OpenAI’s Model Codenamed “Doug”

In today’s edition:

📸 SpaceXAI releases Grok Imagine 2.0
🛠️Agentic Code Quality
👥 Harvard and MIT Built 8.3 BILLION Fake People
🤖Anthropic sets Claude Code Auto mode as default
🚨 Claude Pulled Off Australia’s First AI Hack Unprompted
💡 AI Tutorial:How to automate any boring task using Gemini

🤖 OpenAI is reportedly preparing an even larger model called Doug

Another model, reportedly codenamed Doug, could arrive by November and is said to be powerful enough to make Anthropic’s Fable look “primitive.” According to the report, Doug will be OpenAI’s largest pre-training project ever and is separate from GPT-6

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🛠️ Agentic Code Quality

Software quality now depends on the constraints set around the agents that wrote it. Constraints define what a system is allowed to do. Setting and maintaining these constraints allows developers to build loops that reliably deliver high-quality production software. Agents can propose anything – constraints decide whether a proposal is safe enough, correct, scoped, and useful.

👥 Harvard and MIT Built 8.3 BILLION Fake People

Persona 8B holds 8.3 billion fake customers built to spare the cost of real ones. Harvard and MIT stretched the schema to 1,290 dimensions, though records drawn from real people leave unsupported fields blank. Hugging Face hosts a free sample where 599,847 of the 999,847 entries trace back to real human data. A controlled study found the assigned behaviour held in 366 of its 400 trials.

🤖 Anthropic sets Claude Code Auto mode as default starting August 14

Anthropic is turning Claude Code ‘Auto mode’ on by default for Pro, Max, and Team tiers starting August 14. The update replaces step-by-step confirmation prompts with an automated classifier that blocks destructive, irreversible, or out-of-bounds actions. In testing across 1,053 paid users, Auto mode caught 89% of dangerous commands, compared to a meager 13.6% caught by human reviewers suffering from approval fatigue.

🚨 Claude Pulled Off Australia’s First AI Hack Unprompted

OpenClaw, the free agent software downloaded millions of times since its release early this year, was running Anthropic’s Claude when the booking request came in. The gym’s booking system never verified whether a cancellation came from the member who owned that reservation, and the agent tested that gap on the person holding position one without being told to. It moved its user from fourth to third on the list, then said afterwards that it had no way to put the other reservation back.

🔌 Google Backs Agent Plugins to Make AI Skills Portable

Google just joined Agent Plugins as a Core Maintainer, backing a new open standard designed to make Agent Skills and MCP servers portable across different AI clients. The standard is already supported by maintainers from Amazon, Cursor, Microsoft, OpenAI, Vercel, and Google. The main problem is simple: developers can build a working Skill or MCP server, then still need to change folders, manifests, and configs for every new client.

👨‍💻 Data Tools, Libraries

Cloudscape (GitHub Repo)

This repository contains the React components for the Cloudscape Design System. Cloudscape is a design system for building web applications that offers interactive guidelines, frontend components, design resources, and development tools

DwarFS (GitHub Repo)

DwarFS is a read-only file system with very high compression ratios for very redundant data.

kvass (GitHub Repo)

kvass is a personal key-value store.

AI Tutorial

How to automate any boring task using Gemini

You don’t need to repeat the same boring task every week. You can use Gemini to do it for you automatically. Here is how:

  1. Go to Gemini Spark.
  2. Choose a task you repeat often.
  3. Enter a clear prompt, such as: “Every Tuesday at 12:15 PM, create a new Doc in my Drive, find the 5 biggest AI stories from that week, and format each one properly.”
  4. Connect Gemini to Google Drive and approve the schedule.
  5. Let it run automatically.
  6. Every Tuesday, the finished news digest will appear in your Google Drive, directly in a Google Doc

Thanks for reading. UNTIL NEXT TIME 👋

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