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Hello, I'm Rijul. I'm building git-lrc, a micro AI code reviewer that runs on every commit. It's free...

A deep dive on Go 1.24’s map redesign, where the runtime moved from the classic bucket + overflow-chain model to a Swiss Table-inspired implementation: how the old map layout worked and where pointer-chasing hurt cache locality how control-byte metadata + h2 filtering changes lookup behavior why this can improve practical load factor and memory efficiency Go-specific constraints (iteration semantics, GC/runtime integration, incremental growth behavior) benchmark context: large microbench wins vs smaller full-application geomean gains trade-offs still being worked on (cold-cache and certain delete/clear-heavy paths) submitted by /u/OtherwisePush6424 [link] [留言]

These are the AC units we’ve trusted to cool our homes for months, if not years.

US robotics researchers flew to Venezuela with snakebots after getting a call.

This essay was written with Barath Raghavan, and originally appeared in The Guardian . Major benchmarks measure what AI can do. None measure whether it does what you mean: the distance between what you ask an AI to do and the unspoken assumptions about how you want the AI to do it. We propose a new metric: the Genie coefficient. There’s often a gap between one person’s request and another’s understanding. Most of the time, we bridge it using general knowledge. For example, if you ask a friend to get you coffee, they’ll pour a cup from the pot or buy one from a coffee shop. They won’t bring you a bag of raw beans or snatch a cup from a stranger and hand it to you. You never specified any of this. You never had to...

The TK705STi’s highly responsive, low-latency performance makes gameplay ultra-realistic. For movies and TV? It’s a purely average home projector.

Never mind the “heat death of the universe.” Entropy is just a bridge between two levels of reality.

Facebook is planning some big changes to try and keep its users from jumping to rival social platforms like TikTok - changes that sound dubiously similar to becoming a TikTok clone. Facebook head Tom Alison announced today that the platform will begin testing a "reimagined experience" later this year that will put a subset of […]

Airbus selected Scaleway as its sovereign cloud provider after a tender that scored protection against non-European extraterritorial legislation alongside technical capability. Airbus frames it as complementing multi-cloud, not exiting AWS. Practitioners note the pattern is spreading past hyperscalers to small US SaaS vendors, and that sovereignty claims still require verifiable controls. By Steef-Jan Wiggers

From musical instruments and games to play pieces that encourage balance and dexterity, these toys will engage your child’s mind and body.

Analysis of satellite images of Myanmar shows dozens of alleged scam compounds have appeared in recent months, despite a purported crackdown on the criminal organizations.

Building an operating system is a project I have had my eyes set on ever since I discovered free will in the realm of programming. Years ago, I did a reasonable amount of research, paying extra attention to the subject during my computer science degree and I was able to understand Operating System Theory and how it works from first principles but I never really got around to building one. I had only flimsy reasons for not embarking on it like "why build one when there are tons of working ones out there? The theoretical knowledge is enough" . More recently, I am ignoring the need to not re-invent the wheel for the joy of programming. So if you are interested in also rebuilding stuff because you can, join me on this series as I document how I am going to be building kluster. kluster is in its infancy and the direction is not clear but the one certain thing is that I will be building it entirely in Rust, save some assembly instructions and a linker script and I will be explaining every single line of code along the way. It will also be designed to target the raspberrypi 4 & 5, on qemu and on real hardware respectively. This is an opportunity for anyone who wants to see how Rust works at the lowest of levels to hop on and join the ride. Note that this series will be your biggest lesson on delayed gratification because we will write a lot of code before we even get to see anything meaningful on screen but I will foreshadow what you can get by the end of part 3 if you are patient enough: {{ image(src="/images/os-part3-result.png", alt="Part 3 Results OS Dev") }} You can also clone the source code for part 1 from Github and follow along. Project Setup First things first, let us setup the foundation of the project. I'll be straight with you, I love Rust and I enjoy using the Rust ecosystem in its entirety so I will stay true to that and use it as obsessively as any true Rustacean; I won't hold back. Without doubt, all the dependencies we need are freely available as long as

As part of my learning journey with Django, I wanted to build a project that would challenge me beyond the basics. I decided to create RecipeHub, a web application where users can create, manage, and share recipes while exploring recipes from other users. The project started from a Django starter template, but I customized it by adding new features, redesigning the interface, and deploying it online. Features RecipeHub allows users to: Register and log in Create, edit, and delete recipes Browse recipes by category Save favourite recipes Upload recipe images Access a personal dashboard Use the application in both light and dark mode The application is fully responsive, making it easy to use on both desktop and mobile devices. Technologies Used I built the project using: Python Django Django Allauth PostgreSQL Tailwind CSS DaisyUI HTMX Vite Gunicorn Render GitHub was used for version control throughout the project. Challenges One of the biggest challenges was deployment. While everything worked locally, deploying to Render required configuring PostgreSQL, environment variables, and static files correctly. I also encountered an issue with uploaded recipe images. Since the application is hosted on Render's free tier, uploaded media is stored on an ephemeral filesystem, meaning uploaded images are lost after redeployment. Learning why this happens gave me a better understanding of the difference between development and production environments. Another challenge was redesigning the dashboards. I wanted them to feel clean and modern instead of looking like a default Django application, so I spent time improving the layout, spacing, and responsiveness. What I Learned This project helped me improve my understanding of: Django project structure Authentication and user management CRUD operations Database relationships Responsive UI design Git and GitHub workflows Deploying Django applications Debugging real-world issues More importantly, it taught me how to troubleshoot proble

xAI has a bold pitch for Grok 4.5: it codes about as well as Claude Opus 4.8, but does it with roughly a quarter of the tokens. That's not a "we're smarter" claim. It's a "we're just as good for far less money" claim, which in 2026 might matter more. Someone actually put it to the test, so let me walk through what the numbers say and why you should care. The claim and the pricing Grok 4.5 landed on July 8, 2026. xAI says it matches Opus 4.8 on coding while using about 4.2 times fewer output tokens to get there. The sticker price already favors Grok. It runs $2 per million input tokens and $6 per million output. Opus sits at $5 and $25. That's less than half the price on both sides before you even factor in the token efficiency. Stack the two together and the cost gap gets dramatic. What the benchmarks say The benchmarks mostly support the marketing, with a catch. On Terminal-Bench 2.1, which measures real command-line work, Grok 4.5 scored 83.3 percent to Opus 4.8's 78.9. But on SWE-Bench Pro, the harder test of fixing real open-source bugs, Opus still comes out ahead. So the honest read is not "Grok is better." It's "Grok is about as good, for a lot less." Different claim, and a more interesting one. The hands-on test Benchmarks are one thing, real work is another. The New Stack ran a head-to-head, giving both models the same three jobs in one real Rust project (the fd file-finder) inside Cursor, and tracked every token. I'm summarizing their results here, credit to them for actually measuring it. The three tasks were a bug fix, a multi-file refactor, and a feature build. The code both models produced was nearly interchangeable, so the story came down to tokens, time, and cost. On the small bug fix, Opus actually won. Both wrote an identical fix with all tests passing, but Opus did it faster and on fewer tokens. Grok's efficiency edge showed up on the bigger jobs. On the refactor, Grok used about 197K tokens versus Opus's 954K for the same result, roughly a fifth.

Microsoft has intervened to stop Windows 11 users with LG monitors from being bombarded with annoying McAfee trial pop-ups. In response to complaints about the LG bloatware, Microsoft's Windows chief, Pavan Davuluri, said that LG has agreed to immediately disable the McAfee pop-up from its LG Monitor App Installer, and pledged that Microsoft will "keep […]

Hetzner is experimenting with LLM inference. That is not a sentence I expected to write, but I think it is pretty interesting :) Before anyone moves their production AI workloads to Hetzner: this is very much an experiment . There is no billing, no SLA, no production guarantee, and currently only one model. Hetzner says it wants to learn whether people actually want this, how the system scales, which features matter, and what kind of load it can handle. So this is not a finished product launch. It is Hetzner putting something early in front of users and seeing what happens. I really like that approach. What Is Hetzner Inference? Hetzner Inference is an OpenAI-compatible API running on Hetzner's own infrastructure. You create an API token in the Experiments dashboard, point an OpenAI client at Hetzner's base URL, and use it like most other inference APIs. Right now, the only available model is Qwen/Qwen3.6-35B-A3B-FP8 . It is a 35-billion-parameter Mixture-of-Experts model with 3 billion active parameters. It accepts text and images, has a 262K context window, and uses FP8-quantized weights. That is a perfectly reasonable model for an experiment. It is small enough to serve without a ridiculous GPU cluster, but still useful enough to test the API with real workloads. Hetzner also published a short tutorial for connecting OpenCode to the API , if you want to try it without writing any code. I Tried It Because the API is OpenAI-compatible, there is almost nothing special about the integration: pip install openai from openai import OpenAI client = OpenAI ( base_url = " https://inference.hetzner.com/api/v1 " , api_key = " YOUR_TOKEN " , ) response = client . chat . completions . create ( model = " Qwen/Qwen3.6-35B-A3B-FP8 " , messages = [ { " role " : " user " , " content " : " Explain why the sky is blue in one sentence. " } ], extra_body = { " chat_template_kwargs " : { " enable_thinking " : False , } }, ) print ( response . choices [ 0 ]. message . content ) The enabl

After Trump publicly backed pillow salesman turned election denier Mike Lindell for governor of Minnesota, the local GOP pushed back strongly.

The article discusses an experiment where a code assistant had to design an agentic system using LangChain4j documentation. The assistant created a coding framework capable of writing, testing, and debugging code autonomously. Results showed that two architectural patterns—supervisor and workflow—offered different trade-offs between flexibility and execution speed during debugging tasks. By Kevin Dubois, Mario Fusco

EU law gives researchers unprecedented rights to scrutinize Big Tech. Researchers say Tik Tok, X, and Meta are still deciding who gets to look inside.

submitted by /u/mttd [link] [留言]