Digital IDs, surveillance capitalism, deplatforming, internet shutdowns, social credit systems. The centralized internet is becoming a liability. This guide shows you how to build a completely self-contained, portable, internet-independent network for your family, community, or organization.

Goals:

• ✅ No internet dependency
• ✅ Portable and self-contained
• ✅ Open source everything

I spent hours fighting filesystem corruption on my Proxmox media server. The root cause? LVM thin pool with only 20MB of physical free space. Here's what went wrong and how to avoid it.

• Hardware: Proxmox VE 8.4.1 with Seagate SlimBUP 931GB external drive
• Storage: LVM thin pool (SlimBUP) with containers for Plex and media services

A complete system for building, deploying, and measuring Claude skills using real captured traffic.

This stack enables evaluation-driven development for Claude skills:

1. Build skills from documentation (Skill Seeker)
2. Expose skills via MCP middleware (Director)
3. Capture real Claude Code API traffic (CC Trace)

A comprehensive breakdown of @0xFramer's professional AI animation pipeline for creating comedy sitcom animations with consistent characters, voices, and arbitrary lengths.

This workflow enables creation of full animated episodes using AI tools, combining image generation, video animation, audio synthesis, editing, and post-processing. Based on 3+ years of AI cartoon production experience and the foundation of "Cartoon Hero 2.0" course.

Example Metrics:

• 51-second animated clip: 10 hours of work

An OpenAI-compatible LLM proxy with telemetry, usage tracking, and multi-provider routing for Elixir/Phoenix applications.

Package name: rec_llm_gateway

Purpose: Single OpenAI-compatible endpoint that:

• Accepts standard OpenAI Chat Completions format

Use case: publish the JTBD DSPy agent to Modaic Hub, confirm the revision landed, and sanity-check the hosted build from a clean cache. Format matches our gist shipments (narrative + copy/paste snippets).

• Python 3.10+ with the repo installed editable.
• Export the Modaic + LLM secrets (keep them out of git):

This gist captures the full story of how we wired Early Experience (EE) and Agentic Context Engineering (ACE) together for two very different domains:

• SWE-bench Lite – developer workflows where we validate patches by applying them and running the real test commands.
• MagicBrush – instruction-guided image edits where we clamp outputs via perceptual metrics (MSE/SSIM).

It is organized in the same way we’ve been running the system: narrative ➜ technical ➜ exploratory ➜ explanatory.

1. Seed – Start with expert demonstrations (JSONL). Feed them into the Early Experience (EE) pipeline.
2. Explore & Reflect – Roll out trajectories, compare alternatives, and generate self-reflections.
3. Guard – Apply deterministic guardrails (tests, formulas, MSE/SSIM) to clamp outputs to canonical results.
4. Distill – ACE ingests the guardrail-corrected lessons and appends delta updates to the playbook.
5. Self-Improve – The playbook informs the next loop; the agent keeps teaching itself.

Turning microscopic heuristic checks into reusable tactics for Agentic Context Engineering.

• Domain heuristics (e.g., ±0.4 % drift, missing %) act as precision sensors for the Generator → Reflector → Curator loop.
• The Reflector distills the failure into a lesson, the Curator stores a typed delta, and the Runtime Adapter replays it immediately.
• Result: the agent cannot repeat the mistake—mirroring the +8.6 % gains ACE reports on FiNER/XBRL benchmarks.

Turning microscopic heuristic checks into reusable tactics for Agentic Context Engineering.

• Domain heuristics (e.g., ±0.4 % drift, missing %) act as precision sensors for the Generator → Reflector → Curator loop.
• The Reflector distills the failure into a lesson, the Curator stores a typed delta, and the Runtime Adapter replays it immediately.
• Result: the agent cannot repeat the mistake—mirroring the +8.6 % gains ACE reports on FiNER/XBRL benchmarks.