Plus: A ransomware group is now stealing data in person, BusPatrol wants to hand its license plate surveillance data to the cops, and more.

The biggest risk in using open-ended LLMs for research is their tendency to hallucinate or invent sources. Andrej Karpathy's method of building an LLM Wiki addresses this by creating a closed-loop system: the model is trained only on your trusted raw source docs. This acts as a smart search engine for your own library, grounding all responses in verifiable documents. I've been using Recall, an AI knowledge base, to easily implement this closed retrieval system. It ensures that when Claude answers a question about my research, it's strictly based on the PDFs and papers I uploaded. Does anyone disagree that this closed-system approach is essential for high-stakes research? submitted by /u/AdarshXDD [link] [留言]

The company is hoping to sell 10 million wearables in the second half of 2026, according to 'The Information.'

I’ve added an Easter egg to my portfolio. I wanted to run "Bad Apple" I started experimenting with ASCII art, but then I remembered that you can also print pic in console in the latest versions of Chrome that bad apple didn’t exist before, so I went ahead and did it: NPM : https://www.npmjs.com/package/bad-apple-console Github : https://github.com/alienpingu/bad-apple-console full video : https://www.youtube.com/watch?v=lDpTDnPwZhk let me know if it is usable, thanks! submitted by /u/alienpingu [link] [留言]

I'm trying to understand the intuition behind Word2Vec training using a neural network. In Word2Vec (CBOW or Skip-gram), we often hear that the weight matrices learned during training contain the vector representations (embeddings) of words. However, I don't understand why the weights of the hidden-to-output layer (or output weight matrix) end up representing semantic features of words. Why do these weights become meaningful vector representations instead of just being parameters used to make predictions? I've explored multiple YouTube videos, blog posts and even asked ChatGPT several times, but I still haven't found an explanation that truly clicks for me. Most resources explain that the weights become embeddings, but not why this happens intuitively and mathematically. Could someone provide a clear intuition or mathematical explanation of why the output-layer weights end up encoding semantic information about words? Any good resources that explain this particularly well would also be appreciated. submitted by /u/aaryantiwari26 [link] [留言]

Tired of sleeping like a mummy in a bag? Improve your backcountry sleep and carry less weight with an ultralight quilt.

I’ve added an Easter egg to my portfolio. I wanted to run "Bad Apple" I started experimenting with ASCII art, but then I remembered that you can also print pic in console in the latest versions of Chrome that bad apple didn’t exist before, so I went ahead and did it: NPM: https://www.npmjs.com/package/bad-apple-console Github: https://github.com/alienpingu/bad-apple-console full video: https://www.youtube.com/watch?v=lDpTDnPwZhk let me know if it is usable, thanks! submitted by /u/alienpingu [link] [留言]

Google Cloud's automated systems suspended Railway's production account without notice, triggering an eight-hour platform-wide outage affecting 3 million users. The cascade took down workloads across all providers including AWS and bare metal because Railway's control plane was hosted on GCP. Railway is demoting GCP to backup-only status. By Steef-Jan Wiggers

Trump admin wants to rely on states for coal ash monitoring, enforcement, allow them to bypass national standards.

Just to showoff one little project of my own. I've been building a lots of SaaS (5k euros mrr currently) and I finally wrote the whole recipe in a tool. Write your idea in plain language, it will evaluate who your competitors could and if your project could generate money. With that it will build a full roadmap over 30 days, from the landing page, to the marketing and billing. https://letmecookit.app Happy to get your feedback! submitted by /u/InnerPhilosophy4897 [link] [留言]

22 Astro Best Practices: The Bookmark-Worthy Tips At QuotyAI I'm using Astro to build landing pages and blog posts, so I have hands-on experience how to use it properly and how to vibe-code without headache. Astro is the best framework for content sites right now - #1 in developer satisfaction in the State of JS 2025 survey, with Cloudflare backing it since January 2026. But like any tool, it rewards people who use it the way it was designed. This is the reference I wish I had when I started. Whether you're building your first Astro project or vibe-coding a blog at 2am, these are the habits worth forming from day one. Heads up on versions: This article covers Astro 6.x (released March 2026) and Astro 6.4 (released May 2026). Some APIs from older tutorials are now deprecated - those are called out explicitly below. Always check the upgrade guide when moving between majors. 🖼️ Assets & Media 1. Use <Image /> instead of <img /> Astro's built-in <Image /> component does a lot of work at build time that plain <img> tags leave on the table: it converts images to WebP, generates the right width and height attributes to prevent layout shift, and compresses everything without you touching a single config file. --- import { Image } from 'astro:assets'; import hero from '../assets/hero.png'; --- <!-- ✅ Optimized: converted to WebP, compressed, no layout shift --> <Image src={hero} alt="Hero image" /> <!-- ❌ Skips all of that --> <img src="/hero.png" alt="Hero image" /> For art-direction scenarios (different images at different breakpoints), reach for <Picture /> instead. 2. Use the Astro 6 Built-in Fonts API Almost every website uses custom fonts, but getting them right is surprisingly complicated - performance tradeoffs, privacy concerns, self-hosting, fallback generation, and preload hints. Astro 6 added a built-in Fonts API that handles all of it for you. Configure your fonts in astro.config.mjs : // astro.config.mjs import { defineConfig , fontProviders } from ' astro/conf

Building a Unix-Domain-Socket IPC server for ECS-on-EC2 services that need to talk fast, cheap, and reliably A while back I was looking at a flamegraph of a service that, on paper, should not have been having any performance problems. The producer and the consumer were the same Docker image's worth of trouble — colocated on the same EC2 host, in the same ECS cluster, sharing the same instance type, the same kernel, the same RAM. By every reasonable measure they were neighbours. And yet every event was making a round trip that looked roughly like this: producer → kernel TCP stack → ENI on the producer task → AWS VPC → internal load balancer → ENI on the consumer task → kernel TCP stack → consumer. TLS handshake. HTTP framing. JSON over the wire. Connection pool. Retry policy. The whole circus. I wasn't doing anything wrong. This is what the platform funnels you toward. ECS with awsvpc networking gives every task its own ENI. The default story for "service A talks to service B" is "give B a DNS name, put a load balancer in front of it, configure a security group, point A at the LB." Even if A and B are physically on the same box, the bytes are still leaving the kernel, traversing the VPC, and coming back. There's a fix for this. It's been a fix for fifty-something years. It just hasn't been the default fix, because cloud-native architecture grew up assuming services would be scattered across hosts and the network was the abstraction that mattered. This article is about building a proper IPC server using Unix Domain Sockets, deployed as a sidecar pattern on ECS-on-EC2, with a wire protocol robust enough to ship in production. We're going to design it from scratch — the transport choice, the wire format, the backpressure model, the failure modes, the deployment topology. I'll show you real pseudo-code from the implementation and call out the small number of places where, if you get it wrong, you'll spend a weekend debugging it. The intended outcome is something you coul

The Problem We Were Actually Solving We were not building a demo. We needed to let Veltrix operators run A/B experiments on synthetic user journeys without melting the underlying SQL warehouse. The real question was: how close could we push the warehouse to the AI inference layer before the planner started dropping predicates and the warehouse returned rows that made no sense for the user journey. The warehouse in question was a Snowflake XL on AWS, billed by the second. Our synthetic user model generated 250 k journeys per minute during peak. The AI layer had to annotate each journey with intent tags (shopping, support, fraud) within 200 ms to stay ahead of the next batch. That was the operating envelope, not the sales slide. What We Tried First (And Why It Failed) First cut: put the intent model in a sidecar container next to the Spark cluster that generated the journeys. We picked ONNX Runtime v1.14 with a DistilBERT fine-tuned on our own corpus because the latency slide said 30 ms. Reality: ONNX packaged the tokenizer as a separate DLL. Tokenization alone took 85–110 ms on c6i.large instances, pushing the total inference time to 190 ms when the warehouse was cold and 280 ms when Snowflake decided to spike the warehouse cluster. The operator dashboards immediately showed orange pings; the business called it a red fire drill. Worse, the tokenizer DLL leaked memory. After two hours on a 64-core cluster, each pods RSS climbed to 2.4 GB, and the Kubernetes scheduler evicted five pods in a row. The warehouse downstream received duplicate rows with NULL intents, so every metric we exported was off by 7–12 %. The Architecture Decision We ripped out the sidecar entirely. Instead, the Spark jobs write raw event JSON to an S3 bucket every 60 seconds. A Lambda function (Python 3.12 runtime) picks up the bucket, tokenizes offline, and stores the tokenized blobs back in S3. A nightly Kubernetes job then loads the tokenized chunks into Snowflake as temporary tables. The AI inf

GitHub: github.com/vivek1504/forge Live Demo: forge.vivekjadhav.xyz i built this project that runs full IDE entirely client-side. I've attached a quick demo video showing it in action. It uses WebContainers under the hood. You can pick a framework (React, Vue, Svelte, or plain Node), write code in a Monaco editor, and get a live preview with HMR. It includes a functional file explorer and a real integrated terminal without spinning up any cloud VMs or Docker containers. submitted by /u/viks98 [link] [留言]

How a shared filesystem became the cheapest, fastest outbox I've ever built — and why FSx for OpenZFS is the version of that idea that finally scales I was staring at an AWS bill last quarter where a single Kinesis Data Streams line item was costing more than the entire S3 footprint sitting behind it. The events on that stream had a useful lifetime of about ninety seconds. They were written by one service, read by another, processed, and dropped. We were paying full streaming-bus price for bytes that barely outlived a TCP timeout. That bill is what got me thinking about transitional data as a category that deserves its own architecture, and about why every "use the right tool" instinct I had — Kinesis, Kafka, MSK — was the wrong tool for this particular shape of work. The right tool, it turns out, is a filesystem. Specifically, AWS FSx for OpenZFS, used as an outbox between producers and consumers, with only a tiny pointer message traveling through whatever messaging bus you already have. This article is the case for that pattern. It's also the design, the failure modes, the code, the cost math, and the honest list of when not to do it. I'll walk you through the architecture from first principles, show you the safe-write protocol that makes it correct under crashes and concurrent retries, compare the cost against Kinesis, MSK and EFS at a realistic petabyte-class workload, and explain why the recent addition of FSx Intelligent-Tiering changes the cost story in a way that makes the pattern attractive even for teams that don't ingest petabytes. If you've ever felt the queasy sensation of paying twice for the same bytes — once to land on a stream, again to land in storage — this is for you. What "transitional data" actually means Most data falls into one of two cleanly shaped buckets. Durable data is the stuff you keep — user records, orders, financial events, audit trails. It needs to live for years; you pay storage costs for those years and you get value over those y

You enter a domain and it runs a full security stack analysis in one shot: Redirect chain - traces every hop from HTTP to HTTPS, flags mixed content and redirect loops SSL certificate - expiry date, issuer, SANs, grade (A–F), protocol versions (TLS 1.0/1.1/1.2/1.3) Security headers - HSTS, CSP, X-Frame-Options, Permissions-Policy, Referrer-Policy, and more, each rated HTTP/2 & HTTP/3 - detects ALPN negotiation and confirms QUIC via actual UDP connection DNSSEC & CAA - checks if the zone is signed and if CAA records restrict which CAs can issue certs HSTS preload status - tells you if you're on Chrome's preload list (or why you're not) Mixed content scanner - crawls the page and flags any HTTP resources loaded over HTTPS Beyond the one-off check there's also: Bulk check - up to 10 domains at once Cert expiry reminders - email alert before your cert expires Domain monitoring - periodic re-checks with email diff when something changes No account required. API available for automation. Do you find this useful? Looking for feedback. submitted by /u/EveningRegion3373 [link] [留言]