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Netflix has shared details about Service Topology. This internal system creates and updates a live dependency graph for thousands of microservices. It helps engineers see how services connect and resolve issues more quickly. The system merges three separate data sources into a single, queryable graph. It updates almost in real-time as traffic patterns shift. By Claudio Masolo

Arm CPU cores, a powerful GPU and gobs of unified RAM? That sounds familiar!

“Why don’t we just frickin’ call Apple?”

EveryPlate is an actual budget meal kit whose plates taste delicious. Options and ingredients are fewer, but simplicity can also be a virtue.

The superior RX 9070 also launched for $549 just over a year ago.

Back in 2002, Porsche fans sputtered with rage as the Cayenne made its debut at the Paris Motor. More than 20 years later, Porsche now sells more SUVs than anything else in its lineup. Last year, the Macan and Cayenne accounted for 62 percent of all Porsche sales. Now, these SUVs are trolling traditionalists in […]

I kept shipping AI apps with no idea what was happening under the hood — prompts going in, responses coming out, costs creeping up, and zero visibility into any of it. So I built LogLens. Add one line of code and it logs every single AI call your app makes — the full prompt, completion, latency, token count, and cost — all in a clean dashboard. Works with Anthropic and OpenAI out of the box. No framework lock-in. npm install loglens const anthropic = wrapAnthropic(new Anthropic(), { apiKey: 'your-key' }) // that's it — every call is now logged Built the whole thing in ~48 hours using Claude Code. Still early but fully working. Free early access here: llm-watch.vercel.app Would love feedback — what features would make you actually use this day to day? submitted by /u/ProcessAutomatic6941 [link] [留言]

“Why wouldn’t you want to be in both Pepsi and Coke?” says one venture capitalist. “It’s the same here.”

From battery life to privacy, there are many hurdles to the idea taking off.

Over the past few months I’ve been building VoiceIntego, an AI voice agent that answers calls and books appointments for service businesses (dental clinics, HVAC, plumbing). Here are some of the technical lessons that surprised me along the way. Latency is the whole game With text chatbots, a 2-second delay is fine. On a phone call, anything over ~800ms feels broken — people start talking over the AI. The hard part isn’t the LLM response; it’s the round trip: speech-to-text → LLM → text-to-speech, all streaming. You have to stream every stage and start TTS before the full response is generated. Interruptions break naive pipelines Real callers interrupt. “Actually, can we do Tuesday instead—” mid-sentence. A simple request/response loop can’t handle this. You need barge-in detection: monitor the incoming audio stream and cancel the current TTS playback the moment the caller starts speaking again. Booking logic needs guardrails, not vibes Letting the LLM “decide” availability is a recipe for double-bookings. The reliable pattern: the LLM extracts intent (date, time, service), then deterministic code checks the actual calendar API and confirms. The model handles language; your code handles truth. Confirmation loops matter more than you’d think Always read the booking back: “So that’s a cleaning on Tuesday the 9th at 2pm — correct?” Phone audio is noisy and names/times get misheard constantly. One extra confirmation turn cuts errors dramatically. Phone numbers and edge cases everywhere Voicemail detection, callers who mumble, background noise, people who say “yeah” to mean no. The happy path is maybe 20% of the work. If you’re building something in this space, happy to compare notes. You can see what I’m working on at VoiceIntego .

Most powerful AI/agent tools nobody talks about, and it leaves you behind IMO 1. Instructor define a Pydantic model, get clean structured JSON out of any LLM every time → https://github.com/567-labs/instructor 2. Octopoda gives any AI agent persistent memory and catches it when it loops and quietly burns your tokens. open source → https://www.octopodas.com 3. E2B secure cloud sandboxes so your agent can actually run the code it writes without nuking your machine → https://e2b.dev 4. Firecrawl turn any website into clean, LLM-ready markdown in one API call → https://firecrawl.dev 5. Composio plug your agent into 1000+ apps (Gmail, Slack, GitHub) with the auth handled for you → https://composio.dev 6. LiteLLM one API for 100+ models across OpenAI, Anthropic and local, swap without rewriting a line → https://github.com/BerriAI/litellm what are yours, let me know and I will add it to the list next month! submitted by /u/DetectiveMindless652 [link] [留言]

TL;DR Reconciliation is the part of a payments stack nobody architects for on day one and everyone pays for on day 200. The job: prove that every internal transaction matches the acquirer's settlement file, in the right currency, with the right fees, on the right value date — or surface the diff fast. The mechanics: normalize files → land into an events table → project to a read model → diff against the internal read model → buckets for ops to resolve. The boring details (file formats, fee parsing, FX rounding, value dates) are where 90% of the work lives. If you've ever opened a CSV from an acquirer at the end of the month, sorted by amount, and tried to "just match it in Excel" — yes, this post is for you. What "reconciled" actually means A transaction is reconciled when, for the same logical payment, three views agree: What you sent — your internal record of the charge/payout (your read model). What the acquirer says happened — their settlement file or API report. What the bank actually credited / debited — the bank statement. Disagreements are normal. Persistent disagreements are how you lose money slowly and never know. The shape of a settlement file Across the major acquirers, settlement files look broadly similar — and broadly different in the places that matter: Field Variants you'll see Transaction reference acquirer's transaction_id , sometimes plus a merchant_reference round-tripped from you Gross amount minor units / decimal; transaction currency vs settlement currency Fees inline per-row, or aggregated at the file footer, or in a separate fees file FX inline rate vs separate FX file; sometimes only the converted amount Value date when the bank actually moves money — often T+1/T+2 from event date Adjustments refunds, chargebacks, fee corrections, reserves — usually mixed in Encoding UTF-8 if you're lucky; CP1252 / fixed-width / SWIFT MT940 if you're not Granularity one row per transaction or daily aggregates per merchant or both There's no industry-clean

The test hung for 30 seconds. The response had already fired. One moved line fixed it. The test hung for 30 seconds, then timed out. The browser had received the response. The page had loaded. The data was there. The test was still waiting. The wizard I was writing a helper to walk through a 4-step booking wizard. After clicking "Next" on step 1, the page does a full navigation — window.location.href to step 2. Step 2 immediately loads doctor data from the API. The helper looked like this: await Promise . all ([ page . waitForURL ( /step=2/ ), step1Next . click ()]); await page . waitForResponse ( r => r . url (). includes ( ' /doctors ' )); Standard pattern: wait for navigation, then wait for the data request. Timeout. Every time. What I checked first The URL pattern. Maybe /doctors wasn't matching. Opened the network tab. The request was there: GET /api/v1/doctors , 200, 47ms. Correct URL, correct response. The page looked fine. The data was rendered. The test said it was waiting for a response that had already happened. Added waitForLoadState . Still hung. Added an explicit waitForSelector for an element that was clearly on the page. That passed. Then waitForResponse hung again. The response existed. The test couldn't see it. What was actually happening page.waitForResponse() is not a query. It doesn't look at what happened. It registers a listener — from that exact moment forward — and waits for the next matching response. The sequence in my code: Promise.all resolves when the URL changes to step=2 By the time the URL changed, step 2 had already loaded Step 2 had already sent and received /api/v1/doctors Then waitForResponse registered its listener Now it's waiting for the next /doctors response Which never comes Playwright doesn't buffer missed events. If the response fired before the listener was registered — it's gone. The fix await Promise . all ([ page . waitForURL ( /step=2/ ), page . waitForResponse ( r => r . url (). includes ( ' /doctors ' )), step1Next

These are the cleaning robots, water monitors, and toys actually worth buying for pool season.

An “urban explorer” tells WIRED their mother checked in to make sure they weren’t one of the people seen scurrying out of a manhole with their friends.

I added real-time activity logging and security scoring to my Claude Code dashboard The problem with just seeing costs Knowing how much you spent is useful. But it's not enough. The real question is: what is your AI actually doing? Which files did it read? Which commands did it run? Is your environment even safe to run it in? I couldn't answer any of those. So I built the answers in. What's new in v0.1.17 Activity Log — see every action in real-time Claude Code logs everything via hooks. Every file read. Every command executed. Every API call. Risk-labeled. Timestamped. Live. Set it up once in ~/.claude/settings.json : { "hooks" : { "PostToolUse" : [{ "matcher" : ".*" , "hooks" : [{ "type" : "command" , "command" : "curl -sf -X POST http://localhost:3000/api/actions -H 'Content-Type: application/json' --data-binary @- 2>/dev/null || true" }] }] } } Then open http://localhost:3000/activity . Watch your AI's actions stream in real-time. This is the audit layer AI agents have been missing. Security Score — how safe is your Claude Code environment? Scored out of 100. Checks 7 things: Is Bash(sudo *) in your allow list? (-20) Is ~/.ssh/** in your deny list? (-20) Is Bash(curl *) unrestricted? (-15) Are .env files protected? (-15) Is strictMode enabled? (-10) Is Bash(rm *) restricted? (-10) Are hooks configured? (-5) I scored 90/100. What's yours? The point isn't to shame anyone. It's to make the invisible visible — so you can make informed decisions about what your AI is allowed to do. Try it npm install -g @notenkidev/claude-token-dashboard claude-token-dashboard Open http://localhost:3000 GitHub: https://github.com/notenkitoclient-cpu/claude-token-dashboard This started as a simple token counter. It's becoming something bigger — an observability layer for AI agents. More coming.

Indie Hacking the App Store: Navigating Apple's Guidelines for Niche Catholic AI Applications The era of building generic software-as-a-service (SaaS) platforms is shifting. For independent developers and indie hackers, the real opportunity now lies in underserved, highly specific markets. One of the most fascinating and complex niches emerging today is the intersection of artificial intelligence and religious utility. Building a catholic ai application presents a unique set of technical, ethical, and regulatory hurdles. Developers must create highly accurate systems while navigating strict platform guidelines. Unlike general-purpose chatbots, religious applications require absolute precision. A single theological error can ruin user trust. Furthermore, platforms like the Apple App Store have strict rules regarding user safety, privacy, and functionality. This article explores the technical architecture, prompt engineering strategies, and platform compliance steps required to build and launch a successful catholic ai app . Whether you are using Flutter, Swift, or Kotlin, these insights will help you build a robust, secure, and helpful application. Designing a Catholic AI: Aligning with the Catholic Church Stance on AI Before writing a single line of code, developers must understand the domain. Building tools for this community requires respect for established doctrines and traditions. Fortunately, the Vatican has provided clear guidance on this technology. The Catholic Church Stance on AI The Vatican has taken a proactive and surprisingly technical approach to modern computing. Under the leadership of Pope Francis, the Church has introduced the concept of "algorethics"—the ethical development and deployment of algorithms. The catholic church stance on ai emphasizes that technology must always serve human dignity, protect personal privacy, and promote truth. For developers, this means your application must prioritize: Truthfulness: Minimizing errors in theological ou

We’re a tiny team of 2–9 engineers who believe business messaging should be simple, reliable, and accessible to everyone. Today we’re officially opening up BulkSMSOnline to the Dev.to community, and we’d love your feedback. What’s BulkSMSOnline? A global bulk SMS platform that lets you send campaigns, alerts, OTPs, and notifications via: A clean web portal A REST API An HTTP API It’s designed for developers who want reliable global delivery without fighting arcane telecom protocols or opaque pricing. Why We Built It We noticed a pattern: most SMS platforms either overcomplicate things with bloated SDKs or hide behind enterprise gatekeepers that don’t listen. We wanted something different a lean, transparent API backed by real people who actually care about your deliverability. So we built BulkSMSOnline around three principles: Reliability : Messages must arrive, every time. Radical simplicity : A clean API you can integrate in minutes. Transparency : Honest pricing, clear limits, no surprises. Quick Start: Send an SMS in Under 5 Minutes Here’s how simple it is with our REST API. For full docs, check out our developer portal . curl -X POST https://api.bulksmsonline.com/v1/sms \ -H "Authorization: Bearer YOUR_API_KEY" \ -H "Content-Type: application/json" \ -d '{ "to": "+1234567890", "message": "Hello from BulkSMSOnline!", "sender": "MyApp" }' You’ll get a JSON response with a message ID and status. That’s it. No multi-page setup or carrier negotiations. What else can you do? Send bulk messages with a single API call Track delivery in real time via webhooks Pull reports programmatically Use our HTTP API for legacy systems Who’s Behind This? We’re a small, agile team (2–9 people). That means: No bureaucracy: Fixes and features ship fast. Direct access: When you email support, you reach the engineers who built the platform. Your feedback shapes our roadmap: Many of our recent features came from developer conversations. Tech stack we love: Python, Node.js, PostgreSQL, an

When we started working on Krenalis , we spent a lot of time reviewing how customer data typically flows through a modern data stack. One pattern kept showing up often enough that we started questioning it. In many modern stacks, customer data already lands in a warehouse. Yet we often copy that same data into a CDP before we can start building customer profiles. During one of those discussions, someone asked a question that sounded almost naive: Why are we moving all this data in the first place? Nobody had a particularly strong answer ready. The answer was mostly: Because that's how CDPs work. We expected the question to have an obvious answer. It didn't. The warehouse is no longer just for analytics Over the last few years, the role of the data warehouse has changed significantly. Warehouses are no longer just analytical systems. They're increasingly becoming the place where organizations centralize the context used by applications, AI agents, copilots, and business processes. Customer data from systems like Shopify, Stripe, CRMs, support platforms, and internal applications often ends up there long before anyone starts thinking about segmentation or activation. In many organizations, the warehouse is already the place where teams answer questions about customers, revenue, retention, and product usage. That made us wonder: If the warehouse is already becoming the operational center of the data stack, why does customer identity usually live somewhere else? Consider a customer who buys through Shopify, pays through Stripe, opens support tickets in Zendesk, and uses the product under a different email address. In many organizations, all of those records already end up in the warehouse. Yet building a unified profile often requires exporting that same data into another platform before identity can be resolved. The cost of another copy To be clear, data duplication is not inherently bad. Most software systems rely on some form of replication, caching, or denormalizati