Launched this week
Double
Your AI career agent that gets you hired
116 followers
Your AI career agent that gets you hired
116 followers
Double is your personal AI career agent. Just text it, and it manages your entire career: finding your roles, applying for you, growing and maintaining your network, and making recruiters chase after you.
Spycost
Really like the focus on quality over mass-applying. A career agent that can tailor roles, applications, and networking from real outcomes feels much closer to what candidates actually need. How do you decide when Double should apply automatically versus ask the user for approval first?
@glebarios Hi Hlib, great question! Double's actions are completely controlled by the user! You can choose between ask and agent mode, where ask requires confirmation for any actions and agent where double acts on your behalf. You can toggle between the two and save settings for certain actions so double only automates as much as a user wants.
When it applies for me, how much control do I get before something goes out? Recruiters can tell when an application is automated, so I'm curious if I approve each one or if it just runs and I find out later.
@talhakhalidmtk You have the option to toggle between 'approve' mode and 'auto' mode! We'll always ask for input if we don't have the full picture - no AI slop applications!
auto-applying at scale is the part that worries me a bit - a lot of companies quietly blacklist candidates who spam the same generic application across dozens of roles at their org. does Double tailor each application enough to avoid that, or is it optimizing purely for application volume?
@omri_ben_shoham1 Hi Omri, thanks for the comment! I went through this briefly in an earlier comment:
"You are completely correct about the volume and mass-apply issue. This is one of the biggest issues in the job market today, and leads to a viscous cycle of: volume applying leading to more rejection leading to more applying...
We want to solve this issue by doing what all our competitors don't: advise people on the best next steps to take, growing their online portfolio/presence by showcasing their projects + contributions, and connecting them to the right people to help them. We want to think about the applying as a 'given' - nobody wants to fill in applications and all we do is simplify the tedious process. We do not support mass apply, and specifically made it so that every application is carefully optimized and tailored. We are continuously searching for better ways for candidates to stand out, ones that don't rely on spray-and-pray which is killing the job market.
In the future, we are also thinking about adding a layer of friction for people that apply to roles that don't fit. We believe that everyone can shoot for any role, but if it isn't a match, Double takes the steps to advise them how to become a fit.
Side note: We have already built the feature that carefully recommends select jobs to users and are shipping that today! I've also written a few blog posts on this exact issue, would love if you could check it out: https://trydouble.ai/articles/youre-a-lemon as well as https://trydouble.ai/articles/attention-is-all-you-need."
Really glad to see that people are starting to see the flaws of spam auto-applying. That is definitely NOT the direction we want to develop towards. Happy to answer any further questions!!
would love to see a way to track which conversations turned into actual opportunities, like a simple dashboard showing response rates and next steps so i can see whats actually working.
@tlinzheb Shipping that by the end of the week!
minimalist phone: reduce your screentime
Which data it "reads over the internet" in terms of getting information about the job positions? Also LinkedIn?
@busmark_w_nika we crawl 80k+ company career pages weekly and get job listings STRAIGHT from the source, so no aggregators like linkedin or indeed as they are quite unreliable.
minimalist phone: reduce your screentime
@lucapiekarski Aaaa, okay, thank you for clarification! :)
The idea to simplify job search with AI is at least 2 years-old.
Could you take a day or two and think about how would you compete with Jobright AI (launched in 2024 and growing)?
@wvitalik Hey Vitalii! Thanks for the comment. This is a good question. Jobright has certainly paved the way for a lot of career tools, but we differentiate in two ways:
Double is an agent instead of a tool. Jobright is more of a copilot, users decide what to do, when to do it, and you run each feature themselves. Double actually knows what your career context, and what you should be doing (next steps) and drives the process for you. In other words, you don't have to remember to follow up or build out your search strategy. Furthermore, Double doesn't stop at just getting people hired, it keeps managing and growing your career by growing + maintaining your network & helping you shape your portfolio + presence online.
We focus on getting you seen, not just applying more. AI made everyone's applications look basically identical, so blasting out more of them stopped working. So we put as much weight on making you visible and reachable to the right people as we do on the applications themselves. We are still actively improving our product, but if you want to see our philosophy, check out: https://trydouble.ai/articles/youre-a-lemon and https://trydouble.ai/articles/attention-is-all-you-need
Let me know if this answers the question - would be more than happy to explain further!
Lancepilot
Nice launch , qq how does the agent balance token limit optimization when reviewing long multi-page corporate career documents alongside deep user profile sets?
@istiakahmad We don't feed raw docs into context. Long documents and profiles get extracted into structured data against our own skills/occupation taxonomies, then we use embeddings to retrieve only what's relevant to the task at hand, backed by a persistent user profile so the agent isn't re-reading everything each time. The model spends tokens reasoning, not re-parsing boilerplate. Happy to go deeper if you're building in this space!