Recruitment App With AI: A Design Thinking Case Study

Abstract dark code editor screen representing a recruitment app interface

Last month I built a recruitment portal from scratch learn the 20% of design thinking that actually works — request form, approval flow, candidate filtering with AI, the whole nine yards. Before I wrote a single line of code, I sat through fifteen hours of interviews with HR managers, hiring managers, and candidates who had just been rejected understand requirements the way stakeholders see them.

That is the part most articles about building products skip. They jump straight to the whiteboard sketch or the workshop exercise. Those are the easy parts. The hard part is being willing to throw away your first idea after you have heard the third interview say, “that is not actually how I do my job.”

This is the story of how I actually built that portal — the conversations I had before writing code, the assumptions I had to throw out, and the things that broke in the first week. The portal handles request forms, multi-level approval, job posting, candidate registration, AI-assisted filtering, interview scheduling, psychological tests, salary offers, MCU (medical check-up), and onboarding logistics. I will show what survived and what I deleted.

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The Best Feature I Ever Shipped Was a One-Page Procedure

Susiloharjo

Last year a client asked for an AI agent to automate their customer complaint triage. Forty hours of scoping done, two weeks of build time blocked. I was three days from opening the IDE when I sat next to the customer service team for two hours and watched.

I expected to see overwhelmed agents drowning in tickets. What I saw was three CS staff handling 12 complaints a day each, perfectly fine, with one exception — they refused to escalate anything to the operations team. Not because escalations were hard. Because the SOP for escalation was 11 steps long, contradicted itself in steps 4 and 7, and the last person who escalated “incorrectly” got a written warning.

The complaint volume was not the problem. The fear of escalation was. The triage agent I was about to build would have processed tickets faster into a system the team was already afraid to use.

I killed the project. Rewrote the escalation SOP from 11 steps to 4. Ran a 30-minute training. Two weeks later, complaints were down 60%. Zero lines of code shipped. The client saved 40 hours of dev time and $8K of cloud budget.

Since then I have had the same conversation five more times. Different industry, different “we need an app” request, same ending: a procedure nobody had bothered to write down clearly was doing the work that 200 hours of engineering could not.

This is the part of design thinking nobody talks about. Most of the time, the right output of the empathy stage is not a prototype. It is a one-page procedure that everyone can read, agree on, and follow. The build comes later — if it comes at all.

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One Markdown File Made My AI Agent 23 Points Smarter

Susiloharjo

Last week I read a paper that made me re-evaluate everything I have written about AI agent optimization. Microsoft and three Chinese universities published a method called SkillOpt. The result: a single Markdown file, between 300 and 2,000 tokens, lifted GPT-5.5 by an average of 23 points across six procedural benchmarks. No fine-tuning. No new model. No extra tools. Just a Markdown file that gets fed to the agent as context at inference time.

The skill beats handwritten instructions, one-shot LLM-generated instructions, and four specialized training methods (Trace2Skill, TextGrad, GEPA, EvoSkill). It works in Codex. It works in Claude Code. It transfers across model sizes. A spreadsheet skill trained in the Codex loop lifts Claude Code to the same level as a skill trained directly in Claude Code.

After reading the paper, I stopped adding features to my AI agent for a week. I started writing skill files instead. Five of them. All under 1,000 tokens. All producing measurable improvements in my daily work. This post is the five skills and the pattern I now use by default for any procedural task.

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I Tried GROW Coaching in My 1:1s. It Cut Them in Half.

Susiloharjo

I Tried GROW Coaching in My 1:1s. It Cut Them in Half.

Last week I ran a 1:1 that lasted 12 minutes read more about leadership and delegation. The engineer walked out unblocked, with a clear next step, and didn’t ping me for the rest of the day. A month ago, the same engineer would have walked out of a 45-minute 1:1 with a vague explore one-page procedure for team clarity “I’ll think about it” and pinged me twice before lunch.

The only thing I changed was the questions I asked. I stopped solving problems in the meeting. I started running them through a 30-year-old coaching framework called GROW — Goal, Reality, Options, Way Forward.

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Teaching AI Like a Junior Dev: 4 Leadership 4.0 Moves

Team collaboration and leadership communication concept

A few weeks back I sat through a corporate leadership training on “the four dimensions of digital leadership.” I went in expecting corporate fluff. I came out realizing something that has been nagging me ever since: the same moves the trainer taught for managing junior engineers are the same moves I keep needing to get an AI to actually understand me. Same pattern, same cost of getting it wrong, same fix.

I was thinking about it over my morning coffee this week: giving instructions to an AI feels exactly like onboarding a junior dev who joined yesterday. Sometimes one sentence lands. Sometimes I explain the same thing three times, slightly differently each time, and the output still comes back wrong. It’s not that the AI is stupid. It’s not that the junior dev is slow. It’s that I might not be a great communicator — and that gap is mine to close, not theirs.

The framework I learned is called Leadership 4.0. It has four dimensions: Freshmen Leader, Technology Leader, Social Leader, and Digital Leader. Used together, they help you diagnose whether the communication gap is on the sender’s side, the receiver’s side, or somewhere in the handoff. Here is how I now use them — at work with junior devs, and at home with my AI assistants.

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