The simpler way to automate.
Most workflow tools expect you to think like both a product manager and a software engineer.
You have to:
- Map out triggers and actions in detail
- Navigate a builder UI or config file
- Translate your human thought into rigid, machine-friendly steps
The problem is this isn’t how work really starts. Work starts from intent, a conversation, a quick thought — a “we should probably…” moment.
Previously, the issue has been that the tools and infrastructure didn’t exist to make this ‘vibe coding’ system work as one. Between the UI, the moments of delight, and the full-stack control, software engineers couldn’t have access to the detail they wanted, and non-technical builders would run into barriers when they suddenly had to fix something through code, or when they simply wanted something instantly usable.
Vibe coding removes this translation layer. You just describe what you want in plain language, and the system generates the structure, integrations, and execution plan automatically.
How vibe coding works
Say you want to:
“Ping everyone who hasn’t reviewed the doc by EOD, then summarize comments tomorrow.”
Just ask, and CodeWords will:
- Understand the intent – identify actions like reminders, filtering, and summarization.
- Connect to the right tools – e.g., Slack, Notion, Calendar.
- Build the flow automatically:
- 3PM today → Remind non-reviewers
- 10AM tomorrow → Summarize comments and post them
No drop-downs. No syntax. No manual setup. Just your words, turned into a fully functioning workflow.
Before now, this hasn’t been possible. Using tools like n8n or Zapier has required a lot of manual setup, and has typically cut out a portion of the prospective user base. Instead, tools like Cursor and Lovable have enabled more individuals to become ‘developers’.
Essentially, vibe coding involves prompting AI via natural language to help you write code, sometimes writing this code entirely. Yes, there are still limitations, but the coding scene has never looked more malleable. The barrier to entry is lower than ever before, and CodeWords is filling this vibe coding space for automation building.
Why it matters.
Traditional workflow building starts from structure, whereas vibe coding starts from context. This building journey is naturally much faster. Rather than starting to build from planning and sequences of events, you can just start typing your context and Cody, the CodeWords builder agent, will put the workflow together for you.
This matters when tools like n8n still involve hours of setup and a good level of technical expertise to start giving your team the returns it’s after. If any team member feels empowered to start building automations that they knew would save you hours, they’ll now be able to do this, regardless of their technical background.
Traditional automation → Vibe coding with CodeWords
Start in a builder tool → Start in conversation
Think like a developer → Speak like yourself
Define structure first → Let structure emerge from intent
Suited for known, repeatable processes → Works for fluid, evolving work
This means more of those “we should automate this” ideas actually get implemented, right when you think of them.
Examples for your business use cases
Marketing
“Save 'customer love' quotes from Slack into my Notion database.”
→ CodeWords maps Slack messages from your #customer-love channel into your Notion ‘Quote library’ and fills in the database automatically.
Engineering
“Flag any PR open for more than 2 days without a review in standup.”
→ CodeWords watches the repo and adds alerts to your daily summary.
Sales/Customer Success
“After onboarding calls, create a follow-up checklist with owners.”
→ CodeWords links call notes to a workspace checklist instantly.
The challenge and the opportunity
Vibe coding isn’t just a shortcut, it’s a technical leap.
This is because:
- Natural language is messy and ambiguous
- Understanding intent isn’t enough, you need accurate and structured translation
- Execution demands deep integrations and persistent memory
The insight we believe makes a different is that most intent lives in chat. We communicate most commonly and genuinely through natural language. Yet, there are few ways to-date that bridge this informational barrier between chat and technical building.
Chat is where work starts, and that’s where we can capture intent before it’s lost.
Getting started
The good thing, is that you don’t have to change your process. You can keep working as you always do — just ask CodeWords to detect your mentions of tasks, next steps, or actions, and it will create the workflow that matches. You always have the option to refine or customize it later, but the heavy-lifting is done automatically.
This is vastly different to the tools that still require endless setup, altered workflows, ongoing maintenance, and many team hours trying to align on the foundational processes that have to work once they’re implemented because they're too time-consuming to build again.
Making your work move faster shouldn’t have been this hard, and now it doesn’t have to be.
CodeWords is part of the new generation of tools that are rooted in natural language, are endlessly customizable, and allow you to get started instantly.
The shift
Work used to flow from config, but now it can flow from conversation.
For fast-moving teams, vibe coding means less friction, more momentum, and fewer missed opportunities to automate. Your job is to keep the ideas coming, and let CodeWords handle the execution.
Vibe coding: instantly turn ideas into automations.
Vibe coding lets you turn ideas into automations instantly. Instead of mapping triggers, navigating UIs, or writing code, you just describe your intent in plain language. CodeWords interprets it, connects the right tools, and builds the workflow automatically. This shifts automation from rigid, technical setup to fluid, conversational creation — making it accessible to anyone, not just developers.
As workflow automation evolves, 2025 brings a range of Zapier alternatives offering better pricing, deeper integrations, and AI-driven capabilities tailored to custom business needs.
As workflow automation evolves, 2025 brings a range of Zapier alternatives offering better pricing, deeper integrations, and AI-driven capabilities tailored to custom business needs.
As workflow automation evolves, 2025 brings a range of Zapier alternatives offering better pricing, deeper integrations, and AI-driven capabilities tailored to custom business needs.
As workflow automation evolves, 2025 brings a range of Zapier alternatives offering better pricing, deeper integrations, and AI-driven capabilities tailored to custom business needs.
We document NeurIPS 2024 and the notable shift in the AI research community: rather than scaling model size indefinitely, researchers are unlocking performance gains through refined post-training methods and smarter inference strategies.
We document NeurIPS 2024 and the notable shift in the AI research community: rather than scaling model size indefinitely, researchers are unlocking performance gains through refined post-training methods and smarter inference strategies.
We document NeurIPS 2024 and the notable shift in the AI research community: rather than scaling model size indefinitely, researchers are unlocking performance gains through refined post-training methods and smarter inference strategies.
We document NeurIPS 2024 and the notable shift in the AI research community: rather than scaling model size indefinitely, researchers are unlocking performance gains through refined post-training methods and smarter inference strategies.
Agemo released arcsolver, an open-source Python library designed to tackle the ARC-AGI challenge by combining large language models with an object-centric framework for visual reasoning, achieving up to 85% accuracy on the public training set.
Agemo released arcsolver, an open-source Python library designed to tackle the ARC-AGI challenge by combining large language models with an object-centric framework for visual reasoning, achieving up to 85% accuracy on the public training set.
Agemo released arcsolver, an open-source Python library designed to tackle the ARC-AGI challenge by combining large language models with an object-centric framework for visual reasoning, achieving up to 85% accuracy on the public training set.
Agemo released arcsolver, an open-source Python library designed to tackle the ARC-AGI challenge by combining large language models with an object-centric framework for visual reasoning, achieving up to 85% accuracy on the public training set.
No-code tools promised accessibility, but often fall short—steep learning curves, limited flexibility, and developer dependencies persist. We’re changing that.
No-code tools promised accessibility, but often fall short—steep learning curves, limited flexibility, and developer dependencies persist. We’re changing that.
No-code tools promised accessibility, but often fall short—steep learning curves, limited flexibility, and developer dependencies persist. We’re changing that.
No-code tools promised accessibility, but often fall short—steep learning curves, limited flexibility, and developer dependencies persist. We’re changing that.
At Agemo, our mission to democratize software creation through AI led us to an unexpected challenge: tackling the ARC-AGI benchmark. In this post, we explore how our research in neurosymbolic reasoning, skill acquisition, and guided program search intersects with the ARC framework—highlighting our custom DSL, object-centric grid modeling, and early results with LLM-based solvers. This first installment unpacks the core problem and outlines the strategy that bridges visual reasoning with AI-powered software generation.
At Agemo, our mission to democratize software creation through AI led us to an unexpected challenge: tackling the ARC-AGI benchmark. In this post, we explore how our research in neurosymbolic reasoning, skill acquisition, and guided program search intersects with the ARC framework—highlighting our custom DSL, object-centric grid modeling, and early results with LLM-based solvers. This first installment unpacks the core problem and outlines the strategy that bridges visual reasoning with AI-powered software generation.
At Agemo, our mission to democratize software creation through AI led us to an unexpected challenge: tackling the ARC-AGI benchmark. In this post, we explore how our research in neurosymbolic reasoning, skill acquisition, and guided program search intersects with the ARC framework—highlighting our custom DSL, object-centric grid modeling, and early results with LLM-based solvers. This first installment unpacks the core problem and outlines the strategy that bridges visual reasoning with AI-powered software generation.
At Agemo, our mission to democratize software creation through AI led us to an unexpected challenge: tackling the ARC-AGI benchmark. In this post, we explore how our research in neurosymbolic reasoning, skill acquisition, and guided program search intersects with the ARC framework—highlighting our custom DSL, object-centric grid modeling, and early results with LLM-based solvers. This first installment unpacks the core problem and outlines the strategy that bridges visual reasoning with AI-powered software generation.