Vibe‑Coding: Transform Data into Actionable Code

What Is OpenCode?

OpenCode is an AI‑driven coding assistant that helps developers write, test, and optimise software without leaving their favourite IDE or terminal. Built on a modular architecture, it can plug into any Large Language Model (LLM) you prefer, from open‑source models hosted on‑prem to cloud‑based services such as Ollama Cloud. The platform’s core promise is “code‑first AI” – you give it a high‑level intent, and it returns ready‑to‑run code, documentation snippets, or even complete project scaffolds.

OpenCode’s documentation describes it as a “practical guide for AI‑coding assistants” and highlights three main workflow pillars: Plan, Build, and Vibe‑Coding [1].

Vibe‑Coding: Turning Real‑World Data Into Code

Vibe‑Coding is OpenCode’s specialised mode for scraping, analysing, and reacting to live information streams. Instead of feeding the model static prompts, you let it “listen” to the internet, RSS feeds, or APIs and generate code that processes that data in real time. Typical use‑cases include:

• Monitoring breaking security news and automatically generating alerts.
• Pulling product listings from e‑commerce sites and feeding them into a recommendation engine.
• Watching social‑media sentiment and updating a dashboard on the fly.

The key advantage is that the AI creates the data‑pipeline for you, so you spend more time interpreting results than wiring together HTTP requests, parsers, and storage layers.

Connecting Ollama Cloud and Running Kimi 2.5 – Pros and Cons

Ollama Cloud is a managed hosting environment for open‑source LLMs. It offers a simple API, automatic scaling, and GPU‑backed inference – all while keeping your data inside the provider’s secure enclave. Kimi 2.5, the latest release from the Kimi model family, is tuned for code generation, reasoning, and instruction following. Pairing these two with OpenCode yields a powerful, self‑hosted coding assistant.

Pros

Cons

Overall, the combination works best for teams that value speed, privacy, and the ability to fine‑tune their LLM, and are comfortable managing modest cloud costs.

Planning a Prompt for OpenCode’s Plan Function

The Plan stage is where you define what you want to achieve before any code is produced. A well‑structured plan prompt gives the LLM enough context to design a sensible architecture, select appropriate libraries, and outline a step‑by‑step workflow.

Steps to Craft an Effective Plan Prompt

1. State the Goal Clearly – Use a single sentence that describes the end‑result.
2. Specify Input Sources – Mention APIs, RSS feeds, or file formats you intend to consume.
3. Define Output Requirements – Detail the data structure, storage destination, and any UI components.
4. List Constraints – Include language version, library bans, latency limits, or compliance rules.
5. Ask for a High‑Level Outline – Request a numbered list of modules and their responsibilities.

Example Plan Prompt

“Plan a Python project that monitors the latest cybersecurity breaking news from the US‑CERT RSS feed, extracts the headline, CVE identifier, and severity, stores the records in a PostgreSQL table, and sends a Slack alert for any CVE rated Critical. Use Python 3.11, the feedparser and psycopg2 libraries, and limit the total latency from fetch to alert to under 5 seconds.”

OpenCode will respond with a concise architecture diagram, a module list (Fetcher, Parser, DB‑Writer, Notifier), and a brief rationale for each choice.

Prompting for OpenCode’s Build Function

Once the plan is approved, the Build stage asks the model to generate the actual code. The prompt should reference the plan’s numbered steps and ask for complete, runnable snippets, not just fragments.

Build Prompt Structure

1. Re‑state the Plan ID – “Based on Plan #3…”
2. Request a File‑by‑File Implementation – “Create fetcher.py, parser.py …”
3. Ask for Inline Comments – “Explain each function in a single comment line.”
4. Demand a Test Stub – “Provide a minimal main.py that wires the modules together.”
5. Include Dependency List – “Generate a requirements.txt with exact versions.”

Example Build Prompt

“Build the project described in Plan #2. Generate the following files:

• fetcher.py – pulls the US‑CERT RSS feed using feedparser.
• parser.py – extracts headline, CVE, and severity, returning a dictionary.
• db_writer.py – inserts the dictionary into a PostgreSQL table named cve_alerts.
• notifier.py – posts a Slack message when severity is Critical.
  Include a requirements.txt and a main.py that orchestrates the flow. Add a single‑line comment above each function explaining its purpose.”

OpenCode will output ready‑to‑run code blocks, each file clearly labelled, and a short README that summarises the execution steps.

Vibe‑Coding in Action: Scraping Breaking Cyber‑Security News

Below is a complete, end‑to‑end example of how you could set up Vibe‑Coding to scrape public cyber‑security news, extract CVE information, and store it for analysis. The example assumes you have already connected OpenCode to Ollama Cloud with the Kimi 2.5 model.

1️⃣ Define the Vibe‑Coding Goal

“Continuously monitor the Krebs on Security blog, the Threatpost RSS feed, and the official National Vulnerability Database (NVD) API for new articles or CVE entries. For each item, capture the title, URL, publication date, and any CVE identifiers. Store the results in a MongoDB collection called cyber_news. Additionally, generate a daily summary email listing the top 5 most severe CVEs discovered that day.”

2️⃣ Prompt the Plan Function

Plan a Vibe‑Coding workflow that:
1. Polls the three sources every hour.
2. Normalises the data into a unified schema.
3. Writes records to MongoDB (Atlas or self‑hosted).
4. Sends a daily email via SendGrid with a markdown table of the top 5 CVEs.
Use Python 3.12, `httpx` for async requests, `pymongo` for DB access, and `jinja2` for email templating.

OpenCode will return a modular plan: scheduler.py, source_fetcher.py, normaliser.py, db_sink.py, summary_builder.py, email_sender.py.

3️⃣ Prompt the Build Function

Build the Vibe‑Coding project from the plan above.
Provide complete, async‑ready Python files for each module, a `requirements.txt`,
and a `docker-compose.yml` that launches MongoDB and a Celery worker.
Add a single‑line comment above every function.

OpenCode produces a set of files, each with clear responsibilities, and a docker-compose.yml that looks like this (simplified for readability):

version: "3.9"
services:
  mongodb:
    image: mongo:7
    ports:
      - "27017:27017"
    volumes:
      - mongodata:/data/db
  scheduler:
    build: .
    command: python -m scheduler
    depends_on:
      - mongodb
volumes:
  mongodata:

3️⃣ Run the Generated Code

# Clone the repository OpenCode gave you
git clone https://github.com/your‑team/cyber‑vibe‑coding.git
cd cyber‑vibe‑coding

# Install dependencies (OpenCode supplied exact versions)
pip install -r requirements.txt

# Start the Docker stack
docker compose up -d

# Kick off the scheduler (it will run forever)
python -m scheduler

You now have a self‑maintaining pipeline that continuously enriches a cyber‑security knowledge base.

4️⃣ Light‑Hearted Check‑In

“If the model ever suggests using a while(True) loop without a sleep, we’ll politely remind it that even AI needs a coffee break!”

Feel free to browse the OpenCode “Providers” guide for more details on switching between Ollama, OpenAI, or Anthropic endpoints: https://opencode.ai/docs/providers/.

Key Takeaway

OpenCode transforms high‑level intent into production‑grade code by separating the Plan and Build phases, and it extends this workflow with Vibe‑Coding for real‑time data ingestion. When paired with Ollama Cloud and the Kimi 2.5 model, you gain a fast, private, and customisable coding assistant that excels at generating and maintaining data pipelines such as a live cyber‑security news monitor.

To get started:

1. Sign up for Ollama Cloud, create an API key, and enable the Kimi 2.5 model.
2. Connect the key in OpenCode’s Providers settings (see the “Providers” page).
3. Use a concise Plan prompt to outline your Vibe‑Coding objective.
4. Feed the approved plan into the Build prompt to receive a full, commented codebase.
5. Deploy the generated files, monitor the scheduler, and enjoy automated alerts without writing the boilerplate yourself.

With the right prompt discipline—clear goals, defined inputs, explicit constraints—you’ll unlock a workflow where the AI does the heavy lifting, and you focus on the insights.

Happy coding! 🎉