based CLI that gives one consistent interface: st pharo install && st pharo run st gt install , st cuis install , etc. Basic package search/install and eval support where available Works on Linux/macOS/Windows (WSL) Repo: https://github.com/hernanmd/st Happy to answer questions, take feedback, or hear what other Smalltalk pain points you'd like addressed. Contributions welcome (especially adding new dialects)!

Genkit Agents API, ORA, Python AI Explainer: New Tools for Workflow Automation Today's Highlights This week, Google's Genkit ships a powerful Agents API for TypeScript and Go, featuring human-in-the-loop capabilities for robust production deployments. Additionally, a new Go-based open-source task orchestrator, ORA, emerges for efficient model routing, alongside a practical Python tutorial for building an AI error explainer. Google's Genkit Ships Agents API with Detached Turns and Human-in-the-Loop for TypeScript and Go (InfoQ) Source: https://www.infoq.com/news/2026/07/genkit-agents-api-preview/?utm_campaign=infoq_content&utm_source=infoq&utm_medium=feed&utm_term=global Google has released a preview of its Genkit Agents API, significantly enhancing its open-source AI framework for building generative AI applications. This new API introduces features critical for deploying robust AI agents in production, specifically "Detached Turns" and "Human-in-the-Loop" functionalities. Detached Turns allow agents to operate asynchronously, handling long-running tasks or waiting for external events without blocking the main workflow, which is essential for complex, multi-step agentic processes. The Human-in-the-Loop feature provides crucial mechanisms for human oversight and intervention, ensuring reliability, safety, and compliance in critical applications where full automation is not yet feasible or desirable. The Genkit Agents API supports both TypeScript and Go, targeting a broad range of developers integrating AI into existing systems or building new agentic workflows. By offering structured patterns for agent interaction, state management, and human review, Genkit aims to streamline the development and deployment of intelligent agents, addressing key challenges in control and reliability for real-world AI applications. Comment: The introduction of Human-in-the-Loop into Genkit's Agents API is a game-changer for production-grade agent systems, offering the control and reliab

For a long time, teams treated browser test reliability as a framework problem. When tests failed, the usual response was to change selectors, add waits, increase retries, or replace one automation library with another. That approach made sense when the main challenge was simply controlling a browser. Modern applications are different. A single user journey may now include an identity provider, multi-factor authentication, a streaming AI response, a background API request, a feature flag, a canary deployment, and a frontend rendered differently across several operating systems. The test framework is still important, but it is only one part of the reliability problem. The bigger question is whether the entire testing system gives the team enough evidence to make a release decision. Headless failures are usually a symptom, not the real problem A common example is a test that passes locally but fails only in headless Chrome. It is tempting to assume that headless mode is simply unreliable. In practice, the difference is often caused by viewport size, rendering behavior, animation timing, fonts, resource loading, or elements being positioned differently when no visible browser window exists. This breakdown of why browser tests fail only in Chrome headless is useful because it separates several failure categories that are often grouped together as “timing issues.” That distinction matters. A test that fails because an element is outside the viewport needs a different fix from a test that fails because a network request completes later in CI. Adding a longer timeout may hide both problems temporarily, but it does not make the test more trustworthy. Retries can make a weak test suite look healthy Retries are one of the easiest ways to reduce visible failures in CI. They are also one of the easiest ways to hide instability. A flaky test that passes on its third attempt still consumed runner time, delayed feedback, created extra logs, and made it harder to determine whether

Everyone can generate a website now. Type a prompt, get a decent page — that part is a commodity. The question nobody's answering is what happens on day 2 : the leads start arriving, a line of copy needs a tweak, someone asks for a section you forgot. That's when a website stops being a design project and becomes a thing you have to run — and where most tools hand you yet another dashboard to log into and dread. Sitelas makes a different bet. Because a Sitelas site lives inside Claude through an MCP connector, the same chat that built the site also runs it . You don't open an admin panel to see who filled out your form, write back, or change the page. You just ask. Here's what "running your site from a chat" actually looks like. First, the 30-second why Claude connects to outside tools through MCP connectors — you already use the ones for Gmail, Calendar, and Drive. Sitelas has one too. Add it once (in claude.ai: Customize → Connectors → Add custom connector , and paste https://sitelas.com/api/mcp ), and Claude can do things with your site, not just talk about it: publish it, read its submissions, restyle it, add a section. Your site becomes an automation endpoint sitting next to your other connectors — the thing a Webflow or Squarespace site can't be. New here? Start with How to Build a Website From a Claude Chat . "Did anyone fill out my form today?" That single question is the whole idea. You ask; Claude reads your site's submissions, surfaces the new lead — Maya, a bakery owner — and drafts a warm reply in your voice. One message, no tabs. It works because every form on a Sitelas site captures submissions to your inbox automatically — no integration required. You can open that inbox in the dashboard any time: …but running your site from a chat means you rarely need to. Claude reads those same submissions straight from your site, so "who wrote in today, and what do they want?" is answered in the thread you're already in — not in a panel you have to remember to ch

If you have written more than a couple of scrapers, you already know the pattern. The first few hundred requests fly through. Then responses slow down, you start seeing 429 Too Many Requests , a captcha wall appears, and finally the target just returns empty pages or a hard 403 . Your code did not change. Your IP did. Scraping at any real volume is less about parsing HTML and more about managing where your requests come from. This post is a practical walk-through of how proxies fit into a scraping pipeline: why a single IP fails, what proxy types actually matter, how rotation works, and how to wire it all up in Python with requests , aiohttp , and Scrapy. There is code you can copy, plus the mistakes that cost me the most time. Why one IP is never enough Every site you scrape sees the same thing: a stream of requests from one address, arriving faster and more regularly than a human ever would. Anti-bot systems are built to spot exactly that. The signals they use are boring but effective: Request rate per IP. Too many hits in a short window trips a rate limiter. Volume over time. Even a slow scraper eventually stands out if every request comes from the same address for hours. Behavioral fingerprint. No mouse, no scroll, identical headers, requests in perfect intervals. Reputation. Datacenter ranges that have been abused before are pre-flagged. You can soften some of these with headers, delays, and a real browser, but there is a ceiling. Once a single IP has made enough requests, it gets throttled or blocked regardless of how polite you are. The only way past that ceiling is to spread requests across many addresses, so no single one crosses the threshold. That is the entire job of a proxy pool. The proxy landscape, minus the marketing Providers love to complicate this. For scraping, the distinctions that actually change your results are these: Shared vs private. Shared proxies are handed to many customers at once. You inherit everyone else's behavior, so an address ca

A scraper fails, someone swaps the User-Agent, someone else adds a proxy, then the job starts passing locally but fails again in CI. That usually happens because Cloudflare did not block “scraping” as one thing. It evaluated several signals, and each failure needs a different fix. This is about authorized automation: your own sites, customer-approved workflows, testing, monitoring, data access you are allowed to perform. If you do not have permission to automate against a site, changing fingerprints or rotating IPs does not make it okay. Start with the failure you actually see Cloudflare failures often get collapsed into “403”, but the page body matters. Common cases: Error 1020 : usually an access denied page from a Cloudflare rule or bot score decision. The HTTP status may still be 403, so inspect the HTML. 403 without a 1020 page : often IP reputation, firewall rules, geo restrictions, or an auth problem. 429 : rate limit exhaustion. Slowing down can help here, but it will not fix a fingerprint problem. Endless Just a moment... page : your client did not complete the browser-side challenge. CAPTCHA or Turnstile loop : Cloudflare still considers the session borderline after earlier checks. Add classification before you add workarounds. Even a basic classifier saves time: import time import requests CLOUDFLARE_MARKERS = { " 1020 " : " cloudflare_access_denied " , " Just a moment " : " cloudflare_js_challenge " , " cf-turnstile " : " cloudflare_turnstile " , " cf-error-code " : " cloudflare_error_page " , } def classify_response ( resp : requests . Response ) -> str : body = resp . text [: 5000 ] if resp . status_code == 429 : return " rate_limited " for marker , label in CLOUDFLARE_MARKERS . items (): if marker in body : return label if resp . status_code == 403 : return " forbidden_unknown " return " ok " if resp . ok else f " http_ { resp . status_code } " def get_with_backoff ( url : str , max_attempts = 4 ): for attempt in range ( max_attempts ): resp = request

A few weeks ago I was setting up a new project and needed to do the usual dance: create a Notion doc, spin up a Linear board, invite the team to Slack, and set up a couple of Zapier automations to connect them all. It took me most of an afternoon. That's when it hit me — I wasn't actually trying to "use" any of these tools. I just wanted the outcome. I wanted the project set up. And somewhere between the fifth Zapier trigger and the third failed webhook, I found myself thinking: why am I the one gluing all this together? That question is basically the whole thesis behind this post. AI agents aren't just a new feature category bolted onto SaaS. They're starting to eat the reason SaaS exists in the first place. The old deal: software rents you a workflow Traditional SaaS sells you a workflow, not an outcome. You pay for Notion, and Notion gives you a very nice, very rigid shape to pour your thoughts into. You pay for HubSpot, and it gives you a CRM shape. You pay for Zapier so you can awkwardly stitch the shapes together. This worked great for twenty years because the alternative was building everything yourself. SaaS was the shortcut. But the shortcut came with a tax: you had to adapt your work to fit the tool, and when you needed two tools to talk to each other, you had to become a part-time integrations engineer. The new deal: software does the workflow for you An AI agent flips that relationship. Instead of "here's a tool, go operate it," it's "here's the outcome, go figure out how to get there." You tell an agent "onboard this new client" and it can read the contract, create the folders, send the welcome email, schedule the kickoff call, and post a summary in Slack — using whatever tools it has access to, without you clicking through five different dashboards. That's the part that's easy to miss if you only think of agents as "chatbots with extra steps." A chatbot answers questions. An agent does multi-step work: It breaks a goal down into subtasks It calls tools

Most automation advice assumes you're willing to pay for Zapier or spend weeks learning n8n. The business-automation-architect skill by @1kalin takes a different angle: your AI agent is already capable of running workflows on its own, using cron jobs, scripts, and built-in reasoning. No third-party automation platform required. The Core Premise Your agent has access to APIs, file systems, schedulers, messaging channels, and web tools. That's everything you need to automate business processes without installing anything else. The skill teaches you to think like an automation architect — finding the highest-value processes to automate, designing the workflow, implementing it with agent tools, and measuring the return. The philosophy is grounded: only automate processes that happen at least five times per week OR cost more than thirty minutes per occurrence. Below that threshold, the automation overhead rarely pays off. The 5x5 Automation Audit The first phase is a structured discovery process. The skill provides a scoring matrix across five dimensions — frequency, time cost, error impact, complexity, and number of systems involved. Each dimension is scored 0-3, giving a maximum score of 15. Processes scoring 12 or above are immediate candidates. Those between 8-11 go into the next sprint. Anything below 8 is left manual. The discovery questions are worth asking directly: what breaks when someone is sick? Where do things pile up waiting for a person? What data gets copied between systems every day? These are the real automation opportunities, and they rarely show up in generic automation advice. Designing the Workflow The skill defines a clear workflow architecture template covering triggers, inputs, steps, error handling, outputs, and monitoring. The trigger types supported are schedule (cron), webhook, event, manual, email, and file-based. Steps can be fetch, transform, send, decide, wait, or notify — each mapping directly to what an agent can actually do. Error hand

In my last post I said that for normal HTML pages, element-based automation ( find / read_page ) beats coordinates every time. This post is about the apps where that advice is useless. Flutter Web apps. Canvas-rendered editors. Every button and panel you can see on screen doesn't exist in the DOM — it's all pixels painted onto a single canvas. find returns nothing. read_page 's accessibility tree is effectively empty. I got Claude to drive the Rive editor (an animation tool built with Flutter) all the way through selecting assets and exporting them. Here's the procedure that survived contact with reality. Step zero: confirm you're actually in this situation Coordinate automation is fragile, so you should only accept it after ruling out the alternative. The test is quick: run read_page . If the visible UI has almost no corresponding nodes, you're looking at a canvas-rendered app, and coordinates are the only interface you have. The four rules 1. Wait for the window size to settle before anything else Same failure mode as my previous post: right after load, the viewport hasn't reached its final width (I measured 1664→1920 over 2–3 seconds), and clicks based on an early screenshot land to the right of the target. Read innerWidth via javascript_tool twice; only proceed when two consecutive reads match. But matching innerWidth alone isn't enough — also confirm devicePixelRatio hasn't changed since the screenshot you're about to act on (a follow-up to my previous post surfaced this: when DPI or scaling changes, the whole coordinate space rescales the same way, but the new values stabilize immediately, so an innerWidth -only check can't catch it). Canvas apps deserve extra paranoia here, because there is no element-based fallback when a click misses. 2. Read text by zooming, not by extracting Text painted on canvas can't be pulled out of the DOM. To read a menu item or panel label, zoom into that region and read the enlarged screenshot as an image. Full-page screenshots ma

DoorDash RAG Architecture, AI Agent Mesh, & Open-Source Supply-Chain Scanner Today's Highlights This week, we explore advanced AI agent orchestration, a detailed production RAG architecture, and an open-source tool for supply-chain security auditing. These stories provide practical insights into deploying and managing AI frameworks in real-world workflows. How DoorDash Built an AI Shopping Assistant That Doesn’t Rely on the LLM Alone (InfoQ) Source: https://www.infoq.com/news/2026/07/doordash-ai-ask-assistant/?utm_campaign=infoq_content&utm_source=infoq&utm_medium=feed&utm_term=global This article from InfoQ delves into the intricate architecture behind DoorDash's "Ask DoorDash" AI-powered shopping assistant. Unlike many solutions that solely depend on large language models, DoorDash's approach integrates an LLM with a complex retrieval-augmented generation (RAG) system and a comprehensive intent classification pipeline. This multi-layered framework ensures accuracy and relevance, particularly for tasks like recommending specific items or answering detailed product queries within their extensive catalog. The system also employs sophisticated filtering and ranking mechanisms to refine results, moving beyond simple keyword matching to provide highly personalized and context-aware suggestions. The technical deep-dive covers how DoorDash engineered this system to handle the nuances of user intent and data retrieval efficiently in a production environment. Key aspects include leveraging structured and unstructured data sources, managing latency for real-time interactions, and implementing robust feedback loops for continuous improvement. The article offers valuable insights into building scalable, reliable AI assistants that can augment LLMs with proprietary data and business logic, providing a blueprint for enterprises looking to deploy similar advanced applied AI solutions. Comment: This provides a fantastic real-world case study for augmenting LLMs with custom RAG and