Jung von Matt IMPACT vs Obsidian

Five Experts Discuss Signal Value in Video Planning

This article is an expert roundtable held ahead of the ADZINE CONNECT VIDEO 2026 conference. Five industry professionals from agencies and adtech companies discuss the optimal use of signals in video advertising planning. They debate the value of various data sources such as audience data, context, attention, purchase intent, and platform data, questioning whether more signals automatically lead to better decisions. Key themes include the importance of signals that drive concrete planning decisions, the risk of signal noise, the need for testing to validate signal contribution, and the challenges of measuring success in CTV. The experts also address the economic costs of signals, the issue of platform-specific data lock-in, and the need for a common language across platforms.

• ADZINE CONNECT VIDEO 2026 takes place on November 12, 2026 in Hamburg.
• Five experts from agencies and adtech companies share their views on video advertising signals.
• Alexander Kraft (Jung von Matt Impact) argues a signal must influence planning decisions.

Jung von Matt IMPACT ist offizieller TikTok Marketing Partner

TikTok hat Jung von Matt IMPACT als Marketing Partner ausgezeichnet. Damit sind wir im TikTok Partner Directory gelistet und offiziell als Agentur für TikTok Strategie, Content und Ads anerkannt.

OMR Reviews 2026: Jung von Matt IMPACT auf Platz 1

Jung von Matt IMPACT ist auf OMR Reviews Nummer 1 in Online Marketing, Social Media und Influencer Marketing.

Robust Nightly LLM Log Drain Survives Sleep and Races

A technical walkthrough of a macOS shell script (vault-auto-ingest.sh) that nightly ingests Claude conversation logs into a local Obsidian vault, commits them to a private Git repo, and generates a daily brief. The author describes three real failure modes that drove the final design: sleep-related freezes when the Mac sleeps, double-execution races between scheduled and manual runs (noted on 2026-06-10), and a service-side timeout/outage that caused uniform slot failures on 2026-06-13. The resilient design uses four launchd slots (4:55, 8:20, 10:45, 12:15), caffeinate -i -s, atomic lockdir via mkdir, idempotent step markers for partial retry, explicit timeouts with --kill-after, preflight checks (network, TCC/Full Disk Access), and deterministic overwrites for LLM-generated numbers.

• The author built a macOS shell script to drain Claude conversation logs into an Obsidian vault, commit to a private repo, and leave a daily briefing on the desktop.
• Three real failure incidents drove the design: a double-execution race on 2026-06-10, a service-wide timeout where every slot timed out on 2026-06-13, and repeated sleep-related freezes.
• The script runs in four launchd slots at 04:55, 08:20, 10:45, and 12:15 and uses per-step markers (STEP2A/STEP2B) to allow idempotent partial retries.

LLM Wiki: Protecting Human-Refined Notes with Curated Layer

The article describes agnosticBrain, a GitHub implementation that extends Andrej Karpathy's LLM Wiki pattern by adding explicit ownership layers to protect human-refined knowledge from being overwritten by LLM ingests. agnosticBrain defines directory-level ownership rules — including a read-only curated/ folder for human-canonical notes — and introduces workflows (/curate, /propose) so the LLM can propose diffs but never modify curated content without explicit human approval. The project documents nine kernel operations (AGENTS.md) and provides a proposal, logging, and index-based pipeline to close the learning loop while preserving manual edits.

• Andrej Karpathy published his LLM Wiki gist in April 2026 proposing a compiled, persistent wiki maintained by an LLM.
• agnosticBrain (GitHub: Macorreag/agnosticBrain) extends the LLM Wiki pattern by introducing an explicit curated/ read-only layer to protect human-refined notes.
• agnosticBrain defines four directories with strict ownership rules: raw/ (human immutable), wiki/ (LLM-writable), curated/ (human-canonical, LLM never modifies), and archivo/ (system archive).

AI-native Second Brain Using Multi-RAG and Knowledge Graphs

The article outlines a proposed architecture for an AI-native "Second Brain": a persistent knowledge layer that combines multiple retrieval strategies (Multi-RAG), knowledge graphs, long-term memory, and an MCP API to connect LLMs to structured organizational knowledge. Instead of relying solely on vector search, the design integrates semantic search, keyword/full-text search, knowledge-graph queries, metadata filtering, and reranking. Sources such as Obsidian notes, GitHub, Slack, databases, and documents are ingested, processed into a knowledge layer (vector index, search index, graph store) and exposed via a retrieval engine and MCP so LLMs (e.g., Claude, GPT) can both read context and write back structured memories. The goal is a Knowledge OS that preserves knowledge independently of any single model and provides richer evidence+relationship context for reasoning.

• The author proposes an AI-native "Second Brain" that combines Multi-RAG, knowledge graphs, long-term memory, and MCP to provide unified knowledge to LLMs.
• Multi-RAG combines multiple retrieval methods: semantic (vector) search, full-text/keyword search, knowledge graph queries, metadata filtering, and reranking.
• The architecture ingests sources including Obsidian (notes), GitHub (code/PRs), Slack (conversations), databases, and documents into a knowledge layer with a vector search index, search index, and graph store.