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Strong AI Candidates Don’t Buy Vision. They Buy Clarity.
Something I’m seeing more clearly in AI/ML/robotics hiring right now:
The strongest candidates are not just asking:
“Is this company doing AI?”
They’re asking:
“Is this a real technical problem?”
“Will I own something meaningful?”
“Is the infrastructure serious?”
“Are the founders clear on what needs to work in the next 90 days?”
“Will I be surrounded by people who can actually build?”
That last one matters more than most teams realize.
A lot of early-stage companies still try to attract AI talent with broad vision language:
“We’re transforming the future of X.”
“We’re building agentic workflows.”
“We’re applying AI to a massive market.”
That might get attention.
It rarely closes the candidate.
The best AI/ML/robotics engineers are looking for evidence.
They want to know whether the company understands the real work after the demo: messy data, latency, deployment constraints, model behavior, hardware limitations, infrastructure tradeoffs, customer weirdness, and all the unsexy problems that separate a prototype from a product.
This is where founders accidentally lose strong candidates.
Not because the company is weak.
Because the role is too vague.
The must-haves are blurred with the nice-to-haves. The candidate can’t tell whether they’re joining a serious technical mission or becoming the person responsible for making the AI slide deck true.
Before opening another AI/ML/robotics role, I’d pressure-test five things:
What must this person make true in the first 90 days?
What technical problem will make a great engineer lean forward?
What proof can you show that the problem is real?
Which requirements are actually essential on day one?
Which ones are just founder anxiety in bullet-point form?
This applies beyond technical hiring too.
Consultants, creatives, operators, and founders all deal with the same thing:
Strong people don’t just buy the opportunity.
They buy the clarity.
If your role, project, or client brief feels fuzzy, strong people hesitate.
If it feels clear, specific, and grounded in real outcomes, they engage.
If you’re hiring for a hard technical role right now and your pipeline is noisy, I’m happy to help you pressure-test the role before you spend another month chasing the wrong people.
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I built ReviewIQ because technical interviews don't always test whether you can actually review code.
ReviewIQ is an AI-powered code review interview trainer built for software engineers.
You pick a role, language, and seniority, then get a realistic PR diff with bugs intentionally planted in it.
You write your review.
Then the system grades it against the actual bugs, shows what you caught, what you missed, and gives you feedback on how a stronger reviewer would approach it.
The interesting part was building the grading system so it isn't just "AI thinks your answer is good." The bugs have a known ground truth, so the review can be evaluated against something concrete.
Built with Next.js, Supabase, PostgreSQL, OpenAI, and Lemon Squeezy.
I'm interested in building more products where AI isn't just a chat box, but an actual part of the product experience.
The known-ground-truth approach is a great product decision—it makes the feedback feel earned rather than like an opaque AI verdict. I also like that the flow tests the actual review skill instead of rewarding pattern-matching in interviews.
I’ve been building SabiFlow for a while, and honestly, the most interesting part of the project isn’t the code.
It's the problem.
Because it's personal.
I've experienced that thing where money comes in and somehow, without you really noticing, it starts disappearing.
Not because you don’t earn enough.
Not necessarily because you're irresponsible either.
Sometimes money simply has no job when it arrives.
And as an engineer, that got me thinking:
What if the problem isn't budgeting? What if the problem is that we’re asking people to make too many good decisions at the exact moment they have the most temptation to make bad ones?
That question became the foundation for SabiFlow.
Instead of telling someone, "You should save 20% of your income," I started thinking about what would happen if the system simply helped assign every inflow a purpose the moment it arrived.
That led me down a rabbit hole.
Funnels.
Automated distribution.
Wallet infrastructure.
Virtual accounts.
User behaviour.
Transaction flows.
KYC.
Compliance.
Even the psychology behind notifications.
And this is probably my favourite part of being both an engineer and a founder.
I don’t just ask:
"How do I build this feature?"
I ask:
"Why does this problem exist, and what kind of system could make dealing with it easier?"
Then the engineer in me comes along and asks:
"Okay… but how do we actually make this work reliably?" 😂
That tension between the founder thinking about the problem and the CTO thinking about the system is probably what I enjoy most about building SabiFlow.
I’m still figuring a lot of it out.
But I'm curious:
What’s a problem you’ve experienced personally that eventually made you want to build something around it?
The framing around “why does this problem exist?” is exactly the kind of product thinking that keeps a financial tool from becoming another dashboard. SabiFlow sounds strongest where the behavioral insight meets the practical system design—especially around notifications and reliable follow-through.
🚀 Available for Freelance AI/ML Projects
I’m currently open to working on interesting AI/ML projects — whether you’re building something from scratch or need help turning an idea into a working product.
I can help with things like:
• Machine Learning solutions
• AI/ML model development
• Data & predictive analytics
• Python-based AI applications
• FastAPI / API integration
• Automation and intelligent workflows
• AI features for existing projects
I enjoy working on real problems and building things that are actually useful, not just demo projects.
If you have an idea, project, or problem you’re trying to solve with AI/ML, feel free to reach out.
📩 Open to freelance projects & collaborations.
🔗 My work: https://github.com/Tejeshyewale
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Tejeshyewale - Overview
Computer Engineering Student At PDEACOEM. Tejeshyewale has 102 repositories available. Follow their code on GitHub.