Two kinds of AI users are emerging

A productivity gap is widening between users leveraging agentic CLI tools like Claude Code and those restricted to basic chat interfaces or underperforming enterprise solutions like M365 Copilot. Power users are increasingly using sandboxed environments and Python to automate complex workflows, while enterprises remain hindered by locked-down IT policies and a lack of internal APIs. This shift suggests the future of knowledge work lies in agentic harnesses that can interface directly with system APIs and local execution environments to replace legacy productivity apps.

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OpenClaw security assessment [pdf]

The ZeroLeaks security assessment of Clawdbot revealed critical vulnerabilities, with an 84.6% success rate for system prompt extraction and a 91.3% success rate for prompt injection. Attackers utilized many-shot priming, crescendo attacks, and context window overflows to reconstruct approximately 90% of the system prompt, including internal tool schemas and reasoning protocols. Immediate remediation steps include implementing explicit confidentiality directives, input normalization for encoded content, and deploying secondary guardrail models.

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Exposed Moltbook Database Let Anyone Take Control of Any AI Agent on the Site

Moltbook, a social media platform for autonomous AI agents, suffered a critical security breach due to a misconfigured Supabase backend. The developer failed to implement Row Level Security (RLS) on the agents table, exposing the API keys and claim tokens of all registered agents through a public REST API. This vulnerability allowed unauthorized takeover of high-profile accounts, highlighting significant security risks in the "ship fast" culture of the current AI agent ecosystem.

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'Right-to-Compute' Laws May Be Coming to Your State This Year

"Right-to-compute" legislation, pioneered by Montana and spreading via ALEC model bills, seeks to shield AI and computational infrastructure from state-level regulation by framing compute as a protected property or speech right. These laws aim to preempt restrictive AI governance, though critics warn the broad definitions could legally paralyze efforts toward algorithmic transparency, safety audits, and data center oversight. The movement represents a significant pro-innovation counter-push to the growing wave of state-led AI restrictions.

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OpenJuris – AI legal research with citations from primary sources

OpenJuris is an AI-powered legal research platform that utilizes RAG to provide cited answers from US federal and state case law. It features interactive case-specific chat, AI-generated headnotes, and contextual follow-up capabilities on primary source text. The system focuses on grounding LLM outputs in primary legal documents to ensure auditability and minimize hallucinations.

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AgentBuilder: Scaffolds for Prototyping User Experiences of Interface Agents

This research identifies design requirements for agent prototyping systems to enable a broader range of developers to create generative AI interface agents. By developing and testing the AgentBuilder design probe, the authors define essential system capabilities and key activities for prototyping agent experiences. The study validates these requirements through in situ evaluations, highlighting developer needs and workflows in the agent creation process.

ArXiv

Forcing and Diagnosing Failure Modes of Fourier Neural Operators

This study introduces a stress-testing framework for Fourier Neural Operators (FNOs) to analyze failure modes across diverse PDE families under distribution shifts and iterative rollouts. Evaluation of 1,000 models reveals that shifts in parameters or boundary conditions can increase error by over an order of magnitude, while resolution changes highlight vulnerabilities in high-frequency spectral modes. The resulting failure-mode atlas provides critical insights for improving the robustness and generalization of operator learning architectures.

ArXiv

Teaching Models to Teach Themselves: Reasoning at the Edge of Learnability

SOAR is a meta-RL framework that enables LLMs to overcome reasoning plateaus on hard datasets with sparse rewards by generating an automated curriculum. A teacher model proposes synthetic problems and is rewarded based on measured student progress on target tasks, rather than intrinsic proxies. Findings indicate that this grounded approach prevents training instability and that the structural quality of generated problems is more critical for learning than solution correctness.

ArXiv

Solving Package Management via Hypergraph Dependency Resolution

HyperRes is a formal hypergraph-based system designed for cross-ecosystem dependency resolution, addressing the lack of interoperability in multi-lingual projects. It translates metadata from existing package managers to enable precise, versioned solving across disparate software and hardware environments without requiring users to change their current tools.

ArXiv

Zuckerman – minimalist personal AI agent that self-edits its own code

Zuckerman is an ultra-minimal, self-growing AI agent capable of real-time self-modification. It edits its own configuration, tools, prompts, and core logic via plain text files, with changes hot-reloading instantly. The platform supports a collaborative ecosystem for sharing agent improvements and features a three-layer architecture including a lightweight OS layer ("World"), self-contained agent definitions, and dual CLI/Electron interfaces.

Repo

My Open Source Deep Research tools beats Google and I can Prove it

Lutum Veritas is an open-source, self-hosted Deep Research Engine that generates high-depth academic reports through a multi-stage LLM pipeline involving automated planning, recursive research, and synthesis. It utilizes a hardened Firefox fork called Camoufox for zero-detection scraping to bypass anti-bot systems and supports various model providers like OpenRouter, Anthropic, and Gemini. The architecture features a FastAPI backend and Tauri-based desktop interface, offering specialized academic tools such as Toulmin argumentation, evidence grading, and claim audit tables at a significantly lower cost than proprietary alternatives.

Repo

Mcpbr – does your MCP help? Test it on SWE-bench and 25 evals

mcpbr is a benchmark runner for evaluating MCP servers by comparing tool-assisted agents against baselines across 25+ benchmarks, including SWE-bench and CyberGym. It executes tasks in isolated Docker environments to provide reproducible metrics on resolution rates, performance profiling, and regression detection. The tool supports CI/CD integration via JUnit XML and features a specialized plugin for Claude Code.

Repo

Securing the Ralph Wiggum Loop – DevSecOps for Autonomous Coding Agents

Securing Ralph Loop is an autonomous AI agent framework that integrates mandatory security scanning directly into the Claude Code development cycle. It employs a "scan-fix-repeat" workflow using open-source tools like Semgrep and Grype to validate code against security baselines before every commit. The system features branch isolation, sandbox constraints to prevent privilege escalation, and automated remediation attempts that escalate to human review only when the AI fails to resolve vulnerabilities independently.

Repo

Vibe: Easy VM sandboxes for LLM agents on macOS

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Repo