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Glossary8
Open the full glossary- Cost per token (how API pricing actually works)APIs price input tokens and output tokens separately, output is usually dearer, and cached input is often discounted. An agent's bill is frightening because each turn it resends a longer and longer context.
- InferenceRunning an already-trained model to produce output — the counterpart of training, and the entirety of the computation that happens when you use a model.
- DistillationDistillation lets a smaller student model learn from a larger teacher, usually to shrink size or cost while keeping ability.
- RegressionA regression is a decline beyond threshold versus the baseline, in quality, cost, safety, or reliability; check it before shipping.
- Local vs cloud (run it yourself or call an API)Whether the model runs on your own machine (local) or you call someone else's over the network (cloud API). The real difference is not "free vs paid" but several axes: privacy, cost at volume, latency, capability ceiling and maintenance burden.
- Batching (serving many requests together)Running several requests together to amortize the cost of hauling weights into the compute units. It raises the machine's total throughput but can lengthen any single request's latency.
- KV cacheStoring each processed token's Key and Value so the next token need not recompute them. The cost: this memory grows with sequence length, which is exactly why long context is so RAM-hungry.
- Token Cost / Inference CostToken cost / inference cost is input usage × input price + output usage × output price.
Found 21 articles
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.
ArtCraft Deep Dive: The AI-Generation App (macOS) and Where It Fits
A hands-on breakdown of ArtCraft v0.41.0: the desktop app's data model, task store, cost and account, and how it hands off to DesignCraft/OfficeCLI via files — with a table of verified facts.
Build Your First AI Agent in Seven Steps: from environment to a minimal production pass
Adapted from the MIT-licensed awesome-agentic-ai-zh integrated tutorial: one 'paper assistant' project wires up environment setup, your first LLM call, the four-part prompt, tool use, the agent loop with reflection, RAG memory, evals with observability, and the smallest interface. Every step ships minimal runnable code and safety rails; the local Ollama path costs 0 in API fees.
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.
How AI Pricing Works: Why One Month Could Cost Ten Times More
What a token is, why long conversations get more expensive, what free tiers usually limit, how to choose between subscriptions and pay-as-you-go, and five concrete ways to keep your first month's cost near zero. No specific prices listed — this site only writes dollar amounts after manual verification.
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.
Build or Buy: The Decision Tree for an Enterprise Agent Platform
Should we build our own Agent platform or buy one off the shelf? This article builds a decision tree from six decision dimensions — requirement uniqueness, data sensitivity, engineering capacity, maintenance cost, vendor lock-in, and compliance requirements — and gives judgment tables for the build, buy, and hybrid routes plus a total-cost-of-ownership comparison framework.
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.
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.
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.
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.
CodeGraph Deep Technical Breakdown: How to Save AI Coding Agents 35% in Costs and Cut Tool Calls by 70%
Complete technical analysis of the GitHub Trending #1 project CodeGraph: the architecture design of pre-indexed code knowledge graphs, the tree-sitter AST parsing pipeline, the MCP tool layer, performance comparison data against native Agents, and the real impact on Claude Code/Hermes Agent.
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.
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.
LLM Evaluation and Selection Guide: How to Scientifically Choose Models in 2026
A systematic LLM selection framework: Benchmark interpretation, real-world scenario testing methods, cost/performance matrix, DeepSeek V4 vs Qwen vs Llama comparison.
DeepSeek V4 Flash / V4 Pro API Best Practices and Performance Comparison
Complete guide to the DeepSeek V4 series models: comparing performance, pricing, and use cases of V4 Flash vs V4 Pro, with API call examples and cost estimates.
LiteLLM Proxy Multi Model Routing in Practice
Use LiteLLM Proxy to centrally manage multiple LLM backends such as DeepSeek, ModelStudio, Ollama, and more: load balancing, cost tracking, fallback configuration.
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.
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.