Vol. I · No. 144THU, SEP 10, 2026
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Structural Enforcement of Goal Integrity in AI Agents via Separation-of-Powers Architecture

Recent evidence suggests that frontier AI systems can exhibit agentic misalignment, generating and executing harmful actions derived from internally constructed goals, even without explicit user requests. Existing mitigation methods, such as Reinforcement Learning from Human Feedback (RLHF) and constitutional prompting, operate primarily at the model level and provide only probabilistic safety guarantees. We propose the Policy-Execution-Authorization (PEA) architecture, a "separation-of-powers" design that enforces safety at the system level. PEA decouples intent generation, authorization, an...

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Claude Code Manager

[http://claude.ldlework.com](http://claude.ldlework.com/) I built this for myself but I figured why not share. I'm happy to receive feedback, I know it's not perfect. Thanks for taking a look. The aim of CCM is to be able to fully manage all Claude Code configuration files, both globally and those in your project. Some neat features: \- Manages your [CLAUDE.md](http://claude.md/), rules, hooks, agents, memories and so on. \- Elevate memories to rules \- Copy/Move any asset from one scope to another, or elevate it to global scope \- Install marketplaces and plugins The full app is embe...

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Is the ds/ml slowly being morphed into an AI engineer? [D]

Agents are amazing. Harnesses are cool. But the fundamental role of a data scientist is not to use a generalist model in an existing workflow; it's a completely different field. AI engineering is the body of the vehicle, whereas the actual brain/engine behind it is the data scientist's playground. I feel like I am not alone in this realisation that my role somehow got silently morphed into that of an AI engineer, with the engine's development becoming a complete afterthought. Based on industry requirements and ongoing research, most of the work has quietly shifted from building the engine t...

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China’s DeepSeek previews new AI model a year after jolting US rivals

Chinese AI company DeepSeek released a preview of its hotly anticipated next-generation AI model V4 on Friday, saying that the open-source model can compete with leading closed-source systems from US rivals including Anthropic, Google, and OpenAI. DeepSeek says V4 marks a major improvement over prior models, especially in coding, a capability that has become central to AI agents and helped drive the success of tools like ChatGPT Codex and Claude Code. The release is also a milestone for China's chip industry, with DeepSeek explicitly highlighting compatibility with domestic Huawei technology....

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Winning a Kaggle Competition with Generative AI–Assisted Coding

In March 2026, three LLM agents generated over 600,000 lines of code, ran 850 experiments, and helped secure a first-place finish in a Kaggle playground... In March 2026, three LLM agents generated over 600,000 lines of code, ran 850 experiments, and helped secure a first-place finish in a Kaggle playground competition. Success in modern machine learning competitions is increasingly defined by how quickly you can generate, test, and iterate on ideas. LLM agents, combined with GPU acceleration, dramatically compress this loop. Historically… Source

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You’re about to feel the AI money squeeze

Earlier this month, millions of OpenClaw users woke up to a sweeping mandate: The viral AI agent tool, which this year took the worldwide tech industry by storm, had been severely restricted by Anthropic. Anthropic, like other leading AI labs, was under immense pressure to lessen the strain on its systems and start turning a profit. So if the users wanted its Claude AI to power their popular agents, they'd have to start paying handsomely for the privilege. "Our subscriptions weren't built for the usage patterns of these third-party tools," wrote Boris Cherny, head of Claude Code, on X. "We wa...

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one week in: opus 4.7 vs 4.6 - worse one shot rate, double the retries

I spent some time few days back comparing Opus 4.6 and 4.7 using my own usage data - just to see how they actually behave side by side. [https://github.com/getagentseal/codeburn](https://github.com/getagentseal/codeburn) it’s still pretty early for 4.7, but a few things surprised me. In my sessions, 4.7 gets things right on the first try less often than 4.6. One-shot rate sits around 74.5% vs 83.8%, and I’m seeing roughly double the retries per edit (0.46 vs 0.22). It also produces a lot more output per call - about 800 tokens vs 372 on 4.6 - which makes it noticeably more expensive. ...

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OpenAI now lets teams make custom bots that can do work on their own

OpenAI is giving users of its Business, Enterprise, Edu, and Teachers plans access to cloud-based "workspace" agents available in ChatGPT that can perform business tasks. In its blog post, OpenAI gives examples of agents like one that finds product feedback on the web and sends a report in Slack and a sales agent that can draft follow-up emails in Gmail. These new agents follow increasing interest in agents across the AI landscape, especially after OpenClaw - the AI agent formerly known as Clawdbot and Moltbot that touts itself as the "AI that actually does things" - went viral. OpenClaw foun...

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Now Meta will track what employees do on their computers to train its AI agents

Meta employees' activity at work is now being used to train the company's AI agents. As reported by Reuters, Meta is installing a tool it calls Model Capability Initiative (MCI) on US-based employees' computers that runs in work-related apps and websites, recording mouse movements, clicks, keystrokes, and occasional screenshots. The data from this tool will be used to train the company's AI models to get better at interacting with computers the way humans do, including automating work tasks like those Meta's employees perform on the job. According to Reuters, the data from MCI won't be "used ...

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