New top story on Hacker News: Show HN: JobLens AI-powered job search for 'Who Is Hiring'
Show HN: JobLens – AI-powered job search for 'Who Is Hiring'
4 by hubraumhugo | 0 comments on Hacker News.
There are existing HN job aggregators, but I thought we could take it a step further. Inspired by an insightful comment on a previous thread ( https://ift.tt/V1DjsGJ ), I built a tool that aggregates job postings and intelligently categorizes them based on user-specific preferences: * Country and remote work preferences * Employer type (e.g., startup, corporation, government) * Industry * Technologies used * Role type (developer, architect, product owner, etc.) * Salary range (where available) One of the superpowers of LLMs is reformatting information from any format X to any other format Y. We leverage this to map all the unstructured job postings into the same unified structure. The new GPT functions feature and the extended context windows are really helpful for this. Instead of having to build a custom NER pipeline, it works very well with GPT out-of-the box. One challenge is keeping the filters consistent and merging of duplicates. Embeddings help with that. What's next: * Integrate additional sources. We can generate web scrapers and data processing steps on the fly that extract and transform data into the same structure. * Add location distance filters. * Expand beyond jobs to monitor personalized data like events or real estate. Imagine using AI to rate local events from multiple sources based on your preferences, considering factors like your interests and distance from home. * Smaller improvements based on your feedback :)
4 by hubraumhugo | 0 comments on Hacker News.
There are existing HN job aggregators, but I thought we could take it a step further. Inspired by an insightful comment on a previous thread ( https://ift.tt/V1DjsGJ ), I built a tool that aggregates job postings and intelligently categorizes them based on user-specific preferences: * Country and remote work preferences * Employer type (e.g., startup, corporation, government) * Industry * Technologies used * Role type (developer, architect, product owner, etc.) * Salary range (where available) One of the superpowers of LLMs is reformatting information from any format X to any other format Y. We leverage this to map all the unstructured job postings into the same unified structure. The new GPT functions feature and the extended context windows are really helpful for this. Instead of having to build a custom NER pipeline, it works very well with GPT out-of-the box. One challenge is keeping the filters consistent and merging of duplicates. Embeddings help with that. What's next: * Integrate additional sources. We can generate web scrapers and data processing steps on the fly that extract and transform data into the same structure. * Add location distance filters. * Expand beyond jobs to monitor personalized data like events or real estate. Imagine using AI to rate local events from multiple sources based on your preferences, considering factors like your interests and distance from home. * Smaller improvements based on your feedback :)
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