标签：#privacy

I haven’t thought about the privacy issues surrounding professional athletes and wearables. Wearables present serious privacy issues for “Average Joe” consumers, who are entrusting tech companies to safely store and protect their biometric data. Imagine the stakes for a professional athlete, whose entire livelihood could be affected by a single biometric data point. To give one of many realistic hypotheticals: a basketball player has a terrible game, and the coach wonders if they showed up to the gym hungover. The coach has access to the player’s wearable data, and checks to see when they went to sleep, as well as what their heart rate looked like during the night. Should the player have been out partying before a game? No. Should the coach be able to surveil them? Definitely not...

Hey DEV community! 👋 How many times a day do you need to format a messy JSON string, convert a CSV file, or parse a timestamp? And how many times do you find yourself pasting that data—which might contain API keys, user emails, or proprietary code—into a random website you found on Google? We’ve all done it, but in an era of constant data leaks, it’s a massive security risk. That’s exactly why I built Parsify . What is Parsify? Parsify is an all-in-one data converter and developer toolset designed to handle your daily formatting, parsing, and data manipulation tasks completely offline and client-side. No servers, no tracking, and absolutely no data leaks. Everything happens right inside your browser sandbox. 🚀 Key Features 100% Secure & Offline: Your data never leaves your local machine. Once the page loads, you can literally pull your internet plug and it will still work perfectly. All-in-One Toolkit: No more bookmarking ten different sites for ten different tasks. From JSON formatting and base64 encoding to data conversions, it’s all under one roof. Built for Speed: A clean, lightning-fast UI with batch-processing support to keep your workflow uninterrupted. Privacy by Design: Zero tracking scripts, zero ads, and zero database logging. Why I Built It Most online utilities are bloated with tracking pixels, pop-up ads, and cookies. Worse, you have no idea what happens to the data you paste into their input fields. As a developer, I wanted a tool that felt like a local desktop app but possessed the accessibility of a web app. Parsify is the bridge. It gives you the convenience of a web utility with the strict security boundaries of local execution. Check It Out (It's Free!) If you’re tired of compromising on data privacy for quick utilities, give it a spin: 👉 parsify.tools I’m actively working on adding more tools and converters to the suite. I would absolutely love to get the DEV community's feedback! What converters or formatting utilities do you use daily that you

Using Claude Fable 5 or Mythos 5 on Amazon Bedrock requires opting into provider_data_share, sending prompts and outputs to Anthropic for 30-day retention with human review. Previous Bedrock models kept inference data inside the AWS boundary. Three days after launch, Anthropic asked AWS to revoke access to both models citing US export control compliance. By Steef-Jan Wiggers

When it comes to our deepest thoughts, secrets, and mental health struggles, "the cloud" can feel like a very crowded place. In an era where data privacy is paramount, sending your private journal entries to a central server for analysis feels... risky. But what if you could have the power of a world-class LLM like Llama 3 running entirely on your MacBook? Thanks to the Apple MLX framework, local LLM execution is no longer a pipe dream—it’s a high-performance reality. By leveraging privacy-preserving AI and advanced Llama 3 quantization , we can build a personal mental health assistant that provides Cognitive Behavioral Therapy (CBT) insights without a single byte ever leaving your machine. 🚀 Why Apple MLX? 🍏 Apple's MLX is an array framework designed specifically for machine learning on Apple Silicon. It’s essentially "NumPy meets PyTorch," but optimized to squeeze every drop of power out of your M1/M2/M3 chip's Unified Memory Architecture. The Architecture: 100% Local Data Flow Here is how our private assistant handles your data. Notice the absence of any "External API" or "Cloud Storage" blocks: graph TD A[User Private Journal Entry] --> B{Local Python App} B --> C[Apple MLX Framework] C --> D[Quantized Llama 3 - 4bit/8bit] D --> E[CBT Sentiment Analysis] E --> F[Empathetic CBT Feedback] F --> B B --> G[Local Encrypted Storage] subgraph MacBook Pro / Air C D E end Prerequisites 🛠️ To follow this advanced guide, you’ll need: An Apple Silicon Mac (M1, M2, M3 series). Python 3.10+ . mlx-lm : The high-level library for running LLMs with MLX. Step 1: Setting Up the Environment First, let's create a virtual environment and install our dependencies. We are using mlx-lm because it handles the complexities of quantization and model loading seamlessly. mkdir private-mental-health-ai && cd private-mental-health-ai python -m venv venv source venv/bin/activate pip install mlx-lm huggingface_hub Step 2: Downloading & Quantizing Llama 3 Llama 3 8B is a powerhouse, but it's a bi

Most cloud systems make one authorization decision at login. Everything after runs on trust established at authentication time. For systems handling regulated data, that gap is where breaches happen. This article presents a continuous authorization architecture covering risk-tiered evaluation, behavioral baselines, privacy-preserving audit trails, and a phased and incremental rollout. By Venkata Nedunoori

Originally published at dragosroua.com . Last week, Anthropic released Fable 5. Three days later, the US government ordered them to shut it down — for people outside US. Anthropic said they couldn’t filter users by nationality fast enough, so they pulled the plug on the whole thing. Like any good ol’ miracle, it lasted only 3 days. That was a very much needed cold shower. When you realize someone can take away your workforce just like that, running local, private AI models, suddenly becomes the number one priority. Why You Should Run Your Own Local AI Models In no particular order (because all of them count): No one can take it away. Local AI models on your machine don’t care about export controls, government directives, or provider board decisions. No usage limits. No rate limits, no subscription tiers, no “you’ve used your monthly tokens.” Play as much as you want. Nothing leaves your machine. Your code, your documents, your client data — none of it hits a third-party server. Local AI models are private by default. Fixed cost. You pay for hardware plus electricity. No surprise price hikes mid-year. No API dependency. Your workflow doesn’t break when a provider has an outage, deprecates a model, or gets a compliance letter. You can modify it. Fine-tune, quantize, run on your own data. Build something they can’t sell you. Make your own local, private AI model factory. What It Actually Costs I hear you: but I don’t have the money to build a data center in my basement. Fair play. But here’s the thing: you don’t have to. Here are four realistic options, as of June 2026 money: MacBook Pro M4 Max (~$3,000–4,500) : 546 GB/s memory bandwidth. Runs 70B models at around 70 tokens/second with 4-bit quantization. Fast enough to feel snappy. This is the “you might already own this” option. Mac Studio M3 Ultra (~$5,000–10,000) : 800 GB/s, up to 512 GB unified memory. Runs DeepSeek R1 — a 671-billion-parameter model — at 17–18 tokens/second. That’s a model that costs real money p

A local-first, single-file SPA where multiple agents debate your decision and hand you a verdict. The problem: every AI I asked just agreed with me I almost named this project wrong. I'd picked a name that sounded powerful. I asked ChatGPT, and it loved it. I asked Claude, and it nodded along. Nobody warned me about the trademark conflict, the wrong search intent, or the SEO fight I'd pick with the BBC. That was the moment I realized the problem wasn't the name. It was the feedback loop. Most AI assistants are tuned to please, so they hide your blind spots instead of showing them. When you need to make a consequential decision, "sounds great" is the most expensive answer you can get. So I built the opposite: a council of AI agents that disagree on purpose. What NoFlattery does NoFlattery puts 2–4 agents in a room, gives them different reasoning biases, and makes them debate your decision. The output isn't another chat transcript. It's a Decision Record: a clear verdict, the reasoning behind it, the main risk, what would change the call, and a next step. Use it for product decisions, pricing, tech stack, hiring, or any call where one perspective isn't enough. Key product choices: Local-first: your chats and API keys stay in your browser. BYOK: bring your own OpenAI, Anthropic, OpenRouter, or Ollama key. One-time price: no subscription, no account, no data harvesting. The stack The whole app is a single-file SPA built with: React 19 + TypeScript Zustand for state Dexie over IndexedDB for local-first storage Vite + vite-plugin-singlefile for a single index.html deploy An OpenAI-compatible provider runtime so users can plug in their own keys Why single-file? Because the deploy becomes dead simple. One HTML file. No server for the data. No build orchestration. I can ship the app to Cloudflare Pages and forget about it. The turn engine: deterministic, not magical The heart of NoFlattery is a turn-based multi-agent engine. One user message triggers one round. Each agent sp

Leaked files show the invite-only network grades members by their money and fame, shaping who’s in, who’s out, and who pays.

HiveTalk.space is a privacy focused chat app. HiveTalk.space should not be confused with hivetalk.org. While both platforms focus on communication, they are separate projects with different goals and feature sets. HiveTalk is closed source and cloud hosted, making it easy to start chatting without setting up your own server. Despite not being self-hosted, privacy remains a core focus. Private conversations are designed with privacy in mind, allowing users to communicate without unnecessary tracking or intrusive data collection. Every account includes generous free limits. Users can upload files and videos up to 1 GB each, send unlimited messages , and sign in using supported social login providers or a traditional account. Creating communities is simple, with the ability to make your own chat rooms for friends, gaming groups, project teams, schools, or fanbases in just a few clicks. HiveTalk also aims to provide a modern messaging experience with features such as polls, rich text formatting, media sharing, and room management tools, while keeping the interface simple and easy to use. Whether you want a private conversation, a small group chat, or a larger community, HiveTalk is designed to scale without placing artificial limits on everyday usage. Unlike many messaging platforms that reserve key features for paid subscriptions, HiveTalk offers its core functionality for free. The goal is to make private, feature-rich communication accessible without requiring users to pay just to unlock basic messaging features. As the platform continues to develop, new features and improvements are regularly added, with a focus on privacy, usability, and giving communities more control over how they communicate.

Artist Morry Kolman will be livestreaming feeds of the NBA champions’ ticker-tape parade from NYC’s traffic cameras—and this time, the city’s Department of Transportation isn’t demanding he stop.

You think your AI is just helping you write code. In reality, it's built a logging system on your machine that you never knew existed. Every conversation. Every code snippet. Every file path. Every time you asked "what was my password again?" — permanently archived, without your knowledge. How It Started: An Accidental Discovery I was about to sell my old laptop and decided to clean up my data first. I opened Claude Code's config directory — ~/.claude/ — intending to just remove my API key. Then I saw this: history.jsonl 243 KB / 695 lines sessions/ conversation metadata session-env/ environment variables shell-snapshots/ command execution snapshots telemetry/ 63 telemetry files projects/ 19 project directories ├─ interview-prep/ 31 sessions / 20 MB ├─ spring-ai/ 11 sessions / 13 MB └─ ... 17 more I thought I was just writing code. My computer thought it should record everything. What's Inside These Files history.jsonl — Everything You Ever Asked 695 entries. Every single thing I typed into Claude Code. Including: "I forgot my database password — can you check what passwords were configured in the project files?" "How do I view the database password?" Pasted code snippets Every /model , "who are you?", and project path You casually ask about a password once. It's permanently stored. projects/ — Full Conversation Transcripts (43 MB) If you think history.jsonl only storing user input isn't so bad — you haven't seen this yet. Inside ~/.claude/projects/ , every project directory contains .jsonl files. Opening one 2.3 MB session file: Content Count AI responses 590 AI internal thinking blocks 272 Tool calls 101 Tool call results (including file paths) 100 File history snapshots 208 Every conversation. Every AI response. Every internal reasoning step. Every file operation — what was read, what was modified, what was executed — all written to this file. shell-snapshots/ — Traces of Everything You Ran Your system PATH. Installed tools. Java version. All sitting in command s

Internal Home Office tests of age-verification technology show the risks of life-altering errors. It’s moving forward anyway.

Every time I handed my Aadhaar card to someone for KYC, one thought kept nagging me: Where is this data actually going? Most "digital Aadhaar verification" tools out there silently upload your card details to their servers. You have zero visibility into what gets logged, stored, or sold. For something as sensitive as a national biometric ID, that's a pretty terrible default. So I built AadhaarQRCodeReader — a web app that scans the Secure QR on any Aadhaar card, decodes all the identity details, and does the entire thing inside your browser . No backend. No API calls. No data leaves your device. Ever. PtPrashantTripathi / AadhaarQRCodeReader 🇮🇳 Offline Aadhaar QR Reader — scan or upload any Aadhaar card, no server, no data leak. 🇮🇳 Aadhaar QR Code Reader Scan the Secure QR on any Aadhaar card to instantly verify identity details — 100 % offline, no server, no data leaves your device. ✨ Features Feature Details 📷 Live camera scan Uses the rear camera on mobile, front on desktop 🖼️ Image upload Pick any photo containing an Aadhaar QR from your gallery 🔒 100 % offline All decoding happens in the browser — zero network requests 🪪 Full card details Name, DOB, gender, address, mobile last-4, email (if present), issue date 🃏 3D card flip Front (personal) ↔ Back (address) card flip animation 🔗 Shareable URL Result is encoded in ?data= so links can be bookmarked 📱 Mobile-first Works on iOS Safari, Android Chrome, and desktop browsers 📸 Screenshots Scanner Verified Result (Front) Verified Result (Back) Point camera at any Aadhaar QR Personal details on the front face Address & reference date on … View on GitHub 🤔 Wait, what even is the Aadhaar Secure QR? UIDAI added a Secure QR Code to modern Aadhaar cards (and letters) — it's that big QR, not the small one. It's essentially a compressed, binary-encoded snapshot of your Aadhaar record containing: Name, DOB, gender Full address (house no., street, locality, district, state, PIN) Last 4 digits of your linked mobile Email (if yo

There are over a dozen cases around the country where police officers are using the Flock surveillance camera system to obsessively and illegally stalk people . Alternate link .

Roblox's vice president of safety product policy, Eliza Jacobs, told NBC News that Roblox is "optimistic" that its new facial age estimation tech will "continue to get better," saying, "Ticking a box to say you're 13 or older, it's not enough anymore." NBC invited a group of kids to try out Roblox's new video selfie […]

A proposed FCC rule would kill burner phones: phones whose accounts are not attached to a particular person. The FCC plans to do this by legally forcing the country’s telecoms to store a wealth of personal information about essentially all phone customers, including a government issued identification number and their physical address, alarming privacy advocates and civil rights activists who compare the measures to those from authoritarian countries where it can be difficult to buy a mobile phone plan without giving up your identity. The proposed change would drastically shake up how people obtain phone plans in the U.S., and have all sorts of privacy and cybersecurity knock-on effects. The FCC is proposing the data collection partly as a way to combat scammers, with telecoms being required to collect other information on business and foreign customers like the intended use case of their bulk phone plan purchase and their IP address. But the changes would mean telecoms collect data on all new and renewing customers, and the FCC provides a long list of other things that the collected data could help authorities with...

Rank One, whose board includes a former CIA deputy director and a former FBI science chief, supplied face recognition to Meta for internal development of its smart glasses app.

"Files never leave your browser" is becoming standard copy for PDF tools, image editors, and document converters. But a trust claim and a verifiable fact are different things. Here's how to turn "zero upload" into something any user can audit in about two minutes, and how to enforce it at the browser level so it isn't just a promise. Step 1: Read the Network panel Open DevTools → Network, enable "Disable cache", reload. While processing a file, filter by "Fetch/XHR" and "Doc". A genuinely client-side tool should show only HTML/CSS/JS/WASM asset loads — no POST requests, no GETs carrying file content in query parameters. The non-obvious trap: third-party analytics, Google Fonts, and CDNs all show up as outbound requests. If you claim zero uploads, those count too. The honest move is to self-host fonts and scripts and drop analytics entirely, so the request list is genuinely short enough to eyeball. The Network panel is the human-readable check. The next part is what actually makes it hold. Step 2: Enforce egress with CSP connect-src This is the piece people get backwards, so it's worth stating precisely. CSP's connect-src is an egress allowlist the browser enforces before the request is sent . A fetch /XHR to an origin that isn't on the list is blocked by the browser and never leaves the machine. You'll see it fail in the console as a CSP violation, with no entry in the Network tab going out to that origin. This includes no-cors requests. no-cors is sometimes assumed to be an escape hatch, but it isn't one for this purpose. All no-cors does is let you issue a cross-origin request while making the response opaque (you can't read the body). It does not bypass connect-src : if the target origin isn't in your connect-src allowlist, the no-cors request is blocked exactly the same way — it never goes out. So you can't smuggle a file out to a third party with no-cors under a tight CSP. That's what makes CSP the actual proof, not just documentation. Tighten connect-src to 's

If you want files encrypted before they ever reach a cloud provider — so the provider only ever sees ciphertext — rclone crypt is the simplest tool that works with almost any backend (S3, Google Drive, Dropbox, pCloud, Backblaze B2, a plain SFTP box…). This is client-side, zero-knowledge-style encryption you fully control. Here's a clean setup. The idea rclone crypt is a wrapper remote : it sits on top of a normal remote and transparently encrypts file contents and file/dir names on the way up, decrypts on the way down. Your passphrase never leaves your machine. local files -> [crypt remote: encrypt] -> [storage remote] -> cloud (sees ciphertext only) 1. Install curl https://rclone.org/install.sh | sudo bash # or: sudo apt install rclone rclone version 2. Configure the underlying storage remote rclone config # n) New remote -> name it e.g. "drive" -> pick your provider -> OAuth/keys Test it: rclone lsd drive: 3. Add a crypt remote on top rclone config # n) New remote -> name "secret" -> storage: "crypt" # remote> drive:encrypted # a subfolder on the storage remote # filename_encryption> standard # also encrypts file names # directory_name_encryption> true # password> (generate a strong one) # password2> (salt - optional but recommended) Back up the passphrase + salt in a password manager. There is no recovery if you lose them — that's the whole point of zero-knowledge. 4. Use it # Upload (everything is encrypted client-side first): rclone copy ~/Documents secret: -P # List (decrypted view, local only): rclone ls secret: # Mount as a normal folder: rclone mount secret: ~/CloudCrypt --vfs-cache-mode writes On the provider's side you'll see only opaque names like a1b2c3d4... — no filenames, no content. 5. Verify the provider sees nothing rclone ls drive:encrypted # raw view = encrypted blobs + scrambled names If you can read filenames here, filename encryption isn't on — recheck step 3. Gotchas crypt encrypts content + names, not the number of files or their sizes. A m

Every time you use an online PDF converter, your file travels to a server somewhere — processed by a company you don't know, stored temporarily on hardware you don't control. For a random meme? Fine. For a payslip, a contract, or a medical document? That's a real privacy problem. What I built ConvertiZen is a document converter that runs 100% in the browser using WebAssembly and modern JS APIs. Your file never leaves your device. Ever. Verify it yourself: open DevTools > Network tab > run a conversion. Zero outgoing requests containing your file data. What it supports 40+ format pairs: PDF ↔ Word, JPG, PNG, Text, HTML, Markdown Excel ↔ CSV, JSON, HTML Images: JPG ↔ PNG ↔ WebP, GIF/BMP → PDF JSON ↔ CSV, Markdown ↔ HTML, XML → JSON How it works PDF.js for PDF parsing docx library for Word generation SheetJS for Excel/CSV Canvas API for image conversions Everything client-side — no server required Pricing 3 free conversions/day, no account needed. Beyond that: €0.69/conversion, €4.99 for a pack of 10, or €4.99/month for unlimited Premium. Feedback welcome What formats are missing? What would make you trust a browser-based tool over a server-based one? 👉 https://convertizen.netlify.app