OpenAI’s Sam Altman, Anthropic’s Dario Amodei and Demis Hassabis of Google’s DeepMind AI lab with other top execs signed a letter urging Congress to require safeguards when companies order synthetic DNA and RNA, a key step in developing certain vaccines and biotech breakthroughs. submitted by /u/beasthunterr69 [link] [留言]

Problem: I constantly save Instagram Reels/TikToks about restaurants, events, pop-ups, and things to do, but I always forget about them because they get buried in my saves. Solution: I built Cork. You can send a Reel or Tiktok directly to Cork without even needing to leave the app, and Cork will automatically extract the info in the reel and save it in a map + calendar. However, I've been finding it hard to get traction while my competitors have gotten traction/funding. I strongly believe my app

Been using the Artlist AI Toolkit for a while now and honestly just camp out on Nano Banana 2 for image editing and Kling 3.0 for video. Between those two I can pretty much handle everything I need. The toolkit has a ton of other stuff: Veo 3.1, Flux 2.0, GPT Image 1.5, Sora 2, but I haven't felt a strong enough reason to branch out yet. Curious if anyone's actually putting the other models to work or if most people find their two or three go-tos and just stay there. Is Veo 3.1 actually worth trying alongside Kling? And does anyone use the voiceover tools or is that still rough around the edges? submitted by /u/shogunattila [link] [留言]

I'd like to perform the typical operation of giving an AI some text to review and asking it to give me feedback, summarize the document, evaluate the content etc. Except, I want to give it two pieces of text, perhaps two sides of a debate, and I don't want the output to depend on the order of the two inputs. My naive idea is to do it both ways in two separate contexts, then feed those results to each other with a request for convergent results, and repeat until they converge. However, this seems like it would be rather slow and expensive. Are there any existing tools that enable this sort of task without extra tooling and iterative attempts at convergence? submitted by /u/sparr [link] [留言]

Web Security: OWASP Top 10 and How to Fix Them (2026) Security isn't a feature you add later — it's built into every layer. Here's how the top 10 vulnerabilities work and how to prevent them. #1 Broken Access Control // ❌ Vulnerable: User can access anyone's data app . get ( ' /api/users/:id ' , ( req , res ) => { const user = await db . users . findById ( req . params . id ); res . json ( user ); // No check if requester owns this data! }); // ✅ Secure: Always verify ownership app . get ( ' /api/users/:id ' , async ( req , res ) => { // Check: Is the logged-in user requesting their OWN data? if ( req . params . id !== req . user . id && req . user . role !== ' admin ' ) { return res . status ( 403 ). json ({ error : ' Access denied ' }); } const user = await db . users . findById ( req . params . id ); res . json ( user ); }); // ✅ Better: Use middleware for all protected routes const requireOwnership = ( resourceType ) => async ( req , res , next ) => { const resource = await db [ resourceType ]. findById ( req . params . id ); if ( ! resource ) return res . status ( 404 ). json ({ error : ' Not found ' }); if ( resource . userId !== req . user . id && req . user . role !== ' admin ' ) { return res . status ( 403 ). json ({ error : ' Access denied ' }); } req . resource = resource ; // Attach for route handler next (); }; app . get ( ' /api/posts/:id ' , auth , requireOwnership ( ' posts ' ), ( req , res ) => { res . json ( req . resource ); }); #2 Cryptographic Failures // ❌ Storing passwords in plain text or weak hashing const password = " password123 " ; db . users . insert ({ email , password }); // NEVER DO THIS! // ✅ Proper password hashing with bcrypt const bcrypt = require ( ' bcrypt ' ); const SALT_ROUNDS = 12 ; // Higher = slower = more secure (12 is good balance) async function hashPassword ( password ) { return bcrypt . hash ( password , SALT_ROUNDS ); } async function comparePassword ( password , hash ) { return bcrypt . compare ( password , hash ); /

Web Security: OWASP Top 10 and How to Fix Them (2026) Security isn't a feature you add later — it's built into every layer. Here's how the top 10 vulnerabilities work and how to prevent them. #1 Broken Access Control // ❌ Vulnerable: User can access anyone's data app . get ( ' /api/users/:id ' , ( req , res ) => { const user = await db . users . findById ( req . params . id ); res . json ( user ); // No check if requester owns this data! }); // ✅ Secure: Always verify ownership app . get ( ' /api/users/:id ' , async ( req , res ) => { // Check: Is the logged-in user requesting their OWN data? if ( req . params . id !== req . user . id && req . user . role !== ' admin ' ) { return res . status ( 403 ). json ({ error : ' Access denied ' }); } const user = await db . users . findById ( req . params . id ); res . json ( user ); }); // ✅ Better: Use middleware for all protected routes const requireOwnership = ( resourceType ) => async ( req , res , next ) => { const resource = await db [ resourceType ]. findById ( req . params . id ); if ( ! resource ) return res . status ( 404 ). json ({ error : ' Not found ' }); if ( resource . userId !== req . user . id && req . user . role !== ' admin ' ) { return res . status ( 403 ). json ({ error : ' Access denied ' }); } req . resource = resource ; // Attach for route handler next (); }; app . get ( ' /api/posts/:id ' , auth , requireOwnership ( ' posts ' ), ( req , res ) => { res . json ( req . resource ); }); #2 Cryptographic Failures // ❌ Storing passwords in plain text or weak hashing const password = " password123 " ; db . users . insert ({ email , password }); // NEVER DO THIS! // ✅ Proper password hashing with bcrypt const bcrypt = require ( ' bcrypt ' ); const SALT_ROUNDS = 12 ; // Higher = slower = more secure (12 is good balance) async function hashPassword ( password ) { return bcrypt . hash ( password , SALT_ROUNDS ); } async function comparePassword ( password , hash ) { return bcrypt . compare ( password , hash ); /

Most engineers build distributed systems hoping nothing breaks. Amazon S3 was engineered under the opposite assumption: that something is already broken, right now, and the system needs to be fine with that. That one mindset shift explains almost everything about how S3 works — and why it's one of the most reliable pieces of infrastructure on the planet. I went through a deep-dive conversation with Mai-Lan Tomsen Bukovec, VP of Data and Analytics at AWS, and extracted the engineering philosophy underneath the product. Not the marketing version. The real one. Here's what actually matters. 1. Hardware failure is not an emergency. It's Tuesday. S3 manages hundreds of exabytes of data across tens of millions of hard drives, spread across 120 Availability Zones in 38 AWS Regions. It currently stores over 500 trillion objects. At that scale, something is always failing. A disk here. A rack there. An availability zone every now and then. The math is unforgiving. So the S3 team made a deliberate architectural decision early: stop treating failure as an exception. Design it into the system as the baseline state. This means dedicated auditor and repair microservices run continuously in the background — not when something goes wrong, but always. They scan the entire fleet, inspect every byte of data, detect discrepancies, and trigger repairs automatically. No human in the loop. No incident ticket. No war room. There's also a specific property they engineer for called crash consistency — the system is designed so that after any fail-stop event, it automatically returns to a valid state without manual intervention. The failure happens. The system continues. Those two things are not in conflict. The system heals itself because it was designed to assume it's already sick. If you're building distributed systems and your failure handling is reactive — you only respond after something breaks — you've already lost. Design the repair loop as a first-class citizen, not an afterthought.

Hello everyone. In the last two years I spent, as a dev, part of my free time stretching the limits of my knowledge. Not being a mathematician myself, I discovered that formalizing concepts in mathematical language could nonetheless be useful to improve symbolic reasoning about the concepts themselves. I made use of both books and AI, and I followed the development of the latter, mainly with a critical eye. I have several open projects, and from some observations and explorations on one of them

The most expensive mistake of my career wasn't a line of code; it was a 'yes'. That 'yes' not only cost me money but also severely damaged my reputation, which I had built over years. This was a turning point I experienced when my personal project, which I proudly worked on and named "BurnCPU," reached its first 100 users. Today, with 20 years of system architecture and operations experience, I can clearly see the decisions I made back then and the lessons I've learned since. This post is not just a technical error analysis; it's also an intention to share a pragmatic decision-making process, trade-offs, and the courageous stance of an expert. My goal is to spark discussion, encourage thought, and perhaps help you avoid similar mistakes. When Did That 'Yes' Come? BurnCPU was initially a tool I developed for my own needs, aimed at optimizing server resources. The goal was to reduce costs by efficiently utilizing idle CPU time. The development process was enjoyable and, over time, exceeded expectations. When the first beta users started giving positive feedback, my excitement was at its peak. And then the moment arrived; an investor, during this period when my project reached its first 100 users, offered financial support for a major scaling and marketing push. The offer was tempting. It presented an opportunity to reach wider audiences, add more features, and perhaps even commercialize the project. The person opposite me was introduced as a recognized and successful name in the industry. Without delving too deeply into the details of the offer, I said "yes." This simple word marked the beginning of the most expensive mistake of my career. ⚠️ A Risky 'Yes' When making this decision, I did not sufficiently analyze the technical maturity of the project or whether my infrastructure could handle such a load. I overlooked the chasm between the marketing power promised by the investor and my technical infrastructure. After the First 100 Users: Unexpected Problems When we re

I used to think the best AI food logging flow would be simple: Take a photo, let the model identify the meal, confirm it, done. That works surprisingly well for a lot of meals. But while building MetricSync, I learned the awkward product truth: the best input method changes depending on what is in front of the user. A photo is great for a plate. A barcode is better for packaged food. Text is better when the user already knows what they ate or wants to fix one detail quickly. The mistake is treating one input mode like the whole product. Photos feel magical until the meal gets messy Photo logging is the most impressive demo because it removes the blank search box problem. The user does not need to know the exact database name for “rice bowl with chicken and avocado.” They can just show the app what they ate. But meals are messy. A photo might miss the sauce. It might not know if the drink is diet or regular. It might confuse a small serving with a large one. It might identify the food category correctly but still need a portion correction. That does not make photo logging bad. It just means the UX cannot end at “AI guessed something.” The real product is the correction loop. Can the user fix the meal without starting over? Barcode scanning is boring in the best way Barcode scanning is not as exciting as AI, but it is often the right tool. If someone is logging a protein bar, yogurt, cereal, or a packaged drink, asking an image model to infer the nutrition facts is silly. The barcode is more direct. That changed how I thought about the app. AI should not be the star of every interaction. Sometimes AI should get out of the way. The goal is not “use AI everywhere.” The goal is “make logging the thing in front of me take the least effort.” For packaged foods, that means barcode first. For mixed plates, that means photo first. For quick edits, that means text. Text still matters The more AI features you add, the easier it is to forget text input. But text is still the fas

TLDR - auto lenders are using AI bots to negotiate insurance settlements with inaccurate information. How can I Captain Kirk them and get a live person on the phone? I am an insurance claims adjuster. Recently, several high-interest auto loan lenders have begun using AI (both through email and phone calls) to dispute the total loss values for our claims. For those of you that have never dealt with a total loss - the value of a vehicle is (usually) determined by seeing what comparable vehicles are selling for on the market, and making adjustments based on the condition, mileage, etc. between those vehicles and the totalled vehicle. If a customer disagrees, they can hire an appraiser and the company will hire an independent appraiser, and the two will come to an agreement. The lender gets paid the amount minus the customer's deductible, and if it doesn't fully pay off the loan, unfortunately the customer will be responsible for the balance. Lately, AI calls and emails have been coming from these lenders disputing the amounts, and often based on egregiously incorrect information. They provide cherry picked comparisons to try to boost the vehicle values, and sometimes they aren't the same year, make, or model. Sometimes mileage and condition isn't factored in, sometimes they are tricked-out show cars someone advertised on a FSBO site. The real problem is, we have to waste our time researching all of this to see if any of the data is correct. When we respond pointing out the flawed comparisons, they only come back with more flawed comparisons. If we argue long enough, they will invoke the appraisal clause on the customer's behalf. Their appraiser is another AI system with a cutesy name. All efforts to reach humans at these lenders are essentially turned away - we are told we need to deal with the system. I am open to any advice you folks have - how can we get these AI systems to basically give up and get us in touch with a real person? I'm not trying to screw anyone out

Big update. The PDF hub is fully built out and a whole set of Office conversion tools just shipped. PDF hub complete (18 tools): Merge, split, compress, rotate, reorder, extract pages, remove pages, watermark, page numbers, protect, unlock, extract images, and more. Office ↔ PDF (6 tools): Word to PDF Excel to PDF PowerPoint to PDF PDF to Word PDF to Excel PDF to PowerPoint Office ↔ Office (3 tools, fully browser-side, no file upload): Word to Excel Excel to Word PowerPoint to Word Other recent updates: WordPress plugin updated to include the new PDF tools Chrome extension v1.1.0 updated with PDF support 1.6K pages now indexed on Google across 25 languages That brings the site to 61 free tools across three hubs (Image, PDF, Office), no signup required.

Building a Real-Time Chat Feature with Django Channels and React Real-time features have become table stakes for modern web applications. Whether it is a customer support widget, a collaborative tool, or a social platform, users expect instant communication without page refreshes. In this article, I will walk through how we built a production-ready real-time chat feature using Django Channels and React at UCDREAMS. Why Django Channels? Django is traditionally synchronous. It handles one request at a time per worker. This works fine for standard HTTP requests, but WebSocket connections require persistent, bidirectional communication. Django Channels extends Django to handle WebSockets, background tasks, and asynchronous protocols alongside traditional HTTP. The beauty of Channels is that it does not replace Django. It layers on top, letting you keep your existing models, ORM, authentication, and admin panel while adding real-time capabilities. For a team already invested in Django, this is a massive advantage over introducing an entirely separate real-time server. Setting Up the Backend Start by installing Django Channels and a channel layer. Redis is the recommended backend for production use: channels == 4.0 . 0 channels - redis == 4.2 . 0 daphne == 4.0 . 0 Configure your Django settings: INSTALLED_APPS = [ ... " channels " , ] ASGI_APPLICATION = " your_project.asgi.application " CHANNEL_LAYERS = { " default " : { " BACKEND " : " channels_redis.core.RedisChannelLayer " , " CONFIG " : { " hosts " : [( " 127.0.0.1 " , 6379 )], }, }, } Building the WebSocket Consumer The consumer handles WebSocket connections: import json from channels.generic.websocket import AsyncWebsocketConsumer class ChatConsumer ( AsyncWebsocketConsumer ): async def connect ( self ): self . room_name = self . scope [ " url_route " ][ " kwargs " ][ " room_name " ] self . room_group_name = f " chat_ { self . room_name } " await self . channel_layer . group_add ( self . room_group_name , self . cha