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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

Your AI vendor says "trust us" with your data. At the end of June, ByteDance's Doubao (豆包) officially ends its free tier and starts charging for API calls. The discussion in developer communities quickly shifted from pricing to a different question: all this data flowing to cloud AI services every day — where exactly does it go? Around the same time, NVIDIA spent significant stage time at GTC 2026 presenting the full-stack confidential computing capabilities of the Vera Rubin architecture. Jensen Huang's message was clear: future AI chips need to keep data encrypted throughout the computation process, making it inaccessible in plaintext to anyone — including the cloud service provider. Two signals pointing to the same trend: data security in AI services has moved from "someone mentioned it once" to "you need to answer this directly." The Data Path Through Cloud AI Is More Complex Than You Think Most developers have a simple mental model of cloud AI: I send a request, the model returns a result, and my data is gone. The actual data flow is more involved. A typical cloud AI call touches these steps: Request data travels over HTTPS to the service endpoint The service may queue the request while waiting for GPU allocation During inference, input data exists in plaintext in server memory After inference, whether inputs/outputs are cached or used for subsequent training depends on the provider's privacy policy Logging systems may record request metadata or partial content At each step, data is potentially accessible. Providers typically say "we don't look at your data" and "your data won't be used for training" in their privacy agreements. These are contractual commitments. You need to trust that they'll honor them. This is the "Trust Me" model. Trust Me vs Verify Yourself If you roughly categorize data protection approaches in AI services, two paradigms emerge: Trust Me Data leaves your device and is processed by a third party. The provider guarantees security through co

After GTC 2026, one thing is basically settled: the hardware layer for on-device AI is no longer the bottleneck. NVIDIA's RTX Spark packs Blackwell GPU + Grace CPU + 128GB unified memory into a desktop form factor. Apple's M-series chips with unified memory architecture and efficiency-first design let 4B and even 7B parameter models run smoothly on a MacBook. Two different approaches, same destination: consumer hardware now has the compute foundation for running on-device AI agents. Chip vendors have done their part. The next question is: how many layers are still missing between "chip can run an AI model" and "an on-device agent can actually complete useful tasks"? This post maps out the full technology stack for on-device AI agents, examining each layer's maturity, identifying gaps, and tracking what the open-source community has built so far. Layer 1: Silicon (Ready) On-device AI inference has different chip requirements than traditional compute workloads. The core bottleneck isn't peak FLOPS — it's memory bandwidth and unified memory capacity. LLM inference needs model weights fully loaded into memory, with high-frequency data movement between weight matrices and activations during computation. If memory bandwidth can't keep up, raw compute power just sits idle waiting for data. Three main silicon paths exist today: NVIDIA N1X : Blackwell GPU + Grace CPU heterogeneous architecture, 128GB unified memory, petaflop-class compute, targeting desktop workstations Apple M-series (M4/M5) : Unified memory architecture with GPU and CPU sharing memory, optimized memory bandwidth, configurations from 32GB to 192GB Qualcomm Snapdragon X : Targeting laptops and mobile, NPU-accelerated inference, relatively limited memory configurations Different emphases, but one common takeaway: 2026 consumer silicon can run 4B+ parameter models for real-time inference. This layer is ready. Layer 2: Inference Frameworks (Mature) With silicon in place, efficient inference frameworks are neede

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

Anthropic warns that AIs will soon be able to build their own successor, and it's suggesting that companies may have to slow down development.

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

Hi HN, Not sure if anyone would be interested. But, just wanted to share that I've been maintaining my small tool called 'lowfat' that helps me filters some of my verbose CLI output. It's a single binary, works as an agent hook or a shell wrapper. It has a plugin system to customize filters per command. The idea is pretty simple: agents don't need the full kubectl get -o yaml or any 10k-line dump to make decisions. So that lowfat sits in between, strips the noise, and passes through what matters

Your security scanner just came back with 6 flagged items. All missing HTTP headers. You did what any reasonable developer does: Googled each one, copy-pasted the recommended config, and shipped a fix in 20 minutes. Job done. Security score green. PR merged. You also probably shipped at least two of them wrong. Here is the thing nobody tells you about HTTP security headers: knowing what to add is the easy part. Understanding why it matters, when it actually doesn't, and how a misconfigured one breaks your app in production — that's where most developers fall short. This isn't another "add these 7 headers to secure your app" post. This is the one that explains what's actually happening. First, The Contrarian Take Missing a security header is not automatically a vulnerability. If you do bug bounties, this will save you a rejection. If you're a dev, it'll save you from cargo-culting configs that don't apply to your app. Context is king. X-Frame-Options: DENY is a valid security header. YouTube doesn't use it. Because the entire point of YouTube is for people to embed its videos in iframes. Applying that header would break a core product feature. That's not a security oversight — it's a deliberate design decision. A missing Content-Security-Policy header is not a vulnerability in itself. It only becomes relevant if you already have an XSS problem to mitigate. CSP is defense-in-depth. Not a fix for a broken input sanitisation layer. This matters because a lot of developers (and worse, automated scanners) treat these headers like a binary checklist. Present = secure. Missing = vulnerable. Reality is messier than that. Now — with that said — let's talk about what each one actually does. #1. HTTP Strict Transport Security (HSTS) Most developers think HSTS is just "force HTTPS." It's more precise than that. When your app redirects http:// to https:// , that first request is still unencrypted. For a fraction of a second, on a public network, that window exists. An attacker on

This week I’ve been at SXSW London. There’s been music, film, and a lot—and I mean a lot—of talk about AI. I also had the opportunity to sit down with Gloria Mark, a psychologist at the University of California, Irvine, who has spent the last 30 years studying how people interact with digital technologies. Early…

This series provides your roadmap for the machine age, exploring how to move from vulnerable prototypes to resilient systems through layered defense, robust MLOps, and integrated governance. By Claudio Masolo

AI companies are using serif to project humanity. Critics are calling it “tasteslop.”

On June 5, 404 Media reported that attackers had been using Meta’s AI customer support agent to steal Instagram accounts. Their approach was simple: They asked the agent to link the accounts to email addresses that they controlled, and the agent complied. One attacker broke into the dormant Obama White House account and made pro-Iran…

I’m building a company monitoring app that reads Firebase data coming from multiple bus DMS devices and returns KPIs for a Svelte dashboard. Is FastAPI a good backend choice for this, especially for a secure, production-ready, scalable, and maintainable API? If not give me please alternatives . I also need a good FastAPI template or guide to start from, a secure way to connect it with Firebase, and the best way to package the app for both Windows and Android. What I need to use ? submitted by /u/Successful-Life8510 [link] [留言]

so anthropic just dropped a blog post calling for a global pause on frontier ai development, warning that models could start recursively self-improving and spiral beyond human control. sounds scary. sounds noble. let's talk about what's actually going on here. anthropic is reportedly eyeing a $1 trillion+ ipo, and they just happen to be the ones calling for everyone to stop building. analysts are already asking whether this is really just about freezing the status quo so they can hold their lead. putting it plainly: a pause helps anthropic keep its position and probably grow market share too. and here's where it gets a bit hypocritacal: over 80% of the code in anthropic's own codebase is now written by claude. they're absolutely running the playbook they want everyone else to put down. but the thing nobody's really talking about is regulatory capture. this is textbook. you become the dominant player, go to governments, say "this technology is dangerous, we need oversight, we're the responsible ones, let us help write the rules." suddenly the regulations that get passed only you can afford to comply with, locking in your architecture, your safety benchmarks, your evaluations. smaller competitors get crushed under compliance costs, open source gets kneecapped, and you get a moat that no vc cheque can cross. they compared it to nuclear arms control which sounds serious until you realise ai training is far easier to hide than a missile silo, so any agreement just punishes the people honest enough to follow it. the safety concerns might be real. but the timing, the ipo, the regulatory push is all hard to look at all that and not raise an eyebrow. submitted by /u/Complete-Sea6655 [link] [留言]

We're opening up Creaibo 2.0 beta applications, and I'd genuinely love to get feedback from this community. What is Creaibo? An AI-powered creative tool for images, video, and content production. We're focused on giving creators a more coherent workflow rather than yet another single-task generator. Cora is our core AI assistant inside the product. Why post here? Because people here actually use these tools seriously and have real opinions. We've been building based on the frustration that AI tools are great at individual tasks but terrible at keeping your creative context together across a project. Curious if that resonates. What we're looking for in beta testers: Anyone actively creating content with AI, whether that's video, images, marketing assets, or anything in between. Especially useful: people willing to tell us what's broken. Apply here: https://www.creaibo.com/survery We also published a new Cora demo this week if you want to see what the tool actually does before applying: https://www.bilibili.com/video/BV1ETEF6VEHu/ Happy to answer questions in the comments. submitted by /u/Objective_Dirt_9799 [link] [留言]

found this buried in the openai dashboard and honestly surprised more people don’t know about it it’s called the data sharing program. go to your api dashboard, hit data controls, toggle on sharing. that’s it. you get free tokens every single day. up to 2.5 million tokens daily on the lighter models like gpt-4o-mini, o3-mini, gpt-4.1-mini. for the heavier models it’s 250k tokens per day. resets daily. the trade is your prompts and outputs can be used by openai to train their models. so don’t use it for client work or anything sensitive but for side projects, learning, experiments… you’re basically getting free api access every day just for flipping a toggle not a trial. not a promo. it’s an ongoing program and it just sits there unclaimed for most people submitted by /u/NewMuffin3926 [link] [留言]

The CMA's conduct requirement under the UK Digital Markets, Competition and Consumers Act is the first binding law to separate content display rights from AI training data rights at domain and page level, covering Google AI Overviews, AI Mode, Gemini, and Vertex AI simultaneously, with a phased implementation calendar: main publisher controls by December 2026 and page-level grounding controls by March 2027. CMA chief Sarah Cardell explicitly signaled additional Google search requirements in coming weeks, and the CMA's biannual public compliance reporting obligation gives it a fast-acting mechanism if Google stalls. An anti-retaliation clause bars Google from penalizing opt-out publishers in organic rankings, closing the coercion mechanism that has made voluntary consent frameworks unworkable since AI Overviews launched in the UK in late 2025, when zero-click searches rose roughly 30% in health and local news categories. Fair licensing terms were explicitly deferred to a separate proceeding, a gap publisher trade bodies have already criticized and one the CMA has already signaled it intends to fill in its next enforcement phase. More : https://aiweekly.co/alerts/cma-orders-google-ai-search-opt-out-for-publishers submitted by /u/Justgototheeffinmoon [link] [留言]

submitted by /u/ItsGazH [link] [留言]