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Glossary8
Open the full glossary- Standard streams (stdio)The three data pipes every process is born with: stdin to read from, stdout for normal output, stderr for errors — three separate channels.
- DotfileFiles whose names begin with a dot: hidden from listings by default, because they are usually configuration rather than content you browse.
- Status codeThe three-digit number on the first line of a response, telling you whether the call succeeded, whether you made a mistake, or whether the server did.
- Rate limitThe cap on how many requests or tokens a service lets you send in a given window; go over it and you are throttled with a 429.
- DistillationDistillation lets a smaller student model learn from a larger teacher, usually to shrink size or cost while keeping ability.
- BaselineA baseline is the pre-change comparison point: same cases, environment, trial count, and thresholds, or scores cannot be compared.
- RegressionA regression is a decline beyond threshold versus the baseline, in quality, cost, safety, or reliability; check it before shipping.
- Tokens per second (generation throughput)How many tokens a model emits per second. During decode it is set mainly by memory bandwidth, not compute; the vendor's figure and your measured figure often differ by a wide margin.
Found 85 articles
The 74k-star Open-Source Life Guide: What Its Evidence Grading Is Worth Copying
A breakdown of a GitHub project with 74,249 stars: 772 recommendations, 2,597 primary sources, and an A/B/C evidence grading system. Compared against our T0-T5 trust model — three ideas worth borrowing, two not.
DeepSeek's Open-Source Super-Agent Prototype: A Source-Level Breakdown
A source-level breakdown of DeepSeek's open-source dsh-superagent-prototype: the three-plugin architecture, the event-driven core, 11 injected host services — and which designs are worth borrowing, and which are not.
OpenGCUA Guide: The Open-Source Approach to Letting AI Actually Use a Computer
OpenGCUA is an open-source generalist computer-use agent from the Tongyi-MAI team. This guide breaks down its accessibility tree, code execution, and background operation design, checks the paper's 82-task numbers, and gives macOS install steps, three adoption paths, and an isolation plan for its arbitrary-code-execution risk.
Advanced Ontology Design: From a Reasonable Model to a Production-Grade Semantic Layer for Agents
When an ontology has to go to production: pick the right OWL profile, turn constraints into executable checks, handle identity and time, and let agents use it through a tool contract. This is about making it hold up, not making it look pretty.
Tunnel Techniques Explained: When an AI Agent Needs to Pave a Road into the Internal Network
The hard part begins after an AI agent lands inside a network: firewalls block, the target has no outbound internet, and the attack host cannot reach it directly. This illustrated guide explains how three mainstream tunneling techniques work (SOCKS5 proxy, HTTP-disguised tunnel, TUN virtual adapter), when each applies, and what defenders can observe in traffic.
Insider Threat and Access Governance: The Hardest Attack Surface Is Inside
Insider threat is not simply "having a bad actor": it covers three distinct types — malicious, negligent, and coerced — each needing different countermeasures. This article breaks down their behavioural signatures and detection logic, and why access governance beats employee surveillance.
Multi-Agent Coordination: How Planners and Workers Avoid Fighting Each Other
Hand a task to several AI agents in parallel and three problems appear fast: duplicated work, information silos, and dependency mis-ordering. This article unpacks four design techniques from a public agentic system — role red lines, process-level information exchange, a shared todo list, and a single intent generator — and how they map onto any multi-agent workflow.
The Economics of Ransomware: Why It Persists and How to Make It Unprofitable
Ransomware is not a technical problem; it is a business. Understanding its cost structure, revenue model, and risk pricing is what allows effective defence. This article analyses the business through an economic lens and explains three levers for making attacks unprofitable.
Zero Trust Architecture: Not a Product Purchase but a Change of Assumption
In vendor decks, "zero trust" often becomes a product name; fundamentally it is only a change of assumption — no longer assuming the inside is safe. This article unpacks three core principles, four implementation layers, five common misconceptions, and why it is the same work as credential governance and insider threat management.
OfficeCLI Deep Dive: Control Word, Excel and PowerPoint from the Command Line
A hands-on guide to officecli v1.0.156: the L1/L2/L3 three-layer fallback model, resident mode, atomic batch edits, interactive watch editing, specialized skills and MCP integration, with real rendered screenshots.
OkHuman Architecture Deep Dive: A Minimalist Agent Framework Written in Go
A four-layer teardown of OkHuman: 15,861 lines of dependency-free Go, one bash meta-tool, nine decoupled plugins, and three design tradeoffs that explain every architectural choice.
How We Fixed OkHuman Semantic Search and Built It a Web Search Plugin
A root-cause chain from a lying health endpoint to a one-line --pooling last fix, plus a zero-dependency search plugin with three providers and automatic fallback.
Agent Interfaces: choosing between CLI, API, browser use, computer use, and sandboxes
Which door does an agent act through? Adapted from the MIT-licensed curriculum's Stage 8 concept chapter: eight recurring terms, a 'smallest interface first' decision table, the three tables to draw before letting an agent act (allowlist, actions, approval gates), and the four questions to ask of any browser/computer-use benchmark score. The bigger the door, the higher the risk — the first principle is to pick the smallest, most inspectable one.
After You Press Enter: From 0.03 s to 0.85 s, What Your Question Goes Through
One real API call dissects the full chain of a question: 35 ms to establish the connection, microseconds to tokenize, 474 ms for the model to read your whole question, 380 ms generating it token by token — 854 ms in total. Includes the complete reproducible measurement method, and one counterintuitive conclusion: 96% of the time is not in the network but inside the model.
A/B Test Automation and Analysis
How do you build an automated A/B testing pipeline — from hypothesis generation, experiment design, and traffic allocation to statistical-significance judgment and applying results? This article teaches you to build a data-driven optimization engine that puts every page element through scientific validation. The focus is on 'avoiding common statistical traps' and 'scaling experiments'.
AI Resume Screening and Candidate Scoring System
Automatically process high-volume resumes, use AI to match candidates precisely and make hiring bias easier to find, and cut screening time by 75%
Automated Code Review Assistant: From Manual Line-by-Line Checks to an AI-Assisted Quality Gatekeeper
Code review is time-consuming, edge cases slip through, and newcomers struggle to learn the team's conventions. This article wires GitHub Copilot, CodeRabbit, SonarQube, and an LLM node into an automated review pipeline: static analysis, security scanning, style checks, semantic understanding, and risk grading, so senior engineers handle only high-risk changes and the machine takes the repetitive work.
Smart Email Classification and Reply Template System
Drowning in email every day? This article teaches you how to use AI to classify incoming messages automatically, match the best reply template, and generate drafts for you to confirm and send quickly. The focus is on designing the 'confidence threshold' and the 'human review mechanism', so the system saves time without ever sending the wrong thing.
The Automated Employee-Onboarding Checklist: Structured Guidance from Day One to Day 90
The most expensive part of onboarding is not time, it is the things that slip through. This article uses Workday, BambooHR and AI to build an automated checklist running from offer signature to the end of probation, so every new hire gets a consistent experience and every task has an owner and a due date.
Performance Review Data Aggregation and Visualization: From Fragmented Feedback to Structured Insights
The most expensive thing about performance reviews is not the time — it is the bias. This article uses Lattice, 15Five, and AI together to build automatic aggregation of multi-source feedback, theme extraction, and trend visualization, so managers decide based on data instead of impressions.
The AI Coding Tools Map: Recognize the Form Factor First, Memorize Names Later
Cursor, Trae, Claude Code, Codex, OpenClaw, Hermes Agent, DeepSeek Harness, WorkBuddy and Doubao Work sorted into three categories by form factor: IDE-embedded, terminal agent, and orchestration platform. For each category: what it gives you, how much control and visibility it takes away, and how people actually mix them in practice — so you can decide which category your first tool should come from.
How to Cost AI: Subscriptions, Tokens, and Hidden Labor
AI adoption budgets are often severely underestimated because most people count only the subscription fees. This article teaches you to compute all three cost layers item by item — per-user monthly subscriptions, usage-billed API tokens, and the hidden labor of review and operations — and provides an estimation-sheet template you can fill in directly.
Three Levels of AI: Chat, Collaboration, and Agent
They are all called AI, yet their capabilities and cost structures differ enormously. This article breaks the tools on the market into three levels — chat, collaboration, and agent — and gives you a decision table to first clarify which level a need belongs to, then talk tool selection and budget, so you avoid spending money on the wrong tool.
A Framework for Choosing AI Tools: Five Questions That Eliminate 90% of Options
Facing a dizzying list of AI tools, trialing them one by one is the most expensive way to choose. This article gives you five screening questions — data sensitivity, error tolerance, integration needs, usage scale, and cost of failure — each mapped to which category of tool to pick, and ends with a decision-flow table that walks you through the whole judgment.
Git and GitHub for Beginners: Why AI Tools Keep Asking You to Clone
Why every AI programming tool's installation guide assumes you know Git. This article explains what repository, commit, branch, clone, fork, and pull request each mean, how they connect, and why non-programmers need to understand the first three.
How Machines Read Financials: From PDF to Structured Data
The first bottleneck of automating financial-statement analysis is that machines cannot read the PDF. This article breaks down the three paths — text-layer extraction, OCR, and layout analysis — explains why financial tables are especially hard to read and why human proofreading is mandatory after OCR, and gives the design concepts of structured output (JSON schema).
How the Web Talks: HTTP, Status Codes, Headers, and Invisible Protocols
Why API docs are full of status codes and headers. This article walks through a real request to clarify the four parts of HTTP — request, response, status code, header — then explains the difference between WebSocket and polling, why CORS blocks you, what a proxy does in the middle, and why SSH and private keys belong to the same family.
Is My Data Safe? What Actually Leaves Your Computer When You Use AI
'Is it safe?' is too blunt a question to ever get a useful answer. This article breaks it apart: the four kinds of things that actually leave your machine when you use cloud tools, why local tools are different, what the sentence 'we don't train on your data' does and does not promise, and three rules you can actually enforce. No legal advice included.
Hallucination Risk in Finance: The Three Most Expensive Mistakes
The cost of AI hallucination in financial scenarios is severely underestimated. This article dissects the three most expensive mistakes — fabricating numbers, confusing periods and companies, and filling gaps with inference — explains what causes each one, and gives four implementable lines of defense: mandatory citation, separate statistics, low-confidence abstention, and human review.
Three-Statement Articulation and Cash-Flow Quality Checks: Spotting the Book-Profit Trap
Profit is an opinion; cash is a fact. This article takes apart the reconciliation relations of the income statement, balance sheet, and cash-flow statement, gives check dimensions such as the cash conversion ratio, working capital, and capitalized spending, adds a 'red-flag signal → account to check → verification method' cheat sheet, and demonstrates writing these checks as an automatically executable, traceable rule engine.
What Is an AI Agent: From ChatGPT to AI That Acts on Its Own
Break down the essence of AI Agents with four components — LLM, context, tools, and loop — contrast chatbots with Agents, walk through daily, developer, and business examples plus real failure modes, and learn to judge whether your needs actually call for an Agent.
What Is Claude Code: The Reference Template for Terminal Agents
The flow of one real session explains what Claude Code is: a coding agent that lives in your terminal, reading files, editing files, running commands and iterating until the task is done. With installation methods, the CLAUDE.md memory mechanism, form factors beyond the terminal, the billing model, and the three kinds of people who should not start with it.
What Is Cursor: The AI Coding Tool That Lives in Your Editor
Three everyday actions — Tab completion, the Agent panel, and Plan Mode — explain what Cursor is, how it differs from a chatbot and from a terminal agent, which platforms it supports, what its billing model looks like, and the three kinds of people who should not start with Cursor.
What Is Doubao Work: The Office Agent That Operates a Virtual Desktop and Plugs Deeply Into Feishu
Doubao Work is ByteDance's AI office agent: a desktop client plus remote task assignment from your phone, an agent that actually operates inside a virtual desktop instead of only replying with text, deep integration with Feishu enterprise knowledge (meeting minutes, group chats, documents), and three modes — Fast, Expert and Task. With the billing model and the audiences it fits.
What OpenClaw Is: An Open-Source Assistant That Runs on Your Own Machine and Lives in Your Chat Apps
OpenClaw is an open-source AI assistant framework: the body runs on your own machine and appears through one Gateway in twenty-odd chat channels at once — WhatsApp, Telegram, Slack, Discord and more. One article covering its three-part architecture, the Skills ecosystem, its "models are swappable" philosophy, and the safety responsibilities you must read before using it.
What Is Trae: ByteDance's AI IDE, and the Open-Source Agent That Shares Its Name
Sorting out the three easily confused things called Trae: ByteDance's AI IDE (IDE mode and SOLO mode), the office assistant TraeWork, and the open-source trae-agent command-line framework on GitHub. With platform support, the billing model, and advice on picking between them.
Your First AI Task: How to Give Instructions It Gets Right
Why first tasks usually fail — not because the tool is weak, but because the instruction has no output, no input, and no destination. Three elements, three real examples (each with its vague version for contrast), and how to judge how much freedom to give.
DCF Assumptions and Sensitivity Analysis: Make the Valuation Challengeable
The value of a DCF is not in computing one number but in whether the assumptions behind it can be examined, challenged, and recomputed. Using a hypothetical model for teaching, this article demonstrates the assumptions register, the three-stage cash-flow structure, the composition facets and trade-offs of WACC, the risks of the two terminal-value methods, the design of single-factor and two-factor sensitivity matrices, and how to present a valuation as a defensible range rather than a fragile single point.
Installing Claude Code: A Getting-Started Path for the Terminal Agent
Anthropic's official terminal coding agent Claude Code, from system requirements, four install methods (native install script, Homebrew, WinGet, npm), the verification command, and browser sign-in, to permission modes and your first change. Every step spells out the output you should see and the real error messages listed in the official docs.
Installing the OpenAI Codex CLI: Sign-In, Permissions, and Your First Task
OpenAI's terminal coding agent Codex CLI, from system requirements, three official installation methods, ChatGPT-account sign-in and API-key sign-in, to the three /permissions presets and non-interactive mode. Every step spells out the output you should see and flags which commands I could not verify against the official documentation.
Announcement and Research-Report Monitoring Agent: Alert Me Only When Something Actually Changes
How to design a monitoring agent that notifies you only on substantive change: three classes of monitoring target, fetching and deduplication strategy, the three gates that filter noise, where summary ends and impact assessment begins, tiered delivery, and the balance between never missing something material and never causing alert fatigue.
Terminal, Shell, CLI: The Difference Between the Three Terms, and Ten Survival Commands
The first line of most AI-tool install guides is 'open a Terminal,' but nobody explains how Terminal, Shell, and CLI actually differ. This article separates the three terms, gives you ten commands that really are enough, shows you how to read error messages, and covers the habit that costs beginners the most.
300-Page Annual Report to Structured JSON: The Three Statements and Segment Data
Building on the PaddleOCR field practice, this pushes 'reading out the text' to 'usable data': locating the financial-statement pages, handling cross-page tables, field mapping and unit normalization for the three statements and segment information, JSON schema design, and a complete pipeline and checklist for automated proofreading via reconciliation relationships.
Building Organizational AI Capability: From Individual Heroes to Repeatable Processes
A few people in the company being great at using AI does not mean the organization has AI capability. This article is about turning personal experience into organizational assets: capability inventory, seed users and a three-stage rollout path, consolidating personal prompts into a skill library and a template library, tiered training and documentation, and the dimensions and common failure modes for measuring whether it really landed.
Build Your First AI Workflow Without Writing Code
You can build a usable AI application with your own hands even if you cannot write code. Using a customer-support knowledge-base Q&A as the scenario and no-code platforms like Dify, Coze, and n8n, this article walks you through six steps — prepare the data, build the knowledge base, set the prompt, test, launch, monitor — each concrete enough to follow, without depending on interface details that go stale.
A Multi-Agent Investment-Research Pipeline: Collect, Analyze, Report in Three Stages
A single Agent cannot finish an investment-research task: the context blows up, attention scatters, and errors cross-contaminate. This article gives the complete design for a three-stage collect / analyze / report division of labor — each stage's responsibilities and negative list, the structured JSON contract between stages, the write-scope partition, failure isolation and contamination detection, where to place the verification gates, and the cost-and-latency trade-off framework, with an architecture diagram and a failure-mode list.
Build a Working Prototype Website in a Weekend: From Idea to Shareable Link
To validate a product idea you do not have to wait for engineering capacity. Using AI generation tools like v0, Lovable, and Bolt, this article walks you through converging the idea into a one-page spec, producing the first version, wiring a database and login, deploying to a shareable link, and putting it in front of real users — and spells out which requirements mean you should stop and find people instead.
What Is Vibe Coding: Fast, but Fragile
Vibe coding turns plain-language prompts into a working prototype in minutes. So why does it collapse in production? Three failure modes and five guardrails, so you can move fast without breaking things.
A Source-Level Architectural Dissection of DeepSeek Harness: How an Everything-Is-a-Plugin Agent Framework Is Built
Dissecting the complete architecture of DeepSeek Harness (dsh) from 219 workspace packages and 450k lines of TypeScript: the Cordis plugin foundation, Event Sourcing session log, the three roles of capability seams, a single home for policy + fail-closed approvals, four layers of context engineering defense, and an equally plugin-based frontend. Includes a porting assessment for in-house Agent infrastructure.
YC Open-Sources QM: Source-Level Teardown of the 'Whole-Company Agent Operating System' That Hit 12K Stars in 7 Days, with More Security Code Than Model Loops
On July 31, 2026, Y Combinator open-sourced its internal Agent system QM (Quartermaster). Clone test: 241K lines of TypeScript, 379 test files, 4 interchangeable harnesses (Pi/OpenCode/Codex/Claude Code), egress proxy re-resolves DNS for each request, three-person gate 'a wall, not a hole', npm supply chain 7-day cooldown period: complete source-level dissection
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.
Three Departments and Six Ministries vs Loop Engineering: A Technical Dissection of Institutional Process Enforcement
A comparison of how the two GitHub 'Three Departments and Six Ministries' systems achieve process enforcement using a State Machine, Permission Matrix, Review Gate, and 4-layer Gateway, plus the implications for refactoring our Loop Engineering system.
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.
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 Curator: When Your Agent Has 125 Skills but Only 64 Survived
A practical record of skill curation: the fully automated process from 'never adapt after download' to '99.2% health'. Six-stage full lifecycle management, three-layer diagnostics, six-language auto-injection, and scenario generation.
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.
PaddleOCR in Practice: Extracting Hong Kong Stock Annual Report Financial Data in 83 Seconds
A hands-on test of Baidu PaddleOCR, the open source document AI engine with 81K Stars. PP-OCRv5 with 96.5% accuracy extracted 102 text regions from a Hong Kong stock annual report P&L page, discovered a KMeans clustering bug (n_clusters=0) in the PP-StructureV3 Transformers engine, built a custom DBSCAN row and column clustering parser, and completed automatic structuring of three financial statements end to end in 83 seconds.
A Complete Tutorial for Matt Pocock Skills: The 115K Star AI Coding Engineering Discipline System, a Full Walkthrough of 15 Skills from Installation to Practice
mattpocock/skills: 35 Agent Skills released by TypeScript legend Matt Pocock from his own .claude directory, with 115K+ Stars, 2.7M+ installs, and 60K newsletter subscribers. This is not vibe coding; it is an AI coding discipline system condensing decades of software engineering experience.
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.
Triple Strike: OMP Error #15, PEP 668, and Python Dependency Hell
The MemoryHub daemon crashed and restarted repeatedly over three hours: ModuleNotFoundError → OMP Error #15 → silent crash loop. An in-depth analysis of the faiss + torch dual libomp conflict, the Homebrew PEP 668 block, and the structural fragility of the Python scientific computing ecosystem. Includes a complete prevention plan.
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.
Polyglot Persistence in Practice: A Full-Pipeline Evaluation of a Ten-Database Memory System
From BGE-m3 vector embeddings and ten-way synchronized writes to a comparison of search quality and speed across nine backends: a complete stress test of an AI memory system. Qdrant is the king of semantics, FAISS the king of speed, and Neo4j the king of relationships.
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.
Complete Comparison of Top 10 Vector and Full-Text Databases: MemoryHub Backend Storage Selection Guide
An in-depth review of ten databases, including Qdrant, Chroma, LanceDB, SQLite-vec, FAISS, Redis, PostgreSQL (pgvector), Elasticsearch, MongoDB Atlas, and Neo4j, in AI memory system scenarios, covering performance, deployment difficulty, resource consumption, and use cases.
Complete Guide to 14 Financial Services AI Skills: From Deal Sourcing and M&A Models to Catalyst Calendars
A complete introduction to the 14 OpenClaw financial skills derived from Anthropic Claude for Financial Services, covering three major areas: investment banking (7), private equity (6), and equity research (1), including each skill's command meanings, use cases, and hands-on tutorials.
May 2026 LLM API Pricing Landscape: Complete Comparison of DeepSeek, Qwen, GLM, Kimi, MiniMax, and Doubao
Cross-verified through multiple search engines including Exa, Tavily, and Brave, this compiles per-million-token pricing for DeepSeek V4, Qwen3.6-plus, GLM-5, Kimi K2.5, MiniMax M2.7, Volcano Engine Doubao Seed 2.0, and Claude/GPT, with a Sub2API cost setup guide.
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.
A Deep Dive into Search Channels: The Underlying Principles and Division of Labor Strategies of DuckDuckGo, Tavily, and Brave
A complete technical primer on three search channels: their respective indexing mechanisms, ranking algorithms, return formats, and cost models, plus how to divide work precisely within AI Agent workflows.
May 2026 W2 AI Roundup: Skills Ecosystem Explodes, Terminal Open-Source Wave, Enterprise AI Arms Race
A full record of this week's key events in the AI world: Matt Pocock open-sources 17 Agent skills and detonates GitHub, Warp terminal goes open source, DeepClaude cuts costs 17x, and OpenAI $10B vs Anthropic $1.5B face off on the same day.
In-Depth Analysis of Addy Osmani's agent-skills Open Source Project
A complete introduction to Google Chrome Engineering Director Addy Osmani's agent-skills project: the design philosophy behind 21 AI-assisted development skills, real-world applications, and how it inspired the upgrade of our slash command system.
Agent Framework Capability Matrix: Hermes → OpenClaw → Claude Code Full Capability Overview
One matrix to understand the full capability distribution of the three-layer Agent framework: search API, LLM backend, MCP protocol, skill system, role division, communication channels.
AI Security and Red Teaming: Prompt Injection, Jailbreak Defense
A full landscape of security threats to AI systems: Prompt Injection, Jailbreak, Data Poisoning, and practical defense strategies and red team testing methods.
MCP (Model Context Protocol) Ecosystem Explained: The Three Core Primitives of Tool, Resource, and Prompt
A complete guide to Anthropic's MCP protocol: the design principles behind Tools, Resources, and Prompt Templates, practical configuration, and Claude Code integration.
Brave Search API Configuration and Tavily Scenario Comparison
Configuration of Brave Search API in a three-tier Agent framework, plus a scenario selection guide for Tavily vs Brave.
Comparison of Other Inference Frameworks: A Complete Guide to llama.cpp, MLX, and ExLlamaV2
An in-depth comparison of three major local LLM inference frameworks: llama.cpp (GGUF), Apple MLX, and ExLlamaV2, covering installation, performance, and use cases.
LM Studio Installation and Usage Guide: GUI Local LLM Management
Complete LM Studio guide: download, manage, and run local LLMs on Mac through a graphical interface, supporting GGUF and MLX formats and providing an OpenAI-compatible API.
Three-Engine Search Strategy: DuckDuckGo + Tavily + Brave Complementary Configuration Guide
Role division, scenario selection, and cost comparison of three search engines in an Agent framework. DuckDuckGo is free and fast, Tavily provides AI summaries, Brave covers news and communities comprehensively.
Cross-layer Configuration of Tavily Search API in Agent Frameworks
Configuration methods and usage scenarios for the Tavily AI Search API across three frameworks: Hermes Agent, OpenClaw, and Claude Code.
Web Fetch and Web Scraping in Agent Applications
Configuration and scenario guide for using Web Fetch / Puppeteer / fetch_url to retrieve web content in a three-tier Agent framework.
The Role and Configuration of the MCP Protocol in a Three-Layer Agent Framework
The respective roles and configuration of the Model Context Protocol in Hermes Agent (not supported), OpenClaw (orchestration layer), and Claude Code (execution layer).
Three-Layer Framework LLM API Configuration Summary: DS V4 Pro, Qwen 3.6, oMLX Local
A complete guide to configuring DeepSeek V4 Pro, Qwen 3.6-Plus, and oMLX local Qwen3 Coder Next across Hermes Agent, OpenClaw, and Claude Code.
HuggingFace Community Guide: A Complete Walkthrough of Models, Datasets, and Spaces
A complete guide to using the HuggingFace platform: how to search for models, use Datasets, deploy Spaces apps, and participate in community discussions.
Agentic Research Weekly AI Briefing: May 2026 W2
This week's AI highlights: GitHub Skills ecosystem surge, Kaltura open-sources Agent Skills, DeepClaude cuts costs by 17x, Warp terminal goes open source, GitHub Spec-Kit released. Three-engine (DuckDuckGo+Tavily+Brave) cross-validation.
Three-Layer Agent Collaboration Framework: Hermes → OpenClaw → Claude Code Architecture Explained
An in-depth analysis of a three-layer AI Agent collaboration architecture built on Hermes Agent, OpenClaw, and Claude Code: the responsibilities of each layer, the communication mechanism, and why it is designed this way.
Open Design Gamified App Skills Guide
Multi-frame gamified mobile prototype: three iPhones side by side showing the cover, quest list and quest details, with an XP level system, bottom Tab Bar and pastel quest tiles.