Manifesto
Remove applications.
The job market is built around people looking for jobs.Amble is built for people who aren’t.
Recruiting has worked the same way for decades.
A company publishes a job. People find it. They apply. Someone filters the applications.
- The internet made it possible to apply to more jobs.
- LinkedIn made everyone searchable.
- ATSs made it possible to filter more people.
- Then AI made applying almost free.
Now we can generate applications faster than anyone can meaningfully read them. Companies respond with more automation, more screening and more filters.
More applications. More filters. More noise.
A better application keeps the same loop.
The application is the problem.
Most people aren’t looking.
And that’s the part the current system handles badly.
Some of the best people for a role are already doing good work somewhere else. They aren’t browsing job boards. They aren’t updating their CV. They aren’t applying.
Aiko Tanaka
Product designer
Not lookingWould move forthe right problemDaniel Okafor
Backend engineer
Open to workLucía Serrano
Data scientist
Not lookingWould move forremoteTom Weber
Staff engineer
Not lookingPriya Nair
ML engineer
Not lookingWould move formore ownershipJonas Berg
Platform engineer
Not lookingWould move forthe right number
But not looking isn’t the same as not listening.
There may be a company they’d join. A problem they’d want to solve. More ownership they’d take. A team they’d want to work with. A number that would change the equation.
Today, discovering that requires one side to interrupt the other. Recruiters send cold messages. People browse jobs they mostly don’t want. Both sides repeat information the other could already know.
There should be a better way to discover mutual intent.
Give everyone a Rep.
Your Rep knows what you’ve actually done, what you can demonstrate and what would make you move.
- Then you go back to work.
- Your Rep listens.
- Most weeks, nothing happens.
- That’s a feature.
When something deserves your attention, it comes to you, already filtered against your work, your conditions and the things you’ve taught your Rep to care about. It arrives with one sentence:
I found something worth your attention.
37 read this week
- Senior ML EngineerAdtech✕ Office 5 days
- Founding AI EngineerSeed✕ Solo on-call
- Evaluation LeadKestrel✓ Worth your time
Your Rep
Listening
You
At work
1 worth your time
What we build by
- 01
Describe each person on their own.
What you’re worth depends on who is asking and for what. So your Rep describes you, on your own terms, one person at a time.
- 02
Every claim has a source.
Every claim your Rep makes points to where it read it. Without a source, it stays quiet, even when saying more would flatter you.
- 03
Name the gaps.
A good representative is precise about its own uncertainty. “I can’t tell yet” is a useful sentence.
- 04
Ask who decided.
Much of today’s work is written with an agent. So your Rep asks you to explain your decisions, and only claims what you can defend.
- 05
Retract, and keep the record.
When your Rep stops believing something, it keeps the record of why. A system that can’t unlearn inflates everyone it reads.
- 06
The no is the data.
Nobody lists their own dealbreakers correctly. They show them, one trade-off at a time. Your Rep learns most from what you turn down.
- 07
Silence is a feature.
Most weeks, the right number of opportunities is zero, and your Rep says nothing at all.
- 08
Your data is yours.
Private by default, portable as plain text, deletable for good. You can always see why your Rep thinks what it thinks, and change its mind.
- 09
Built in the open.
With the people who use it, and in public where we can. The format your profile is written in will be open for anyone to read and use.
Eventually, the search itself goes away.
- Companies will have agents of their own.
- They’ll describe the work, the evidence they need, the conditions and the range.
- Their agent will find the people who could actually do it.
- Your Rep will decide whether it’s worth your attention.
Each side reveals more as interest grows on both.
Your agent talks to theirs.
And when both sides want the conversation, humans talk.