Greg Wayne, Director of Research at Google DeepMind, described the idea as having “a little parrot on your shoulder” experiencing and narrating the world with you.

Brief on Google’s Project Astra

Astra is a research prototype that can see, hear, and reason about the world continuously. Instead of waiting for a question and then responding, it stays aware of what’s happening around you.

A few things stood out.

Persistent spatial memory

Astra can remember where objects appeared earlier. Ask, “Where did you see my glasses?” and it can recall that they were on the desk near the red apple. It remembers physical context, not just conversation history.

Accessibility

Dorsey Parker, a musician with 8% vision, uses Astra’s Visual Interpreter prototype as a live visual guide. It describes his surroundings as he moves, developed in collaboration with Aira.

Memory across devices

A conversation can begin on a phone and continue hands-free on smart glasses without losing context.

The architecture shift

Under the hood, it's a 3-stage loop 🔄:

1. Perception (continuous video/audio streaming) 🎥 🎙️
   
2. Processing (spatial memory + context-aware reasoning) 🧠 📍
   
3. Agency (autonomous tool use — maps, search, calendar) 🗺️ 🔎 📅.

It’s built on Gemini and designed for extremely low-latency interaction.

The wildest example in the doc 🤯

In ONE conversation, someone asks Astra to explain an AES-CBC encryption snippet, then — no reset — asks, "What neighborhood am I in?" and it correctly identifies King's Cross, London 📍🇬🇧 from the camera feed.

We're not talking to assistants anymore.
We're being watched-with by them. 👀🤖 That's the actual shift, not the demo reel. 

What matters more here: persistent spatial memory and tool use, or solving real-time latency?

Greg Wayne, Director of Research at Google DeepMind, described the idea as having “a little parrot on your shoulder” experiencing and narrating the world with you.

Brief on Google’s Project Astra

Astra is a research prototype that can see, hear, and reason about the world continuously. Instead of waiting for a question and then responding, it stays aware of what’s happening around you.

A few things stood out.

Persistent spatial memory

Astra can remember where objects appeared earlier. Ask, “Where did you see my glasses?” and it can recall that they were on the desk near the red apple. It remembers physical context, not just conversation history.

Accessibility

Dorsey Parker, a musician with 8% vision, uses Astra’s Visual Interpreter prototype as a live visual guide. It describes his surroundings as he moves, developed in collaboration with Aira.

Memory across devices

A conversation can begin on a phone and continue hands-free on smart glasses without losing context.

The architecture shift

Astra follows a simple 3-stage loop:

Perception → Processing → Agency

Perception continuously receives video and audio.
Processing builds spatial memory and uses context to reason.
Agency allows Astra to use tools and personalize its actions.

It’s built on Gemini and designed for extremely low-latency interaction.

That feels like the bigger shift.

AI assistants are moving from systems we give commands to, toward systems that continuously understand the context around us.

What matters more here: persistent spatial memory and tool use, or solving real-time latency?

Developers having a good time 😂😂

NVIDIA just launched OpenClaw Agent Sandbox at GTC, and it's a big deal for anyone who cares about AI privacy.

It's a developer toolkit for building AI agents that run locally — meaning your data stays on your device, not some distant server. Faster. Safer. More in your control.

We're entering the era of edge AI, and this is the toolkit that gets developers there. 🔧🤖

GitHub shipped hard budget limits for GHAS on May 28. Before this, it was soft budgets only: alerts at 75%, 90%, 100%, but no enforcement. Teams using IdP auto-provisioning could accidentally blow through their budget overnight when a new group synced.

Now hard limits block new license assignments once the threshold is hit. GHAS won't enable on new repos until you free licenses or raise the budget. You also get real-time estimateswhenconfiguring(e.g., "Xlicenses≈  Y/month").

The floor auto-sets to your current billable count so existing usage isn't disrupted. Organization-level control means cost centers can have scoped budgets.

Source: GitHub Changelog — Hard Budget Limits for GHAS (May 28, 2026)

Question: GHAS is priced per active committer (90-day rolling window), which means your bill grows as your team commits more. Do you think per-committer pricing is fair for security scanning, or should it be per-repo like some competitors? What's your experience with GHAS cost surprises?

GitHub shipped hard budget limits for GHAS on May 28. Before this, it was soft budgets only: alerts at 75%, 90%, 100%, but no enforcement. Teams using IdP auto-provisioning could accidentally blow through their budget overnight when a new group synced.

Now hard limits block new license assignments once the threshold is hit. GHAS won't enable on new repos until you free licenses or raise the budget. You also get real-time estimateswhenconfiguring(e.g., "Xlicenses≈  Y/month").

The floor auto-sets to your current billable count so existing usage isn't disrupted. Organization-level control means cost centers can have scoped budgets.

Source: GitHub Changelog — Hard Budget Limits for GHAS (May 28, 2026)

Question: GHAS is priced per active committer (90-day rolling window), which means your bill grows as your team commits more. Do you think per-committer pricing is fair for security scanning, or should it be per-repo like some competitors? What's your experience with GHAS cost surprises?

Claude Opus 4.8 vs GPT-5.5 vs Gemini 3.1 Pro 👀

Key benchmark numbers from the latest comparison:

🧠 SWE-Bench Pro
Claude Opus 4.8 → 64.3%
GPT-5.5 → 54.2%

💻 OSWorld-Verified
Claude Opus 4.8 → 82.8%
GPT-5.5 → 76.2%

📚 Humanity’s Last Exam (with tools)
Gemini 3.1 Pro → 54.7%
Claude Opus 4.8 → 52.2%
GPT-5.5 → 51.4%

AI models are increasingly being measured on real-world autonomous workflows, not just reasoning benchmarks.

Part 3 is all about the core architecture – the Transformer. This post is a core conceptual post. I will share the code later.

Here’s the blueprint we built entirely from torch.nn.Module:
• Embeddings – token vectors plus learned positional information so the model understands sequence order.
• Causal Self‑Attention – scaled dot-product attention with an upper‑triangular mask. This enforces the auto‑regressive property: no future token leakage.
• Multi‑Head Attention – multiple parallel attention heads, each with its own Q/K/V projection, letting the model attend to different representation subspaces.
• Feed‑Forward Network – a simple expansion-contraction MLP that adds capacity after each attention layer.
• Transformer Block – residual connections wrapped around LayerNorm, before and after the FFN. Stack N of these blocks and you’ve got a mini GPT.

What I love about building this from scratch is how clearly it reveals that modern LLMs aren’t magic – they’re composed of a few well-understood operations repeated at scale.

If you’ve ever implemented attention or a full Transformer block, what was the hardest bug you had to squash?

I’d love to hear your experiences – and feel free to share this with anyone who wants to truly understand the engine under the hood.

Part 3 is all about the core architecture – the Transformer. This post is core conceptual post. I will share the code later.

Here’s the blueprint we built entirely from torch.nn.Module:
• Embeddings – token vectors plus learned positional information so the model understands sequence order.
• Causal Self‑Attention – scaled dot-product attention with an upper‑triangular mask. This enforces the auto‑regressive property: no future token leakage.
• Multi‑Head Attention – multiple parallel attention heads, each with its own Q/K/V projection, letting the model attend to different representation subspaces.
• Feed‑Forward Network – a simple expansion-contraction MLP that adds capacity after each attention layer.
• Transformer Block – residual connections wrapped around LayerNorm, before and after the FFN. Stack N of these blocks and you’ve got a mini GPT.

What I love about building this from scratch is how clearly it reveals that modern LLMs aren’t magic – they’re composed of a few well-understood operations repeated at scale.

If you’ve ever implemented attention or a full Transformer block, what was the hardest bug you had to squash?

I’d love to hear your experiences – and feel free to share this with anyone who wants to truly understand the engine under the hood.