No-Code Agentist

top anthropic engineers don't code the way you do, and nobody told you why they're not prompting harder or buying bigger subscriptions - they stopped treating Claude as a chat window actual shift: workflows that loop, verify their own output, and keep running after you stop watching most people use Claude like a search engine with a longer answer. engineers at Anthropic use it like a factory floor - one instruction triggers a chain, agents hand off automatically pause at 0:29, watch his fingers - he's not counting vibe coding trends. he's counting stages in a workflow most people haven't built yet while you type prompt 7, that system already finished 8 through 14 and flagged the one that needed a retry gap isn't talent or access - it's that they stopped treating Claude as a partner and started treating it as infrastructure and that decision compounds every day

Tencent WorkBuddy is now becoming China’s #1 PC-based productivity AI agent. Tell it what you need, then it reads files, calls tools, writes reports, builds decks, analyzes data, uses 100+ expert roles. Connects to GitHub, Jira, Notion, Gmail, Google Drive, Slack and more through MCP, runs tasks in a sandbox, and can even be controlled from Slack, Telegram, Discord, or WeChat when you are away from your desk. WorkBuddy breaks a big task into smaller jobs, picks the right skills or connected apps for each job, and for complex work it can use Expert Teams where multiple specialized sub-agents work in parallel while 1 lead agent coordinates the final output. So if you ask for a report, it is not just generating text. It can read the file, send the data-analysis part to an analyst-style expert, send the writing part to another expert, use connectors like Google Drive or Gmail if needed, and then combine everything into a finished file. 👋 Here are a few practical use cases you can do immediately with it. - Read PDFs, images, and documents, then organizes the extracted content. - Create reports, proposals, manuals, and presentations from raw material. - Analyze spreadsheets, finds trends, and turns data into charts. - Create platform-ready posts, scripts, articles, and content ideas. - Automatically research news and sends scheduled summaries to your channels. - Run desktop tasks from Slack on your phone. Manage Calendar and Drive tasks directly through conversation. - Build working apps without needing you to code. - Turn repeated workflows into reusable WorkBuddy skills. For my own workflow, I installed Tavily AI Search because I post a lot about research papers on X. And paper content needs outside context: project pages, GitHub repos, author links, related papers, previous methods, and the reason a paper is worth posting about. @TencentAI_News

What if AI agents didn't have to start from zero every time? -No repeated setup -No lost context -No relearning the same workflows That's the future EvoMap is building 🧵 https://t.co/Z3U9NaR6i6

how I’m building an agent company inside my agency. the structure looks like this: Agency gBrain → Orchestrator Hermes Agent → Department verticals → Specialist agents → Scoped sub-agents gBrain is the company brain. It gets ingested with the data and experience we already have: > transcripts > chats > previous campaigns > client learnings > strategy docs > internal workflows > examples of what good looks like That brain is maintained by a human champion plus an orchestrator Hermes Agent. Under the orchestrator, we have different department verticals inside the agency. Each vertical has its own specialist agents. Some of those specialist agents have even narrower scoped agents underneath them. I’ve found that narrow scope improves output quality and reduces drift. > a general “marketing agent” is too vague. > a lifecycle email agent with access to the right campaigns, voice rules, approval gates, and examples can get very good. > a technical SEO agent with its own tools, checklists, and source standards can get very good. > a content research agent with narrow inputs and a clear definition of done can get very good. The narrower the job, the easier it is to improve the agent. I use different harnesses for this. Mostly Hermes Agent, but also CLI harnesses like Codex and Claude Code depending on the job. I’m still looking for a good bare-bones harness for model routers to run on. To keep track, I maintain an org chart inside the company gBrain. The org chart shows: > top-level orchestrator > department verticals > specialist agents > scoped sub-agents > which brain each agent reads from > which tools each agent is allowed to use > where human approval is required For clients, I do downstream pods. Think of them as new agent companies that are isolated from the agency brain, but can still communicate with our agency agents when needed. A client pod has its own: > client gBrain > client orchestrator > client specialist agents > client-specific workflows > client-specific approvals > client-specific memory This is important. You do not want client context bleeding across accounts. You do not want one agent with every client’s data, every tool, and every permission. Scope is what keeps the system useful. The powerful part is that once you build one vertical agent well, you can fork it. Not copy-paste blindly. You still need to customize the context, examples, approvals, voice, tools, and workflows. But you are not starting from zero. You might have 75% of the agent already done. That changes the agency model. You no longer need a full traditional department for every function before you can deliver a well-rounded marketing service. One or two strong marketing engineers can run an output surface that used to require a much larger team. But this only works if the agents are actually good. It takes iteration, taste, source material, QA, workflow design, and real marketing experience. Bad agents do not become good because you connected more tools. Vague agents just create vague output faster. TLDR: > turn the agency’s knowledge into a brain > turn repeated work into scoped agents > turn each client into an isolated pod > let skilled operators run the system

$500/month for a human assistant. replaced by a laptop and a $20 Claude subscription Obsidian + Claude AI. local vault setup. runs for solo founders. nothing lost in messy apps. no more productivity systems that break on a bad day > Obsidian: 8 folders to structure. one file called Claude.md for full context of your entire life > Claude AI: 5 automated workflows. fast enough to sort your chaotic thoughts into clean databases while you sleep > Personal OS: morning briefings. hands-off capture logs. weekly review generation. automatic project health checks month one savings: $300-500. every month after: 15 hours saved per week your vault. $0 maintenance. a digital executive director that runs forever