Enhanced Exception Notifications with User Context

🚀 I'm working on a project. First and difficult step is to monitor webpage. The idea is to automatically check whether a website or API is working correctly instead of manually testing it. Mejor things to check: - Verifies if a page is reachable or not. Means status codes. - Measures response time to detect slow pages. - Validates API responses to make sure the expected data structure is returned. - Checks HTML structure to detect missing elements - Detects broken images on pages - Logging the results so issues can be tracked over time - means tracking all the urls, status code or severity. This helped me understand how monitoring tools detect problems before users notice them. It also connects directly to the goal of my project like- automatically detecting bugs and prioritizing them based on severity. Note: everything is in python libs, ' bs4 ' or ' request ' Next step could be - Expanding it to classify errors and devs could work on them and resolve it. #Python #BackendDevelopment #WebMonitoring #LearningInPublic

Scraping job boards looks easy… until you hit your first 403. 🚫 Your Requests + BeautifulSoup script works on a static site. Then you try Indeed and get blocked, empty pages, or inconsistent results. Modern job boards rely on: ⚙️ JavaScript rendering 🕵️ Browser fingerprinting 🌍 IP tracking 🛡️ Anti-bot protection We just published a new guide showing: 🐍 Why basic Python scrapers fail ✅ How to use ZenRows to handle JS rendering, proxies, and anti-bot systems 📈 How to scale across multiple job boards reliably Read the tutorial: https://lnkd.in/gT_mVkVy

When I set up a new agentic workflow, I don't write the Python scripts. Claude Code writes them. Each tool has one job. Scrape a site. Parse a response. Save a file. The agent builds them based on what the workflow needs. Here's the part that still surprises me. When a tool hits an error, the agent reads the message, looks up the fix, rewrites the script, and tests it again. I don't debug. I don't patch. It handles it. On one workflow, it hit a Unicode encoding issue on Windows. Found the problem, rewrote the function, verified the fix. 90 seconds. I'd have spent 20 minutes on that. What tasks do you still manually debug that probably don't need you in the loop? #ClaudeCode #AIAutomation #BuildInPublic #AgenticAI

On agents and how you must watch them like a hawk… my agent permissions are set to ALWAYS ask for the “rm” command and also allows for “uv run <script>”. So now my agent makes an adhoc Python script to remove files as I’m watching it. It also self reports that “it must remove files by script because it’s prevented from running ‘rm’ without asking the user for permission” You won’t find me OpenClaw’ing anytime soon.

𝗪𝗵𝘆 𝗥𝘂𝗻𝗻𝗶𝗻𝗴 𝗨𝘀𝗲𝗿 𝗖𝗼𝗱𝗲 𝗼𝗻 𝗬𝗼𝘂𝗿 𝗦𝗲𝗿𝘃𝗲𝗿 𝗜𝘀 𝗗𝗮𝗻𝗴𝗲𝗿𝗼𝘂𝘀 While building a Python script execution API, I realized how risky it is to run user-submitted code on a server. A script could try to delete files, access sensitive directories, run infinite loops, or consume all system resources. What sounds like a simple feature quickly becomes a serious security and reliability problem. To explore this, I built 𝗣𝘆𝘁𝗵𝗼𝗻𝗦𝗰𝗿𝗶𝗽𝘁_𝗦𝗲𝗿𝘃𝗶𝗰𝗲𝗦𝗮𝗻𝗱𝗯𝗼𝘅, which executes scripts inside an isolated environment using 𝗻𝘀𝗷𝗮𝗶𝗹 locally and 𝗴𝗩𝗶𝘀𝗼𝗿 in production, enforcing filesystem restrictions and resource limits. The real challenge was 𝗱𝗲𝘀𝗶𝗴𝗻𝗶𝗻𝗴 𝗴𝘂𝗮𝗿𝗱𝗿𝗮𝗶𝗹𝘀 that safely handle untrusted code. If you're curious, you can go through the project and some of the test cases to see how the sandbox handles different scenarios 👇 https://lnkd.in/eZP5mMhP

agentpad is live. A multi-language runtime for AI agents that actually runs your code — bash, Python, JavaScript, SQL - against a real project directory. Not a sandbox pretending to be one. Most agent runtimes fake the environment. agentpad doesn't. You get full control over what the agent can touch, what it can write, and exactly what happened when it's done. > Overlay mode to stage changes before they stick. > Read-only mode when writes aren't allowed. > Glob allowlists and timeouts so nothing runs wild. > Structured output every time - stdout, stderr, exitCode, files written. > A full session run log so nothing is a black box. > OpenAI tool helpers so you're not rewriting the function-calling loop from scratch. Just run npm install agentpad Or pip install agentpad Website/Docs: https://slaps.dev/agentpad #openSource #aiAgents #developerTools #typescript #python

When I was building agents-js and Slapify, the browser side was only half the story. The other half was always: run this in the repo - a script, a test, a SQL check, a one-off patch - and get back something structured I could log, replay, or hand to the next step. I didn't want another bespoke subprocess wrapper per project. agentpad is that layer. Bash, Python, Node, SQL against a real working tree, with timeouts, glob allowlists, and a clear result object every time. Same idea in TypeScript and Python, because agents don't care which language your stack speaks. The piece I reach for most is overlay mode - full temp copy of the directory, agent edits there, then apply or discard. It made "let the model touch files" feel survivable in production, not reckless. If you're wiring agents, CI, or internal copilots on top of real codebases - try it and tell me what breaks. Github: https://lnkd.in/gi4Ccqb2 Website: https://lnkd.in/grJXTicc #openSource #aiAgents #developerTools #typescript #python

Testing shouldn't be an afterthought. Dyne comes with a built-in, asynchronous test client that makes verifying your API as easy as writing it. The `app.client` uses the familiar Requests-style API, allowing you to perform GET, POST, and other requests against your app without a live server. With support for `url_for()` and automatic JSON/YAML media parsing, you can write robust unit and integration tests in seconds. Ship with confidence every time: https://lnkd.in/eqEc4Tuw #Python #Pytest #SoftwareTesting #Backend #WebDev #DyneFramework

Crawling an entire website used to take: A Python script. Playwright or Selenium. Proxy rotation. Rate limiting logic. Error handling. 3 hours of debugging why page 47 returned a 403. Now it's one API call. Every web scraping startup that raised millions to solve this problem just became a single endpoint. Every freelancer charging $500 to "extract website data" just lost their entire business model to a /crawl command. HTML. Markdown. JSON. Pick your format. No scripts. No browser. No headache. The entire web scraping industry just got reduced to one line of code. Someone is going to use this to clone every competitor's website by Friday. 💀

I faced an endless number of challenges while developing a scraping script for our competitors. This seems to be a game-changer.