#append-only log
29 related articles
·2 min
Apache Maka: An Auditable, Local-First AI Agent Workspace
Deep dive into Apache Maka, a local-first AI Agent workspace built on append-only logs. Explore its architecture, privacy-first design, and unique value for production AI Agent deployment.
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·3 min
DeepSeek Harness Explained: An Agent Runtime Where Everything Is a Plugin
Deep dive into DeepSeek Harness (DSH): its Agent=Model+Harness formula, Cordis plugin system, four runtime modes, and Trajectory traceability for modular Agent development.
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·3 min
GitHub Daily · August 22: The Rise of Agent Meta-Frameworks and Local-First AI Workbenches
GitHub Trending Aug 22: Agent meta-framework ruflo leads with 68K+ stars, Apache Maka pioneers local-first Agent governance, signaling AI's shift to orchestration and observability.
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·3 min
DeepSeek Harness Deep Dive: The 'Everything Is a Plugin' Paradigm for AI Programming
DeepSeek's open-source Harness framework, built on Cordis Kernel, uses an 'everything is a plugin' design for ultimate customization. Supports multi-model integration, built-in traceability, and Creator mode self-extension.
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·4 min
DeepSeek-Harness Open-Source Framework Explained: An Agent Architecture Where Everything Is a Plugin
Deep dive into DeepSeek-Harness open-source agent framework: from Codis kernel to plugin architecture, four preset modes, transparent session logs, and its open-source ecosystem strategy.
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·2 min
Memory Revocation Mechanisms for AI Agents: Why Agents Need an 'Undo' Button
Exploring why AI Agent memory systems need an undo function. Analyzing risks of irreversible memory from error accumulation to memory poisoning attacks and privacy compliance, plus technical solutions.
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·3 min
The Overlooked "Boring" Infrastructure Behind Production-Grade AI Agents
Exploring the core challenges of AI Agents moving from demo to production: idempotency, approval states, retries, action ledgers, audit tables, and other critical infrastructure design patterns.
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·3 min
Prompt Caching Explained: The Key Technology for Saving Money with AI Agents
Deep dive into Prompt Caching: how it works, why AI Agents repeatedly send tokens causing costs to skyrocket, and best practices to slash LLM costs by up to 90%.
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·4 min
Tau Open-Source Coding Framework Explained: A Python Port of Pi
Deep dive into the Tau open-source coding framework: tree-based session management, JSONL persistence, skills system, and custom prompts. Learn how this Python port of Pi delivers a new AI coding agent experience.
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·3 min
Beyond OTel + Logs + Databases: The Three Missing Pieces of AI Agent Auditing
Beyond OpenTelemetry tracing, log archiving, and database snapshots, AI Agent auditing still has three structural gaps: decision reasoning trails, model version snapshots, and forensic-grade retention of unstructured artifacts.
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·3 min
Supabase in Production: A Deep Dive into Cron and Queue Reliability Issues
Deep analysis of Supabase pg_cron and pgmq reliability issues in production, including task loss, execution uncertainty, and observability gaps, with practical architecture optimization advice.
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·3 min
Supabase in Production: A Deep Dive into Cron and Queue Reliability Issues
Deep analysis of Supabase production reliability issues with pg_cron and pgmq, covering task loss, execution uncertainty, and observability gaps, with practical architecture optimization advice.
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·4 min
SQLite in Production: A Deep Dive into WAL Mode, Concurrency Control, and VFS Optimization
A deep dive into three core SQLite production optimizations: WAL mode for read/write concurrency, single-writer concurrency model best practices, and VFS layer tuning with mmap and custom locking.
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·4 min
GPT-5.6 Value Maxing Practical Guide: From Token Stacking to Output Optimization Methodology
Deep dive into OpenAI GPT-5.6 Value Maxing strategies covering Sol/Terra/Luna model selection, KV cache optimization, Prompt compression, and programmatic tool calling to help developers achieve more output with fewer Tokens.
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·3 min
Using AI Agents to Write Research Experiment Logs: A Complete Methodology and Workflow
A detailed guide on using AI Agents to build Research Logs for scientific experiments, covering skeleton structure, daily workflows, pre-experiment thinking standards, code change tracking, and Agent-researcher division of labor.
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·3 min
Using AI Agents to Write Research Experiment Logs: A Complete Methodology and Workflow
Learn how to use AI Agents to build a Research Log for scientific experiments, covering structure, daily workflows, pre-experiment thinking, code change tracking, and human-AI division of labor.
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·2 min
LLM Agent Long-Term Memory Security: A Six-Stage Attack Surface and Defense Framework
Deep analysis of LLM agent long-term memory security threats, covering persistence, statefulness, and propagation of memory poisoning, with a six-stage lifecycle defense framework.
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·4 min
Agent Loop in Practice: A Guide to Building Self-Improving Automation with Guardrails
A deep dive into building verifiable, self-evolving Agent automation loops with Claude Code and Codex — covering Loop Contracts, four trigger types, three-phase execution architecture, and Evolve Loops.
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Deep Dives
Deep Dive into AI Agent Skill Design: …
·3 min
Deep Dive into AI Agent Skill Design: Engineering Practices from Anthropic and Perplexity
A deep dive into Skill design philosophy from Anthropic's Claude Code team and Perplexity's Agent team, covering the Tax Test, Gotchas Flywheel, progressive disclosure, and Eval-First practices for building high-quality AI Agent skill systems.
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·3 min
AI System Retroactive Changes Trigger a Trust Crisis: The Core Challenges of Transparency and Auditability
When AI systems silently modify historical data without users' knowledge, trust begins to unravel. This article examines retroactive changes and the path to trustworthy automation.
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