Today we're launching @metix_ai for recruiters. Type your role in one sentence, then pick how far it goes: 👉 Run Fast Search for an immediate list to explore candidate pools and size up the market. 👉 Run Deep Search for the best fits. It starts by clarifying what your role needs and what matters most, then digs into every candidate. 👉 Run the Agent to hand off sourcing, emails and follow-ups. It puts interested candidates on your calendar for interviews. We send interviews, not resumes. metix.ai Try Fast Search on a role you're hiring for.

Should engineers move to a city with less competition? We checked six big US cities, comparing each role with other jobs in the same city. Software engineering is a little harder to land than the typical job in every one of them. The city barely changes that. Your profile can! Link below. @metix_ai

Every recruiter search creates a ranking. Most people have never seen one they're in. Today you can see yours @metix_ai. Paste your LinkedIn and see where you rank against people with your title, in your area, at your experience level. You don't need to sign up. go.metix.ai/recruiters-view/

a short and sweet clip to recap our @Techweek_ boba debate🧋 see yourself in there? tag yourself 👀 w/ @zhilinjerrywag @ZooWorkAI @XTrace_ai @flatkey101 @hellyeah_ai

Spent some time testing #Jev from @typesafeai on a use case I care about: matching a resume against real job descriptions. I built a small demo called Rolewise. @metix_ai handles job retrieval; Jev handles the fit decision. The setup: - Parse the resume and generate editable search filters. - Retrieve jobs and full descriptions through the Metix AI API. - Send each resume–JD pair to Jev, with 20 requests in flight. - Ask for one decision: suitable or not suitable. One run: 838 jobs evaluated in 61.3 seconds. That excludes parsing and retrieval. I started with multiple scoring dimensions, then simplified the task to a single fit decision. For this demo, I wanted to see whether Jev could help someone narrow a list of opportunities without adding another scoring system to interpret. The recording shows actual requests completing—no simulated progress. Retrieval and evaluation are separate steps, so you can inspect the jobs before running Jev. This is a throughput result, not an accuracy benchmark. I haven’t established how reliably those decisions agree with a human reviewer yet. That’s the next thing I’d test, especially on borderline cases. Sharing this as a concrete Jev example. If you’re building something similar, I’d be curious how you’re defining “fit” and checking the results. Job data/API: platform.metix.ai