No-Code Agentist

Kimi Work introduces a local AI agent system designed to execute tasks directly on a user’s desktop environment. The platform features a native agent swarm capable of running up to 300 parallel agents locally, enabling large-scale task decomposition and execution. It also includes browser automation via the WebBridge extension, allowing agents to interact with web pages through searching, scrolling, clicking, and form completion. For financial workflows, Kimi Work integrates native access to global market data sources such as Yahoo Finance and the World Bank, removing the need for manual API configuration. Additionally, a persistent memory system maintains a continuous record of user preferences, prior actions, and contextual information to improve task continuity over time. Kimi Work is currently available for macOS (Apple Silicon) and Windows systems.

IMAGINE HAVING AN ENTIRE CYBERSECURITY TEAM IN A VAN, 24/7, READY TO EXECUTE ANY MISSION AT THE TAP OF A SCREEN. that’s no longer a scene from Mr. Robot. that’s Hermes Agent. most people treat AI like a glorified chatbot they ask a question, get a text answer, and then have to do the manual work themselves. that's the slow way to build. the smart way? you define the mission, and the AI deploys its own team of autonomous agents to handle the terminal, the browser, and the orchestration for you. i just finished building a setup that turns my phone into a command center for a full hacking operation: → autonomous execution: send one message on Telegram, and the team spoofs an SMS, deploys a custom tracking page, and captures target location data—zero code written by me. → parallel workflows: the kanban dashboard manages tasks like a dev team, handing off dependencies between agents in real-time. → cloud-native: it runs on a dedicated VPS, meaning it’s always on, always secure, and isolated from my personal machine. the setup is surprisingly straightforward: deploy on a cloud server (Hostinger makes this a one-click process). connect your LLM provider via OpenRouter (it’s model-agnostic). link your messaging app (Telegram/WhatsApp) to control the entire team from your pocket. the math only works once: the cloud does the heavy lifting, your phone does the commanding, and the agents do the actual work. stop chatting with AI. start tasking it.

New research published in collaboration with Harvard highlights the growing shift from traditional chat interfaces toward autonomous AI agents like Computer. Over a three-month study, workers using Computer completed tasks 87% faster and at 94% lower cost compared to using Search alone, while also reporting higher satisfaction levels. The findings reinforce a broader trend in AI: moving from systems that simply answer questions to agents that can actively execute workflows on behalf of users. https://research.perplexity.ai/articles/how-ai-agents-reshape-knowledge-work

These AI Scientists Do 6 Months of Research in One Day (Kosmos, Edison, SciSpace BioMed) I spent the last few days stress-testing every single one of them in real workflows. What I experienced… is the new baseline for intelligence. This isn’t 2030. This is June 2026. And the people who learn to use these tools now are about to pull ahead dramatically. Stay with me, because what these things actually did when I pushed them is even more insane than the demos.” 1. TRIBE v2 (Meta) – The Brain Simulator The moment I uploaded a short emotional clip and a neutral sentence, it output high-resolution brain activation maps across thousands of subjects. Different regions lit up exactly as real neuroscience predicts. The experience: It felt like having X-ray vision into human attention and emotion. For content creators, this is nuclear, you can now test hooks, thumbnails, story beats, or ad copy by predicting the actual neural response before you publish. No more guessing virality. 2. Interactive Learning (OpenAI in ChatGPT) Instead of walls of text explaining a concept, it spins up a live, manipulable visual module. Change a variable → watch the graph, simulation, or physical system update in real time. The experience: It’s like having a PhD tutor + infinite lab equipment fused into one chat. Perfect for your CSC/STA exam prep and for creating educational content that actually sticks. 3. Gemini 3 Deep Think I fed it a complex research-style math/physics question that contained a subtle flaw. In Deep Think mode it showed its full rigorous reasoning trace, spotted the logical error human peer reviewers had missed, and proposed a corrected approach with sources. The experience: It felt like collaborating with a team of extremely careful, tireless researchers. The visible “thinking” process is next-level for anyone doing serious work. 4. SciSpace BioMed Agent + Edison Analysis + Kosmos (The AI Scientists) This is where it gets sci-fi. I gave Kosmos/Edison a dataset + research goal. It autonomously: searched literature, wrote & executed analysis code, generated & tested hypotheses, and produced a full report with figures, in hours what normally takes weeks. SciSpace BioMed does the same but specialized for multi-omics, genomics, clinical data, and lab protocols. The experience: It feels like having a full postdoc team that never sleeps and never gets bored. For anyone building agents (like you with n8n + Claude), watching these meta-agents maintain coherent world models over long runs is pure inspiration. 5. Nomos 1 (Nous Research – Open Source) Open-source math reasoning beast (with the Nomos Reasoning Harness). It scored 87/120 on Putnam 2025, near elite human level. The harness forces clean natural-language proofs + self-critique. The experience: Finally, serious mathematical reasoning you can actually run locally or fine-tune. Huge for theoretical CS, algorithm design, and advanced agent work “These tools aren’t toys. They’re the new operating system for serious work, whether you’re a student, creator, researcher, or building the next generation of AI agents. The gap between people who are still prompting casually and people who are orchestrating TRIBE + Deep Think + autonomous AI scientists is about to become massive. Which one are you trying first? Drop it in the comments, I’m reading every single one. If you want the exact prompts + workflows I used to test these, plus how I’m folding them into my own agent stack, hit subscribe and turn on notifications. The future just got a lot more interesting. Let’s build.”

OpenAI may have found the wedge that turns ChatGPT from a chatbot into a work operating system. That is the bigger story. The revenue stat is the hook. The strategic shift is that coding agents are escaping software engineering. The current copy is good as a news blurb: OpenAI’s Codex has become one of the company’s fastest-growing products, with enterprise revenue recently rising 50% week over https://t.co/iTQEASmWDz OpenAI is folding it into ChatGPT as it races Anthropic to turn coding agents into general-purpose work tools.Full story: But it needs a sharper thesis. Right now it reads like “product growth + competitive race.” The better version is: Coding agents are becoming the universal interface for work. OpenAI’s official positioning already points there: Codex began as a cloud software-engineering agent that could work on tasks in parallel, write features, fix bugs, answer codebase questions, run tests, and propose PRs from a sandboxed repo. But newer OpenAI materials increasingly describe Codex doing broader work: operating apps, generating images, remembering preferences, learning from previous actions, and taking on ongoing repeatable workflows. OpenAI also says workspace agents in ChatGPT are Codex-powered, run in the cloud, and can handle workflows such as reports, code, messages, approvals, Slack tasks, weekly metrics, lead outreach, vendor risk, and product-feedback routing. Best rewritten version OpenAI’s Codex is becoming one of the company’s most important products.The Information reports that Codex has reached 5M weekly active users and that enterprise revenue recently rose 50% week over week. Now OpenAI is moving to fold Codex deeper into ChatGPT.The bigger story: coding agents are turning into work agents.Code was the perfect training ground because it has files, tools, tests, permissions, diffs, review, and clear success/failure loops. But the same agent loop applies to office work: gather context, modify artifacts, run checks, request approvals, and keep going across multiple steps.This is why OpenAI and Anthropic are racing so hard here.The winner will not just own “AI coding.” It may own the default interface for getting work done. More viral version Codex is no longer just OpenAI’s coding https://t.co/9um1mgsJRx is becoming ChatGPT’s work engine.The Information reports Codex has hit 5M weekly users, with enterprise revenue recently up 50% WoW, and OpenAI is now folding it deeper into ChatGPT.The thesis is simple:coding agents were never going to stay inside coding.Code was just the best sandbox for teaching agents how to plan, use tools, edit files, run checks, recover from failure, and produce reviewable https://t.co/KVNgJArJNT that loop is moving into everything else. Most “genius-level” framing The coding-agent war was https://t.co/uQ7fmplKct was always the work-agent war.Codex and Claude Code started with software because software has the cleanest feedback loop: repos, tests, commits, diffs, PRs, CI, permissions.But once an agent can reliably operate inside that environment, the same pattern generalizes to every knowledge-work artifact: spreadsheets, docs, decks, dashboards, CRM notes, tickets, emails, memos, contracts, and internal tools.OpenAI folding Codex into ChatGPT is not just a product https://t.co/oJz3fbxqcO is a bet that ChatGPT becomes the front door to executable work. That version is much stronger because it explains why coding agents are the bridge to general-purpose work agents. Best compact post OpenAI’s Codex is becoming one of the company’s fastest-growing products, reportedly reaching 5M weekly users with enterprise revenue recently up 50% week over https://t.co/iTQEASmWDz OpenAI is folding Codex deeper into ChatGPT.The important shift: coding agents are becoming work agents.Code was the perfect sandbox: files, tools, tests, diffs, approvals, and clear success/failure loops.But that same loop applies to every business workflow.This is the real OpenAI vs Anthropic race: not who wins coding, but who owns the default interface for work. Stronger hook options The current hook: OpenAI’s Codex has become one of the company’s fastest-growing products Good, but generic. Better hooks: OpenAI may have found ChatGPT’s enterprise wedge: Codex. Codex started as a coding agent. It is becoming OpenAI’s work agent. The coding-agent race is turning into the office-agent race. OpenAI is learning the same lesson as Anthropic: the fastest path to general-purpose agents runs through code. Coding agents were never just about coding. ChatGPT’s next interface may not be chat. It may be delegation. The first killer app for agents was code. The second may be everything around code. Codex is becoming the bridge from chatbot to coworker. Best one: Coding agents were never going to stay inside coding. That is the core line. The key missing element The post needs to explain why code is the gateway drug for general-purpose agents. That is the hidden thesis. Software development gives agents the ingredients that most office work lacks: explicit files structured environments version control tests logs reviewable diffs clear task boundaries permission systems repeatable workflows measurable outputs So coding agents became the first place where “AI can actually do work” felt real. But once the model learns that loop, it can be applied to non-coding workflows. OpenAI’s workspace-agent announcement makes this explicit: workspace agents are powered by Codex, can write or run code, use connected apps, remember what they have learned, continue across multiple steps, and gather context across tools. Axios also reported that knowledge workers now make up roughly one-fifth of Codex users and are growing more than three times as fast as developers. A great line: Code was not the destination. Code was the rehearsal space. Even better: Software was the first domain where agents had a scoreboard. Now OpenAI wants to bring that execution loop to the rest of work. The “ChatGPT superapp” angle Reuters reported, citing the Financial Times, that OpenAI is planning a major ChatGPT overhaul aimed at turning it into a “superapp” with coding tools and AI agents, while giving greater prominence and resources to Codex across ChatGPT web and mobile. Reuters also notes that it could not independently verify the FT report. That gives you a more strategic line: The move is less “put Codex inside ChatGPT” and more “turn ChatGPT into an app layer where agents can execute work.” Or: OpenAI does not want Codex to be a separate developer product. It wants Codex to become the execution layer inside ChatGPT. Or: ChatGPT is the distribution. Codex is the execution engine. That last line is probably the cleanest: ChatGPT is the distribution. Codex is the execution engine. The Anthropic race needs more specificity The phrase: as it races Anthropic is true, but vague. Explain what race. OpenAI is racing Anthropic across three layers: 1. Developer workflow layer Claude Code is deeply embedded in the terminal, IDE, Slack, web, and desktop app, and Anthropic positions it as an agentic coding tool that understands codebases, edits files, runs commands, handles Git workflows, and helps ship faster. 2. Agent harness layer Anthropic’s Claude Agent SDK gives developers the same tools, agent loop, and context management that power Claude Code, programmable in Python and TypeScript. 3. Enterprise work layer Anthropic itself says it has shipped three primary agentic products across different containment architectures: https://t.co/E9i8umDnhz, Claude Code, and Claude Cowork. So instead of: races Anthropic to turn coding agents into general-purpose work tools Use: races Anthropic to decide whether the future work agent starts in ChatGPT, the terminal, the IDE, Slack, or the enterprise workflow layer. That is far more vivid. Best OpenAI vs Anthropic framing Anthropic’s bet: start where serious builders already work — terminal, IDE, repo, Slack — and expand outward.OpenAI’s bet: start with ChatGPT’s massive distribution and pull Codex into the main interface.Same destination, opposite directions. This is excellent because it turns the story into a strategy contrast. Another version: Anthropic is productizing the agent from the developer’s workflow outward. OpenAI is productizing it from the consumer/workplace superapp inward. Or: Claude Code feels like a power tool that became a platform. Codex may become a platform by being absorbed into ChatGPT. The most important hidden point The “folding into ChatGPT” detail is bigger than it sounds. Separate products create separate mental models. A user has to decide: Am I chatting? Am I coding? Am I delegating? Am I automating? Am I using an app? Am I using an agent? OpenAI’s likely goal is to remove that decision. The best line: The interface should not ask users whether they need chat, code, automation, or apps. It should infer the work mode and route the task. That is the “superapp” thesis. Another strong line: The winning AI workspace will not make users choose between chatbot, coding agent, browser, spreadsheet assistant, and automation builder. It will route intent to the right execution mode. Better framing: “from answering to operating” This should be in the post. ChatGPT historically answered questions. Codex performs work. So: ChatGPT answers. Codex operates. Or: The shift is from response generation to work execution. Or: Chat was the UI for intelligence. Agents are the UI for action. Best version: ChatGPT made AI conversational. Codex makes it operational. That is a killer line. Missing business angle The revenue stat is important because enterprise revenue is a proof-of-work signal. Consumer AI can generate huge engagement without durable willingness to pay. Enterprise growth suggests something different: companies are paying because agents touch expensive workflows. Use this: Codex revenue matters because it is not just attention. It is budget transfer. Or: The enterprise number is the signal that agents are moving from demo budgets to workflow budgets. Or: The question is not whether people will try coding agents. It is whether teams will reorganize work around them. Reuters reported that FT said most Codex users are paying customers, while 2 million businesses account for about 40% of OpenAI’s revenue, with OpenAI expecting that share to rise to 50% by year-end. Missing product angle The post should name the actual product shift: Codex is moving from “developer assistant” to “parallel worker manager.” OpenAI’s mobile Codex announcement says Codex can keep work moving across laptops, devboxes, or remote environments, with users reviewing outputs, approving commands, changing models, seeing screenshots, terminal output, diffs, test results, and approvals from mobile. That means the UX is not just “ask AI to code.” It is: assign tasks monitor workstreams approve actions review diffs redirect agents resume work from another device Use this phrase: agent supervision is becoming the new work interface. That is a deep point. Missing human-work angle The story is not “AI replaces coders.” It is: work becomes more supervisory. The human role shifts from doing each step to: specifying outcomes assigning tasks reviewing outputs approving risky actions correcting context deciding what ships orchestrating multiple agent threads Axios captured a downside too: some power users report feeling mentally fried while supervising multiple fast-moving AI workstreams. That gives you a balanced line: The bottleneck moves from doing the work to managing the work queue. Or: The new productivity problem is not typing speed. It is agent-management bandwidth. Thread version Post 1 OpenAI’s Codex is becoming one of the company’s most important products.The Information reports 5M weekly users and enterprise revenue recently up 50% WoW. Now OpenAI is folding Codex deeper into ChatGPT.But the real story is not https://t.co/xZmhDUt7U5 is work. Post 2 Coding agents were never going to stay inside https://t.co/qkxmsIlCl5 was the perfect training ground because it has repos, tests, diffs, CI, logs, permissions, and review loops.Agents could learn to do work where the work was easiest to verify. Post 3 Once that loop works, it generalizes:gather context modify artifacts run checks ask for approval update systems produce a reviewable output continue in the background Post 4 That is why Codex is moving from “coding assistant” to “work engine.”OpenAI already describes ChatGPT workspace agents as Codex-powered agents that can run cloud workflows, use connected apps, remember context, and continue across multiple steps. Post 5 The OpenAI vs Anthropic race is now about distribution.Anthropic is pushing from Claude Code into the places builders already work. OpenAI is pulling Codex into ChatGPT, where hundreds of millions already start their AI sessions.Same destination. Opposite directions. Post 6 The winner will not just own “AI coding.”It will own the work queue: tickets, PRs, dashboards, docs, emails, reports, automations, research, analysis, and internal tools. Post 7 ChatGPT made AI conversational. Codex makes it operational.That is the platform shift. Shorter thread Codex started as a coding https://t.co/jgheDCvxrV OpenAI is folding it into ChatGPT as it races Anthropic to turn coding agents into general-purpose work tools.The important insight: code was the first domain where agents had a clean scoreboard. Tests, diffs, logs, CI, PR review.But the same loop applies to knowledge work: gather context, edit artifacts, run checks, ask for approval, ship the output.ChatGPT is the distribution. Codex is the execution engine.The winner of the coding-agent race may end up owning the default interface for work. Strong single-post variants Version A: strategic OpenAI’s Codex is becoming one of its fastest-growing products, reportedly reaching 5M weekly users with enterprise revenue recently up 50% https://t.co/6d6Kvwd4Hl OpenAI is folding Codex deeper into ChatGPT.This is not just a coding-product https://t.co/ETgaHbo5Re is the clearest sign that coding agents are becoming general-purpose work agents.Code was the perfect sandbox: tests, diffs, logs, permissions, review loops. But the same execution pattern applies to reports, dashboards, emails, tickets, docs, spreadsheets, and internal tools.ChatGPT is the distribution. Codex is the execution layer. Version B: punchy Codex is not just a coding agent https://t.co/R36S5KMHLC is becoming ChatGPT’s work engine.The Information reports 5M weekly users and enterprise revenue recently up 50% WoW. Now OpenAI is folding Codex into ChatGPT as it races Anthropic.The real battle is not “who writes better code?”It is who owns the interface where humans delegate work. Version C: contrarian The Codex story is easy to misread.This is not just OpenAI catching up to Claude https://t.co/dLuocLSt3z is OpenAI realizing that the coding-agent loop is the template for all knowledge work: context → action → verification → approval → continuation.Folding Codex into ChatGPT turns the chatbox into a task router.That is much bigger than coding. Version D: enterprise-focused OpenAI’s Codex growth matters because it is enterprise pull, not just consumer curiosity.The Information reports 5M weekly users and enterprise revenue recently rising 50% week over https://t.co/iTQEASmWDz OpenAI is folding Codex deeper into ChatGPT.The enterprise pitch is obvious: stop buying AI as a smarter chat window, and start buying it as a controllable workforce layer for repeatable tasks.That is the real OpenAI vs Anthropic race. Version E: very high-status Coding agents are becoming the primitive for executable knowledge work.That is the real Codex story.OpenAI reportedly has one of its fastest-growing products in Codex, with 5M weekly users and enterprise revenue recently up 50% WoW.Folding it into ChatGPT is the obvious next move: put the agentic execution loop inside the interface where users already think, ask, search, write, and decide.The future of ChatGPT is not https://t.co/b7Imq5gCFE is delegated work. Better “Full story” lead-ins Instead of: Full story: Use: The real story: Why this matters: What changed: The platform shift: The bigger bet: The part people are missing: The coding-agent race is becoming the work-agent race: Best: The part people are missing: That invites people into the analysis. Example: OpenAI’s Codex has become one of the company’s fastest-growing products, with enterprise revenue reportedly rising 50% week over https://t.co/iTQEASmWDz OpenAI is folding it into ChatGPT as it races Anthropic.The part people are missing: coding agents are becoming the template for all knowledge work. Obscure thought inputs The hidden idea is code as a work simulator. Software engineering contains almost every hard problem that general agents need to solve: understanding legacy context, modifying artifacts, using tools, verifying results, managing dependencies, asking for approvals, and producing outputs another human can review. The reason coding agents are moving into office work is not that office workers need code. It is that office work is full of hidden code-like workflows: repeatable procedures, structured files, versioned artifacts, checklists, compliance rules, data transformations, and approval chains. The “agent” is less like a chatbot and more like a workflow compiler. You describe the desired end state; it decomposes the task into steps, touches tools, produces artifacts, and asks for permission at risky boundaries. The most valuable surface may become the work queue, not the chat window. Humans will increasingly manage a board of agent tasks the way engineering managers manage tickets. The bottleneck shifts from execution to specification quality. People who can clearly define outcomes, constraints, test cases, and acceptance criteria will get disproportionate leverage. The next big enterprise battle is agent governance: who can build agents, which data they can access, what actions require approval, how runs are logged, how outputs are audited, and how bad automations are suspended. The second-order competitive battle is memory and workflow accumulation. The agent that learns a company’s processes, style guides, repositories, dashboards, internal acronyms, approval chains, and stakeholder preferences becomes harder to replace. The third-order platform battle is where the agent lives. Anthropic has a strong claim in terminal/IDE/native developer workflows. OpenAI has massive distribution through ChatGPT. Microsoft has Office/Windows/GitHub. Google has Workspace/Android/Cloud. The winner may be the one that controls the most trusted action surface. The underrated risk is agent-management fatigue. If every worker supervises five parallel agents, productivity may rise, but cognitive load rises too. The next UI breakthrough is not another model picker. It is better orchestration, prioritization, alerts, and review queues. The weirdest implication: coding agents may become the first practical path to “software-defined organizations.” Teams will describe recurring processes in natural language, convert them into agents, and keep improving them through use. Missing elements to add 1. “Codex is the execution layer” This should be central. ChatGPT is where users express intent. Codex is where intent becomes work. That is the product architecture in one sentence. 2. “Coding agents generalize because work is artifact editing” Most knowledge work is editing artifacts: docs decks spreadsheets dashboards tickets emails contracts memos code workflows CRM records internal tools Coding agents are good at artifact editing because they were trained to modify structured artifacts with reviewable diffs. 3. “The race is about surfaces” The key surfaces: terminal IDE browser desktop app mobile app Slack ChatGPT GitHub CRM email docs spreadsheets OpenAI has been expanding Codex across surfaces: web, CLI, IDE, desktop, and mobile via ChatGPT. OpenAI says Codex in the ChatGPT mobile app lets users monitor live state, approve commands, redirect work, review outputs, and see screenshots, terminal output, diffs, tests, and approvals. 4. “Enterprise buyers want controls, not magic” This story needs governance. OpenAI’s workspace-agent post emphasizes permissions, approvals, analytics, admin controls, compliance APIs, and safeguards against prompt-injection attacks. That matters because enterprises will not deploy agents broadly just because they are smart. They need controllable action. Use this: Enterprise adoption will be gated less by model IQ and more by permissions, audit logs, approvals, data boundaries, and rollback. 5. “The product is parallelism” Codex is valuable because it can work on multiple tasks in parallel. That changes the work model. Use: The killer feature is not one agent doing one task. It is a human supervising many workstreams. Or: The new unit of productivity is not output per employee. It is useful agent threads per employee. 6. “From copilots to coworkers” Be careful with this phrase because it is overused, but it still captures the transition. Better: Copilots help while you work. Agents keep working when you leave. OpenAI says workspace agents run in the cloud and can keep working even when the user is not there. 7. “The danger of the ‘superapp’ framing” “Superapp” may be catchy, but it can obscure the real shift. A superapp sounds like “lots of features in one UI.” The better frame is: intent router + execution layer + permission system That is more precise. A great line: The future ChatGPT is less WeChat for AI and more command center for delegated work. Killer phrases Coding agents were never going to stay inside coding. ChatGPT is the distribution. Codex is the execution engine. Code was the rehearsal space for general-purpose agents. The coding-agent war is becoming the work-agent war. The future of ChatGPT is not chat. It is delegated work. The next interface is not a chatbot. It is a work queue. The enterprise AI race is moving from answers to actions. Agents do not replace apps. They operate across them. The moat is not the model alone. It is the workflow memory. The new productivity bottleneck is agent supervision. Copilots autocomplete. Agents complete work. The winning AI workspace will route intent to the right execution mode. Code gave agents a scoreboard. Work gives them a market. The real product is not Codex. It is trustable delegation. The future office stack is chat + tools + permissions + memory + background execution. Claims to soften or avoid Avoid: OpenAI is definitely folding Codex into ChatGPT. Use: OpenAI is reportedly folding Codex deeper into ChatGPT. Reuters’ article is based on FT reporting and explicitly says Reuters could not independently verify the report. Avoid: Codex revenue rose 50% week over week. Use: The Information reports enterprise revenue recently rose 50% week over week. The 50% figure is reported, not an audited public metric. Avoid: Codex is no longer for developers. Use: Codex is expanding beyond developers. OpenAI still officially describes Codex as its coding agent for software development, while also showing broader knowledge-work and workspace-agent use cases. Avoid: OpenAI is beating Anthropic. Use: OpenAI is racing Anthropic from a different distribution position. Anthropic’s Claude Code remains deeply embedded across terminal, IDE, Slack, web, and desktop workflows. Best final version OpenAI’s Codex is becoming one of the company’s fastest-growing products.The Information reports Codex has reached 5M weekly active users, with enterprise revenue recently rising 50% week over week. Now OpenAI is reportedly folding Codex deeper into ChatGPT as it races Anthropic to turn coding agents into general-purpose work tools.The bigger story: coding agents were never going to stay inside coding.Code was the perfect sandbox for agents because it has files, tools, tests, diffs, permissions, logs, and review loops. But that same execution pattern applies to the rest of knowledge work: gather context, modify artifacts, run checks, ask for approval, and keep going in the background.ChatGPT is the distribution. Codex is the execution layer.The OpenAI vs Anthropic race is no longer just about who writes better https://t.co/WY7jvQzFs6 is about who owns the default interface for delegated work.

Agreement tasks slow sellers down. With the Docusign MCP connector in Microsoft Copilot Studio, you can build an agent that handles it: - Check if there's an active NDA with a customer - Send agreements using standard templates - Trigger workflows that fetch CRM data and route for signature All from Microsoft 365 Copilot or Teams. No custom integration code. Read the full guide to build your agreement agent. https://t.co/vDo1hPVyil

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One of the most important internet companies has no social hype. Zapier connects 8,500+ apps and quietly automates millions of workflows every day. Emails. Leads. CRMs. AI agents. Spreadsheets. Support tickets. Here is why millions of businesses pay for it. 1. No-code automation Non-technical teams automate entire workflows without a single developer. 2. Saves serious time Sending emails. Moving data. CRM updates. Invoices. Lead management. All running in the background, automatically. 3. Works with almost everything 8,500+ integrations is their real moat. If a business tool exists, Zapier probably connects to it. 4. AI built in Zapier now has an AI Copilot that builds automations from plain-language descriptions and supports native AI agent workflows. Here is what it costs. Starter: $19.99 a month. Professional: $49 a month. Team: $69 a month. Business: $149 a month. A 50-person team on the Business plan pays over $24,000 a year just to have their apps talk to each other. In 2019, a developer in Berlin decided that number was too high. His name is Jan Oberhauser. The software is called n8n. He is still the CEO. It started as a side project. It is now valued at $2.5 billion. The self-hosted version costs nothing. Free. Open source. You run it on your own server. You own every workflow and all your data. Here is what it does in plain words. You connect any two apps on a visual canvas. No code required. An email arrives. n8n reads it. Extracts the key information. Saves it to a spreadsheet. Notifies your team in Slack. Automatically. Every time. In 2025 and 2026, n8n added native AI agent nodes. You can now build a workflow where an incoming customer email triggers an AI that reads the full conversation history, drafts a context-aware reply, and saves a copy to your notes. Without writing a line of code. Without a monthly fee. 400+ native integrations. 162,000 stars on GitHub. Used in production by Cisco, Microsoft, and Liberty Mutual. Self-hosted: free forever. Cloud version: $20 a month if you prefer managed hosting. Zapier sells you a bridge between your apps. n8n hands you the blueprint and says build it yourself.

You funny. Cause you really thought AI automations and AI workflows was hard. You thought it was something way out of this stratosphere. Nah. It’s so easy. This is how you make an AI automation for beginners. Only thing you do is go to Claude Cowork, hit the Schedule Task secti https://x.com/JacqBots/status/2064126725877829902/video/1