I build automation CLIs and AI-agent tooling — sharp single-purpose command-line tools, plus the production patterns (approval gates, schema validation, audit logs) that let agents touch real systems safely.
If you script things, run agents, or want CLIs that do one job well with JSON output you can pipe — this profile is for you. Follow for new CLIs and production-AI patterns as they ship.
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- capcut-cli — CLI to edit CapCut / JianYing drafts (subtitles, timing, speed, templates, cut long-form → shorts). No API; reads
draft_content.jsondirectly - draftcat — governed AI pipelines for service businesses: deterministic-first, operator-approved, single Go binary (MIT)
- agentic-task-system — your task manager is the best agent memory you're not using: hybrid retrieval (RRF) over TickTick / Obsidian
- agent-approval-gate —
draft → validate → approve → dispatch → auditpattern with JSON schemas, n8n workflow, and email-approval example - browserground — local UI-grounding specialist for hybrid AI agents: Qwen3-VL-2B LoRA, screenshot + target → strict JSON bbox. Drop-in for Claude Code, Codex, browser-use
Production AI Automation Notes — a running set of repos and gists on:
- approval gates for AI agents
- MCP server security (permissions, tenants, audit logs)
- Claude Code policies for company repos
- n8n workflows with human approval
- audit-log schemas
Follow if you're building agents that need to work outside demos.
| Gist | Topic |
|---|---|
| Claude Code persistent memory between sessions: task manager as agent memory via MCP (PAAN #3) | claude mcp add / TickTick / Obsidian / PreCompact alternative / no vector DB (June 2026) |
| TickTick MCP server for Claude Code & Claude Desktop | ticktick mcp / OAuth / exceed_query_limit / hybrid RRF retrieval (June 2026) |
| Obsidian vault as agent memory for Claude Code | obsidian mcp / ATS_OBSIDIAN_VAULT / headless, no REST API plugin (June 2026) |
| Agent memory without a vector database: RRF in 15 lines of JS | reciprocal rank fusion / k=60 / hit@1 bench numbers (June 2026) |
| AI agents that can't pick their own next action: deterministic step pipelines in Go (PAAN #10) | ai agents / ai automation / deterministic / ai · approval steps / schema-validated / autonomous agent / Go (June 2026) |
| Project | PR | What |
|---|---|---|
| Tencent/WeKnora | #835 Merged | Parallel tool calling support |
| steveyegge/beads | #2884 Merged | Multi-project support, Notion sync, backup/restore |
| e2b-dev/infra | #2273 Merged | Local dev docs: prerequisites, verification steps, troubleshooting |
| pacifio/cersei | #10 Merged | Native Google Gemini provider + Cohere & SambaNova support |
Stack: Python, Go, TypeScript, Node.js, Kubernetes, Linux, systemd, vector databases, LLM APIs.
Website: renezander.com
A gist series on production-grade patterns for AI pipelines. Each entry covers one pattern with copy-pasteable code.
| # | Topic | Pattern |
|---|---|---|
| #1 | Agent Approval Gates | Schema-validated proposals, human review, deterministic dispatch |
| #2 | Token Budgets | Per-step, per-pipeline, per-day enforcement |
| #3 | Agentic Knowledge Base | Karpathy-style LLM wiki with pluggable adapters |
| #4 | CapCut / JianYing from an LLM agent | Deterministic JSON command boundary |
| #5 | SQLite Dedup + Crash Safety | WAL mode, seen_items, audit log |
| #6 | Prompt-Injection Defense | Input sanitization, schema validation |
| #7 | PDF Cite Verification | Auditable LLM extraction with per-fragment bounding boxes |
| #10 | Deterministic Step Pipelines | Fixed typed steps; the LLM never picks the next action |
Reference implementation for entries #1, #2, #5, #6, #7, #9, #10: draftcat (Go, MIT).