Alibaba has reportedly classified Claude Code as high-risk software.

The US Department of Energy reportedly deleted about 6,000 pages related to energy conservation as a historic heatwave tears across the country. The deletion was suspiciously timed, following Republican outrage over Mayor Zohran Mamdani asking New Yorkers to help reduce strain on the grid by setting their AC to 78 degrees. Republicans like Ted Cruz […]

deck.gl based visualiser of the TFL Api + National Rails to be able to track (with minimal drift) a train along the way in London and to the nearest airports. If you pick one from any platform in https://nexttrain.london basically you can share your train journey along the rails. Build to test Cloudflare workers and their infra along with deck.gl performance that is incredible in my opinion.

Give your AI agents a slack, a task board, and a boss Discussion | Link

NASA has made contact with LINK, the robotic spacecraft made for the Swift Boost mission.

The Matic is our favorite robot vacuum by a pretty comfortable margin. If you’ve been thinking about buying one, you may want to plan on doing it sooner than later. The company will raise its price by $250 on September 9th, going from $1,245 to $1,495. Matic told The Verge that the new price reflects […]

The Yahoo Boys author Carlos Barragán will join Kate Knibbs to answer your questions about Nigeria's romance scammers.

Why Your SOE Resume Needs a Structural Overhaul Chinese state-owned enterprises (SOEs) often have deep hierarchical structures and a culture of collective achievement. But Western tech companies want to see individual impact, autonomy, and data-driven results. Continuing to lead with your former employer's prestige or your rank (e.g., "Senior Engineer Grade 7") wastes valuable space. The solution: reshape every section to answer the question "What did you personally accomplish with data?" The Core Shift: From Hierarchy to Impact In a Chinese SOE resume, it's tempting to list departments you led or teams you oversaw. In a Western senior data scientist resume, focus on the problems you defined, the algorithms you deployed, and the revenue, cost savings, or user metrics that improved. For example, instead of "Led the data analytics team of 10 people," write "Designed and deployed a demand-forecasting model that reduced inventory costs by 15% (¥12M annually)." Three Resume Sections That Require Full Rewriting Professional Summary: From 'Accomplished Engineer' to 'Data Science Leader' Start with your total years of experience, your technical stack, and the types of business problems you solve. Example: "Senior Data Scientist with 10+ years applying machine learning to supply chain and logistics. Expertise in Python, TensorFlow, and Spark. Reduced operational costs by 15-30% through predictive models deployed at [SOE name]." Work Experience: From Role Descriptions to Metric-Driven Bullets For each role, list 3-5 bullets. Every bullet should have a verb, a task, a technology (if relevant), and a quantified result. Avoid vague phrases like "responsible for." Use specific numbers: "Improved forecast accuracy from 70% to 85% by building an ensemble of ARIMA and XGBoost models." Education & Certifications: Emphasize Transferable Skills Your Chinese degree is fine, but add relevant certifications (AWS, TensorFlow, Coursera) to show adaptability. Consider a "Technical Skills" se

This is my first post here, so I'm going to use it as an introduction. I'm Usman, a software + data engineer who primarily works with data pipelines, backend systems. Not a huge fan of frontend development though. Although I do what I can, projects honestly feel incomplete without them, because at the end of the day you do have to showcase a working end to end system when you build something. I'm here after dozens of incomplete personal projects, and projects that never even got past the design phase, you know the drill. Procrastination and imposter syndrome kept stopping me from taking the next step, but I'm here now, gotta keep myself in check fr. I was scrolling through LinkedIn the past few days, and oh my god, the amount of AI-related brain rot there. Every single post written by AI, telling you how to use AI and how not to use AI. I mean I get it, yeah, the paradigm is shifting and AI is essential to development, but where are your personal anecdotes, stuff you solved, stuff you learned, the challenges you faced, how you overcame them. You know what maybe it's my fault, it's my algorithm after all. Anyway, here I am, looking to interact with like-minded engineers and learn from them. I'm also going to post regularly about my progress and what I am building, even though I have quite a bit of experience, and have built and contributed to large-scale production systems and pipelines, I'm going to start with something small, so I can stay consistent and keep myself in check. Software engineering fascinates me a lot, and there are so many domains that I wish to explore and have explored like game development, data engineering, web/app development. My significant other is graduating in a few days, and I'm thinking of making a small game for her, alongside which I'll be working on a small sales lead enrichment pipeline. Hoping to showcase my work and document it publicly, and hoping to get to know and learn from you all! Also, I'd love to know your thoughts on the am

Meta description: RAG keeps your data in a vector database — but n8n AI workflows still ship retrieved chunks to OpenAI in plain text. Learn where n8n RAG pipelines leak PII, why Guardrails can't restore it, and how Privent tokenizes context before the LLM and detokenizes before the user sees the answer. Target keywords: privent, n8n, n8n RAG, n8n AI workflow, n8n workflow, n8n Guardrails, PII tokenization, data loss prevention When it comes to building AI agents and automations in n8n , RAG (Retrieval-Augmented Generation) is widely marketed as the safest way to connect corporate data to Large Language Models. The pitch sounds great: "You don't need to fine-tune models with your data; your data stays securely in your own vector database." But there is a blind spot — and it shows up in almost every production n8n RAG workflow we review. Just because your documents rest in Pinecone, Qdrant, or Milvus doesn't mean they aren't leaking out. The critical question most developers overlook is this: once those highly confidential internal document chunks are retrieved from the vector database, where exactly do they go when they hit the LLM node in your n8n workflow? In this article, we'll break down the hidden dangers lurking in n8n AI workflows built on RAG, what happens to your data when it travels to third-party LLM providers like OpenAI or Anthropic, why common fixes in n8n fall short, and how Privent closes the gap with reversible tokenization — mask on the way in, restore on the way out, without breaking your automation. The illusion of RAG: where is your data actually safe? Let's look at a standard n8n workflow for RAG: [Webhook / Chat Trigger] → [Embed query] → [Vector DB: search] → [Build prompt with retrieved chunks] → [OpenAI / Anthropic LLM] → [Return answer to user] Step by step: The user asks a question — e.g., "What is the new financial projection for Q3?" Your n8n workflow vectorizes the query and searches a vector database containing indexed internal docume

Mistral AI, which offers some open source AI models, has raised significant funding since its creation in 2023, with the ambition to “put frontier AI in the hands of everyone.”

The incident is also being investigated by the National Highway Traffic Safety Administration.

I like neofetch / fastfetch , but I wanted one with a genuinely empty dependency graph — nothing pulled from crates.io. So I built purefetch : a small system-info fetcher written entirely in Rust using only std plus raw Linux syscalls. Disclosure up front: purefetch was built largely with AI assistance (Claude Code). I directed the design, and every change was reviewed and tested — including running it on four architectures under QEMU — but most of the code is AI-generated. I'd rather be honest about that than pretend otherwise. _,met$$$$$gg. ooonea@unicorn ,g$$$$$$$$$$$$$$$P. ────────────── ,g$$P" """Y$$.". OS Debian GNU/Linux 13.5 (trixie) x86_64 ,$$P' `$$$. Host ThinkPad P53 (20QQS0JD01) ',$$P ,ggs. `$$b: Kernel 6.12.94+deb13-amd64 `d$$' ,$P"' . $$$ Uptime 6 days, 15 hours, 30 mins $$P d$' , $$P Packages 2477 (dpkg), 1 (flatpak) $$: $$. - ,d$$' Shell zsh 5.9 $$; Y$b._ _,d$P' Display 1920x1080 (eDP-1) Y$$. `.`"Y$$$$P"' DE GNOME 48.7 `$$b "-.__ WM Mutter (Wayland) `Y$$ Terminal kitty 0.41.1 `Y$$. CPU Intel(R) Core(TM) i7-9850H @ 4.60 GHz `$$b. GPU Quadro RTX 3000 `Y$$b. Memory 15.28 GiB / 62.61 GiB (24%) `"Y$b._ Swap 0 B / 8.00 GiB (0%) `""" Disk (/) 8.52 GiB / 489.57 GiB (2%) Locale en_US.UTF-8 Battery 76% (Not charging) Zero dependencies, really No libc crate, no sysinfo , no nix , no color crate — nothing from crates.io. Almost everything is just reading and parsing /proc and /sys . The result is a single ~484 KiB binary that builds offline. The only things std can't do are statfs (disk usage) and ioctl (terminal size / tty check). Instead of pulling in a binding crate, those are issued as raw Linux syscalls via core::arch::asm! : #[cfg(target_arch = "x86_64" )] unsafe fn syscall3 ( n : usize , a1 : usize , a2 : usize , a3 : usize ) -> isize { let ret : isize ; core :: arch :: asm! ( "syscall" , inlateout ( "rax" ) n as isize => ret , in ( "rdi" ) a1 , in ( "rsi" ) a2 , in ( "rdx" ) a3 , out ( "rcx" ) _ , out ( "r11" ) _ , options ( nostack ), ); ret } Four arch