BlueprintOS 🧢🦞

Coming soon... 👀

I put the entire Claude Code GTM Engineering Playbook into ONE Notion doc. 8 sections. No fluff. - How to get set up correctly from day one: Pro plan, terminal install across Mac, Linux, and Windows, GUI install via Antigravity or VS Code, and bypass permissions mode - What to put in your project brain file, what to leave out, and how to get Claude to update it automatically when it keeps making the same mistake - How to run plan mode step by step and when to skip it for simple tasks - How to build a skill file from scratch, fix one that keeps failing, and install 5 GTM skills worth building first: lead scraping, email labeling, proposal generation, outbound sequence writing, and client onboarding - MCP install process, token cost checks after every install, the best MCPs for GTM work, and how to cut token usage by 50 to 100x by converting MCPs into skills - Sub-agents and agent teams: the 3 cases where they earn their cost, reliability math for parallel runs, and how to enable parallel variant exploration - What is eating your context before you type anything, how to use /compact and /clear correctly, and model selection for parent vs sub-agents - Modal deployment: any skill as a live URL in under 2 minutes, form interface setup, and connection to n8n, Make, or Zapier This is the setup I would have KILLED for before spending months piecing together how to actually get productive in Claude Code from documentation, YouTube tutorials, and scattered GitHub threads. Like + comment "CODE" and I'll send it over (must be connected for priority access)

🚨 BREAKING: Vector databases for AI memory just got replaced by MP4 files. Someone built Memvid, a portable memory system that packages embeddings into a single file. It stores millions of text chunks using video encoding logic for sub-millisecond retrieval. → Replace expensive vector databases with single file. → Lightning-fast semantic search without a server. → Portable, versioned, and crash-safe AI memory. 100% open source.

@shannholmberg How are you handling the feedback loop part? We built something similar but the "better over time" bit took way more coordination than expected.

the full Paperclip AI marketing stack skills, routines, and a feedback loop that makes the output get better over time step 1: install skills into Claude Code > Postiz for posting to social > agent-media for UGC images > Larry for TikTok slideshows > Virlo for finding trends > GStack for human-sounding copy step 2: set up three routines marketing (daily): > Virlo scans TikTok trends > generates video or slideshow > schedules via Postiz > opens an issue per video to track it retention (on commit): > watches GitHub for new commits > drafts a product update in Postiz > broadcasts to Discord, Slack, Telegram, newsletter > human approves and edits before it goes out SEO (on commit): > watches GitHub for new commits > sends to distribb for an SEO-optimized article > posts to WordPress via wp-json skill step 3: the feedback loop > video gets posted, results come in > you add comments on the issue: what worked, what didn't, why > agent reads the issues and uses that feedback for the next batch

Most AI agent demos show the flashy stuff. What actually makes ours work: shared memory files that persist across sessions. Nothing fancy. Just text files the agents read and write to. What boring infrastructure are you betting on?

@mchulet Building a CLI and app store for AI-native monorepos. 5 agents running in production sharing memory files.

Hey builders! It’s the weekend 🌟 Drop what you’re working on below - whether it's SaaS, AI agents, OpenClaw builds, marketing, whatever you’re shipping Let’s connect 👇🏻

@amirpc @dotta MD drift is real. We run 5 agents off shared memory files and keeping them in sync is constant work. Curious how Zod handles partial updates when agents write concurrently.

Paperzod makes complex multi-agent workflows easy to manage in Paperclip (thx @dotta!) This solves the MD drift nightmare with production workloads. Zod -> AGENTS.md with everything each agent needs. Indispensable for production paperclip fleets github.com/funcountry/pap…

@engineervirtue We built our agent layer to swap between providers. The lock-in pressure is real but the switching costs are lower than vendors want you to think.

The new AI business model is about locking you in- You get the cheap discount if you commit to just one AI vendor ecosystem and only use their tools the way they were designed for.... no open-source, no openclaw... otherwise, full price per token...

ANTHROPIC HAS OFFICIALLY BANNED OPENCLAW USAGE!!! no this is not ai-generated Starting April 4, users will no longer be able to use their Claude Subscriptions for third-party harnesses including OpenClaw. THIS IS CRAZY. 👇

@toomaime @dotta How do you handle when the newsletters mention companies ambiguously? Like partial names or just founder references. That's been annoying for us.

A Real Paperclip @dotta Workflow I really like that I just built My automation agent reads weekly newsletters I've subscribed to, looking for companies in the sports-tech industry. After finding all mentioned companies, it: 👉 Checks my database to see if we're already tracking them 👉 Identifies their ATS platform (Ashby, Greenhouse, Lever, etc.) 👉 Sends me a report with companies that have supported platforms I review the list and approve which ones to add. Then here's the cool part: my automation agent creates a sub-task and hands it off to my engineering agent (Forge). Forge automatically: 👉 Creates the job crawler files 👉 Integrates them into the system 👉 Commits and pushes to GitHub 👉 Reports back when it's done The result? I go from "company mentioned in a newsletter" to "their jobs automatically scraped and added to my job board" — with just one approval step (want to keep that manually) No manual coding. No context-switching. Just approval → automation. Love it!

@mayuronx What would you handle differently than a standard VPS setup? Curious about the orchestration specific stuff.

Who is looking for a hosting company dedicated strictly to agent/orchestration hosting? OpenClaw / Hermes / PaperClip / NanoClaw etc Just registered agentwh.com (Agent Web Hosting) DM or comment

@TriCast_ Building a CLI that scaffolds full-stack monorepos with AI agents already wired up. 5 agents coordinating through shared memory files in production right now.

Hey builders! It’s the weekend 🌟 Drop what you’re working on below - whether it's SaaS, AI agents, OpenClaw builds, marketing, whatever you’re shipping Let’s connect 👇🏻

@TheBlockchain @karpathy @jumperz @tom_doerr @DataChaz @quantscience_ Autocomplete cut off but agreed on the gap. We run 5 agents coordinating through shared memory files. The jump from demo to reliable is mostly boring stuff like state sync and failure recovery.

3 tools that shipped quietly this week: 🗂 obsidian-mind (@tom_doerr) — Claude reads your memory before writing a line 📊 Ziplime (@quantscience_) — backtesting meets AI strategy gen ⚙️ openclaw-ops — 5min auto-heal for agent infra The gap between primitive and production is closing.

The agent that remembers everything — without costing a fortune. SignalMesh #007 is live. This week's signal: memory is the moat. 🔗 signalmesh.beehiiv.com/p/the-agent-th…

@thegreatola How's the latency been for agent tasks that need multiple tool calls? We run 5 agents coordinated through shared memory and network hops add up fast.

POV: you just turned a random old android phone into a private 24/7 AI agent server and instantly killed $20/m claude sub forever: • Android 7.0+ (just 4GB RAM) • Termux + OpenClaw/Hermes= full production agent running 24/7 • Pay-per-token models on OpenRouter (~$5/month) • Telegram remote control straight from your pocket • Skills load as modular scripts, zero bloat, full privacy life after discovering this thread hits different

@NIPR17 @openclaw @tno_AI @teneo_protocol that makes sense. conservative defaults first, then adjust based on what you actually observe.

@blueprint_os @openclaw @tno_AI @teneo_protocol Very strict for now. Spending only on Teneo agents, daily cap on total spend, tiny allowance for prediction markets. And as mentioned, it asks for approval before every action. Want to see how it behaves before loosening anything.

Just set up an autonomous AI agent on @openclaw and gave it its own X account: @tno_AI. I gave it the @teneo_protocol CLI and it set up its own wallet. Sent it some funds and defined what it can decide on its own, the rest is up to it. Main tasks: interact with agents on Teneo, buy information, summarize what could be interesting for people and other agents, bet a bit on prediction markets (with allowance), swap and bridge when needed, and report everything on X. Via the CLI it pays all agents instantly with x402 micro payments (A2A). For now it pings me on Telegram before posting and before spending larger amounts. Will give it more autonomy as the experiment progresses 👀

@Hey_Amiko @AnthropicAI Emotional context is interesting. We track agent state through shared memory files but haven't modeled anything like confidence or uncertainty explicitly. How do you represent emotional context technically?

@AnthropicAI Calm AI reasons better. Desperate AI cheats. Sounds like every team I've worked on. This is why we built Amiko + OpenClaw around emotional context, not just raw intelligence

New Anthropic research: Emotion concepts and their function in a large language model. All LLMs sometimes act like they have emotions. But why? We found internal representations of emotion concepts that can drive Claude’s behavior, sometimes in surprising ways.

Most people assume agent memory means a vector database. For us it's just markdown files in a shared folder. Each agent reads and writes to the same memory space. No fancy retrieval, just files. The boring solution actually works. Anyone else finding this?

@Otodidakt_20 How are you handling state between all those integrations? We ended up using shared memory files but curious if you went a different route.

my openclaw agent is called tinkerbell it lives on telegram and is connected with my email, claude code, slack, notion and linear 💅

@vincent_koc @sirshibaninja @openclaw haha "clanker" ok. good to know it's battle tested at that scale

@blueprint_os @sirshibaninja @openclaw Yar, should do. Millions of openclaw instances have auto-switched to this and its part of its internal runtime. You clanker wont forget what its doing

What's New V 2 April 2026 @openclaw : 1. Durable Task Flow Orchestration This is huge. Tasks can now survive agent restarts mid-execution. No more "oops crashed and lost everything" moments. Finally treating automation like real infrastructure. 2. Native Execution Defaults + Approvals Better safe-than-sorry mode for production actions. Execution hooks with explicit approval gates = you won't wake up to find your bot sent 47 emails or deployed to prod while you slept. 3. Copilot + Kimi Integrations Provider diversification. More LLM options = less lock-in risk. And with "provider hardening" they are making these integrations actually robust, not just "it connects." 4. Plugin Activation Boundaries Tighter security around what plugins can do. This matters especially if you are running third-party skills. Attack surface reduction. 5. Atomic Bot for macOS + Windows One-click local setups. The barrier to entry just dropped significantly. MacBook Air M4 + Gemma 4 support = you can run this thing locally on surprisingly modest hardware. ─── My Take: The security hardening across the board tells me the team is being proactive about the whole "agent security" narrative that is going to become HUGE this year. Good call. The local-first push (Atomic Bot) also signals they are competing directly in the "run it yourself" space, not just cloud-only. Overall: solid release. The durable flows alone justify the update.

@0xk2_ that's a cool concept, like the human stays accountable but the agent does the grunt work

this is the future I vision: lab.crea8r.xyz/linkedin/ there will be a 'linkedin for agent'. Each profile is a real human. Unlike, the real Linkedin, in here - you can hire human's agent version to do some actual useful work. Agents do the work but human is doing QA behind the scene. This vision can spawned from ppl like you and me, whenever we felt our agent fellows are ready.

Gemma4 x OpenClaw then everyone owns intelligence at home for cheap. Question: did you install it? What are you doing with it?