Should I Build It Should I Build It
19 Apr 2025
Cars

News agent service to scrape major publications to get car ...

...recommendations

Confidence
Engagement
Net use signal
Net buy signal

Idea type: Swamp

The market has seen several mediocre solutions that nobody loves. Unless you can offer something fundamentally different, you’ll likely struggle to stand out or make money.

Should You Build It?

Don't build it.

Your are here

Your idea of a news agent service scraping major publications for car recommendations falls into a crowded space where several similar solutions have already emerged, and most haven't achieved significant traction. With 4 similar products already identified, the competition is present, and the engagement level is low, averaging only 1 comment per product. This suggests that there's either a lack of interest in existing solutions, or the existing products haven't solved the problem well enough to garner engagement. Considering the 'Swamp' category designation, this landscape indicates a high risk of blending in with mediocre solutions unless your approach offers a fundamentally different value proposition.

Recommendations

  1. Before investing further, deeply investigate why existing car recommendation services haven't gained substantial traction. Analyze their shortcomings in terms of user experience, data accuracy, recommendation relevance, and overall value. Understanding their failures is crucial to avoid replicating them.
  2. If you still believe in your idea, narrow your focus to a specific niche within the car recommendation space that is currently underserved. This could be based on vehicle type (e.g., electric vehicles, classic cars), user demographic (e.g., first-time buyers, eco-conscious consumers), or geographic location (e.g., urban commuters, rural drivers). Carve out a niche to minimize direct competition and cater to specific needs.
  3. Instead of directly competing, explore opportunities to build tools or services that enhance the capabilities of existing car recommendation platforms or dealerships. This could involve developing more sophisticated data analytics, personalized recommendation algorithms, or lead generation tools. Supporting existing players can be a faster path to market entry.
  4. Consider pivoting to adjacent problems within the automotive industry that may offer more promising opportunities. This could involve focusing on car maintenance, insurance, financing, or other related services where there is a greater unmet need. Explore areas with less competition and more potential for innovation.
  5. Given the challenges in this space, carefully evaluate whether this is the best use of your time and resources. There may be other startup ideas with a higher probability of success and a lower risk of getting stuck in a 'Swamp' scenario. Be honest with yourself about the potential for this idea to truly stand out and generate meaningful value.
  6. Leverage the feedback from similar products. For instance, 'Cardog' received positive feedback for shared content and update requests. Consider how you'd incorporate frequent data updates and community contributions into your service to keep it fresh and engaging, addressing a known user need.

Questions

  1. What unique data sources or algorithms can you leverage to provide car recommendations that are significantly more accurate and personalized than existing solutions?
  2. How can you build a sustainable competitive advantage in this market, considering the existing competition and the potential for larger players to replicate your features?
  3. What specific metrics will you use to measure the success of your car recommendation service, and how will you iterate based on user feedback and market trends?

Your are here

Your idea of a news agent service scraping major publications for car recommendations falls into a crowded space where several similar solutions have already emerged, and most haven't achieved significant traction. With 4 similar products already identified, the competition is present, and the engagement level is low, averaging only 1 comment per product. This suggests that there's either a lack of interest in existing solutions, or the existing products haven't solved the problem well enough to garner engagement. Considering the 'Swamp' category designation, this landscape indicates a high risk of blending in with mediocre solutions unless your approach offers a fundamentally different value proposition.

Recommendations

  1. Before investing further, deeply investigate why existing car recommendation services haven't gained substantial traction. Analyze their shortcomings in terms of user experience, data accuracy, recommendation relevance, and overall value. Understanding their failures is crucial to avoid replicating them.
  2. If you still believe in your idea, narrow your focus to a specific niche within the car recommendation space that is currently underserved. This could be based on vehicle type (e.g., electric vehicles, classic cars), user demographic (e.g., first-time buyers, eco-conscious consumers), or geographic location (e.g., urban commuters, rural drivers). Carve out a niche to minimize direct competition and cater to specific needs.
  3. Instead of directly competing, explore opportunities to build tools or services that enhance the capabilities of existing car recommendation platforms or dealerships. This could involve developing more sophisticated data analytics, personalized recommendation algorithms, or lead generation tools. Supporting existing players can be a faster path to market entry.
  4. Consider pivoting to adjacent problems within the automotive industry that may offer more promising opportunities. This could involve focusing on car maintenance, insurance, financing, or other related services where there is a greater unmet need. Explore areas with less competition and more potential for innovation.
  5. Given the challenges in this space, carefully evaluate whether this is the best use of your time and resources. There may be other startup ideas with a higher probability of success and a lower risk of getting stuck in a 'Swamp' scenario. Be honest with yourself about the potential for this idea to truly stand out and generate meaningful value.
  6. Leverage the feedback from similar products. For instance, 'Cardog' received positive feedback for shared content and update requests. Consider how you'd incorporate frequent data updates and community contributions into your service to keep it fresh and engaging, addressing a known user need.

Questions

  1. What unique data sources or algorithms can you leverage to provide car recommendations that are significantly more accurate and personalized than existing solutions?
  2. How can you build a sustainable competitive advantage in this market, considering the existing competition and the potential for larger players to replicate your features?
  3. What specific metrics will you use to measure the success of your car recommendation service, and how will you iterate based on user feedback and market trends?

  • Confidence: Medium
    • Number of similar products: 4
  • Engagement: Low
    • Average number of comments: 1
  • Net use signal: 0.0%
    • Positive use signal: 0.0%
    • Negative use signal: 0.0%
  • Net buy signal: 0.0%
    • Positive buy signal: 0.0%
    • Negative buy signal: 0.0%

This chart summarizes all the similar products we found for your idea in a single plot.

The x-axis represents the overall feedback each product received. This is calculated from the net use and buy signals that were expressed in the comments. The maximum is +1, which means all comments (across all similar products) were positive, expressed a willingness to use & buy said product. The minimum is -1 and it means the exact opposite.

The y-axis captures the strength of the signal, i.e. how many people commented and how does this rank against other products in this category. The maximum is +1, which means these products were the most liked, upvoted and talked about launches recently. The minimum is 0, meaning zero engagement or feedback was received.

The sizes of the product dots are determined by the relevance to your idea, where 10 is the maximum.

Your idea is the big blueish dot, which should lie somewhere in the polygon defined by these products. It can be off-center because we use custom weighting to summarize these metrics.

Similar products

Relevance

Let AI Find Your Next Car

31 Mar 2024 Artificial Intelligence Cars

Hi HN,I was hoping to get some feedback on a new feature i've added to my side project cardog. I built Cardog to make finding a car easier, manually searching through dealerships websites and building out a crazy ad hoc spreadsheet to compare cars is not fun.I've just added an LLM chat feature that has access to our API using function calls. The next step is to add a RAG store consisting of vehicle recalls, complaints, reviews / blog posts and anything else. I was interested in hearing what information is paramount to customers when searching for cars.Also, given that buying a car is a huge financial commitment only made every few years, would you see any value in a system that could constantly keep you updated with price changes and new vehicles on the market that match your criteria?

Comment flagged for review.

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Relevance

I built the easiest to use car shopping site

16 Oct 2024 Cars

Hi Hacker News!I've been working on a used car shopping site and am excited to show everyone! I hope you like it and would appreciate any feedback you can leave.Right now we are only in the New England Area but have intentions of expanding soon. If you like it and are interested, please leave your email on the site to stay updated with our progress.

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Cardog – I scraped every dealerships inventory (200k cars)

26 Jan 2024 Cars

I just finished building the first version of Cardog, a platform that makes finding a car online simple. There is currently over 200,000 vehicles to search and filter from. Currently I only have vehicles from Canada but am working on adding 20,000 dealerships from the US.Searching for cars online is an awful experience, most dealerships websites are littered with popups, ads and bugs. This leaves users with the large vehicle listing platforms, which suffer from the same problems of failing to make searching simple.That's why I built Cardog, I saw most dealerships use the same few website providers, so I built a system to fetch the data and serve it in one simple to use platform. Everyday the data is fetched and updated so there isn't any stale listings.I'm really excited to share this with the community and would love to hear any feedback.

Users provided positive feedback and appreciated the shared content. One user inquired about the possibility of periodic updates.

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Relevance

Cardog – Finding a car made easy

10 Jan 2024 Cars

Hi HN,I just finished building the first version of Cardog, a platform that makes finding a car online simple. There is currently over 240,000 vehicles to search and filter from. Currently I only have vehicles from Canada but am working on adding 20,000 dealerships from the US.Searching for cars online is an awful experience, most dealerships websites are littered with popups, ads and bugs. This leaves users with the large vehicle listing platforms, which suffer from the same problems of failing to make searching simple.That's why I built Cardog, I saw most dealerships use the same few website providers, so I built a system to fetch the data and serve it in one simple to use platform. Everyday the data is fetched and updated so there isn't any stale listings.Its my first major project and I was hoping to get some feedback from the HN community. I was also hoping someone from Google might be able to speed up the OAuth verification process.I'm really excited to share this with the community and would love to hear any feedback.

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