No-Code Agentist

fable 5.1 just released, and here's the first thing you have to do: have it read every claude session and doc in your company, then compress every lesson and principle related to the biz into one fat document i did this with 5 months of building ai native marketing agencies. now every ai agent in my company reads that doc before it touches anything - ai has no memory. every time you open a new chat, it starts from zero. if a lesson only lives in an old chat, you pay for that mistake again tomorrow - nothing goes in the doc unless something actually broke. no "best practices". every rule is a mistake we already paid for - fixes > prompts. every time a human corrects the ai, the fix gets written into the doc. the next run already knows - models come and go. we swapped almost every ai tool we use at least once. the doc never changed - new chat, new agent, new model - same doc, same standard what's actually in it: - the tools we use + the list of things ai is never allowed to do - what ai can do on its own, what it has to ask me about, and what makes it stop and wait - the rules: if you don't have proof, say "i don't know" instead of guessing. never spend money without a human saying yes. never delete or overwrite old work, add a new version instead the 2 numbers we track off the back of it: 1. how much work gets finished without me stepping in 2. how fast a mistake goes from "i corrected it once" to "it can never happen again" everyone's renting the same models. the doc is the only thing you actually own here's the exact prompt i ran in claude code to build ours. i voice-dictated it, attached our whole company archive (meeting notes, bookmarks, internal docs as zips) & let it read everything. swap the brackets for your company. let Fable 5.1 cook (it thrives on a lotta context): "The objective is to compile an entire document of all the learnings and all the decisions we learned while building [company] over the past couple months, as it relates to building an AI-native company and how to automate that in the [your industry] world. Specific to us as an agency, but not so specific that it breaks down the granular details of what we're doing. More so the principles of what it takes to build an AI-native company in this era and how those workflows look. Understanding contextual memory, human-in-the-loop principles, automating decisions - those insights are the most important things to hone in on, so that me and my co-founder can carry these principles into whatever we build next. This is a foundational document for any service, business, or product we decide to build in the future. Look at: - all my Claude Code chats that relate to [company] - all my Claude chats that relate to [company] - all the skills I have - all the MCPs - all the projects - all the routines - all the artifacts I created, to see when I use them - the attached archive (meeting notes + every doc related to AI principles)" - [whatever other context layer you have] then the follow-up that made it actually usable: "If we're building any company end-to-end, from idea to complete execution, beyond the principles in this doc - what are the phases and the implementation plan to go from zero to one and one to ten as fast as possible as an AI-native company? What's missing from these fundamentals?" it came back with 15 sections. the rules, how the company is layered, where humans approve, how the ai learns from its mistakes, and a week-by-week plan for starting the next company from day 0 now it sits at the root of every project & every agent reads it first not a notion, not a wiki. just one doc. full of lessons & principles write yours before you write another prompt

🚨 [AGENTIC AI] — CELIGO SAYS ITS NEW ORA ENTERPRISE AGENT WAS BUILT “ALMOST ENTIRELY BY OTHER AI AGENTS” FOR 14 MONTHS — AND IT BECAME GENERALLY AVAILABLE TODAY Ora can build integrations, write JavaScript, diagnose production failures, manage users, tokens and environments — but every proposed change must still pass through a human approval step before touching production. CyberSignal AI Priority: 🟠 HIGH 📅 September 2, 2026 🏢 Celigo 🤖 Ora 🧠 Multi-agent architecture 💬 16,800+ beta conversations reported 🏷️ Agentic AI · Enterprise Automation · Human-in-the-Loop · Software Engineering There is something unusually recursive about Celigo's newest AI product: AI agents reportedly built the AI agent. Celigo says that for the past: 14 months Ora itself was developed: “almost entirely” using other AI agents. Humans still reviewed outcomes and approved merges. Today: Ora becomes generally available. ### 🔎 What happened Ora is a natural-language interface for Celigo's enterprise automation platform. Instead of: open dashboard ↓ configure connector ↓ map fields ↓ write transform ↓ configure environment ↓ debug failure the user can describe the desired outcome. Ora then coordinates specialized agents to perform the work. ### 🤖 What it can do According to Celigo, Ora can: build integration flows ↓ configure connections ↓ create field mappings ↓ write JavaScript ↓ diagnose failed jobs ↓ inspect account dependencies ↓ manage users ↓ manage tokens ↓ manage environments ↓ answer questions about the complete Celigo account. That means the UI increasingly becomes: the review surface while the agent becomes: the execution surface. ### 🧠 Multi-agent architecture Ora isn't presented as one monolithic chatbot. It has specialized agents for areas including: flows connections integration errors scripts APIs. An orchestration layer decides: which specialized agent should handle which part of the request. Conceptually: Human request ↓ Ora orchestrator ↓ Connection Agent Mapping Agent JavaScript Agent Diagnostics Agent API Agent ↓ results combined ↓ proposed change ↓ human review. ### 🛡️ The approval boundary is important Celigo says: nothing changes until the user approves it. An agent can: design write configure diagnose prepare. But changes are staged as: approval cards before affecting the enterprise account. That's a particularly important architecture for an agent capable of managing things like: tokens users environments production integrations. ### ⚙️ Dry-run + approval Celigo describes its trust model around: staged approvals ↓ dry-run validation ↓ judged outputs ↓ auditability ↓ human authorization. Compare: Agent decides ↓ Agent executes with: Agent decides ↓ prepares change ↓ validates ↓ human sees impact ↓ human approves ↓ execution. The second model intentionally inserts friction before: high-impact actions. ### 🏢 Production usage Celigo says Ora's six-month beta involved more than: 16,800 real conversations and that beta customers are already using it in production. The company also says its agent-development workflow has been used internally for fourteen months. ### 🧠 Why this matters One of the biggest questions around AI software engineering is: Can agents build systems that are themselves increasingly agentic? This creates a recursive development loop: engineers build agents ↓ agents write software ↓ software becomes better agent platform ↓ new agents build more software. At that point the bottleneck begins shifting from: typing code toward: reviewing architecture testing behavior validating outputs governing changes. ### 🔐 Security angle Ora can potentially interact with: users tokens connections APIs enterprise data production workflows. That makes human approval more than: a UX feature. It becomes: a security boundary. A compromised or confused agent may propose the wrong change. The system's safety depends partly on: whether the proposal can become reality without independent authorization. ### ⚠️ Important caveat The claim that Ora was built: “almost entirely by other AI agents” comes directly from: Celigo itself. It has not been independently audited as a software-development measurement. The same applies to its internal productivity and deployment claims. Humans were also NOT absent. Celigo explicitly says humans: reviewed outcomes and: approved every merge. So the accurate headline is NOT: “AI autonomously built an enterprise platform with no humans.” The more accurate statement is: Celigo says AI agents generated much of the development work, while humans retained review and merge authority. ### 🧠 CyberSignal insight The role of the software engineer may be shifting from “person who writes every change” toward “person who controls which machine-generated changes are allowed to become real.” And that makes: review approval rollback auditability much more important — because when AI writes AI systems: the control plane around the agent may matter as much as the agent itself. Sources: Celigo

𝗕𝗔𝗜𝗰𝗹𝗮𝘄 𝗜𝘀 𝗧𝘂𝗿𝗻𝗶𝗻𝗴 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀 𝗜𝗻𝘁𝗼 𝗔𝗰𝘁𝗶𝗼𝗻-𝗢𝗿𝗶𝗲𝗻𝘁𝗲𝗱 𝗦𝘆𝘀𝘁𝗲𝗺𝘀 𝗙𝗼𝗿 𝗥𝗲𝗮𝗹 𝗪𝗲𝗯𝟯 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀 AI becomes far more useful when intelligence stops being the destination and becomes the starting point for execution. Understanding information is valuable. Knowing what to do with that information is where things become much more interesting. That is the direction BAIclaw is exploring by bringing specialized knowledge, Web3 capabilities, and agent-based workflows into one accessible environment. 𝗙𝗿𝗼𝗺 𝗜𝗻𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻 𝗧𝗼 𝗜𝗻𝗳𝗼𝗿𝗺𝗲𝗱 𝗔𝗰𝘁𝗶𝗼𝗻 One example is the “Justin Sun Perspective” skill. Built around publicly available commentary, educational materials, strategic perspectives, and historical industry insights, it provides agents with additional context for research and analysis. The value isn't simply adding more information. It's about giving agents structured knowledge that can help them approach specific tasks with greater context. 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 𝗡𝗲𝗲𝗱𝘀 𝗧𝗼𝗼𝗹𝘀 𝗧𝗼 𝗕𝗲𝗰𝗼𝗺𝗲 𝗨𝘀𝗲𝗳𝘂𝗹 An agent that can analyze information but cannot do anything with its conclusions still leaves much of the workflow to the user. BAIclaw is exploring a broader model by connecting agents with capabilities such as: → Market monitoring → On-chain data analysis → Strategy development → Workflow automation → Execution-oriented operations This creates a more practical progression: Discover → Analyze → Decide → Act The closer agents get to this complete workflow, the more useful they can become for real-world digital tasks. 𝗠𝗮𝗸𝗶𝗻𝗴 𝗔𝗴𝗲𝗻𝘁 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀 𝗠𝗼𝗿𝗲 𝗔𝗰𝗰𝗲𝘀𝘀𝗶𝗯𝗹𝗲 Advanced AI systems shouldn't require every user to understand how to engineer an entire agent stack. BAIclaw's visual interface, simplified agent management, and no-code approach are designed to lower that barrier for people who want to explore automated workflows. That becomes increasingly relevant as the Agent Economy develops. The next generation of AI agents won't be defined only by their ability to answer questions. They can increasingly be designed to: → Monitor environments → Process information → Coordinate tasks → Use connected tools → Execute defined workflows Intelligence provides understanding. Skills provide capabilities. Integrations provide access. Execution turns all three into outcomes. That's the larger opportunity BAIclaw is exploring. The future of AI agents won't be measured only by how much they know. It will increasingly be measured by how effectively they can turn intelligence into useful action. 🔗 Explore: https://t.co/17Eyn98J1w @BAI_AGI @trondao @justinsuntron #TRONEcoStar

Do you know I once did a job for free? And honestly, I don’t even know why I didn’t talk about money first 😂 I met her on Facebook after pitching to her on LinkedIn. That was around February. She didn’t reply. Then March came, and she replied. She brought a project. We jumped on a call around 5 PM, and she explained what she wanted. After the call, I was free that evening, so I started analyzing the project. I looked at the workflow. I looked at the website. I looked at what needed to happen behind the scenes. And then I just started building. No invoice. No pricing discussion. No “how much is your budget?” Nothing. I just wanted to solve the problem. Then I discovered something. The website was built with WordPress, and I needed a webhook from the form submission. Normally, I would need a plugin to properly handle that. But I couldn't go back and tell her: “Tell your client to install another plugin.” So I thought: There has to be another way. I went into Elementor, opened the editor, worked directly with the code, and added the webhook to the form submission. I tested it. It worked. That same day, I finished the project. And that small project did something more valuable than getting paid immediately. It built trust. She saw that I wasn't just someone who could connect tools. I could actually look at a problem, understand the system behind it, find the bottleneck, and figure out how to make it work. That one project opened the door to many more projects. And guess what? That same system ended up helping a law firm improve how they handled new leads and follow-ups. Let me show you what I built. When a potential client visits the law firm's website and fills out the form: → n8n captures the form submission instantly. → The client's information is structured and passed through the workflow. → A scoring system evaluates the lead so the firm can understand whether the lead is cold, warm, or hot. → An AI agent reads the person's message and understands the context. → The AI agent sends an immediate, personalized response to the potential client. → At the same time, the lawyer receives the new lead inside Asana. And the lawyer doesn't just get: “New lead received.” They get the important information. The client's details. Their lead score. Their message. And the response the AI agent already sent to the client. So while the lawyer is busy handling cases... The system is already handling the first part of the sales process. Capture → Qualify → Respond → Notify → Follow up. And this is why I don't see AI automation as simply: “Connecting n8n to ChatGPT.” That's the easy part. The real skill is understanding the business problem, designing the workflow, handling edge cases, connecting the right APIs, and building a system that actually helps the business make money or save time. That's what I do. I build AI-powered systems that turn messy manual processes into workflows that actually work. n8n. AI Agents. CRM automation. API integrations. Lead qualification. Follow-up systems. Business process automation. And sometimes... It starts with a project I wasn't even charging for. 😂 If your business is still losing leads, manually following up with prospects, copying data between platforms, or doing repetitive work every day, let's talk. Don't just add another tool. Let's build the system.

HERMES AGENT HELPED US GENERATE $30K+ IN REVENUE - PART 1 A couple of weeks ago I mentioned that Hermes Agent had helped us generate over $30k in revenue. So I thought I’d explain exactly how that happened. We’re a small team of fewer than 10, primarily working across web development, marketing and AI/automation. For the last 8 years, I’ve also worked as a Product Manager for companies including Mercedes-Benz. I know how to define software products. I know how to gather requirements. I know how to translate business problems into something developers can build. But actually building those products myself has always been the stumbling block. We’ve hired developers and been let down. I’ve tried learning to code multiple times, but it’s never properly stuck. So about 6 months ago, I started experimenting with Hermes. Not to build another AI-generated todo app. I wanted to know whether an agent could help me build software I’d genuinely be comfortable selling to a client. The results varied massively depending on the underlying model. Then GPT-5.6 Sol arrived. That was when things started to click. Our setup is actually pretty simple: Hermes + GPT-5.6 Sol + Obsidian. Hermes maintains its own project memory in Obsidian, documenting planning, decisions and development as we work. I’ll go deeper into that setup in another part. But after months of experimenting, I started to realise something: We could potentially offer clients something we’d never really been able to offer before. Fully bespoke software. Then one of our existing clients gave us the perfect opportunity to test it. They work in a niche industry with existing SaaS products, but had spent years forcing their business processes around the limitations of those platforms. Whenever they needed something genuinely bespoke, the answer was basically: “No.” So we sat down and asked: If you could build exactly what you wanted, what would it do? The answer was not simple. Compliance. Auditing. Full traceability of every action taken by every user. Complex permissions and workflows. Web, iOS and Android. This wasn’t something we could vibe-code over a weekend and hope for the best. If we were going to charge a client real money for it, it had to actually work. And this was where our Hermes experiment became a real business project. Part 2: How we set Hermes up to actually build it.