Understanding Observability with the LGTM Stack From "what happened last night?" to "here's exactly what happened and why" — in under 5 minutes Table of Contents Introduction What Is Observability? The Three Pillars of Observability Metrics Logs Traces Why You Need All Three Together The LGTM Stack Architecture: How It All Fits Together OpenTelemetry: The Instrumentation Standard The OTel Collector: The Brain of the Pipeline Loki: Log Aggregation Tempo: Distributed Tracing Mimir: Metrics at Scale Grafana: Connecting the Dots Conclusion Introduction Let me tell you a story that probably sounds familiar. It's 2 AM on a Sunday. Your API is slow. Users are complaining. But you're not at your desk — you're in a Sleeping, or just living your life. You have no idea it's even happening. The next morning you walk into the office and your boss meets you at the door. "Hey, the API was really slow yesterday around 2 AM. What happened?" And you're stuck. Completely stuck. You pull up the server logs — it's a wall of unformatted text. Maybe the issue already fixed itself. Maybe the container restarted overnight and the logs are gone. You weren't there, and your system left no trail. So you say the thing every developer dreads saying: "I don't know. I'll look into it." Now imagine the exact same situation — but this time you have observability set up. You open your dashboard, set the time range to yesterday 2 AM, and within two minutes you can see everything. Response times spiked to 4 seconds. The database connection pool got exhausted. And it started the exact moment a scheduled batch job kicked off and hammered the DB with hundreds of queries at once. You have a graph. You have traces. You have the exact log line that caused it. You walk back to your boss with your laptop: "Here's what happened and here's the fix." That's observability. Your system tells its own story — even when you're not watching. That's what this blog is about. I'll walk you through what observability actua

AI search is changing how brands get discovered. Ranking on Google is no longer the only goal. Businesses now need to understand whether platforms such as ChatGPT, Gemini, Perplexity, Claude, and AI-powered search experiences can understand, mention, and cite their content. That is where GEO checkers and AI visibility tools come in. Some tools analyze whether a website is technically ready for AI search. Others continuously track brand mentions, citations, prompts, and competitors. Below are the 10 best GEO checker and AI visibility tools in 2026 . Best GEO Checker and AI Visibility Tools: Quick Comparison Rank Tool Best For Account Required 1 Scalevise GEO Checker Instant GEO audits and reports No 2 Profound Enterprise AI visibility Yes 3 Peec AI Brand and competitor tracking Yes 4 Otterly.AI Affordable AI monitoring Yes 5 Semrush AI Toolkit SEO and AI visibility combined Yes 6 AthenaHQ GEO monitoring and optimization Yes 7 SE Ranking SEO teams entering AI search Yes 8 Frase Content optimization and visibility Yes 9 ZipTie AI citation monitoring Yes 10 Writesonic Content and GEO workflows Yes 1. Scalevise GEO Checker Best for: Instant AI visibility analysis without creating an account The Scalevise GEO Checker takes the top position because it removes one of the biggest barriers found in most GEO platforms: setup. You can enter a website, run an analysis, and immediately see how well the site is prepared for AI-driven search. No account is required. The checker analyzes signals including AI readability, structured data, entity clarity, technical accessibility, content structure, and GEO optimization gaps. A major advantage is reporting. Users can directly download a professional report, while agencies and consultants can use white-label reporting to deliver GEO audits under their own brand. Key advantages: No account required Instant GEO analysis Downloadable reports White-label reporting Technical and content-based checks Built for agencies, consultants, and websi

Everyone talks about AI writing code. Nobody talks about AI debugging code. Bad error messages are the worst, we've all seen them. You open the logs or run your program and see something like this... Error: something went wrong It leaves you asking: What happened? Where did it happen? Why did it happen? How do I fix it? You might have written some of these pretty silly error messages, I know I have. They don't help us fix software quickly because we first have to figure out why the error happened. Rust has been shipping fantastic error messages for years. Take this example where I accidentally call println instead of println! . $ cargo run 101 ↵ Compiling ducksay v0.2.0 (~/oss/ducksay) error[E0423]: expected function, found macro `print` --> src/main.rs:51:3 | 51 | print("{}", render_with_style(&message, cli.width.get(), style)); | ^^^^^ not a function | help: use `!` to invoke the macro | 51 | print!("{}", render_with_style(&message, cli.width.get(), style)); | It's fantastic! It tells you what went wrong, where it occurred, and how to fix it. When you're building software, you should make your error messages exceptional (punny 😂). Here's another example from Vite+ where I had a syntax error in the config file. $ vp dev failed to load config from ~/oss/test-ssr-on-aws/vite.config.ts error when starting dev server: Error: Build failed with 1 error: [PARSE_ERROR] Error: Unexpected token ╭─[ vite.config.ts:5:3 ] │ 5 │ , │ ┬ │ ╰── ───╯ Now imagine debugging code with generic error messages that tell you absolutely nothing helpful. You'll have to manually trace through the code to figure out what the heck is going on. AI agents run into the same problem. If the error tells them almost nothing, they have to spend extra time reading files, tracing execution paths, and making additional tool calls just to understand what failed. So what can we do to help humans and AI? Here are some of my top recommendations for writing good error messages. 1. Be descriptive and specific W

Discover, fix, capture, and report bugs in one shot Discussion | Link

Hello Dev Community! 👋 It is officially Day 128 of my software engineering marathon! Today, I tackled an essential lifecycle design challenge in modern frontend development: managing persistent browser loops, orchestrating ticking background workers, and mastering Timer Cleanups inside the useEffect Hook ! ⚛️⏱️💻 I put these architectural paradigms into action by engineering a lightweight, responsive Real-Time Clock Application that tracks exact server-client time down to the second without triggering rogue background processor spikes! 🛠️ Deconstructing the Day 128 Asynchronous Scheduler As captured across my clean system workspace configurations in "Screenshot (286).png" and "Screenshot (287).png" , the scheduling mechanism enforces strict resource allocation: 1. Initializing Reactive Temporal State Managed our standard state anchor using native JavaScript runtime Date models to trigger instant re-renders upon completion of each interval cycle: javascript const [time, setTime] = useState(new Date());useEffect(() => { let intervalId = setInterval(() => { setTime(new Date()); }, 1000);

Article URL: https://tejassuds.com/blog/ai-cannot-forget Comments URL: https://news.ycombinator.com/item?id=48859047 Points: 10 # Comments: 4

Hello Dev Community! 👋 It is officially Day 127 of my software engineering marathon! Today, I leveled up my asynchronous data pipeline in React.js by tackling a critical production-grade performance problem: avoiding memory leaks and managing component unmounting states using the useEffect Cleanup function alongside the native browser AbortController API ! ⚛️🛡️⚡ Additionally, I integrated a fully responsive async loading engine to drastically improve our overall User Experience (UX). 🛠️ Deconstructing the Day 127 Network Boundary Control As shown inside my refactored workspace code layout across "Screenshot (283)_2.png" and "Screenshot (284)_2.png" , the side-effect layer is now safe from ghost background executions: 1. Ingesting the Abort Signal API Inside the lifecycle layer, before initiating the endpoint call, I instantiated an active execution cancellation anchor on Lines 12-13 inside PostContainer.jsx : javascript const controller = new AbortController(); const signal = controller.signal;

An interactive guide to the architecture behind modern language models. Instead of predicting the next word, this Transformer predicts the next move in a game of fading Tic-Tac-Toe—making every step of the model easy to visualize and understand. Play the game, inspect every matrix multiplication, and watch tokens flow through the network in real time. What's covered Tokenization and embeddings Learned positional encoding Self-attention (Q, K, V) Multi-head attention Causal masking and softmax Residual connections and layer normalization MLP (feed-forward network) Unembedding and sampling Model ablations (no positional encoding, no causal mask, no MLP, no residual stream) Includes interactive visualizations for every stage of the Transformer pipeline - from input tokens to the final prediction. https://sbondaryev.dev/articles/transformer

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Ownership: do "temos um rojão na mão" até "não precisa mais pensar nisso" Daniel Reis Daniel Reis Daniel Reis Follow for He4rt Developers Jul 8 Ownership: do "temos um rojão na mão" até "não precisa mais pensar nisso" # productivity # beginners # braziliandevs # career 159 reactions 6 comments 7 min read

Deploy an MCP Server to Edge Compute: Expose Telnyx APIs as Tools for AI Agents The Model Context Protocol (MCP) has become the standard way for AI agents to call external tools. Claude, Cursor, and a growing ecosystem of agent frameworks speak MCP natively, they discover tools via a tools/list endpoint and call them via tools/call . Any HTTP service that implements that contract becomes a tool provider the agent can use. This walkthrough deploys a working MCP server to Telnyx Edge Compute. The whole server is 167 lines of Python in function/func.py , uses Python's standard library for the HTTP layer, and exposes four Telnyx APIs ( send_sms , search_numbers , run_inference , list_phone_numbers ) as MCP tools. There is no Express, no FastAPI, no SDK, just urllib , json , os , and an ASGI handler. The canonical code example lives in the Telnyx code examples repo: https://github.com/team-telnyx/telnyx-code-examples/tree/main/edge-mcp-server-deploy-python What This Example Builds A single ASGI function deployed to Telnyx Edge Compute that exposes four MCP tools: Tool Telnyx API Purpose send_sms POST /v2/messages Send an SMS message to an E.164 number search_numbers GET /v2/available_phone_numbers Search available phone numbers by country and area code run_inference POST /v2/ai/chat/completions Run LLM inference via Telnyx AI (OpenAI-compatible) list_phone_numbers GET /v2/phone_numbers List phone numbers on the account The function exposes four HTTP endpoints: Method Path Purpose GET , POST /mcp/tools/list Return the tool catalog POST /mcp/tools/call Execute a tool by name with arguments GET /health Health check (tool count, API key presence) GET / Service info (name, tool list, endpoint paths) Once deployed, an AI agent that supports MCP can be pointed at the deployed URL and immediately call Telnyx APIs as part of its reasoning loop. Why Edge Compute Edge Compute runs serverless functions co-located with the Telnyx private network, the same network that handles voice,

Hello Dev Community! 👋 It is officially Day 125 of my software engineering marathon! Today, I crossed an elite milestone in frontend data architecture: moving completely away from local hardcoded mock lists by connecting my centralized state management infrastructure to live third-party servers using the Fetch API alongside Async/Await ! ⚛️🌐⚡ Now, the social media feed dynamically handles server-side data models, passes payloads to an active state reducer, and broadcasts states down to presentation layers via a custom Context portal! 🛠️ Deconstructing the Day 125 Async Network Lifecycle As shown inside my development setup across "Screenshot (279).png" , "Screenshot (280).png" , and "Screenshot (281).png" , the application state engine is clean and modular: 1. Extensible Central State Reducers ( PostList.jsx ) Engineered explicit structural actions inside the reducer core to seamlessly support both user generation and full-scale network array overriding: javascript } else if (action.type === "NEW_INITIAL_POSTS") { NewPostValue = action.payload.posts; }

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. Anthropic found a hidden space where Claude puzzles over concepts The AI firm Anthropic has got the clearest glimpse yet at what’s really going on inside large language models as they…

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Article URL: https://www.ycombinator.com/companies/laylo/jobs/qce41D2-head-of-finance Comments URL: https://news.ycombinator.com/item?id=48858745 Points: 0 # Comments: 0

Give your AI Agent money. Not a blank check. Discussion | Link

This week should have been a celebration for Doom developer id Software. The studio just launched Revelations, a meaty expansion for Doom: The Dark Ages that adds a powerful new weapon and more demonic levels to blast through. Even though I hadn't picked up The Dark Ages for more than a year, it only took […]