Articles and tutorials

EchoTime

Time-series similarity, method selection and worked examples.

Articles and examples

• Time-series similarity: choosing between DTW, matrix profiles and learned representationsEnglish · Article
• 时序相似性方法盘点：DTW、矩阵轮廓与学习表示该怎么选中文 · Article
• What Does Similar Mean for a Time Series? A Mechanism-Level Tour of Distances, Alignments and RepresentationsEnglish · Article
• BTC vs VIX in 2024Case study
• AI attention breakout analogsCase study
• Southwest heatwave city temperaturesCase study
• Similarity labInteractive method comparisons

DeTime

Decomposition research, Python workflows, quant research and the Hot Trend Lab.

Research articles

• Time-Series Decomposition in 2026: Algorithms, Tokens, and LossesResearch guide
• STL, SSA, Wavelets, EMD, or VMD? Choosing Python Decomposition ToolsResearch guide

Tutorials and notebook walkthroughs

• ML WorkflowsFeatures and forecasting workflows
• Tutorial: CLI and profilingTutorial
• arXiv Research PulseTutorial
• Hot Trend Lab Source ProtocolTutorial
• Hot Trend Lab OverviewTutorial
• arXiv Category PulseTutorial
• arXiv Agent and Coding-Agent Research PulseTutorial
• Hugging Face Open-Model PulseTutorial
• GitHub AI-Agent Star VelocityTutorial
• Wikimedia Attention and Hype DecayTutorial
• Crypto and Stablecoin Liquidity PulseTutorial
• AI Infrastructure Market PulseTutorial
• Hot Trend Lab NotebooksTutorial series
• Open Models and Developer AttentionTutorial
• Hot Trend Lab Publishing ProtocolTutorial
• Public Attention, Markets, and CryptoTutorial
• Hot Trend Lab: Real Data Trend and Cycle DecompositionTutorial
• Multivariate workflowsTutorial
• ProfilingTutorial
• Backtesting Frameworks for the Quant Trading ColumnTutorial
• Tutorial 01: Real Data and DeTime Feature FactoryTutorial
• DeTime Method-Specific Strategy ExpansionTutorial
• Tutorial 00 - Decomposition-first quant trading roadmapTutorial
• Strategy Lab 01 - Trend-following strategyTutorial
• Tutorial 01 - Real market data and the DeTime feature factoryTutorial
• Tutorial 02 - Moving averages and MACD through decompositionTutorial
• Strategy Lab 02 - Oscillation / residual-reversion strategyTutorial
• Strategy Expansion 03 - Method-specific strategy variantsTutorial
• Tutorial 03 - Residual mean reversion: RSI, Bollinger and residual bandsTutorial
• Strategy Expansion 04 - Component pair trading and cointegrationTutorial
• Tutorial 04 - Donchian and Turtle breakout with DeTime volume confirmationTutorial
• Tutorial 05 - Pair spread decomposition and stat-arbTutorial
• Tutorial 06 - Cross-sectional rotation and portfolio constructionTutorial
• Native SSA high-return / low-drawdown strategy tutorialTutorial
• Quant Trading Tutorial NotebooksTutorial series
• Strategy Map: From Classic Indicators to Decomposition-Aware SignalsTutorial
• DeTime Quant Strategy Lab: Two Concrete Strategy FamiliesTutorial
• Walk-Forward Validation and Audit ProtocolTutorial
• Decomposition-First Quant Trading Tutorials with DeTimeTutorial
• Univariate workflowsTutorial
• Visual tutorial: compare methods on the same seriesTutorial
• Visual tutorial: multivariate decompositionTutorial
• Visual tutorial: one univariate decompositionTutorial

TOMC Memory

Context compression, task memory, prompt caching and evaluation.

Practical guides

• Context Compression for LLM APIs with TOMCPractical guide
• Reuse Project Notes Across Codex and Claude Code with TOMCPractical guide
• How to Measure Context Compression and AI Agent MemoryEvaluation guide

Technical articles and research

• API request optimization and native compactionTechnical article
• API 请求优化与原生压缩的配合中文
• Prompt caching and TOMC — 2026-10-17Technical article
• 提示词缓存与 TOMC — 2026-10-17中文
• TOMC and other memory toolsTechnical article
• Paper readers and coding-agent hostsTechnical article
• 论文 Reader 与编程 Agent 宿主中文
• Windows Codex: context budgets and actual input — 2026-10-05Technical article
• Windows Codex：上下文预算与实际输入 — 2026-10-05中文
• Method semanticsTechnical article
• Current paper resultsTechnical article
• Documented model context limits — 2026-10-12Technical article

Documentation and further guides

• Python APIDocumentation
• ArchitectureDocumentation
• Install TOMC in your assistantDocumentation
• 把 TOMC 装进常用助手中文
• Use TOMC with your assistantDocumentation
• Results and limitationsDocumentation
• Configure TOMC in Cursor or TRAEDocumentation
• 在 Cursor 或 TRAE 中配置 TOMC中文
• Data and licensingDocumentation
• TOMC Memory for DeepSeek HarnessDocumentation
• DeepSeek Harness integration validation — 2026-10-29Documentation
• 在 DeepSeek Harness 中使用 TOMC Memory中文
• TOMC desktop companion (beta)Documentation
• Evidence and claims ledgerDocumentation
• 证据与数字台账中文
• Hugging Face deployment settingsDocumentation
• Legacy compatibility extensionDocumentation
• Linux installation and feedbackDocumentation
• TOMC on macOSDocumentation
• Paper and demo scopeDocumentation
• Platform checksDocumentation
• 各平台检查记录中文
• QuickstartDocumentation
• TOMC Memory 0.3.0 release notesDocumentation
• TOMC Memory 0.3.0 更新说明中文
• TOMC Memory 0.3.1 release notesDocumentation
• TOMC Memory 0.3.1 更新说明中文
• ReproducibilityDocumentation
• This week's cardDocumentation
• 本周战报中文
• Repository traffic and StarsDocumentation
• TOMC usage tipsDocumentation
• TOMC 使用技巧中文
• Validation recordDocumentation
• Windows installation feedbackDocumentation
• Windows 安装反馈中文
• Configure TOMC in WorkBuddyDocumentation
• 在 WorkBuddy 中配置 TOMC中文