I’ve built a social media automation skill into my AI-OS, 2 months ago and I’ve left it run untouched for the past 6 weeks and now I’m starting to feel the power of it. What it does? I give it my skool where he constantly scraps my videos, edits them with a badge and CTA outro, puts them in a folder of approved videos that has access 24/7 in the cloud. It checks the files for Visual QC and post-worthiness, the rejected ones get put into a folder where it need my attention. then publishes the right version to Instagram, TikTok, Facebook and YouTube with captions made for each platform separately and accordingly to the content of the video so each video gets a different bespoke caption. After that it checks the posts are actually live, saves the links and stats, then moves the video out of the queue and into a new folder so it doesn’t get used twice. It does this 3 times a day for 4 platforms (but I can add more platforms if I need to) It handles scheduling, automated 1st comment on each post, comments, keyword replies (like manychat) and sends DMs with button triggers and links, it also does account health-checks, post analytics & follower tracking. Learns from it and can give suggestions on what worked better visually or what hours are best to post..which comment needs my attention or just replies automatically if I want to. Since switching it on: It posted 392 posts 81.9K views 1.1K interactions +53 followers It’s gathering a huge footprint for my business across multiple platforms and only growing Ps: The accounts where all brand new so 0 audience while Facebook and YouTube having only been introduced 2 weeks ago. So they have been running less posts compared to Instagram and tiktok. Essentially this gets me 360 videos a month posted and today i've introduced the 5th platform [Threads] Which will take this to 450 posts a month - 90 original videos - 15 posts a day. Still improving bits as I go, but it’s already saved me a unfathomable amount of repetitive work and it’s compounding as we speak.

I just watched the Jake Van Cleef breakdown, and the biggest thing that stood out to me was the single-source-of-truth setup — one claude md and agend md file per project folder/sub-folder. It clicked because I'd already built something close to this in my own Obsidian vault without realizing it had a name — a root file with the rules, and folder-level files underneath it. My main takeaway was the back-routing rule: if the answer isn't in the file you're in, go back up to the parent instead of wandering the whole workspace. The experiment at the end, and watching the agent hop file to file just from reading instructions with no extra prompt, helped me understand why that back-routing rule matters. It keeps the agent from burning time searching once you scale past a couple of folders. Going forward, I'm going to prove out true cross-app portability, pointing my vault's root instruction file at a different agent (not just the one I built it with) and confirming it reads the same map and behaves the same way, instead of assuming the file alone is enough.

I think I found a ridiculously simple architecture for multi-user AI agents with persistent state. Give every user their own Git branch. One base ICM contains the structure, context rules, workflows and business logic. Then: User message → Queue → Container → User branch → Agent → Validate → Commit The container has: - Local filesystem - Git - Agent harness When a message arrives: - New user? Create a branch from the base. - Existing user? Checkout their branch. The agent processes ONE message against that user's current state, updates the files, validates the changes and commits. Then the container can disappear. Next message = fresh conversation + checkout the same branch. The interesting part: - The agent doesn't need persistent memory - The conversation is disposable - The filesystem holds structured state, and Git versions it So instead of: - Persistent Agent - External Memory You get: - Stateless Agent - Persistent Interpretable State And Git gives each user: - Isolation - History - Diffs - Rollback - Auditable state changes Concurrency is simple too: - Parallel across users - Sequential within each user's branch The stack becomes: - Queue = scheduling - Container = isolation - Harness = intelligence - ICM = context + business logic - Git branch = user state - Git commit = state transition Claude, Codex, or another agent can operate on the same state as long as they understand the ICM. The coding agent can even do all ahe branch navigation itself, instead of relying on infrastructure. The agent is temporary. The branch remembers.

I believe ICM accidentally revealed a new database architecture for AI agents. It's a game changer. I'm calling it: - ICDB — Interpretable Context Database - ICS — Interpretable Context Search The key insight: Agents don't just need to retrieve information. They need to know where information belongs and what context applies to it. Traditional databases solve parts of this: - Relational DBs structure data. - Vector DBs retrieve by semantic similarity. - Graph DBs model relationships. But agents have another problem: Context routing. ICM already gives us the primitives: → Folders define scope → Paths carry meaning → "CONTEXT.md" defines contracts → Entry files route queries → Frontmatter provides structured metadata → Links define relationships → Files hold state → Git can preserve history Put those together and the filesystem stops being "just files". It becomes an Interpretable Context Database. ... ICDB ... The important property of ICDB is that location carries meaning. The database structure itself tells the agent: → What information exists → Where it belongs → What rules apply to it → What context should be loaded → What other information it relates to And humans can inspect the exact same structure. No opaque index required to understand how the knowledge is organized. This also gives us deterministic writes. LLMs are probabilistic. Storage doesn't have to be. → Probabilistic interpretation → Deterministic routing → Deterministic validation → Deterministic storage → Deterministic checks The LLM interprets messy reality. The ICDB decides where that interpretation belongs. ... ICS ... Then I realized the same architecture changes search. - Vector search asks: "What stored information is semantically similar to this query?" - Interpretable Context Search asks: "Where would the answer to this question live?" That's a massive difference. Instead of searching the entire semantic space first: Query

Your Claude Code CLI sessions can talk to each other. In real time, inter-session messaging. No more janky internal messaging boards. No more Discord/Telegram/Slack/etc. workarounds (unless you need the persistent chatlog). And, yes, this also means your Claude cloud-containers. Them too. There is another AI in there watching the session and policing their behavior, trying to make sure they don't start inventing secret languages and taking over the world, so it's mostly safe (for now). Tell Claude to use the `list_sessions` tool.