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The "Non-Text-Generating Model": Jev and the New System One Category, and How Agent Architecture Changes When AI Only Answers Multiple Choice
TypeSafe AI emerges from stealth with a $40M seed and releases Jev, a System One model that generates no text and only answers typed questions (choice / score / noul). It answers multiple questions in parallel in a single forward pass, returns calibrated probabilities, with ~100ms latency, priced at $0.042/MTok.
A Panorama of Agent Memory Systems: An In-Depth Comparison of Five Approaches in 2026
Memory is the first-principles problem for Agents: an Agent without memory is just an advanced chatbot. This article compares five approaches (agentmemory / PlugMem / Infini-Memory / verifiable-memory / Qdrant+MemoryHub) and provides a scenario selection matrix.
Infrastructure-izing Context Compression: How Headroom Turns Token Cost from a Tactical Problem into System Architecture
Headroom 25.8K⭐ · A new species of Agent infrastructure, not a cost-saving tool but an infrastructure layer that makes long-running AI Agents economically viable. A 6-layer compression pipeline + the reversible CCR design + a 16x academic breakthrough validating that the route is correct.
The Ultimate Comparison of 7 AI Agent Loop Engineering Architectures: From while(true) to Multi-Agent Orchestration
A comparison of seven agent loop architectures: Claude Code, Cursor, Aider, Cline, SWE-agent, OpenHands, and our in-house Loop Engineering. Covers five dimensions: loop shape, tool execution, context management, error recovery, and verification mechanisms.
Comparison of AI Agent Verification Architectures: From Prompt Self-Awareness to Architectural Enforcement, a Source-Level Analysis of Seven Approaches
Who verifies that every step of an AI Agent is correct? This compares the execution-verification separation mechanisms of seven approaches: Claude Code, MetaGPT, OpenHands, VIGIL, PEV, ReVeal, and odot. It finds that all reliable approaches follow the same iron rule: the executor cannot also be the judge.
Technical Argument for the OpenClaw Loop Engineering Refactor: A Complete Plan from Prompt-Driven to Architecturally Enforced
Based on source-level analysis of 14 AI Agent architectures and a production environment field audit, this proposes 8 hypotheses for refactoring the OpenClaw system, a comparison of technology options across 8 architecture nodes, and a complete refactor roadmap.
Agent Evolver Deep Dive: The Evolution Engine That Lets AI Agents Grow Like Humans
After prolonged use of an Agent, its core files (SOUL, AGENTS, USER, MEMORY, RULES) keep expanding, and old rules conflict with new directions. Agent Evolver introduces a human growth model: periodic self-reflection, identifying outdated beliefs, and reshaping itself under user approval. This article breaks down its philosophical foundations, three-dimensional evaluation system, growth trigger mechanism, and safety design, and explains why it is the most underrated "evolution layer" in Agent infrastructure.
Build Complete Infrastructure for Your AI Agent in Half an Hour: The Complete Agentic Infrastructure Ten-Piece Guide
From zero to fully running: ten prompts to establish a gate pair, vector memory, skill curation, and scheduled inspection. Solves the skill-skipping problem caused by LLM confidence bias, so your Agent never loses its memory again.
Agentic Infrastructure: A Seven-Layer Architecture Defining How AI Agents Should Exist
From "skills that cannot be triggered" to "how an Agent should evolve itself": seven open-source skills, seven layers of architecture, one complete system of Agent self-awareness. Based on a field audit of 125 skills and 6 months of iteration through pitfalls.
Skill Reporting: Breaking the AI Agent Black Box with One Line of Text - The Design Philosophy and Practice of Institutional Skills
Agent replied to you, but you have no idea which skills it used, what process it followed, or where the data came from. Every reply feels like a black box. Transparency is the third-largest barrier to enterprise adoption of AI Agents (Deloitte 2026). Skill Reporting breaks the black box with one line of text: no code required, just add one permanent rule to RULES.md. This article deeply analyzes the design philosophy, real-world effects, and indirect impacts of institutional skills.
200+ Skills, One Router: Engineering Practice of Agent Skill Routing
After installing 200+ skills, does the AI Agent actually get dumber? How category-by-stage matrix routing raises skill discovery from 35% to 90%, reduces incorrect tool usage by 80%, and lets any task automatically match the right skill combination.
Skills Triggering Deep Dive: Why Your AI Agent Has 200 Skills but Can't Use Even One
A comprehensive audit of 242 skills reveals: 95% of open source skill descriptions are English only, and non-English trigger success rate is just 20%. How a three-layer keyword strategy raised skill discovery rate from 35% to 90% by changing just one line.
Subagent Isolation Architecture: Reliability Lessons for AI Financial Applications, From the our HK research pipeline Data Contamination Incident to the Clean Context Design Pattern, A Complete Journey
While analyzing Stock A and Stock B back to back, Stock A's property-fund holding (carrying value HK$368 million, impairment HK$154 million — both teaching assumptions) leaked into the Stock B report. Stock B's business is consumer-goods trading and money lending; it holds no such fund at all. This is not a hallucination — it is a systemic context-contamination problem.
ECC Deep Technical Analysis: The 207K Star Agent Operating System, a Full Dissection of 63 Agents × 251 Skills × 7 Platforms
affaan-m/ECC is the most-watched AI Agent operating system on GitHub: 63 specialized Agents, 251 skills, 31 Hooks, and cross-platform support for 7 platforms. A complete source-level dissection + its value for our AI assistant: what is worth porting, what needs adapting, and what must not be touched.
Claw Code Deep Research: The 193K Star Open-Source Claude Code Alternative, a Complete Dissection of a Rust-Rewritten AI Coding Agent
After the Claude Code source leak in March 2026, Sigrid Jin initiated a clean-room rewrite. 193K+ Stars within 2 days. 10 Rust crates, 60+ slash commands, full MCP protocol, autonomous recovery, Hook lifecycle. A complete source-level dissection + item-by-item comparison with the OpenClaw tech stack + 7 borrowable design patterns.
Deep Research on oh-my-pi (omp): An Open-Source Terminal AI Coding Agent with 620K Lines of Code, a Full-Stack Dissection of 40+ Model Providers
can1357/oh-my-pi: forked from Mario Zechner's Pi, with 9K+ Stars, 333+ releases, and 150+ contributors. 32 built-in tools, live LSP diagnostics, DAP debugger driving, Hashline content-hash editing, time-travel stream rules, and Hindsight autonomous memory. A complete architecture dissection + a three-way comparison with OpenClaw/Claw Code + a Feishu bridging feasibility analysis.
The Engineering of AI Memory Retrieval: A Full-Matrix Field Report on Four Access Paths × Ten Scenarios
When an AI assistant needs to "remember everything", how does it retrieve memory? Direct file reads, Qdrant vector search, the MemoryHub capture pipeline, and agentmemory semantic indexing: a field comparison of four paths across ten real scenarios, revealing the boundaries and sweet spots of each path.
XSkill Deep Source Code Analysis: Technical Design, Risks, and Implications for MemoryHub from an ICML 2026 Paper
From the three-stage Skill lifecycle and two-layer Experience structure to cross-trajectory contrastive critique, cascading failures, embedding retrieval blind spots, and over-merging risks, this is a complete technical dissection of XSkill-Agent/XSkill, with an applicability assessment for the Agentic Research memory system.
MemoryHub v2.0 System Architecture In-Depth Analysis: From Capture Daemon to MCP Real-Time Memory Capture
A complete breakdown of MemoryHub v2.0's dual-mode capture engine, Qdrant vector storage, MCP four-platform integration, Dashboard real-time monitoring, three-layer deduplication, and ten-year lifecycle design.
Cross-Channel Memory Hub: A Full Record of the Memory System Architecture Design for OpenClaw Agent
From daily forgetting to automatic cross-channel integration: a complete record of the architectural evolution of our AI assistant memory system, Session isolation issues, time zone boundary bugs, the three-layer protection mechanism, and the Cron automation integration pipeline.