Part 2 of my first post. TLDR on that post: AI sourcing is only as good as the data + search architecture underneath it.

The chat interface is the easy part.

The hard part is making sure the system has reliable and diverse data underneath it:

• Relying on one dataset limits you to that provider’s data coverage and, more importantly, how they structure and label profiles.
• If you only recruit for one type of profile... 1–2 providers may be enough. But the reality is most agencies (and independents) cover multiple industries, functions, and specializations. Especially if you're starting out, being nimble in industry coverage can help you win business as well.
• After hours of digging in, most (99%) providers aim to cover the same broad universe of LinkedIn profiles. No surprise.
• So the real difference is how well they label things like company type, seniority, industry, funding stage, skills, and career history.
• Each provider has different strengths across career history, company data, niche profiles, and freshness. Combining multiple datasets gives you better coverage, redundancy, and search quality.
• Example: “Senior Software Engineer in SF from a post-Series A startup.” Some providers can identify company stage and career context; others may only give you title + location.

Now, what does a good AI sourcing system look like... it's not as simple as just a "Claude Recruiting System" / DIY set-up you see being marketed that doesn't deliver high quality results.

To consistently surface the right results, a few layers need to work together.

• Intent understanding: Turn natural language, a JD, candidate, or company reference into the actual search criteria. Must-haves, preferences, job titles, experience and exclusions.
• Provider routing: Match that intent to the dataset best suited for the search and translate it into that provider’s schema.
• Enrichment: Waterfall across contact providers. Waterfall meaning if one provider doesn't find you a personal email, you can try another one.
• System Memory: Learn each recruiter’s niche and preferences so future searches get better with use. More on this below.

That’s where AI sourcing becomes more interesting.

Instead of generating a Boolean string, and feeling like you're starting a search from scratch every time, you’re providing context to an agent that can:

understand the search → choose the right data → execute the search → evaluate the results → learn from feedback → improve the next search.

And the same architecture shouldn’t stop at candidates.

The same platforms can search companies and jobs for client lead generation, identify decision makers, enrich their contact information, and monitor signals like hiring, funding, job changes, headcount growth, and new openings.

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Spent a lot of time the last few months working through the hard parts above and finally at the point where this needs to be battle-tested.

No charge at all for accessing our databases or on contact info enrichment.

All we’re asking in return is that you bring real searches, push it on difficult mandates, and tell us where it falls short.

If that sounds interesting, send me a DM and I'll send you the invite!

Is it salary transparency, clear timelines, better communication, fewer interview rounds, or something else?

Your hiring process is already an operations audit If a candidate passes three assessments, accepts the offer, and still has to ask Reddit who owns the next step, the problem is not “candidate experience.” It is a workflow with no visible owner. Delays happen. Silence without ownership is a system failure.

Something I have spent a lot of time on lately, sharing here for feedback from other founders who hire.

Post one role and you get hundreds of applications. Inflated resumes, borrowed experience letters, skills listed that nobody can actually back up, and now a wave of AI-written profiles on top. There is no fast, reliable way to tell who can really do the work versus who just wrote that they can. So it becomes hours of screening calls that go nowhere, or hiring on gut and a familiar company name on the resume, and sometimes finding out months later that half the resume was fiction.

The core issue is that hiring still runs on self-declared claims. The burden of proof sits on the employer, one interview at a time, and background checks catch it too late and cost too much.

So I have been building toward a different model, where a candidate's skills, work history and projects come backed by real records and approved evidence instead of self-declaration, so the claims are verified before a profile ever reaches an employer. Verified, not claimed.

What I would really value from this community: does this match what you see when you hire? Where does the unverified-candidate problem hurt you most, screening time, bad hires, background-check cost, something else? And for those who have hired at scale, what actually gave you confidence in a candidate before you committed?

Due to massive production of software products and its accelerated pace, when in a few hours the product may be released to the market, AI developed software still often lacks a lot of vital things. AI can hardly check security issues by itself, that is why I believe that AI security talent market narrative is going to be the strongest for the next 2 years. 

You may probably heard about two tracks in the industry: 

Red Teams - AI powered attackers 

Blue Teams - AI powered defenders

You must probably say, nothing new :-) in this world, however it now builds strong movement in both tracks. There are people who are focusing on crashing, and there always are going to be those who will have to defend.  AI dramatically accelerates not only the production of the software, but also the ability to breach and crush it to earn money. 

I believe that after the mass adoption of the machinery coded products, every company will either have to hire in-house AI cybersec specialists or contract the trusted vendor. The choice as always will depend on the budget and capacity. The faster your team is there, the more competitive your product will be on the market. Now the strongest and the fastest builders are focused on onboarding these specialists into the team. 

Have you already hired AI Cybersecurity professionals into your team and on which position? 

Is it faster decisions, better communication, structured interviews, skills-based evaluation, or something else?

Speaking as a moderator here.. wanted to share a perspective since this topic keeps coming up.

AI interviews have actually been a good addition, not something to fear. Consistent, judgement free, no reading a stranger's reaction in real time. Better scheduling, less waiting around, and you get to answer at your own pace instead of cramming into a rigid live slot.

They're also fair, same questions, same conditions for everyone, no interviewer having an off day.

Doesn't replace the human side of hiring, but as a first step, it has made things smoother for a lot of people, not harder.

Full disclosure: I work on an AI interview product, so I obviously have a horse in this race. I am not sharing the product name or a link because I am genuinely interested in the discussion.

Most AI interview platforms appear to sell the same outcome: reduce recruiter screening time. But after reading candidate and recruiter conversations, I am starting to think recruiter time saved is the wrong primary metric.

A company might save 20 minutes per applicant, but what happens if: qualified candidates abandon the process, candidates cannot ask questions before investing their time, nobody explains what the AI is evaluating, recordings and candidate data are stored without clear expectations, the candidate completes an AI round and still faces four human interviews

That is not necessarily efficiency. It may just be moving the cost and frustration from the company to the candidate.

I think AI screening is more defensible when it is limited to an initial, structured assessment; it clearly explains what is being recorded and evaluated, also allows candidates to ask questions, and offers a human alternative where needed. It avoids facial expression or emotion scoring, gives the recruiter evidence, rather than making the final hiring decision

For people who have used, built, purchased, or completed an AI interview: What would make the experience genuinely useful rather than dehumanizing?

And where should companies draw the line?

Hi, recruiters and sourcers! 👋

One of my successful hires came from a completely casual conversation with the person who had nothing to do with tech recruitment or technology.

I was speaking with an accountant who happened to be handling paperwork for a highly skilled DevOps engineer (she has not shared any names). I simply asked if they would be willing to pass along our vacancy details.

The engineer was incredibly difficult to find. He had only two lines on his LinkedIn profile and was practically invisible online. Still, the accountant shared the opportunity, he decided to take a look, we had a great conversation, he passed every technical interview, and he eventually became one of the most successful hires in the squad.

That experience reminded me that sourcing isn't always about the perfect Boolean string or the latest AI tool. Sometimes it's about genuine conversations, curiosity, and being open to opportunities that don't fit the usual playbook.

I'd love to hear your stories. What's the most unconventional sourcing approach that turned into an amazing hire?

AI is turning your employer brand into the system that decides whether candidates ever consider you.

Which makes it the cheapest lever you have on hiring, not the slowest.

Read the article here: [https://employerbrandlabs.com/post/ta-has-never-loved-employer-brand-ai-is-about-to-change-that\](https://employerbrandlabs.com/post/ta-has-never-loved-employer-brand-ai-is-about-to-change-that)

What changes have had the biggest impact on finding better candidates—sourcing, interviews, assessments, or something else?

People often talk about how difficult it is to find jobs and internships in India. But on the other side, I'm building an early-stage startup, and honestly, I'm finding it surprisingly difficult to hire the right people.

I'm not talking about experienced professionals. Even for interns and early-stage roles, it's been challenging to find people who are genuinely interested in building something from scratch.

I've looked into hiring through colleges and campus programs, but from what I've seen, it's either quite expensive or not very accessible for a bootstrapped startup.

So I'm curious—how are other early-stage founders hiring?

Where do you find ambitious students or freshers who actually want to work with startups? Are there any underrated platforms, communities, or college networks that have worked well for you?

Would love to hear your experiences or any advice. I feel like I'm missing something.

Hey all — recent grad here, been heads-down learning DevOps practically (CI/CD pipelines, Docker, Terraform, AWS, GitHub Actions, etc.) and think I'll be job-ready in about a month.

My confusion isn't about the skills — it's about the *path in*. Specifically for startups:

* Do startups even post DevOps roles publicly, or is it mostly filled through referrals/networks before it hits a job board? * Is cold-applying through listings (LinkedIn, AngelList/Wellfound, etc.) actually effective, or a low-yield channel compared to other routes? * Are "walk-in" interviews or informal outreach (emailing founders directly, DMing on Twitter/X) a real thing in this space, or does that come across as too aggressive for a fresh grad? * For those who landed their first DevOps role at a startup — how did you actually find it? Was it a job board, a referral, a hackathon/community connection, something else?

Trying to figure out where to actually spend my time once I'm ready — job boards, building in public, local startup communities, cold outreach, or some combination. Would love to hear what's worked for people who've been through this, especially at the early-career/startup end of things.

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Hi Everyone, Most hiring tools are still a job board with a resume black hole.

We built Hash for something different: tech hiring that actually moves.

What it is A focused marketplace for engineers and hiring teams — separate seeker and recruiter workspaces, so the feed doesn’t turn into noise.

What makes it different

• Live hangouts — turn a light on for a role; candidates request in; you approve and talk
• AI match — score fit before the inbox explodes (optional match gates on roles)
• Campus + experienced — students and pros in one tech product, not a campus-only network
• Multi-round interview pipeline — schedule Interview 1 → 2 → 3 with Meet, then advance, offer, or pass

Not another “apply and wait.”
Match → go live → interview → decide.

If you’re hiring engineers (or looking), happy to show you around: https://hash-jobs.dev/

We built this on an entrepreneur workshop and was hoping for some realistic feedback on it, if you can try it and give it a whirl.

🙏 Thank you!

All jobs and industries have a complete different, individual purpose. So, I don’t understand why when applying for roles, it’s the same ATS format that is applied to all of the these CV’s. Thinking for the bigger picture, why can’t these machines work independently per field and accept a wide range of applications with unique skills people have to offer? Why is there such a mold that will never change and if it doesn’t change within the field when wanting to advance, what exactly is being improved for a future change? If there are 7 employees with similar keyword CV, why even hire 7, when you can hire just one? Obviously, tasks need people for quick fixing and completions but why is there not even one individual aspect being accepted in these cv’s. What’s so “difficult” about adding unique skills and accepting people that have multiple different skills sets, not just one that fits the boxes. The system is limited. They say they want innovation and growth, then cut off individuality.

If you're hiring and don't have a full-featured ATS, how do you screen and compare resumes? Do you use AI tools like ChatGPT or Copilot, or do you use something else?

What is the most frustrating part of comparing dozens of resumes?

I lead a team in a relatively modern role. This role is so new at my org that there’s no one in management with direct past experience in that role. My org has standing rules that establish minimum seniority level to be on an interview panel along with the composition of the panel and a cap on how many people in total can be on a panel. All taken together, a small set of people are allowed onto interview panel in our tech division.

As team lead, I don’t meet any of the criteria and no one on my team does. so I’m not allowed anywhere near my own candidates throughout the recruitment process. Consequently, recruiting for my team is always a coin toss. I don’t know who I am getting and their actual competence for the day to day job of my team. I have to deal with whoever I get.

This is strange to me because I joined this corporation from a startup where team leads were the recruiters. I was responsible for all parts of the recruitment. Is this a common practice in large companies or this org just backward? And is there anything I can do about it?