NoName13

func NoName13() Dev{
backend, mobile := {Go, C#, Java}, {Dart, Flutter}
cloudServices := {AWS, Azure, Heroku}
ia := {Claude, Codex, APIs, agents}
return
}

[NoName13 @_NoName13_]

@genuinearticles 🫡

[Google Labs @GoogleLabs]

🚨NEW EXPERIMENT 🚨

Playground is an experimental gaming platform that lets you create your own games with zero coding experience. If you can think it, you can play it.

Go to https://t.co/fRUukpuoqM to learn more! Available to users 18+ in the US.

[Avid @Av1dlive]

i spent 202+ days learning inference engineering.

this is the 6-month roadmap i wish i had... 26 weeks, 9 projects, 910 hours.

the exact path to become an inference engineer, month by month.

full guide: https://t.co/NxRk8iTkDM

[Avi Chawla @_avichawla]

https://t.co/sLoULsUfEt

[Uber Engineering @UberEng]

https://t.co/p4OzJicyw8

[Google DeepMind @GoogleDeepMind]

Introducing Gemini 4 Argon – our new frontier model.

It’s built for complex workflows across coding, enterprise knowledge work, and cybersecurity defense – rolling out today to a set of trusted testers through our Fairwind Program.

[OpenAI @OpenAI]

Please welcome GPT-6 Sol and GPT-6 Luna to the GPT-6 universe.

GPT-6 Sol and Luna build on the advances behind GPT-6 Astra, bringing much of its strengths into faster and more affordable models to support work at scale.

We’ve also made caching and inference more efficient, and we’re passing the savings directly to you: 50% lower API prices for Sol and Luna compared with GPT‑5.6 promotional pricing.

[NoName13 @_NoName13_]

@genuinearticles 0x69cd13c112816d397A3e9c95DA4C7A9F54be58ff

[The Fourth Musketeer @IV_Musketeer]

Why ancient humans ate rotten meat on purpose?

[GitHub @github]

We are investigating unauthorized access to GitHub’s internal repositories. While we currently have no evidence of impact to customer information stored outside of GitHub’s internal repositories (such as our customers’ enterprises, organizations, and repositories), we are closely monitoring our infrastructure for follow-on activity.

[NoName13 @_NoName13_]

gaugo: a go-native eval framework for AI apps (RAG, agents, chatbots, prompts).

a side project i'm working on and wanted to share:
https://t.co/aTGjsW3Iu1

[Claude @claudeai]

New in Claude Code: agent view.

One list of all your sessions, available today as a research preview.

[NVIDIA AI Infrastructure @NVIDIAAIInfra]

As quantum computing rapidly converges with AI and accelerated computing, the skills students need are evolving fast. Educators now face a key question: how do you prepare students for what’s next?

Join us for CUDA Live: CUDA-Q Academic Demo Day on May 20 to explore free, open-source CUDA-Q Academic resources designed to make quantum computing accessible, practical, and industry-relevant. You’ll see modular, classroom-tested notebooks, interactive visualization tools, and live demos, with resources that can be run across platforms like NVIDIA Brev, Google Colab, qBraid, and Amazon Braket.

We’ll also dive into how AI is reshaping quantum education—and what an agentic-first future means for your curriculum.

📅: May 20 | 11:00 AM–12:30 PM CDT
🔗: https://t.co/rYEDg0Dqd5

[Chrome @googlechrome]

Skills in Chrome are now available. Get started saving and customizing your favorite prompts with our quick tutorial.

What Skill are you saving first? ⬇️

[GitHub @github]

Tomorrow on Open Source Friday 👇

We're breaking down Spec Kit: what it is, the problems it solves, and how clear specs make collaboration actually work. Principal Software Engineer Manfred Riem explains live.

Set a reminder. 🔔
https://t.co/g0xrLf3Hb5

[Claude @claudeai]

We’ve agreed to a partnership with @SpaceX that will substantially increase our compute capacity.

This, along with our other recent compute deals, means that we’ve been able to increase our usage limits for Claude Code and the Claude API.

[Google for Developers @googledevs]

Breaking LLM inference’s autoregressive bottleneck 🛠️

We've teamed up with @haozhangml, @YimingBob, and @aaronzhfeng, among others from UCSD to achieve a massive 3.13X speedup for LLM inference on Google Cloud TPUs using Diffusion-Style Speculative Decoding (DFlash).

Read the blog: https://t.co/bIugAUJm8S

[Brij Pandey @LearnWithBrij]

Claude Code ships with 5 architectural layers most engineers never open.
Not features. Not settings. Layers — each solving a distinct problem that LLMs alone can't solve. And four of them have nothing to do with prompting.
Here's the full Agent Development Kit:

Layer 1 — CLAUDE.md → The Memory Layer
Architecture rules, naming conventions, test expectations, repo map. Always loaded. Always active.
Two scopes:
• ~/.claude/CLAUDE.md → global
• .claude/CLAUDE.md → project
This isn't context you paste in before every session. It's context that never needs repeating. The agent's constitution.

Layer 2 — Skills → The Knowledge Layer
Each SKILL.md carries a description. Claude matches it at runtime and forks the skill into an isolated subagent. On-demand, never always-on.
Task-specific knowledge without inflating your main context window. Modular by design.

Layer 3 — Hooks → The Guardrail Layer
PreToolUse → PostToolUse → SessionStart → Stop → SubagentStop

This is the layer most teams skip. And the one they regret skipping first.

Hooks are NOT AI. They're deterministic event-driven shell commands.
• Auto-lint on every Write
• Hard-block on rm -rf
• Slack notification on Stop
Event fires → Matcher checks → Command runs
Quality enforced at the infrastructure level. Not the prompt level.

Layer 4 — Subagents → The Delegation Layer
Each subagent gets its own context window, model, tools, and permissions.
Main agent delegates down. Receives results up. That's it.
No infinite recursion — subagents can't spawn subagents. Main context stays clean. Hard boundaries by design.

Layer 5 — Plugins → The Distribution Layer
Bundle your skills + agents + hooks + commands into a plugin. One install. Whole team inherits the behavior.
Think npm packages — but for what your agent knows how to do.

Wrapping everything:
→ MCP Servers on the left (GitHub, databases, APIs, custom integrations)
→ Agent Teams on the right (parallel execution, message passing, shared permissions)

The 5-layer stack in one line:
CLAUDE.md sets rules → Skills provide expertise → Hooks enforce quality → Subagents delegate work → Plugins distribute to the team
Most production failures in agentic systems trace back to one missing layer.

Which one is the gap in your current setup?

[0xMarioNawfal @RoundtableSpace]

SOMEONE SHARED THE MOST EFFECTIVE CLAUDE PROMPT STRUCTURE

BOOKMARK THIS NOW

[Google en español @googleespanol]

¡Felices 20 años, Google Translate! 🎂🌎

Llevamos dos décadas rompiendo las barreras idiomáticas. Pero hoy, en vez de pensar en todos puentes que construimos, ¡queremos celebrar cómo nos comunicamos en nuestra región! 🧵👇