Show HN: SIEMatic, a fair-sourced observability and security platform

Mixture-of-Kittens: our open-source MoE megakernel for NVL72s

Retries don't fix eventual consistency

The Pedagogy Behind the Studio

The Judgment Reservoir

How React Renders

This is the first article in the React Internals series, focused on fibers, reconciliation, keys, and the storage model behind useState, useEffect, and useRef. React's public model is simple: describe the UI for a given state, and React updates the screen. That sentence hides the part that matters when you are debugging a strange remount, a broken hook, or a list that loses input state. React does not update the DOM directly from your component function. It builds an internal tree, compares it w

When AI Benchmarks Plateau: A Systematic Study of Benchmark Saturation

Encoding or Compression: Why not both?

The Software Supply Chain Is Under Siege. Devs Are Still the First Line of Defense

A new report from Omdia focuses on security issues in the software supply chain, how organizations are responding, and where the biggest gaps remain  In the heat map of cybersecurity vulnerabilities today, among the most intense hot spots is the software supply chain. In fact, it was the shift of the modern attack surface away from isolated systems to the software supply chain that connects them—and Docker’s role in safeguarding that interconnected reality—that first drew me to Docker.

Expertise Formation as the Deeper Consequence of the GenAI Revolution

A few weeks ago, we participated in a meeting of faculty members and researchers discussing the integration of generative AI (GenAI) into academic work. As expected, the conversation revolved around opportunities, challenges, productivity gains, teaching, research, and the future of the university. Then, one faculty member made a comment that immediately caught our attention. “Recently,” he said, “I realized that new graduate students slow my work down.” Several other faculty members

Sending Emails Using Python

You probably found this video course because you want to send emails with Python to automate confirmation messages, password resets, or scheduled notifications. Python’s standard library covers the whole pipeline, from making a server connection to building the message and sending it to one or many recipients. This course walks through every step in working code. By the end of this course, you’ll understand that: A safe testing setup uses a throwaway Gmail account with an app password, a lo

RFC 6238 in Common Lisp

I wanted to implement 2FA as per RFC 6238. This is the Time-based One-Time Password (TOTP) algorithm that is used by Google Authenticator and other 2FA apps. This was originally `vibe coded`. The vibe coding got me 80% of the way there, and I made a manual pass to turn it into a more functional style. Feel free to use this under an MIT license. ;;; -*- mode: lisp; coding: utf-8-unix; -*- ;;; RFC 6238: TOTP (Time-Based One-Time Password Algorithm) implementation in Common Lisp ;;; This im

Quiz: K-Means Clustering in Python: A Practical Guide

In this quiz, you’ll test your understanding of K-Means Clustering in Python: A Practical Guide. By working through this quiz, you’ll revisit how k-means groups data into clusters, how the main clustering categories compare, and how to build and tune a k-means pipeline with scikit-learn. k-means is one of the oldest and most approachable clustering methods, which makes it a great entry point into unsupervised machine learning with Python. [ Improve Your Python With 🐍 Pyth

Quiz: Sending Emails Using Python

In this quiz, you’ll test your understanding of the concepts covered in Sending Emails Using Python. By working through this quiz, you’ll revisit how SMTP carries a message from your code to a mail server, how the EmailMessage class assembles plain text, HTML alternatives, and file attachments, and how SMTP_SSL secures a connection to a real provider. You’ll also check what you know about CC, BCC, and reply-to headers, base64 encoding for binary attachments, and when a transactional email

The Weekly Challenge 383: Similar RGB

I’ve been doing the Weekly Challenges. The latest involved list comparison and colour mangling. (Note that this ends today.)

Homebench – Benchmark local LLMs for speed, memory, and quality

My brain hurts after LinkedLists in CtCI

I am currently working myself through “cracking the coding interview” and implementing everything in Rust. I can highly recommend it for getting better in rust because the exercises have a great length and there is very little overhead of things you need to do besides the actual algorithmic challenge. Chapter 2 of Exercises is about linked lists… this is where the Option<Box<Node<T>>> nesting starts and it is a completely new challenge because handling these nested objects is so different

Why Large Language Models Fail at Tabular Prediction

Learning-Rust.Github.io: Rust Programming Language Tutorials for Everyone

This Month in Ladybird – July 2026

The Future of Gnome Boxes

Your AI Sounds Certain. Look at Where It Learned That.

Your AI Sounds Certain. Look at Where It Learned That. You ask a question. The answer comes back clean, structured, confident, and wrong. Not obviously wrong. Wrong in the specific way you can only catch if you already knew the answer. The date is off by a year. The number belongs to a different study. The quote is real but the person never said it in that context. If you knew the subject, you caught it. If you didn’t, you just learned something false from a machine that never once sounded uns

Harness Engineering for Self-Improvement

BorrowSanitizer

Organiser une équipe de développement à l’ère de l’IA - Part II.1

'impact de l'IA agentique sur la création de valeur dans le développement logiciel. Celle-ci se déplace progressivement de la production de code vers la compréhension du métier, la qualité du contexte et la prise de décision.

Quiz: Managing Imports With Python's __all__

In this quiz, you’ll test your understanding of Managing Imports With Python’s __all__. By working through this quiz, you’ll revisit how wildcard imports work, what role the __all__ variable plays in modules and packages, and how to define a clean public API for your Python code. [ Improve Your Python With 🐍 Python Tricks 💌 – Get a short & sweet Python Trick delivered to your inbox every couple of days. >> Click here to learn more and see examples ]

Soppo - Go, with the features it's missing

harlan: language for GPU computing

Cache-Consistent Dynamic Load Balancing for Kubernetes Controllers

arXiv:2608.00454v1 Announce Type: new Abstract: As Kubernetes clusters grow, the scalability of controllers can become a bottleneck for the performance of the system. Distributing the load dynamically across multiple controller instances, however, raises the following two problems, and a controller can therefore be run only as a single instance today. The first problem is the cost of reassignment. A controller retrieves objects on the basis of the Labels attached to them, so in a naive design i

Scheduling a recursively enumarable language

So here is my algorithm overview https://github.com/Mjz86/String/blob/main/mjz_lib/graph/scheduler.md Btw im a solo open source maintainer , please have mercy if its not what you exactly want ( E.g : why not write it in rust ) submitted by /u/cppenjoy [link] [comments]

LLMs are (still) mostly powered by imitative learning, not RL

ZX Spectrum System Tour: Text Mode – Bumbershoot Software

Le vibe coding ringardise t-il le no-code ?

Le vibe coding pourrait remplacer les outils no-code pour les citizen developers grâce à l'accessibilité des IA génératives, qui permettent à des non-informaticiens de créer des applications plus facilement. C'est une perspective largement évoquée dans les milieux de l'informatique.

Mathematicians Need to Act

LLMs reward expertise

Diagnosing Swap Usage with smem on Linux

If you've ever looked at your server and wondered why it's using swap even though plenty of RAM is available, you're not alone. This is a common scenario on Linux systems, and it can impact performance if not addressed.Continue reading...

Computing Education When Writing Code Is No Longer the Challenge

Imagine holding the line for another generation, banning the use of GenAI in computing education, and avoiding the hard work of reinventing objectives, assignments, assessments, courses, and curricula. Students in 2040 arrive in our CS1 lectures having relied on these tools in the same way as today’s “digital natives” have relied on ubiquitous Internet access and communication tools. Continuing with longstanding tradition, we ask them to average rainfalls or check whether a string is a pal

OpenAI's Unreleased Model Astra Solves Ten Major Open Mathematics Problems

Learning Rust: Updated / Human-Authored (From 2016)

This is a screenshot captured between 2018 and 2020 from https://learning-rust.github.io . The project started in 2016 as a Medium publication and GitBook but later moved to https://github.com/learning-rust/learning-rust.github.io I was updating section by section from time to time. No lies! keeping a Rust tutorial up to date is very tough. Plus, you end up repeating what you already know. It is even tougher, when you have to write code in another language for work. https://learning-rust.githu

Branchless Rust: Making a Filter 4x Faster by Removing an if

Kelly Criterion Simulator

Use Task Runners for Common Coding Tasks

Wallr - a native Wayland wallpaper engine I've been building for a few months (Rust, wgpu, wlr-layer-shell)

I published this to GitHub a few days ago, so the commit history will look recent even though this has been in progress for a few months. Wanted to mention that in case it looks odd. Wallr draws its own layer-shell surface and renders transitions itself with wgpu. It's not a wrapper around swww or hyprpaper, the point of the project was to own the rendering path so transitions are GPU-driven and timed to wall clock duration instead of tied to refresh rate. What it does: 11 transition effects (f

GEM Training: How Meta Doubled the Efficiency of Its LLM-Scale Ads Foundation Model

Meta’s Generative Ads Recommendation Model (GEM), the foundation model behind ads recommendations across Instagram and Facebook, now trains at LLM scale on several thousand of the latest-generation GPUs. This post goes into the details on how we achieved: doubling end-to-end (E2E) training efficiency to 20–25% Model FLOPs Utilization (MFU) while scaling training FLOPs 4x in 12 months, by co-designing kernels, precision, parallelism, networking, and memory together. Training GEM presents uniq

Massively Parallel Postgres Backups

Sane Development Environments for Ansible Automation Code

Table of Contents Table of Contents The Stack I Actually Use Dev Container and Execution Environment Are Different Jobs Host Preparation The devcontainer.json Those Privileges Are Not Decorative What Is Actually in the Image? Why This Is Better Than a Carefully Tended Venv Upstream Alternative: Community Ansible Dev Tools The Editor Is a Separate Choice A Practical Team Policy Further Reading Ansible has a deceptively low barrier to entry. Install a Python package, write a YAML

LambdaClass:: Lean raises the bar: it converts logical errors into compilation errors. .... If It Compiles, It Is Correct: A Practical Introduction to Lean 4 for Engineers

Discovered: Aug 3, 2026 16:25 (UTC) LambdaClass:: Lean raises the bar: it converts logical errors into compilation errors. …. If It Compiles, It Is Correct: A Practical Introduction to Lean 4 for Engineers My only concern is that it is written in terrible and unsafe language C++. Oh well most of lean is written in lean so I guess we’ll have to live with the C++ part. At least it uses C++ 17 i.e. modern C++ according to Wikipedia. And if I don’t start programming in Lean it won’t be

SearXNG in Rust

Computer Science is Not Just Coding and Its NOT Dying

The discussions about the future of computer science and of computer science education are unending. We are seeing huge numbers of layoffs in the tech industry as coders are (attempted to be) replaced by artificial intelligence. Universities and high schools are seeing drops in signups for computer science courses as students don’t see the same economic future in coding that they once did. Some are trying to counter the “CS is dying” narrative. For example, the two opinion pieces linked be

The ACM AI Leadership Summit: Shaping the Future of AI

Artificial intelligence is reshaping how knowledge is produced, organizations operate, and people learn, work, create, and participate in society. This transformation is occurring faster than we are prepared for. Decisions made today about AI’s design, deployment, evaluation, and governance will have consequences for societies worldwide, for decades to come. As the world’s largest association of computing professionals, the Association for Computing Machinery (ACM) has both the opportunity a

If AI Outputs Aren't Speech, Who Has to Prove They're Human?

What's the largest software project AI can complete on its own?

Sanedit: Modal text editor

I have been building a hobby text editor project sanedit https://codeberg.org/lote/sanedit It's a terminal based modal text editor with language server protocol (LSP), parsing expression grammar (PEG) based syntax highlighting and multicursor support. There is a million different text editors out there so why is this different? It's not, however I did not just slap the common combination of treesitter, ropey and LSP together. I made the editor to support very large files without slowing down to

Umair Shahid: PostgreSQL Lockup vs Stale Connection: How to Tell Them Apart

“The database is locked up.” We heard some version of that sentence more than once this past week, from the same team, about what looked like the same problem. It was not the same problem. Once, Postgres had genuinely stopped responding. Every other time, Postgres was fine, and a connection sitting in the application’s pool had quietly died somewhere between the app and the database. Both failures produce the same page at 2am: the app cannot reach the database. Only one of them means th

We Became Editors-in-Chief, and Nobody Trained Us

An essay on how AI-assisted development turns engineers into full-time reviewers. We spend our days evaluating AI-generated diffs instead of writing code, and that shift carries a new cognitive fatigue and may widen the senior/junior gap. Read more

Finding zombies in our systems: A real-world story of CPU bottlenecks

PhpStorm 2026.2 Released

JetBrains has released PhpStorm 2026.2 with a new Laravel tool window, a FileReference attribute for PHP, PHP 8.5 pipe operator support, and an agent skills manager. The post PhpStorm 2026.2 Released appeared first on Laravel News. Join the Laravel Newsletter to get all the latest Laravel articles like this directly in your inbox.

The Growing AI Divide

What happens to the holdouts as AI eats the world

30 years of CPUs at Tom's Hardware – looking back on three decades of processors

Empty sandboxes break developer experience

I work on Docker Sandboxes, so I spend a lot of time talking about isolation, microVMs, disposable filesystems, blast radii, all the good infrastructure things. But the Docker Sandboxes feature I keep reaching for in daily use is kits. Kits sound like a packaging detail until you try to use a sandbox for real work. An empty sandbox is a good boundary. It’s also (eventually) ephemeral and empty, and that combination means annoyance and repeated setup work. The agent gets a clean filesy

Smaller, faster, safer: running Kimi and GLM at scale

Workers AI runs inference for some of the best open models in the world on GPUs in Cloudflare data centers close to your users. Two of the most capable, and most demanding, are Moonshot's Kimi K-series and Z.ai's GLM. They are large, long-context, mixture-of-experts models, and they are wonderful to use. They are also very hard to serve efficiently because of memory constraints.We've written before about how we serve large models on Workers AI and about separating the prefill and decode phases o

3 Seconds of compilation shaved by metadata analysis

No AI is used in my work; or research; or writing; or anything else really. (=｀ェ´=) Go to your Rust codebase and pick a dependency. How much do you use that dependency? How many functions do you use from bitflags? From chrono? From hashbrown? How many items does your dependency pull from other dependencies? And how many end up being linked in the final binary or re-compiled during compilation? Better insights without target bloat （=´∇｀=） Incremental Systems Rethought started as a

Quiz: Bypassing the GIL for Parallel Processing in Python

In this quiz, you’ll test your understanding of Bypassing the GIL for Parallel Processing in Python. The global interpreter lock stops your threads from executing Python bytecode at the same time, which caps the speed of CPU-bound code no matter how many cores you have on hand. By working through this quiz, you’ll revisit how CPU-bound and I/O-bound tasks differ, how process-based parallelism sidesteps the lock entirely, and which strategies finally let your threads spread across every core.

netcat (nc) Command in Linux, with Examples

The nc command (netcat) is a lightweight but surprisingly powerful networking tool for port testing, file transfers, banner grabbing, and connectivity debugging. This practical guide covers the flags and real-world usage patterns that Linux users and sysadmins actually reach for.Continue reading...

C++ to Rust Migration

Show HN: MPEdb - Drop in replacement for SQLite3 with concurrency support

Emulating ALiBi with Rope

GraphForge: An embedded, openCypher-compatible graph engine with a Rust core, Arrow results, and Parquet persistence — for research and investigative workflows

I've been working with graph shaped data for years and have really wanted to have a good local way to work with big datasets - without having to run memgraph or neo4j. I first built a version in python but it couldn't handle datasets larger than about 1m edges without choking. So I used that as a basis to design what to refactor into a rust project. 15 crates later we have fully embedded graph data science algo's, vector/fts search, and full openCypher compatibility. image is of the vs code ext

Rust project goals: Immobile types and guaranteed destructors

Practical Memory Safety

An old-new take on argument parsing in Rust

Resigning from Arch Linux

ST Rust drivers

Why "AI is just the next compiler" doesn't hold up

At a recent talk I compared AI adoption to the introduction of compilers: a new layer of abstraction that lets us work at a higher level, the same way compilers let us stop writing assembly by hand. In the hallway afterwards, a few people pushed back on that comparison. They were right to. Here's the point I should have made from the stage: agents are not deterministic, and that single difference breaks the analogy. Compilers follow rules A compiler is not a magical black box. You give it co

Imbalanced Data Clustering via Targeted Data Augmentation Using GMM and LLM

arXiv:2607.28635v1 Announce Type: new Abstract: In Natural Language Processing (NLP), dealing with underrepresented topics is challenging, especially in unsupervised tasks where clustering might not adequately capture minority topics. To tackle this challenge, our paper presents a novel unsupervised data augmentation method that integrates Gaussian Mixture Models (GMMs) and Large Language Models (LLMs). Due to their flexibility and robustness, GMMs can detect clusters corresponding to underrepr

Can LLMs Really Understand Item Difficulty Levels? Implications for Automated Item Generation Using LLMs

arXiv:2607.28634v1 Announce Type: new Abstract: The estimation of item difficulty plays a key role in both formative assessment and large-scale high-stakes summative assessments. This study explores how large language models (LLMs) perform in predicting item difficulty levels using items from a large-scale Reading and Writing test. The study investigated various prompting strategies and parameter settings across multiple LLMs. LLM performance was compared with encoder-only language models and f

Scaffolding Critical Engagement with GenAI: Transforming Ethnic Minority Preparatory Students' Collaborative Discourse in Prompt Engineering Tasks

arXiv:2607.28630v1 Announce Type: new Abstract: Generative AI (GenAI) holds significant promise for advancing educational equity among ethnic minority students by broadening access to learning resources and mitigating linguistic barriers. However, these benefits are counterbalanced by the risk of cognitive laziness, whereby students may treat GenAI as an answer engine or shortcut rather than as a partner in thinking. This design-based research investigated how pedagogical scaffolding can shift

LLM Framework for Discovering Major Mathematical Conjectures: AI's Quest for the Next Riemann Hypothesis

arXiv:2607.28632v1 Announce Type: new Abstract: Major mathematical conjectures still depend heavily on expert intuition, so a unified method for the systematic generation and validation of conjectures with substantial mathematical potential remains unavailable. We present a three stage pipeline for major conjecture discovery, with region search from explicit local evidence modules, reflective validation for foundationality, novelty, and potential significance, and formal validation in Lean 4 an

Can AI Evaluate AI Scientists? A Benchmarking Study of Autonomous Research Generation Systems Using Automated Multi-Model Review

arXiv:2607.28631v1 Announce Type: new Abstract: AI Scientist systems capable of autonomous research have the potential to significantly accelerate scientific discovery. However, evaluating and comparing the quality of AI-generated papers remains an open challenge. We propose and implement a rigorous benchmarking protocol using an automated peer-review system that harnesses frontier large language models to assess scientific papers across four core dimensions: originality, scientific rigor, clar

OpenClaw and Ollama in Agentic AI: Toward Fully Autonomous and Scalable AI Agent Systems

arXiv:2607.28629v1 Announce Type: new Abstract: The rapid transition from reactive large language models (LLMs) to persistent, action-capable systems has exposed critical gaps in the architectural understanding of Agentic AI, particularly in separating inference, orchestration, and execution layers for autonomous AI agents. Despite recent advances, unified frameworks for designing and evaluating full-stack agentic systems remain limited. This paper presents a comprehensive, layered architecture