--- title: Can Regular People Code Now? My Journey from Skeptic to Builder description: Deploying apps as a guy without coding know-how image: https://brage.ai/hubfs/image-Jan-09-2026-11-01-25-4290-AM.png --- [Skip to content](https://brage.ai/brage-ai-blog/can-regular-people-code-now-my-journey-from-skeptic-to-builder#main-content) [![](https://brage.ai/hs-fs/hubfs/CI2_114.png?width=114&height=114&name=CI2_114.png)Homepage](https://brage.ai/) - [Products](https://brage.ai/products) - [Blog](https://brage.ai/brage-ai-blog) - [Company](https://brage.ai/company) Get started - [Products](https://brage.ai/products) - [Blog](https://brage.ai/brage-ai-blog) - [Company](https://brage.ai/company) Get started ![](https://brage.ai/hs-fs/hubfs/image-Jan-09-2026-11-01-25-4290-AM.png?width=1024&height=1024&name=image-Jan-09-2026-11-01-25-4290-AM.png) Agents AI # Can Regular People Code Now? My Journey from Skeptic to Builder ![Christian Blom](https://7528315.fs1.hubspotusercontent-na1.net/hub/7528315/hubfs/raw_assets/public/mV0_d-cms-elevate-theme_hubspot/elevate/images/avatar-placeholder.jpg?width=48&height=48&name=avatar-placeholder.jpg) Christian Blom January 11, 2026 My journey in AI began a little over a decade ago with **machine learning**. back then, it was purely a tool for recognizing patterns and structures in data—invaluable for tasks like commissioning renewable power plants or optimizing battery sizing based on energy consumption. The next stop was **deep neural networks and machine vision**. Fuelled by the self-driving car revolution, this technology made analyzing images and video reasonably simple. Suddenly, satellite imagery or live video streams could be ingested and processed by small on-site computers, like the Nvidia Jetson Nano. Then, in 2023, **Large Language Models (LLMs)** arrived. At first glance, the performance was impressive, though often marred by "hallucinations." From a business perspective, it looked promising, but I remained skeptical: was this just a fancy search engine, or perhaps a glorified translator? To find the answer, I spent the last few months diving deep into LLMs—specifically from the perspective of a guy who **cannot code**. I am resurfacing now with a wealth of new knowledge, a few painful experiences, and a fresh perspective on the corporate utility of LLMs. In my context, LLMs fall into two distinct buckets: 1. **The Application:** Software solutions that utilize LLMs to deliver business value. 2. **The Build:** The actual tools used to create that software. Let’s focus on the latter. **Can I, with no background in coding, build my own software?** The simple answer is **yes**. I have identified three main approaches to doing this: **No-Code Automation**, **Hosted "Vibe Coding"**, and **Agentic IDEs**. --- ### 1. No-Code Automation (The Integrator) *Tools tested: n8n, Make, Zapier.* These tools allow you to build workflows using visual nodes. Tools like **n8n** are particularly powerful because they are built on solid open-source frameworks like LangChain, making the underlying code for flows and agents very robust. **The Experience:** Most of what I initially needed could be done here. However, the downside is that you still need to understand system logic. Building a web scraper is possible, but you must figure out exactly which nodes to use. While AI helpers exist within these tools, they tend to gravitate toward paid third-party solutions and cannot "invent" new nodes for you. - **Pros:** Great for "If X, do Y" logic. - **Cons:** Complex logic becomes a hassle; reliability issues arise with scale; no native Front-End/UI (you are stuck connecting to Google Sheets, Forms, or relying on external UIs). **Winner:** **n8n** (for its deep integrations and open template approach). - *Verdict:* Excellent for backend logic, but not a complete software solution alone. **Apps I built with n8n:** - **RAG Pipeline:** Connected to Google Docs for document retrieval. - **Blog Post Writer:** An automated flow tied to a Lovable front-end, Google Forms, and Hubspot for publishing. --- ### 2. Hosted "Vibe Coding" Solutions *Tools tested: Lovable, Base44, Riff.* As the name implies, these are hosted environments that provide an "Agentic IDE." You prompt, and they handle the code and hosting. **Lovable** focuses on glossy, consumer-facing apps with a "prototype" feel, while **Riff** targets enterprise customers building internal tools. **The Experience:** While impressive, my challenge is that neither solution is ready for a corporate environment. They are still stuck in a "single developer/startup" mindset. I encountered four specific governance gaps: 1. **Fragmentation:** I wanted a unified look and feel across all apps with a shared admin dashboard. Currently, I am forced to build one massive app to achieve this, as I cannot monitor or theme multiple small apps centrally. 2. **Architecture Drift:** I want a "system prompt" at the account level to enforce architectural standards (e.g., "Always use Airtable for database," "Always use LangSmith for monitoring"). Without this, the AI hallucinates a new tech stack for every app. 3. **API Key Management:** There is no centralized `.env` file. I have to manually paste API keys (Gemini, OpenAI) into every single app. A centralized secret manager with user-level access controls is a must-have for business use. 4. **Black Box Frameworks:** These tools often "vibe code" without a declared framework. I want to instruct the builder to use **LangGraph** for complex agents to ensure the code is trustworthy and stable. If the user doesn't know to ask for this, the output is random and potentially unstable. **Winner:** **Riff** (due to its focus on internal business tools). - *Verdict:* Promising, but I hope they solve the governance issues above. **Apps I built with Riff:** - **Deep Research Agent:** A 5-agent system in LangGraph with LangSmith tracing. Four agents conduct research (via Tavily) and fact-checking, while the fifth segments leads and updates the CRM (Hubspot). - **Web Monitor/Scraper:** Monitors key pages, stores data in Airtable, and works with a Lead Identifier agent. --- ### 3. The Agentic IDE (The Powerhouse) *Tools tested: Visual Studio Code (Copilot), Kiro (Amazon), Anti Gravity (Google).* This is the most powerful but also the most complex category. **Cursor** started this trend, but the field is evolving rapidly. These systems differ from hosted solutions because you are closer to the raw code. **The Experience:** Development happens at lightning speed. I tested **Kiro** early on, but eventually jumped to **Anti Gravity** as it introduced significant improvements. Like the hosted solutions, these tools are immature but improving daily. The major advantage here is control—things like `.env` files and API keys are much easier to manage. However, you need to know *what* to ask for, and you must verify that the agent hasn't "forgotten" early instructions during long sessions. The biggest historical hurdle here has been **deployment**, but this is being solved. **Winner:** **Anti Gravity** (by a significant margin). Three features set it apart: 1. **Browser Testing:** The agent can actively click and test the app in a web browser to verify its own code. 2. **Parallel Agents:** You can have multiple agents working simultaneously, allowing them to maintain shorter, more focused context windows. 3. **Direct Deployment:** Seamless integration with Google Run and Kubernetes. **Apps I built with Kiro:** - **IoT Edge App:** An Edge Lambda application in a Greengrass runtime, deployed to a Raspberry Pi 5. - **Local AI App:** Running on a laptop with local LLM support. **Apps I built with Anti Gravity:** - **AI Hub:** A suite of 8 connected apps. - **Backstage Automation:** A set of 4 separate agents handling background tasks. - **Meeting Support:** A Chrome extension for live meeting assistance. - **Voice Agent:** An app integrating ElevenLabs for voice conversation. --- **What’s Next?** In the following blog posts, I will break down each of these apps in detail—sharing exactly how they were made, the specific prompts used, and how they are performing in the real world. 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