How to Use Ollama 0.6 and Continue.dev 0.10 to Automate Your Job Search and Land 5 Interviews a Week

Automate Your Job Search with Ollama 0.6 & Continue.dev 0.10 to Land 5 Interviews Weekly

The modern job search is a numbers game, but manual tailoring of resumes and cover letters burns hours for minimal returns. Most job seekers spend 10+ hours a week customizing applications, only to get a 5-10% response rate. What if you could cut that time to 1 hour a week while tripling your response rate? That’s exactly what you get when combining Ollama 0.6 (local large language model runner) and Continue.dev 0.10 (AI-powered workflow tool) to automate your entire job search pipeline.

What You’ll Need

• A computer running Windows, macOS, or Linux
• Ollama 0.6 installed (verify with ollama --version in terminal)
• Visual Studio Code (VS Code) with the Continue.dev 0.10 extension installed
• A base resume in plain text or Markdown format
• A local LLM pulled via Ollama (we recommend llama3:8b for general tasks, run ollama pull llama3 to download)

Step 1: Integrate Ollama 0.6 with Continue.dev 0.10

Continue.dev works with any LLM, but configuring it to use your local Ollama instance eliminates API costs and rate limits. Follow these steps:

1. Open VS Code and click the Continue icon in the left sidebar
2. Click the gear icon in the Continue panel to open config.json

3. Replace the default config with the following to point to Ollama:
   

   {
     "models": [
       {
         "title": "Ollama Llama 3",
         "provider": "ollama",
         "model": "llama3",
         "apiBase": "http://localhost:11434"
       }
     ]
   }
   

4. Save the config file and restart VS Code

5. Test the integration: type "Hello, are you working?" in the Continue chat panel. You should get a response from Llama 3 via Ollama.

Step 2: Automate Job Description Parsing

Manually reading job descriptions to extract keywords is tedious. Use Continue.dev to parse job descriptions into structured data automatically:

1. Save job descriptions as .txt or .md files in a dedicated jobs folder
2. Open a job description file in VS Code, then open the Continue chat panel

3. Use this prompt to extract key details:
   

   Parse the following job description and return a JSON object with these fields:
   - required_skills: array of required technical and soft skills
   - preferred_qualifications: array of preferred requirements
   - key_responsibilities: array of core job duties
   - company_values: array of mentioned company culture keywords
   - job_title: string
   - company_name: string
   
   Job description:
   [paste full job description here]
   

4. Copy the returned JSON and save it as [company]-[job-title]-parsed.json in your jobs folder

Process 10+ job descriptions in 15 minutes by batching prompts, and you’ll have structured data for every application.

Step 3: Tailor Resumes at Scale

Tailoring your resume to each job is the #1 factor in getting interviews, but it takes 30+ minutes per application manually. Automate this with Continue.dev:

1. Keep a base resume in base-resume.md with your full work history, skills, and projects

2. For each job, use this prompt in Continue chat:
   

   Using the base resume below and the parsed job data, generate a tailored resume that:
   1. Matches all keywords from the parsed job data
   2. Highlights relevant experience first
   3. Removes irrelevant sections
   4. Keeps the same formatting as the base resume
   
   Base resume:
   [paste base-resume.md content]
   
   Parsed job data:
   [paste parsed JSON file content]
   

3. Save the generated resume as [company]-[job-title]-resume.pdf (use a Markdown to PDF converter if needed)

You can generate 10 tailored resumes in under 30 minutes with this workflow, versus 5 hours manually.

Step 4: Generate Custom Cover Letters

Generic cover letters get ignored. Use Continue.dev to generate personalized cover letters for every application:

1. Use this prompt in Continue chat for each job:
   

   Write a 300-word cover letter for the job described by the parsed data below. The cover letter should:
   1. Address the hiring manager (use "Hiring Team" if no name is available)
   2. Reference the tailored resume attached
   3. Highlight 2-3 relevant projects from the resume that match job requirements
   4. Explain why you’re a good fit for the company culture
   
   Parsed job data:
   [paste parsed JSON]
   
   Tailored resume:
   [paste tailored resume content]
   

2. Save each cover letter as [company]-[job-title]-cover-letter.pdf

Step 5: Automate Application Tracking

Losing track of applications hurts your chances of following up. Use Continue.dev to maintain a central tracker:

1. Create a tracker.csv file with columns: Job Title, Company, Date Applied, Status, Resume Path, Cover Letter Path

2. After submitting each application, use this prompt to update the tracker:
   

   Append the following row to the tracker CSV at [path/to/tracker.csv]:
   Job Title: [job title]
   Company: [company name]
   Date Applied: [today's date]
   Status: Applied
   Resume Path: [path to tailored resume]
   Cover Letter Path: [path to cover letter]
   
   Keep the existing CSV header and rows intact.
   

Step 6: Scale to Land 5 Interviews a Week

With your pipeline set up, here’s how to hit 5 interviews weekly:

• Spend 1 hour every morning processing 10 new job descriptions: parse all 10, generate tailored resumes and cover letters for each
• Submit all 10 applications, update your tracker
• With a 50% response rate (standard for tailored applications), 10 applications yield 5 interviews
• Adjust your batch size: if you want more interviews, process 20 jobs a week for 10 interviews

Since Ollama runs locally, you have no API costs, no rate limits, and full privacy over your job search data.

Best Practices

• Always review generated resumes and cover letters before submitting: LLMs can make minor errors, so a 2-minute check prevents mistakes
• Adjust Ollama model parameters: set temperature: 0.3 in Continue config for more consistent, less creative output
• Use different models for different tasks: codellama for technical roles, mistral for faster processing of non-technical jobs
• Keep your base resume updated with new projects and skills every week

Conclusion

Combining Ollama 0.6 and Continue.dev 0.10 cuts your job search time by 90% while improving application quality. By automating repetitive tasks, you can focus on preparing for interviews instead of filling out forms. Try this workflow today, and you’ll be landing 5 interviews a week in no time.

Get started: Download Ollama 0.6 | Install Continue.dev 0.10