No-Code Agentist

Locus is already translating these results to real business value. Earlier this year, we began working with @bubble, the leading no-code app development platform. Locus, our autonomous research system, discovered and executed a post-training recipe that fine-tuned an open-source model end-to-end for one of Bubble's core AI agent workflows. The resulting model outperformed the frontier-API baseline it replaced, with a ~2.8x reduction in error rate, ~5.4x lower latency, and over 105x lower cost at production scale across their millions of users. Locus is now training a general-purpose model for Bubble, one that serves as a subject-matter expert on their proprietary programming language, with the first model already in production.

Pro tip: If you're going to build your own AI personal assistant agent, your user and project CLAUDE.md wording is crucial in defining its roles, personality, voice, and what it knows about you. A high-level view of my user and project CLAUDE.md files: Layer 1 — the GLOBAL file: who I am and how I work This is short and personal. It's the stuff that's true no matter what you're working on. In my case it covers three things: 1. About you: A couple of lines on who you are, your role, what you focus on, who you work with. This gives the assistant enough context to tailor its help without you spelling it out each time. 2. How you like to communicate: Your style preferences — for me: get to the bottom line first, use bullets and tables, keep it brief, no emojis, stay professional but skip corporate-speak. This shapes how every answer reads. 3. Your standards / "rules of the road.": The non-negotiables about how work should be done. Mine are mostly about doing technical work carefully, e.g., "don't take sloppy shortcuts," "show evidence before claiming something works," "understand what a change affects before making it," "always test." For a non-technical assistant these would instead be things like "always double-check dates and dollar amounts," or "never send anything on my behalf without asking." The point of the global file: capture your identity, your voice, and your quality bar. Layer 2 — the PROJECT file: this assistant's job and toolkit This one is bigger and more operational. It turns a general-purpose AI into a specific assistant with a specific job. My project file for my executive assistant covers: 1. The assistant's mission: One clear statement of what this assistant is for. For mine: "keep track of everything and help me get it done, and act as a second brain." Everything else serves that. 2. Background it should always know: Pointers to a few reference files that get loaded automatically about you, your work, your team, your current priorities, your goals. So the assistant starts every session already "read in." 3. What tools it's allowed to use: The concrete systems it can touch: email, notes, calendar, code repositories and, importantly, what it must NOT touch (e.g., my work systems are off-limits, and certain data must never be sent to outside services). Boundaries matter as much as capabilities. 4. Reusable workflows ("skills"): Named routines the assistant has learned for recurring tasks, e.g., "write up my daily to-do list," "prep me for my 1-on-1," "research a topic and save a report," "back up my data." Instead of re-describing a multi-step task, you just invoke the skill. New ones get added as patterns repeat. 5. Memory and a decision log: Rules for what to remember long-term (preferences, ongoing projects) and a running, append-only log of important decisions and why, so the reasoning behind choices isn't lost. 6. Housekeeping conventions: Small "how we keep the workspace tidy" rules: where different kinds of files live, keep context files current, and (a nice one) never delete finished work, move it to an archive instead. The point of the project file: define the assistant's role, wire up its tools and guardrails, and give it a growing library of workflows and memory. ---

Google AI Studio just killed the manual chatbot. No more babysitting every AI prompt. No more paying for expensive agent tools. Here’s the Google AI Managed Agents play 👇 → Autonomous execution: One prompt, one finished project. → Smarter reasoning: Gemini models now build and run code. → Environment Hooks: Set rules to block or approve AI actions. → Scheduled Triggers: Set your AI to run on a weekly loop. → Free Tier: Test and build full workflows for $0. Stop prompting and start building. The era of autonomous AI is finally here. Want the full guide? DM me.

Most people use AI to complete isolated tasks. The real leverage comes from connecting those tasks into a system. Here is the 7-part AI Build Stack that turns one person into a product team. https://t.co/BA8AI76oay

BAIclaw is making AI Agent creation more accessible with a simple, no-code approach. Instead of dealing with complex configurations or writing code from scratch, users can now design, build, and manage AI Agents through an intuitive visual interface. Key highlights: ▫️No-code agent creation ▫️Free to use ▫️Local-first operation ▫️Multi-platform support This approach lowers the barrier for non-technical users who want to explore AI Agents while still giving experienced builders a faster and more efficient way to prototype and manage their ideas. As AI Agents become a bigger part of digital workflows, tools that combine simplicity with flexibility will play an important role in bringing more people into the ecosystem. @BAI_AGI @justinsuntron #TRONEcoStar