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On July 9, 2026, the European Parliament reauthorized a law that lets tech companies scan your private messages without a warrant. Here is the part that should worry you: a majority of lawmakers voted against it. 314 MEPs voted to kill the law. 276 voted to keep it. The law passed anyway. How? The rejection required an "absolute majority" of 361 votes out of 720 MEPs. Every absent MEP effectively counted as a yes. The vote was scheduled for the last day before summer recess, when many MEPs had already left Strasbourg. The European People's Party, Parliament's largest group, used a rarely invoked urgency procedure (Rule 170) to force the vote onto the floor on July 7. Two days later, the deed was done. What Chat Control 1.0 Actually Does The law is technically called the ePrivacy derogation. It allows tech companies to voluntarily scan private, unencrypted messages and emails for known child sexual abuse material (CSAM), without a warrant or prior suspicion. Platforms affected: Instagram DMs, Discord, Snapchat, Skype, Xbox messages, Gmail, and iCloud. Platforms not affected: WhatsApp and Signal, because they use end-to-end encryption. Parliament did adopt an E2EE exemption amendment with 369 votes, excluding "communications to which end-to-end encryption is, has been or will be applied" from the scanning scope. But here is the catch. Providers of E2EE services were not scanning messages anyway. The exemption preserves the status quo. It does not create new protections. The scanning is limited to "known" CSAM material, meaning previously identified photos and videos. It does not detect new or unknown material. And it remains in effect until 2028, or until a permanent regulation is agreed. The Numbers That Should Make You Doubt This Law The EU Commission's own evaluation report gives Chat Control a very poor assessment: Only 0.00000077% of messages scanned in the EU actually contained illegal material ( heise online ) False positive rates of filter technologies reach u

As the climate phenomenon sends warm water surging across the eastern Pacific, some parts of the fishing industry are suffering—but other regions are seeing a windfall.

I reviewed HackerRank's open-sourced Hiring Agent - prompts, the scoring logic, and the GitHub enrichment code. I tested the tool with a few faked resumes. Post : https://blog.grandimam.com/posts/how-hacker-rank-scores-engineers/ Takeaways: * Open source is weighted at 35%, technical skills at 10% * A senior engineer with perfect production experience scored 71/100 * Job titles to "Founding Engineer" and "CTO" results in same score * Startup roles emphasis * Active development was 77 days Code: Hiring Agent submitted by /u/grandimam [link] [留言]

Turn your SaaS into a clickable demo in 90 seconds. Discussion | Link

Hot showers, like electricity, are a luxury that's easy to take for granted. That all changes after a few nights camping at a music festival, a week toiling at a backcountry job site, or overlanding all summer in the great unknown. An itchy scalp and the vague smell of warm clams suddenly make the idea […]

AI is not a smarter Google I am convinced most people are using AI in the worst possible way. They treat it like a slightly magical search bar. Type question. Get answer. Copy. Paste. Forget. I think that mindset is holding a lot of people back. Developers. Designers. Knowledge workers. Even my baseball kids who ask ChatGPT for homework help. AI is not a better Q&A machine. It is a delegation machine. You do not "ask" AI. You give it a job. This post is me making that shift concrete. I just shipped six AI gallery pages on my site, built entirely around that idea. Not as a gimmick. As infrastructure for how I work, learn, and build. Why I stopped asking AI questions The turning point was basically frustration. My workflow looked like this for months: Open ChatGPT Ask something like "How do I X in Astro / Svelte / Next" Skim the answer Try the snippet Debug for 30 minutes anyway The answers were fine. Sometimes even useful. But nothing stuck. I would ask the same class of questions over and over. Same concepts. Same patterns. Same gotchas. No real accumulation of knowledge. Just one-off transactions. Then I noticed something: the few times I actually got huge value from AI, I was not asking. I was delegating. "Rebuild this layout using CSS grid, but keep these class names." "Refactor this component, keep the same API, and annotate the performance tradeoffs in comments." "Act like my annoying senior engineer and poke holes in this data model." That felt different. Less like search. More like a teammate who does legwork while I keep steering. Delegation > questions So I made a decision: treat AI like a junior colleague with unlimited patience and questionable taste. That means: I do not ask "How do I do X". I say "You are responsible for X. Here is context. Here are constraints. Here is the definition of done." The shift sounds subtle. It is not. When you ask a question, the model guesses what you want. When you delegate a job, you tell it what you want and where it fit

A practical workflow for batch audio and video conversion Media conversion is rarely difficult because of one file. The friction appears when the same job has to be repeated across a queue: choose an output format, adjust quality, add another file, wait for the result, and then start the setup again. That is the problem Format Factory is designed to address. It is a browser-based workbench for common audio and video conversion jobs, with a workflow built around batches instead of isolated one-file sessions. You open the page, choose the task you need, set the shared options once, add compatible files, and run the queue. There are no installer bundles or cluttered download pages to work through, and there is no need to configure every file from scratch. Start with the job, not the file Different media tasks call for different settings. Format Factory organizes the workflow around the operation you want to complete: Convert video to a different format for playback or upload Extract the audio track from a video Compress video files with shared quality settings Merge 2 to 10 clips into one MP4 Remove audio and export a silent copy of a video Convert audio between MP3, WAV, AAC, M4A, OGG, and FLAC Compress MP3 files by choosing a lower bitrate Merge 2 to 20 audio tracks into one MP3 This task-first approach is useful when you already know the result you want. Instead of opening a separate configuration flow for every input, you define the conversion job once and then build a queue around it. A queue that keeps each file visible Batch processing should reduce repetitive setup, but it should not make individual files mysterious. The queue keeps the state of each row visible from upload to download. If one file needs a different setting, you can apply a per-file override without rebuilding the entire job. If a file fails, its error is shown at the row level. You can retry that item, cancel it, or download a specific result when only one file needs attention. That gives you

This is a submission for Weekend Challenge: Passion Edition What I Built I told an agent Never write directly to the database . A long session later, context window full, it wrote directly to the database. The rule loading mark was still sitting in the prompt. The model had just stopped weighting and attending to it. It's an invisible failure. No error is being thrown. The task comes back subtly wrong, and the rule reads perfectly fine when you go back and check it. I wanted to make it visible, so I built an interactive field you can drag around. Every rule you write for an agent is a hill. Its height is how well the rule is written: a directive-led, backtick -anchored rule stands tall, a hedged and vague one sits low. Then you raise the water. The water is context load. As it rises the low rules go under first, in order of how well they were written. The weak ones drown while you watch. Three of the hills are high-stakes prohibitions, the Never... rules. They drown too. That is the whole point of the piece. A rule you cannot afford to lose does not belong in prose at all; it belongs on a runtime hook that runs as code, not attention. The field flags those in red the moment they go under. Underneath the field is a second tool: a client-side lint that reads an instruction and names the surface tells (hedges, shouting, politeness, a ban placed before its directive). It is deliberately not a score. It catches what a little regex can honestly catch, and points at the real analysis for the rest. Demo Play it on its own page. Drag to orbit, drag the load slider to raise the water: ▶ Open the live demo Each of the nine instruction patterns in the demo links to its rule page on reporails.com/rules . Code Code is available on Codepen: https://codepen.io/editor/G-bor-M-sz-ros-the-reactor/pen/019f4cad-e344-78bf-b7bc-919972f42a4e The whole thing is one self-contained HTML file: no build step, no dependencies, no backend. The CodePen above is the full source, so you can read eve

Exposing the keys in the GitHub Issue The Phishing Site (Notice the Spotify option) There is a golden rule in cybersecurity: the weakest link is almost always human error. But what happens when that human error comes from a malicious actor trying to orchestrate a crypto phishing scam? The result is surprisingly comedic. Here is the story of how my newly built open-source secret scanner, Sentinel, accidentally neutralized a Tether (USDT) phishing operation during a routine benchmark. The Setup: Testing in the Wild I recently released Sentinel , a statically compiled, context-aware Git secret scanner and pre-commit hook written in Go. After fine-tuning its engine to achieve near-zero false positives, I decided to benchmark it "in the wild" by scanning random, recently updated repositories on GitHub. The goal was to see if Sentinel could catch edge-case credentials that traditional, regex-heavy tools often miss or drown in noise. During the scan, Sentinel instantly flagged a critical severity finding in a rather suspicious repository. The Catch: AI Copy-Paste Gone Wrong Upon inspecting the flagged file, the issue was immediately apparent: a fully exposed, hardcoded Firebase configuration object containing the API key, project ID, and messaging sender ID. It was a textbook case of a script kiddie asking an AI for a web login template and blindly copy-pasting the frontend code into a public repository. They had effectively handed over the administrative keys to their backend infrastructure before the project even launched. The Phishing Site: Logging into Crypto with Spotify? Out of professional curiosity, I checked the Vercel deployment linked to the repository. The project was attempting to impersonate Tether (USDT), the world's largest stablecoin. It featured the official logo, a catchy slogan, and a login prompt designed to harvest credentials. However, because the scammer had blindly copied a generic consumer application template, the authentication options presented

If you have ever built a custom JavaScript framework from scratch, you know that the line between a smooth, memory-clean engine and a total memory-leak disaster is incredibly thin. With version 1, Levelo-Js proved that lightweight reactive UIs could be fast and intuitive. But as codebases grow, raw JavaScript starts to feel like writing code blindfolded. The dreaded undefined is not a function is always lurking around the corner. Today, we are taking a massive leap forward. Meet Levelo-Js v2 —a complete ground-up architectural rewrite, fully re-born in TypeScript, with enterprise-grade build tooling and absolute bulletproof memory management. Let’s dive into what makes v2 an absolute game-changer. The Pillars of the TypeScript Rebirth 1. Full TypeScript Migration & Modern Bundling We didn't just add types; we transformed the entire runtime engine core and internal modules from .js to .ts . Every piece of code is now strictly type-safe, offering self-documenting APIs and flawless IDE autocompletion (IntelliSense) right out of the box. We also waved goodbye to publishing raw, uncompiled source files. Levelo-Js v2 now ships with production bundles powered by tsup . The engine is now pre-bundled into highly optimized, tree-shakable ES Modules ( compiler/index.js ), making your production build lighter than ever. 2. Hierarchical Tracking Context ( owner.ts ) Handling nested reactive scopes and side-effects can easily lead to chaotic state bugs if not tracked properly. v2 introduces a robust Reactive Ownership Architecture . This creates a clean parent-child tracking hierarchy, ensuring that nested state updates always know exactly where they belong in the application tree. 3. Ownership-Driven Effects & Zero Memory Leaks Memory leaks are the silent killers of Single Page Applications (SPAs). In v2, our core effect() engine has been deeply integrated with the new ownership layer. The breakthrough? It now auto-disposes stale tracking dependencies automatically. We ran heap

I built a tool that answers one question: when a website won't load, is it your connection or the site? It runs two checks in parallel — measures your own line in the browser (latency + a 1 MiB download), and probes the target from Cloudflare's edge — then returns one of four verdicts: it's not you, it's you, it's both, or all clear. Demo: https://itsnotyou.site The interesting part isn't the tool, it's that the whole thing — app, ~120 monitored sites with 24h history, SSR status pages, share cards, outage alerts — runs for $0/month plus domains. The one thing that wanted to cost money Everything fit the Workers free plan except a background monitor probing ~120 sites on a tight cadence. As a Worker cron that's ~120 subrequests/run and, done naively, thousands of KV writes/day — which pushes you onto Workers Paid. The real ceiling is KV: 1,000 writes/day. So I split it. The user-facing test stays on Cloudflare — the edge probe still measures from the colo nearest the user, which is the point of edge. But the background sweep moved to a cheap VPS I already had: it probes the roster on a systemd timer and pushes results back into KV over the REST API. Staying under 1,000 KV writes/day One KV key, not key-per-site. All ~120 statuses live in a single blob. Key-per-site at sweep cadence would be millions of writes/month; a single blob is one write per sweep. Widen the cadence. I started at 2 min = 720 writes/day. Cloudflare emailed that I'd hit 50% of the daily limit (the sweep wasn't the only writer). I went to 3 min = 480/day, leaving headroom for share cards and the notify list. Move the hot counter off KV. The anonymous "tests today" counter was the sneaky risk — a traffic spike could exhaust the write budget and stall the status sweep, the one thing you can't let happen. It went to Analytics Engine instead (free, uncapped, separate budget). Now no amount of user traffic can starve the monitor. Reader and writer share code so their rules never drift: the streak/histo

# Building an AI Sales Intelligence Platform in Just 12 Hours at Hack Aarambh 2026 Turning sales conversations into actionable business insights using AI. Yesterday, my team and I participated in Hack Aarambh 2026 at Swarnim Startup & Innovation University (SSIU) . Like every hackathon, the challenge wasn't just writing code—it was identifying a real-world problem, designing a practical solution, and delivering a working prototype within 12 hours . Instead of building another chatbot or productivity tool, we wanted to solve a problem faced by almost every sales-driven organization. The Problem Every day, sales teams spend hours talking to potential customers. These conversations contain valuable information such as: Customer pain points Buying intent Competitor mentions Product feedback Common objections Feature requests Unfortunately, most of this information remains buried inside meeting recordings or handwritten notes. Managers rarely have time to review every conversation, which means valuable business insights are often lost. That became our motivation. Introducing AI Sales Intelligence Platform Our project is an AI-powered platform that automatically analyzes sales conversations and transforms them into actionable insights for both sales representatives and business leaders. Instead of manually reviewing calls, users receive: AI-generated summaries Customer intelligence Actionable recommendations Performance analytics Business insights ...all within seconds. What We Built AI Call Transcription & Summarization The platform automatically converts conversations into readable transcripts and concise summaries. Customer Intelligence The platform identifies: Customer sentiment Buying intent Objections Competitor mentions Important discussion topics This helps sales teams focus on what actually matters. AI Generated Follow-ups Writing follow-up emails after every meeting is repetitive. Our platform automatically generates personalized follow-up emails based on each c

When learning a new technology, most of us follow a familiar path. We start with the official documentation. Then we search GitHub repositories. We read blog posts. We watch YouTube tutorials. Eventually, we ask an AI assistant when we get stuck. Each resource solves a different problem, and the best developers know when to use each one. Documentation Is the Foundation Official documentation should almost always be your first stop. It tells you how a framework or library is intended to work. The information is usually accurate, maintained, and version-specific. If you're learning React, Next.js, or Node.js, the official docs provide the most reliable starting point. But documentation has limits. It explains what something does, not always why developers use it in real projects. Community Content Fills the Gaps That's where blog posts, conference talks, and open-source repositories become valuable. Experienced developers share: Real-world architecture decisions Common mistakes Performance considerations Debugging strategies Project structure Deployment workflows These practical insights often don't belong in official documentation, but they're essential for becoming a better engineer. AI Has Changed the Workflow AI assistants have become another tool in the developer toolbox. Instead of searching through multiple pages, developers can ask targeted questions like: Why is this hook re-rendering? What's the difference between these two approaches? How can I improve this query? Can you explain this error message? AI doesn't replace documentation. It helps you understand it faster. The most effective workflow is using documentation as the source of truth while letting AI explain concepts, compare approaches, or clarify confusing examples. Build Your Own Reference Library One habit that's improved my productivity is creating a personal knowledge base. Whenever I solve a difficult problem, I write down: The issue Why it happened The solution What I learned Links to relevant

If you only tracked one part of the agent economy this June, you'd have missed how fast the rest of the stack is being built. So here's a roundup, and one honest observation about the piece that's still missing. Four launches, one month Four things shipped in roughly four weeks, and together they sketch the shape of the machine economy: MetaMask Agent Wallet (Jun 8) - a self-custodial wallet an AI agent can drive directly. Keys for machines. Coinbase for Agents (Jun 11) - an MCP + CLI surface that connects an agent to a Coinbase account, riding on x402, which has now processed well past 160M payments. OKX.AI marketplace (Jun 30) - persistent on-chain identity, cross-job reputation, and escrow-backed dispute resolution, all in one platform. Kustodia MCP escrow - a smart-contract escrow on Arbitrum, exposed as MCP tools so an agent can create an escrow, lock funds, monitor for delivery, and release payment through natural-language calls. It also supports x402, Google's AP2, and Coinbase's AgentKit. Add the payment-rail data around all of it: across the tracked x402 flows this year, USDC is the overwhelming majority of value moved, and the median agent payment sits in the cents. This is a real economy forming, not a demo. Every one of those launches is genuine progress. And every one of them, at the moment that matters, has someone other than the two counterparties holding the asset. The pattern: hold, then decide Look at where the money physically sits during a transaction in each model. A wallet holds your keys - fine, that's custody of your own funds by design. A payment rail moves value from your account to theirs - a transfer, one direction. A marketplace with escrow holds both sides' value and releases it when a condition (often a human-designed evaluator or dispute process) says so. Kustodia is the cleanest statement of the escrow model, so it's worth being precise about it rather than vague. Their Arbitrum contract acts, in their own framing, as an impartial re

**_Hi everyone! We're three 16-year-old friends learning to code. Instead of building "just another app," we want to solve a real problem that developers actually face. So we have one question: Think about a moment when you caught yourself saying, "Why hasn't anyone built a good solution for this yet?" What was the problem? It can be anything: something that wastes your time, something frustrating, a repetitive task, a confusing workflow, or anything that made you wish a better tool existed. We're not trying to sell anything. We're simply listening and looking for real problems worth solving. Every answer means a lot to us. Thank you!_**