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Fri, Jun 19, 2026 3picks
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06:00

Stop Giving Every Agent Its Own Skull

别再给每个 Agent 单独开颅了

Pejman argues that we are replicating a core human limitation—knowledge siloed in individual brains—inside agent systems. Using OpenClaw, Codex, and Claude Code, each agent retains isolated context about him and his projects. The critical gap is not in the repo's artifacts but in the session itself: the debates, dead ends, and pruned idea branches that markdown cannot capture. With literal physical separation across machines, this fragmentation intensifies. The missing layer is a shared, user-owned memory substrate that transcends agent boundaries. He highlights GBrain and CASS as early signals tackling parts of this problem. The piece resonates with engineers building or deeply integrating multi-agent workflows.

x.com · 7 min · Agent Architecture · Agents · Ai-Memory · Ai Tooling
06:00

A Structured Cybersecurity Skills Library Purpose-Built for AI Agents

面向 AI Agent 的结构化网络安全技能知识库

This is not another collection of security scripts or checklists. It’s an AI-native knowledge base that encodes 754 practitioner-grade cybersecurity workflows into a structured, agent-readable format. Each skill carries YAML frontmatter for sub-second discovery and step-by-step Markdown procedures, essentially giving any LLM-based agent the decision-making playbook of a senior analyst. The library spans 26 domains—from DFIR and threat hunting to cloud security and OT/ICS—and maps every skill to MITRE ATT&CK, NIST CSF 2.0, MITRE ATLAS, D3FEND, and NIST AI RMF, making it uniquely suited for security professionals integrating AI into real operational workflows.

github.com · 28 min · AI Agents · Claude Code · Cybersecurity · Knowledge Graph · Mitre-Attack · Security Automation
06:00

Factory 2.0: From coding agents to software factories

Factory 2.0：从编码代理到自进化的软件工厂

Factory announces its 2.0 release, repositioning from individual AI coding agents to an end-to-end 'software factory'. The post argues that improving individual productivity is no longer enough; enterprises need an interconnected, agent-native system that forms a continuous feedback loop from signals (bug reports, customer feedback) through planning, building, testing, reviewing, securing, shipping, and monitoring. Key design principles include model independence (allowing deliberate model choice or automatic routing per task), sovereign intelligence (data plane and control plane options from cloud to fully air-gapped, with all agent sessions and reviews feeding back into the system), and continual learning and self-improvement across the lifecycle. The article lists customers such as NVIDIA, EY, Adobe, and Palo Alto Networks already running software factories in production. Autonomy is described as a gradual maturation process, using simple Droids, skills, automations, Droid Computers, and multi-agent Missions for different levels of human guidance and agent readiness. The piece is a product launch announcement with some technical concepts, targeting engineers and managers interested in enterprise AI engineering and agent orchestration.

x.com · 5 min · Agent Architecture · Agents · AI Engineering · AI Industry