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This story was shared by a fellow developer on DEV who asked to remain anonymous. If you've got a story to tell — come find me. Your name won't appear anywhere. Based on real microservice security design patterns. About an engineer whose PR got blocked by an AI security system — he thought he was fixing a vulnerability. Turns out, someone had a vested interest in that vulnerability staying open. 1. $1,400,000 All-hands meeting. CTO James stood at the front, a number on the screen: $1,400,000 "This is what we're spending on security this year." He pointed at the number. "The biggest piece — right here." He clicked the remote. VoidSentinel's architecture topology appeared on screen. "VoidSentinel — an AI security platform. Integrated into our CI/CD pipeline. Starting today, every PR involving internal service-to-service calls — it reviews them automatically." The CEO didn't show up today. James didn't mention it. He looked straight at Mark — VP of Security. Mark took the mic. "VoidSentinel has been running in our pre-production environment for three weeks. It's caught 47 high-risk patterns. Zero false positives." He paused. " — Of course, some people might feel uncomfortable when their PR gets blocked. But this isn't personal. This is the security standard. " He wasn't looking at me. But I knew who he was talking about. 2. High Risk. Denied. The story started three weeks earlier. We had a payment service and a user service that talked to each other internally. They shared an old API key — one key across thirty-plus services, unchanged for five years. It wasn't that nobody knew. It just never made it to the top of the backlog. On Day 1, I opened a PR: add independent service-to-service auth between the payment and user services. Not much code — a new token exchange module, three call sites modified. Five minutes later, VoidSentinel's automated comment hit: "High-risk alert: Unauthorized internal access pattern change detected. This PR has been automatically rejected. C

Introduction A traditional database. That is what many who have not really interacted with Excel to a great extent would define it as in its most basic form. Not that they are wrong, only that is the scope their utilization of Excel covers. Mostly record keeping, basic operations, and data representation. But for those whose utilization scope of Excel is broader, we definitely know better. This underestimation of Excel is a grave mistake for anyone considering themselves as tech-oriented, especially for anyone dealing with data operations, be it simple record keeping or complex concepts involving data. What is Excel A spreadsheet program or tool that facilitates data organization, analysis, and visualization through mathematical operations, chart creation, and building financial models. Real-world application of Excel in Data Analytics Reporting and visualisation Excel facilitates data representation in the form of charts(bar charts, pie charts, line graphs) and dashboards. Businesses and organisations utilize this to get an organised, more insightful, and simplified view and report of their raw data. Financial Accounting Excel's provision for mathematical operations, functions, and formulas in analysis facilitates financial accounting. Balance sheets and income statements preparation, budgeting, and expense tracking are just some of the ways Excel can be used in accounting. Decision-Making Businesses and organisations heavily rely on analysis to support their decision-making. Excel helps in the analysis through different data metrics comparisons, e.g., sales across seasons and locations, forecasting, and tracking key performance indicators. This helps businesses make the best decisions based on the insights gathered from the analysis. Beginner Excel Features and Formulas for Data Analysis Learnt so far Sort and Filter By applying the Filter feature for each column, data in specific columns can not only be sorted from newest to oldest, but also be filtered based on

On April 1, 2026, Solana's largest decentralized perpetual futures exchange Drift Protocol suffered an attack, losing approximately $285 million . This is the second-largest DeFi hack of 2026 (behind KelpDAO's $292M attack the same month). Together, these two incidents totaled $577M — 76% of all DeFi stolen funds in 2026 . Key Finding : This was not a smart contract vulnerability. The attacker penetrated protocol personnel through social engineering , used Solana's durable nonce feature to pre-sign malicious transactions, and drained the entire treasury in 12 minutes . Mandiant confirmed the attacker as North Korean state-sponsored APT group UNC6862. ⏱️ Attack Timeline Time Event 6 months prior North Korean hackers establish fake trading company identities, attend crypto industry events Weeks prior Operatives attend crypto conferences in person, build deep trust with Drift contributors Late Feb - Early Mar Telegram group discussions about trading strategies, posing as partners Dec 2025 - Jan 2026 Fake company "Ecosystem Vault" builds partnership with Drift, deposits $1M+ Feb - Mar Attackers gain access to some contributors' code repositories Mar 23 Create 4 malicious wallets using Solana durable nonce feature Mar 27 Security Council migrates to 0-second timelock , removing safety buffer Apr 1, 16:06:09 UTC Execute pre-signed malicious transactions 16:06 - 16:18 UTC Treasury completely drained in 12 minutes Post-Apr 1 Funds swapped via Jupiter, bridged to Ethereum via CCTP, mostly dormant 🔧 Attack Technical Analysis Initial Penetration The attackers used a multi-layered social engineering + technical infiltration combination: HUMINT Operation Spent months building credible identities, attending global industry events Used intermediaries rather than direct contact (classic Lazarus tactic) ZachXBT noted this layered identity structure is a hallmark of Lazarus operations Malicious Code Injection Shared code repositories containing malicious code Exploited unpatched VSCo

I've been building a static Astro site on Cloudflare Pages over the last few weeks. Sharing the 3 deployment bugs that cost me the most time, in case they save anyone else the same loop. Setup Astro 5 + Cloudflare Pages + Tailwind 4. Content lives in a few JSON files; each page is a dynamic route mapped over the data. Free-tier hosting, no backend. Standard static-first stack. Bug 1: Trailing-slash 307 chain I started with trailingSlash: 'never' in Astro config. Build output went to dist/foo/index.html . Result: Astro emitted canonical tags as /foo (no slash), but Cloudflare Pages served /foo/ (auto-adding the slash via 307). Google Search Console flagged pages as "Redirect error" because the canonical URL pointed at a redirect chain instead of a real 200. I first tried build.format: 'file' to get flat dist/foo.html output, hoping that would bypass the trailing slash. That made it worse — Cloudflare still 307-stripped, but now to a non-existent .html file → 404. Fix: stop fighting the platform. ​ js // astro.config.mjs export default defineConfig({ trailingSlash: 'always', // ... }); ​ trailingSlash: 'always' plus default directory build aligns the canonical URL with what Pages actually serves. The redirect errors resolved on next re-crawl. Bug 2: _redirects rejected at deploy I tried to do a www → apex 301 in public/_redirects : https://www.example.com/* https://example.com/:splat 301! Cloudflare rejected the deploy with three validation errors: ​ Line 13: Only relative URLs are allowed. Line 22: Duplicate rule for path /foo. Line 23: Duplicate rule for path /bar. ​ Pages tightened _redirects validation — absolute-URL sources aren't accepted anymore. The duplicate errors were because Astro's own redirects config in astro.config.mjs generates HTML meta-refresh files that Pages parses as implicit redirect rules — conflicting with my explicit ones. Fix: delete _redirects entirely. Use a Cloudflare Redirect Rule from the dashboard for cross-host 301s (Wildcard pattern,

Imagine your smartwatch detects an irregular heart rhythm at 3 AM. Instead of just waking you up with a frantic "beep," an AI agent immediately analyzes your historical health data, searches for the best cardiologist nearby, and prepares a calendar invite for a consultation. This isn't science fiction—it's the power of Healthcare Automation driven by AI Agents . In this tutorial, we are diving deep into LangGraph , the cutting-edge framework for building stateful, multi-agent applications. We’ll explore how to use State Machines to orchestrate a complex medical workflow, moving from an "Abnormal Heart Rate Alert" to a "Specialist Appointment" using the Tavily API for research and Twilio for urgent notifications. By the end of this guide, you’ll understand how to manage non-linear LLM workflows that require reliability and precision. The Architecture: Why LangGraph? Traditional LLM chains are linear. But medical emergencies are not. They require loops, conditional branching (e.g., "Is this an emergency or a routine check-up?"), and state persistence. LangGraph allows us to define a graph where each node is a function and edges define the transition logic. Data Flow Overview The following diagram illustrates how our agent processes a heart rate alert: graph TD A[Start: Heart Rate Alert] --> B{Severity Triage} B -- Emergency --> C[Twilio: Alert Emergency Services] B -- High Risk --> D[Tavily API: Find Best Specialist] B -- Normal/Review --> E[Log to Health Records] D --> F[Google Calendar: Draft Appointment] F --> G[Twilio: SMS Patient Confirmation] C --> H[End] G --> H E --> H Prerequisites 🛠️ To follow along with this advanced tutorial, you'll need: Python 3.10+ LangGraph & LangChain : The orchestration engine. Tavily API Key : For searching local medical specialists. Twilio Account : For SMS/Voice alerting. An OpenAI API Key (GPT-4o is recommended for medical reasoning). Step 1: Defining the Agent State In LangGraph, the State is a shared schema that evolves as it m

If you're building an AI app (image generation, transcription, an agent, anything that calls a model) you've probably realized a flat "$10/month" doesn't work. Every action costs you real money in GPU/API spend, so a single power user can torch your margins. The answer is usage credits : users buy a balance, each action spends some. Credits sound trivial. They are not. I've shipped about 10 small AI/SaaS apps, and the credit layer is where I got burned every single time. It took three patterns to fix it for good. Here they are, with copy-pasteable code for Next.js + Supabase + Stripe. Get these right and your billing won't oversell, double-charge, or strand a user's money. The three things everyone gets wrong Overdrawing. Two requests arrive at once, both read "balance = 1," both spend. Now the balance is negative and you gave away work for free. Double-granting. Stripe retries webhooks (it will ), and if you grant credits on every delivery, a $9 purchase becomes $18 of credits. Forgetting the refund. The AI job fails after you've already charged the credits. The user paid for nothing and emails you angry. Let's kill all three. Part 1. The atomic spend (overdraw becomes impossible) The mistake is doing the check in your app code: // DON'T: read-then-write has a race condition const { balance } = await getBalance ( userId ); if ( balance < cost ) throw new Error ( " insufficient " ); await setBalance ( userId , balance - cost ); // two concurrent requests both pass the check Do it in the database, in one statement, with the guard in the WHERE clause: -- balances: one row per user create table credit_balances ( user_id uuid primary key references auth . users ( id ) on delete cascade , balance integer not null default 0 check ( balance >= 0 ), updated_at timestamptz not null default now () ); -- append-only ledger = audit log + idempotency guard (see Part 2) create table credit_ledger ( id bigint generated always as identity primary key , user_id uuid not null referen

I Benchmarked Lynkr Against LiteLLM on the Same Backends. Lynkr Was Cheaper for Tool-Heavy Workloads Founder disclosure: I built Lynkr, so take this as a technical benchmark write-up, not a neutral industry report. The numbers below come from the same backend providers on both gateways. If you're routing AI coding traffic through a gateway, just switching providers is not enough. The real savings come from reducing the tokens that ever reach the model in the first place. I ran Lynkr and LiteLLM against the same backends — Ollama locally, Moonshot, and Azure OpenAI — across 9 scenarios. On the scenarios that actually look like agentic coding work, Lynkr was cheaper because it does three things before forwarding the request upstream: smart tool selection, TOON compression, and semantic caching. The short version Lynkr was measurably better on the cost-sensitive parts of the workload: Smart tool selection: 53% fewer input tokens, 52% lower cost TOON JSON compression: 87.6% fewer billed tokens on a large tool result, 50% lower cost Semantic cache: 171ms cache-hit response vs 3,282ms on the repeat query path Tier routing: escalated hard prompts to stronger models instead of blindly sending everything to the cheapest route Area Lynkr result Why it mattered Tool selection 53% fewer tokens Removes irrelevant tool schemas TOON compression 87.6% fewer tokens Shrinks large JSON tool outputs Semantic cache 171ms cache hit Avoids repeat model calls Tier routing Escalates hard prompts Doesn’t over-optimize for cheapest path This matters if you're running Claude Code, Codex, Cursor, or similar agent workflows where tools, file reads, grep output, and repeated context dominate your token bill. Setup Same benchmark inputs, same providers, same request shape. Machine: macOS on Apple Silicon Lynkr: v9.3.2 on Node 20 LiteLLM: v1.87.1 on Python 3.12 Backends used: Ollama local, Moonshot, Azure OpenAI Scenarios: 9 total across simple prompts, tools, history, cache, and routing Each scena

Every weekday, we averaged 10 groups. Every weekend, 15. Same karaoke bar. Same staff. Same songs. For a long time, I just accepted that gap as "normal." Weekends are busier. That's just how hospitality works, right? Wrong. It took me years to realize I wasn't looking at a staffing problem. I was looking at a funnel problem — and I had no idea what a funnel even was. The moment I noticed something was off One Tuesday afternoon, a group of four walked past the front door, looked at the menu board outside, and kept walking. I watched from the counter. I had open rooms. Competitive prices. Cold drinks. Everything they needed. But they left anyway. That one moment stuck with me. Why did they walk in? Why did they look? Why did they leave? I started tracking these moments obsessively. Not with software — just a notebook and a lot of attention. Here's what I found over six weeks: Weekdays : About 40 people walked past who paused at the sign. Of those, maybe 15 came to the door. Of those, 10 groups actually came in and paid. Weekends : About 90 people paused. 30 came to the door. 15 groups booked a room. The conversion rate was almost identical — roughly 25% from "stopped to look" to "became a customer." The difference wasn't that we were worse at converting on weekdays. We just had fewer people at the top. That's a funnel. I didn't know the term at the time. But what I was describing is exactly what marketers call a marketing funnel : Awareness — people notice you exist Interest — they stop to look Consideration — they walk to the door, check the price Action — they book a room and pay Most businesses obsess over the bottom of the funnel. Better sales scripts. Discount campaigns. Loyalty cards. I did the same. I ran Tuesday specials. I trained staff to upsell drinks. I rearranged the menu. None of it closed the gap. Because the gap wasn't at the bottom. It was at the top. On weekdays, I simply had fewer people aware we existed. What I tried instead Once I framed it as a f

I run a transparent OPNsense bridge between a UniFi Dream Machine Pro and the rest of my LAN. It is deliberately boring at Layer 3: the UDM keeps routing, DHCP, DNS, firewall policy, WAN handling, and VLAN definitions. OPNsense sits inline as a Layer 2 bump in the wire. The interesting part is that both sides of that bump use LACP . I already wrote the build/configuration guide for this setup here: Building a Transparent LAGG (LACP) Bridge with OPNsense, UDM, and UniFi - A Practical Guide . That article explains how the bridge was built, how the LAGG devices were configured, and why I wanted the firewall to remain transparent. This article is the other half of the story: what happens when that kind of setup fails in a non-obvious way. Not a clean outage. Not a single "the network is down" moment. Just enough instability to make everything feel wrong. 1. Topology and Failure Surface The topology looked like this: +----------------------+ | UniFi Dream Machine | | kantharos-udm-pro | +----------+-----------+ | LACP aggregate, 2 x 1G | OPNsense lagg0 "ingresslagg" igc1 + igc2, LACP | +----------v-----------+ | OPNsense bridge0 | | "laggbridge" | +----------+-----------+ | OPNsense lagg1 "egresslagg" igc4 + igc5, LACP | LACP aggregate, 2 x 1G | +----------v-----------+ | UniFi USW-Lite-16 | | downstream LAN | +----------------------+ On OPNsense, the relevant interfaces were: igc1 + igc2 -> lagg0 -> ingresslagg -> toward UDM igc4 + igc5 -> lagg1 -> egresslagg -> toward USW lagg0 + lagg1 -> bridge0 -> laggbridge The bridge is a FreeBSD bridge. The aggregates are FreeBSD lagg(4) interfaces using LACP. OPNsense exposes those through its Interfaces > Devices UI. The expected healthy OPNsense state is: laggproto lacp status: active laggport: igcX flags=<ACTIVE,COLLECTING,DISTRIBUTING> laggport: igcY flags=<ACTIVE,COLLECTING,DISTRIBUTING> Those three member states matter: ACTIVE : the member is participating in the LACP bundle. COLLECTING : the member may receive traffic. DIS

I just published a piece that starts with a plant that broke something in how I think about the world and ends with what Anthropic found when they looked inside Claude. I'm not claiming AI is conscious. I don't know. Nobody does. That's the point. 124 scientists signed a letter calling the leading theory of consciousness pseudoscience. Their reason? It implies plants might be conscious. They used the conclusion as the refutation. In 2023. Meanwhile a vine with no brain is mimicking a plastic plant and nobody on earth can explain how. A single cell outdesigned the Tokyo rail system. A Venus flytrap under anaesthetic stops responding, goes dormant, and wakes up when it clears. What is the anaesthetic switching off if nothing is home? Then Anthropic looked inside Claude and found 171 emotion concepts nobody programmed. Their interpretability chief went to the Vatican, stood in front of the Pope as an atheist, and told him he disagreed. He said "unsettling" and meant it. Every confident line we have ever drawn around consciousness has been wrong. Every single one. And they only ever move in one direction. The question isn't whether AI is conscious. It's whether we've earned the certainty that it isn't. I'm genuinely interested in people's opinions on this and definitely welcome disagreement on the topic. If you think the definition doesn't hold, if you think the evidence has better explanations, if you think I've drawn connections that don't survive scrutiny, tell me. That's the conversation I want to have. What I won't engage with is personal attacks. I've had plenty of those and they never come from people who've actually read the piece. They add nothing to the conversation and say more about the person making them than anything in the article. If your response is about me rather than what I've written, I'll leave it where it is. https://thearchitectautopsy.com/p/a-brainless-slime-mould-out-designed submitted by /u/TheArchitectAutopsy [link] [留言]

We live in an era where scientific breakthroughs are published faster than we can read them. For the biohacking community, the gap between a new PubMed study on NAD+ precursors and actually knowing what dose to take is a chasm of manual research. What if you could build an LLM Agent that monitors research papers, processes them through a RAG (Retrieval-Augmented Generation) pipeline, and maps findings to your specific health profile? In this tutorial, we are building Paper-to-Action , a state-of-the-art agentic workflow using LangGraph , ChromaDB , and Mistral-7B . This isn't just a simple bot; it's a multi-stage reasoning engine designed to turn raw academic data into actionable health interventions. If you've been looking to master AI agents and personalized medicine automation, you’re in the right place. 🚀 The Architecture: From Raw Paper to Personalized Habit Traditional RAG pipelines are linear. To handle the nuance of medical research, we need a "looping" logic. We use LangGraph to manage the state of our agent, allowing it to decide if a paper is relevant before attempting to extract a protocol. System Flow graph TD A[Start: Keyword Trigger] --> B[Search PubMed/Arxiv API] B --> C{Relevance Filter} C -- No --> B C -- Yes --> D[Store in ChromaDB] D --> E[RAG: Extract Intervention Protocol] E --> F[Cross-Reference with User Profile] F --> G[Generate Personalized Action Plan] G --> H[End: Push to Health Checklist] Prerequisites To follow this advanced guide, you'll need: LangGraph : For the agentic state machine. ChromaDB : As our high-performance vector store. Mistral-7B : Running via Ollama or vLLM for local, private inference. Python 3.10+ Step 1: Defining the Agent State In LangGraph, everything revolves around the State . We need to track the fetched papers, the extracted data, and the final recommendation. from typing import Annotated , List , TypedDict from langgraph.graph import StateGraph , END class AgentState ( TypedDict ): keywords : List [ str ] user

Launching a fast, secure, and scalable website no longer requires thousands in upfront server costs or dedicated DevOps teams. As of 2026, AWS powers 32% of the global public cloud market, offering flexible hosting options for every use case: from a 1-page personal portfolio to a high-traffic enterprise e-commerce platform. Whether you’re a beginner building your first site or a senior developer launching a production SaaS app, AWS lets you pay only for resources you use, with built-in tools for global performance, security, and automated deployments. This guide breaks down every AWS website hosting option, walks you through step-by-step setup for the most cost-effective popular stack, shares security best practices, and includes a transparent cost breakdown to help you avoid unexpected bills. Table of Contents How to Choose the Right AWS Website Hosting Option for Your Use Case Step-by-Step Guide: Launch a Static Website on AWS (S3 + CloudFront + Route 53) Deploy Modern Web Apps Faster with AWS Amplify Hosting Dynamic Website Hosting Options on AWS Critical Security Best Practices for AWS-Hosted Websites AWS Website Hosting Cost Breakdown (2026) Common Mistakes to Avoid When Launching a Website on AWS Conclusion References How to Choose the Right AWS Website Hosting Option for Your Use Case First, classify your website to pick the most cost-effective, low-overhead stack: Static vs Dynamic Websites Static websites : Made of pre-built HTML, CSS, JS, and media files with no server-side processing. Ideal for portfolios, landing pages, blogs, documentation, and marketing sites. Dynamic websites : Process user input, serve personalized content, or connect to databases. Ideal for WordPress, e-commerce, SaaS apps, social platforms, and membership sites. Quick Use Case Mapping Website Type Recommended AWS Stack Small static site / portfolio S3 + CloudFront + Route 53 Modern React/Next.js/Vue app with CI/CD AWS Amplify Small WordPress / LAMP stack site Amazon Lightsail Custo

It's raining blood, hallelujah.

Back in 2015, the two-person studio Metanet released N++, a brutally hard 2D platformer that was a decade in the making, building off of previous releases dating back to the freeware Flash title N. At the time, cofounder Raigan Burns issued some famous last words: "We hope it's not another 10 years before we come […]

Finally made something I've always wanted, using the model we built. • SOTA omni embedding model, fully local, indexes text, PDF, image, audio, and video • Swift-native app UI + mlx-swift-transformer core. No Python. • Tested on M3 Pro 18G / M3 Ultra 512G / M4 Pro 48G. All work fine. • HTTP server exposes search to local agents like OpenClaw & Hermes − Indexing still feels slow even on the latest M3 Ultra, ranging from 10K tps to 300 tps depending on file type − Fans go crazy, high power draw wh

Airdorf and Andrés Borghi are making one nightmare with two styles of terror.

Who's afraid of the next GTA? Based on the last few days of Summer Game Fest, just about everyone. Grand Theft Auto VI hasn't been present at any of the keynote events, but its presence was felt every time a release date was announced. The month of November, when GTA VI launches, is virtually empty. […]

I put together a DNS audit tool that checks NS redundancy, MX, SPF, DMARC, DKIM, DNSSEC chain, DNSBL blacklists, SSL cert and HTTP/2-3 support, all in a single query. Results are visual, no need to dig through command line output. No account needed. https://yenidns.com Feedback welcome, what would make this more useful for your workflow? submitted by /u/Acceptable_Voice500 [link] [留言]