I was interested in building a product for which 1. the agent is the whole product, not just a component of it; and 2. solves a specific problem out of the box.
Tavi is a deep people search agent that lives in Slack. We used it to find our founding engineer and our first customers are a mix startups, recruiters and VCs.
I'm building this: https://teeming.ai/jobs. It aggregates jobs in AI startups enriched with investor-grade info. Can be navigated with a chat agent, filters, and has automatic CV/Linkedin matching.
I am building something in this space: teeming.ai/jobs
While we are focused specifically on technical roles in AI startups, and we don't "verify" per se, we do enrich jobs data with investor-grade intelligence on the startups themselves, so you can see which companies have legitimate backing.
I'm working on https://teeming.ai, trying to solve the information asymmetry problem in the job market.
The project has been a huge learning curve for me - I started out as a skeptic of how generative AI could solve real problems (rather than just create noise) but now think that, like the internet, it can create a new kind of abundance that will be harnessable in all sorts of interesting ways.
OP here. Appreciate the feedback - the login barrier is definitely something we will take on board. I think some level of identity authentication is a must to counter the scourge of bot traffic, but we will think of ways to be cleverer about this.
With regard to job posting accuracy - absolutely this is a problem. Normalising what different job descriptions actually _mean_ is one of the key challenges we want to solve.
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