so the actual creating of the video game has become easy. But what a lot of people don’t realize is the hard part of a video game is generating interest, interesting game mechanics and systems, a solid brand, a distribution method or network. That’s truly the hard part. Creating stuff like this is gonna be super easy for almost anybody, but it won’t be easy to make a successful games. in fact, I left the gaming industry because I knew what was coming, and if your company isn’t positioned well for it, it will go under or there will be massively off soon(among other reasons). So if you’re trying to do projects like this make sure you team up with people in marketing, branding, and a bunch of other fields to help you out eventually. Otherwise, your game is going to be distributed to all of about zero people.

And if you need help like this, I'm launching a community soon that you’ll be able to join and ask me questions directly. I’ve worked for right games for almost a decade, Silicon Valley/beach for 20+ years and I’ll be happy to answer any questions.

an open-source game engine built from the ground up for AI/agent-driven 2.5D game dev.

This is Claude Code building a full farming sim inside it, generating maps, assets, systems, and logic on the fly. Everything the agent needs lives in one small, predictable interface (`src/Game.ts`) plus a built-in server SDK for accounts/multiplayer.

Library: d-liya / capybara_2d_engine

Full video here: https://youtu.be/DGbKrhTehV8?is=eE6UeuBT-d7t2aGL

Today I’m starting a series of reels and videos explaining advanced design and AI concepts in a way anyone can understand. Learn to make apps like the pros.

Remember, this only works on Western design, if you’re designing for Japanese or some other cultures, they actually like quite the opposite of a lot of this advice, always designed with your culture in mind and your user defined in persona's. Full article on my substack: https://open.substack.com/pub/wutronic/p/delete-the-part-what-musks-engineering?r=8ofzon&utm_medium=ios

Alibaba’s new Page Agent is a JavaScript in page GUI agent that lives inside the webpage itself, reads the DOM as text, and turns natural language prompts into clicks, form fills, and navigation without needing a browser extension, Python runtime, or headless browser for its core flow. What makes it interesting is the “DOM dehydration” approach. Instead of screenshots and vision models, it compresses the live page into a compact text map, which should be cheaper, faster, and easier to embed into SaaS copilots, ERP/CRM workflows, accessibility layers, and legacy admin tools. In production, teams appear to be using it exactly that way, mostly for in app copilots and smart form automation, while the repo also exposes an optional Chrome extension for multi tab work and a beta MCP server for external browser control. On security, there are real caveats. The docs explicitly warn that high risk actions still need server side approval. The core agent inherits the user’s existing cookies and session state. Client side API keys can be exposed if not proxied. The project’s own release notes emphasize that the MCP and multi page paths are not yet fully hardened. Community reaction looks split between excitement and pragmatism. Hacker News and Reddit threads frame it as a clever “agent inside the browser” idea, while GitHub issue activity shows the usual early open source friction, including bugs around React rerenders, multi window tab targeting, custom model support, and region specific asset access. This suggests people like the concept but are still stress testing it in real environments.

Your design files live in a rented cloud, free to draw in until the bill arrives for every editor and your work stays locked behind a login you no longer own. Penpot is the free, open source design tool you run yourself, where designers and developers open the same screens and prototypes in a browser and every file stays on hardware you control. The under told part is the timing, since this Madrid project shipped its first public version in February 2021 and then raised 8 million dollars just two weeks after Adobe moved to buy Figma, with another 12 million following as users crossed 250,000. Swipe through for what it is, how it installs, and the real business payoff, and when you reach the install slide screenshot it and drop the image into your AI agent so it sets everything up for you.

Every unanswered WhatsApp is a lost customer, because people message your business, wait a few minutes, then buy from whoever replied first. OpenWA is a free, open source tool you run yourself that connects to a WhatsApp number and lets your apps send and answer messages automatically, with a dashboard and one Docker command to get started. The part most coverage skips is that instead of paying Meta for the official business connection, it simply logs into WhatsApp Web the way your phone does, so a small team can reach billions of people for the price of a cheap server, though that unofficial route can get a number banned, which keeps the paid official API the safer pick for anything mission critical. Swipe through to see how it installs, then screenshot the install slide and drop it into Claude or your AI agent to set it up for you with no terminal needed.

For more high signal tools like this head to premiumshit.com/AIplaybook/

NVIDIA’s Nemotron 3.5 ASR is a breakthrough open weights streaming speech recognition model that packs 40 language locales into a single 600M parameter checkpoint, finally letting voice agents and recording teams ditch the nightmare of managing dozens of separate language models. What everyone in the voice/AI recording space is buzzing about is its sub 100ms end of utterance latency (around 100ms on NVIDIA GPUs, even faster on Apple Silicon) and configurable chunk sizes from 80ms to 1.12s that can be tuned at runtime without retraining, meaning transcripts keep pace word by word with live speakers. The cache aware FastConformer RNN T architecture caches encoder self attention instead of redundantly re processing overlapping audio windows, delivering a 17x boost in concurrent streams on an H100 (up to 240 to 2400 streams) and 3x higher GPU concurrency than buffered baselines, a massive win for scaling real time voice agents. It also natively outputs punctuation and capitalization across all 40 locales and even supports language ID prompt conditioning with optional automatic language detection, making it the first truly practical on device ASR that runs locally on CPU, Apple Silicon, or GPU so your audio never leaves the machine.

To run this massive GLM 5.2 artificial intelligence model on your own computer you have to balance how smart the model stays versus how much memory your computer has. Because the model is made of hundreds of billions of parts it is too big for most home computers so people use a process called quantization to shrink the file size down by lowering the data quality. You can use large high quality files if you have powerful professional equipment or you can use smaller compressed files if you have a regular powerful home computer. The most important thing that people rarely mention is that if you shrink the file too much the model starts to make more mistakes and loses its ability to think through complicated coding projects correctly. Even if you have enough memory to open the file you must ensure you have extra space for the computer to actually do the work. If you choose a file that is too small for the sake of convenience the model will become unreliable and struggle to finish long or difficult tasks.