noticed the AMC theatres site has had queue times of over an hour today… just to get onto the homepage. That’s a bit strange right? AMC has ~650 locations in the US. Assuming ~10 screens per location, ~5 showings per screen per day, and ~300 seats per auditorium (probably a generous estimate), that’s roughly 10 million available seats per day. Even if site traffic is 5x higher than actual ticket sales, we’re still talking about something in the ballpark of 50 million daily visitors. That’s obviously not nothing, but it also doesn’t seem like an absurd amount of traffic for a company this large. I’m curious what the technical/business rationale could be? submitted by /u/u16scharpf [link] [留言]

What are the most valuable skills to learn in the AI era? Not skills like problem solving but more hands on. For someone who likes building stuff submitted by /u/Big_Consequence_5162 [link] [留言]

I am a web developer who specializes in dashboard-like web applications. Due to recent price hikes for GitHub Copilot, I have been considering running a local model to help out with debugging, multi-file edits, learning about the codebase, and small-medium tasks. I intend to continue using GitHub Copilot or Claude Code for more advanced tasks, but I want to minimize token costs. I cannot test a local model myself right now because I lack the hardware to do so, which is why I am asking here. If I decided on using a local model, I would likely need to upgrade my graphics card from a 3080 to a 3090. Has anyone here tried running a local AI model? Which one are you using? How well does it work compared to Claude Sonnet 4.5 or other AI models? I would appreciate any advice or feedback. submitted by /u/Various-Complex-1582 [link] [留言]

Did that Coachella appearance land him the gig?

Don't worry, StudioMDHR is working on another hand-drawn Cuphead game, too.

A massive viral conversation sharing VC horror stories has taken place this week on X. Some are weird. Some are infuriating.

Chronologically, Control Resonant is a sequel to 2019's Control. But in most other ways, the games aren't directly connected. To developer Remedy, they're more like two sides of the same coin. When Resonant was first revealed last year, creative director Mikael Kasurinen said you can play the games in any order. The world of Control […]

While some of my recent posts have involved using the Colab extension for VS Code and the Antigravity IDE, I actually prefer working in the terminal and Vim. The new Colab CLI finally lets me work in my natural habitat, and it opens the door for autonomous workflows! Setup Currently, installation is handled via pip or uv. It's straightforward, though, I'm holding out hope for a brew formula in the future: uv tool install google-colab-cli I'm testing Version: 0.6.dev7+g510115b0c inside Ghostty. The Colab CLI is pretty solid, but I do have some feedback and nitpicks I'd like to share (but more on that later). Creating a new session Creating a session is simple: colab new [-s SESSION_NAME] [--gpu T4|L4|A100|H100] [--tpu v5e1|v6e1] : SESSION_NAME : This is optional. If you leave it blank, the CLI generates a random unique ID for you. --gpu and --tpu : The hardware accelerator flags are optional, but omitting them defaults to a standard CPU-only instance. The specific accelerator chips you can request depend on your Colab tier, which you can check via colab pay. NOTE : If you only have one active session, the CLI targets it by default. This makes the -s flag unnecessary for subsequent commands. Testing Colab CLI's capabilities CLI certainly sounds cool, but how does it handle artifacts and images? More importantly, how debuggable is it? I decided to find out by running a Fashion MNIST PyTorch example. Handling artifacts To get started, I installed my requirements using colab install torch torchvision matplotlib . If you prefer a more standard approach, you can also use colab install -r requirements.txt . Once the environment was ready, I executed the training script using colab exec -f ./fashion_mnist_TRAIN.py and here's the output: [ colab] Using unique session '8c860c' . Using CUDA device. Shape of X [ N, C, H, W]: torch.Size ([ 64, 1, 28, 28] ) Shape of y: torch.Size ([ 64] ) torch.int64 NeuralNetwork ( ( flatten ) : Flatten ( start_dim = 1, end_dim = -1 ) ( linear_re

Just remember that Ueda's last game took nine years to get here.

Have you run into work that feels technically possible in principle, but in practice keeps stalling because of how current AI systems behave? Not asking for: bigger context windows better memory lower hallucination more agentic workflows I mean situations where: You are trying to discover something (not retrieve something), and the AI repeatedly pushes toward premature answers, stable interpretations, optimization, categorization, or coherence before the thing itself has had time to emerge. Cases where the failure isn’t output quality. The failure is that the interaction itself changes the trajectory of the work. If yes: What are you trying to build / understand? What exactly happens when it breaks? At what moment do you realize the AI has moved you onto the wrong path? What would need to be different for progress to resume? Trying to understand whether this is an edge case or a recurring limitation pattern. submitted by /u/iknowbutidontknow00 [link] [留言]

I and I imagine a lot of other folks, don't believe the future of work should be a smaller group of executives commanding a larger system of people and machines. We have seen what AI can do not just to software product quality without guardrails, but to the junior and midlevel team members who are laid off or never hired at all in exchange for better profit rates with AI tokens vs human salaries. That is just the old hierarchy with better software. The history of work has always had this tension. You can go back to the start of US history and look at the military, commissioned officers were trained and trusted to command while enlisted service members carried out the work and risk. In the corporate and business world, executives and managers became the people who planned, measured, and optimized, while workers became the people being measured. Those structures were not only about class, but race and in America they were built inside a society already shaped by racism, classism, unequal education, unequal access to capital, and unequal access to leadership. AI now forces us to confront that history again. If we are not careful, AI will not flatten organizations. It will make the hierarchy invisible. Instead of a manager with a clipboard, we will have an algorithm. Instead of a foreman with a stopwatch, we will have dashboards, productivity scores, automated performance reviews, and AI systems that decide who gets opportunity and who gets replaced. That is not progress. The goal should not be to replace people with AI. The goal should be to replace bureaucracy, repetitive work, bad process, and unnecessary gatekeeping. What I am trying to do at Buildly is simple: AI should remove drudgery, not dignity. Automation should increase agency, not surveillance. Productivity gains should be shared, not extracted. Hierarchy should be functional, temporary, and accountable — not a measure of human worth. This is why we talk about AI-native product development differently. An AI

As an independent researcher I've used various LLMs to help me dive deeply into research projects but I've been frustrated by the fact that LLMs start to become unusable after the thread has accumulated 50-80k tokens. I don't know how many other folks here have experienced the same pain point. So, I decided to do something about it. Over the course of this whole year, I built an inference time tool I call Epistemic Lattice Tethering (ELT). So, here is the full framework in GitHub for everyone's review: The README describing ELT, it's various components and the roadmap. The full ELT stack for Claude /ELT%20Model-Specific%20Forks/ELT-H%20v1.0%20(Claude-Optimized)), ChatGPT /ELT%20Model-Specific%20Forks/ELT-H%20v1.0%20(ChatGPT-Optimized)), and Grok /ELT%20Model-Specific%20Forks/ELT-H%20v1.0%20(Grok-Optimized)). Instructions on how to load ELT into an LLM session are here /README). If you're planning to try out ELT PLEASE READ THIS FIRST! Medium article introducing ELT , its methodology, the problems it is aiming to address, and philosophical framework. Discussion page . Your input is valuable! So, what does ELT do and why should you care? Right now ELT is an inference-time scaffolding framework that's best for those who are frustrated with threads that lose coherence too quickly, hallucinate too quickly, are too fragile and sycophantic, and forget what a project's goals are too soon. If that's a big pain point for you, then ELT might help. If these are not big issues for you and the stock version of your LLM is fine, then ELT probably won't be useful for you. The upshot? The epistemic and ontological stability that ELT provides has produced coherent and productive threads extending to: Claude: ~ 325,000 tokens /Extreme%20Thread%20Length/Claude%20Thread%20325k%20tokens-%20Redacted) (advertised limit: 200k) GPT: ~430,000 tokens (advertised limit: 256k) Grok: ~1,150,000 tokens /Extreme%20Thread%20Length/Grok%20Thread%201M%20tokens-%20Redacted) (advertised limit: 1M) The d

An interactive visualization of how data moves across the Internet: • DNS lookup • TCP handshake • TLS encryption • HTTP requests • Routers and packets Built to make networking concepts easier to understand visually. submitted by /u/nulless [link] [留言]

The abbreviated name, "AI 2," is the scariest thing about it so far.

AI Code Security: Claude's rsync Bugs; Europe's GNSS Interference & GPS Anomalies Today's Highlights This week in security, a deep dive explores how AI code generation might introduce new vulnerabilities, with analysis showing Claude increasing bugs in rsync. We also highlight two critical infrastructure concerns: a powerful GNSS interference source over Europe and the mysterious 'numbers station' broadcasts found on GPS frequencies. Did Claude increase bugs in rsync? (Hacker News) Source: https://alexispurslane.github.io/rsync-analysis/ This article presents an intriguing analysis of how large language models (LLMs) might inadvertently introduce bugs into software. Focusing on Claude's contributions to the widely used rsync utility, the author investigates code changes attributed to AI and compares them against human-written code. The study reveals instances where AI-generated code, while appearing plausible, introduced subtle yet significant defects, raising questions about the reliability of AI assistance in critical codebases. The implications extend beyond rsync , pointing to potential supply chain vulnerabilities if AI-generated code is not rigorously audited. This research highlights a new frontier for AI-specific security, emphasizing the need for developers to employ practical hardening guides and thorough review processes when integrating AI into their development workflows to prevent the unintentional introduction of new attack vectors. Comment: As a developer, seeing concrete examples of AI introducing bugs, even subtle ones, in a fundamental tool like rsync is a wake-up call. It's a reminder that AI-generated code requires diligent human review, especially when security or reliability is paramount, highlighting prompt engineering as a defensive technique. Tracing a powerful GNSS interference source over Europe (Hacker News) Source: https://arxiv.org/abs/2606.03673 Researchers have identified and traced a significant source of Global Navigation Satellite

Dropbox Nova for AI Coding Agents, OpenAI's Codex Sandbox, & Puppeteer MCP Server Today's Highlights This week, we dive into Dropbox's Nova platform for scaling AI coding agents and OpenAI's secure sandbox architecture for Codex, highlighting advanced production deployments. We also examine practical solutions for safer browser automation for AI agents, detailing a custom Puppeteer MCP server. Dropbox Introduces Nova, an Internal Platform for Running AI Coding Agents at Scale (InfoQ) Source: https://www.infoq.com/news/2026/06/dropbox-nova-ai-coding-agents/?utm_campaign=infoq_content&utm_source=infoq&utm_medium=feed&utm_term=global Dropbox has unveiled Nova, an internal platform meticulously engineered to orchestrate and scale AI coding agents. This platform tackles the complex challenges of managing autonomous AI entities performing tasks like code generation, bug fixing, and refactoring across a large codebase. Nova's architecture focuses on reliability, efficiency, and safety, providing a robust environment for thousands of agents to operate concurrently without overwhelming system resources or introducing instability. The platform acts as a critical layer between AI models and the vast codebase, enabling agents to interpret development tasks, interact with repositories, and propose changes in a controlled manner. The significance of Nova lies in its ability to industrialize the use of AI in software development workflows. By abstracting away the operational complexities of agent deployment and execution, Dropbox empowers its engineering teams to leverage AI as a force multiplier, accelerating development cycles and improving code quality. Nova represents a practical, large-scale implementation of AI agent orchestration, demonstrating how companies are moving beyond experimental AI tools to integrate them deeply into core business processes. This showcases a production-grade pattern for applied AI, particularly relevant for "code generation" and "workflow automati