标签：#m

Metanet is bringing minimalism and precise mechanics to consoles and PC next year.

Airdorf and Andrés Borghi are making one nightmare with two styles of terror.

The final chapter will drop simultaneously on PS5, Switch 2, Xbox and PC.

Blood Dungeon is the latest project from the studio behind Nidhogg.

Who's afraid of the next GTA? Based on the last few days of Summer Game Fest, just about everyone. Grand Theft Auto VI hasn't been present at any of the keynote events, but its presence was felt every time a release date was announced. The month of November, when GTA VI launches, is virtually empty. […]

You can watch all ten episodes of the Among Us cartoon on Paramount+.

Bloober Team is publishing (and also kinda helping develop) a new Saw game.

Square Enix has officially announced the third and final game in its Final Fantasy VII remake trilogy: Final Fantasy VII Revelation. It will release on multiple platforms simultaneously - PC, PS5, Xbox Series X / S, and Nintendo Switch 2 - in spring 2027. In footage shown onstage at Summer Game Fest Live, there was […]

More details on the sixth Virtua Fighter game are coming in a dedicated showcase stream.

In the end, the three companies involved all point the finger at each other.

After a very profitable decade on Microsoft's board, Reid Hoffman is stepping down to focus on his AI drug discovery startup Manus.

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.

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

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

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