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One thing I’ve learned from building automation tools:
Writing the code is often the easy part.
The harder part is deciding what the system should do on its own, what should stay under human control, and what should happen if something goes wrong.
A system can work perfectly and still be annoying or even risky if the workflow is badly designed.
These days I think more about reliability and user control than just adding more features.
What do you prefer: full automation, or a system that always asks before taking action?
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𝐉𝐨𝐛 𝐡𝐮𝐧𝐭𝐢𝐧𝐠 𝐨𝐧 𝐡𝐚𝐫𝐝 𝐦𝐨𝐝𝐞
B2 English, based in Argentina. Payment preferably in USDT. Remote only. Quite a combination, right?
I built an AI-assisted job-search system around Freehire and Hirify. Both platforms offer official API/CLI access for automation, which makes them much easier to connect to an agent workflow. Simple Python scripts collect job listings and run the first pass of filtering against strict rules.
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𝐀𝐬 𝐨𝐟 𝐒𝐞𝐩𝐭𝐞𝐦𝐛𝐞𝐫 𝟐𝟔:
✅ 20 applications sent;
✅ 8 HR screenings passed;
✅ 1 video screening completed and submitted.
🚀 Technical interviews are still ahead.
I'm not ready to write off the market. Even with my constraints, things are moving.
A job search deserves 𝐚𝐧 𝐞𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 𝐚𝐩𝐩𝐫𝐨𝐚𝐜𝐡 𝐭𝐨𝐨: clear rules, automation, and checks on the results.
Messy Data In. Clearer Decisions Out. 📊
Over the past year, I've noticed most businesses don't struggle because they lack data — they struggle because that data never turns into a decision.
That's the gap I work in:
→ Cleaning and analyze data with Python & R
→ Turning findings into investor-ready pitch decks
→ Polishing the final write-up so nothing gets lost in translation
If your team has data sitting in spreadsheets with no story attached, or a pitch deck that needs to actually land — that's exactly where I can help.
Open to projects. Let's talk 👇
