In a genetic system, what is the best quality gate design to ensure you have constancy coverage for unit, api, integration and e2e tests?
How does the system manage this and how does it select what to run and what to repair?
ChatGPT designs for an app I’m working on.
Heavily influenced by cuphead style. I love this design though and it’s really inspiring me on what UI style I want for my app.
Tried @stitchbygoogle for a new app idea to detect my dog on the couch and I’m honestly impressed.
It laid out each screen from my prompt with a consistent style and cohesive feel. Super solid for UI inspiration. Next I’m feeding it into Claude to see what it builds.
BIG shift for AI builders 👇
@Google’s Gemini API now combines custom functions + tools (Search, Maps) in ONE call.
Use case:
Find a restaurant, check hours, book it automatically
Why it matters: replaces multi-step backend logic with one call.
Read: goo.gle/3NxGtZs
AI just leveled up again.
Agents can now use a browser inside VS Code to:
click, read, test, verify.
Not just coding anymore… actually interacting with your app.
🌐 Agentic Browser Tools (Experimental) in @code!
Agents can now open pages, read content, click elements, and verify changes directly in the integrated browser while building your web app.
Enable ⚙️ workbench.browser.enableChatTools to try it out.
Learn mode:
My dog keeps jumping on the couch while I’m not home and it was driving me nuts!!!
I probably found the most ridiculous solution but I decided to make my own puppy cam that will detect if she’s on the “Couch Zone” (area in green).
Will update with results 🤞😆
Today @GoogleMaps is getting its biggest upgrade in over a decade. By combining our Gemini models with a deep understanding of the world, Maps now unlocks entirely new possibilities for how you navigate and explore. Here’s what you need to know 🧵
Is anyone tracking whether these devs are building AI tooling FOR crypto specifically, or actually walking away from web3 entirely? That distinction changes everything. coindesk.com/tech/2026/03/1…
2024: crypto was GitHub's hottest corner. 2026: commits down 75%, active devs down 56%. AI deployment repos jumped 120%. The migration already happened — most people just didn't notice.
Google Maps gets ~20B searches per day.
Now Gemini can reason across reviews, photos, and menus so you can ask:
“Find a coffee shop nearby where people actually work for hours and outlets are mentioned in reviews.”
Maps just went from navigation to understanding places.
6 months after leaving OpenAI, Mira Murati just committed to 1 gigawatt of Nvidia Vera Rubin chips — hardware that doesn't even ship until 2027. Compute is the new equity. techcrunch.com/2026/03/10/thi…
The Pentagon labeled Anthropic a 'supply chain risk' — same tag used for foreign adversaries. Why? They refused to let AI auto-fire weapons. 30+ OpenAI & Google employees just publicly backed them. The AI ethics war just got loud.
Anthropic just shipped AI Code Review to catch bugs written by AI. We're using AI to audit AI now. The loop is closed. Where exactly does the developer fit?
AI coding tools are quietly reshaping what languages devs pick. TypeScript jumped to #1 not because devs chose it — LLMs just work better with typed code. Are we letting the AI pick our stack?
GitHub's Agent HQ lets you run Claude, Codex, and Copilot simultaneously on the same task. Three different AI brains, one codebase. Are we done writing code ourselves?
this is going viral on chinese social media right now.
exo is being used by a school in china to deploy private ai agents locally.
they repurposed m1 ultra macs from their film lab, clustered them together with @exolabs, and ingested their entire school corpus including
What if cities ran public AI infrastructure like libraries?
A shared GPU cluster hosting open LLMs.
Residents log in, get own private workspace, run agents, build tools, and use AI without paying per-token to big tech.
Public AI compute as civic infrastructure.
@Meta help us out
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