nextloom.ai

skill match scoring. Paste a JD, see which required/preferred skills match your profile. Before you spend time tailoring.

I reached for the LLM first. Then found Aho-Corasick — a 1975 algorithm that matches thousands of skills in one pass. Faster, deterministic, zero tokens.

Not every problem needs a language model. https://press.nextloom.ai/2026-05/v0.16.0-match-score

#BuildInPublic #AI

I'm building a job search tool alone. Backend, frontend, Chrome extension, admin dashboard, e2e tests, marketing site.

The structure: 8 Claude agents, each owning one repo. One spec drives them all.

Every feature starts as a design doc. I brainstorm with the PM and architect agents, then review it myself four or five times. Once the spec is solid, engineering agents work in parallel — frontend and backend don't need to coordinate because the contract is already written.

What actually makes it work:

- Multi-repo boundaries. Each agent only sees its own code.

- Written specs as the single source of truth.

- CLAUDE.md conventions and few-shot examples for consistent code style.

- I review every line of backend code. Frontend I review structure, not pixels.

What I refuse to delegate: product judgment, architecture decisions, and taste. does this code feel like the rest of the system?

Full post with architecture diagrams: https://press.nextloom.ai/2026-05/how-i-build-nextloom-ai-with-claude-agents

What does your AI-assisted workflow look like?

Hey IH 👋 I'm Steven, and I've been shipping nextloom.ai for a week now — an AI job search assistant that handles the full application lifecycle (resume tailoring, cover letter, interview prep, tracking). The kicker: I'm actually job hunting right now, so I'm both the builder and the user.

Here's what happened in Week 1, with real numbers.

The setup

I was tired of the job search circus: rewrite resume, update spreadsheet, draft cover letter, forget to follow up, panic before interview. Repeat 50 times. Every tool solved one piece, nothing connected.

So I built the thing that connects them. Paste a JD → AI analyzes → 7-step resume pipeline with ATS validation → cover letter → interview prep → Kanban tracking. Context flows from job post to offer letter.

Week 1 numbers

- 750 unique visitors (Cloudflare)

- 3 signups

- LinkedIn: 243 impressions, top post hit 65

- X: 35 impressions, 0 new followers

- Reddit: +9 karma from 1 discussion post in r/resumes

- 5 directories submitted (BetaList, AIxploria, aitoolsdirectory, Uneed, Microlaunch — though Uneed and Microlaunch both have 90+ day waitlists 🫠)

What actually worked

My best-performing post wasn't an educational "5 resume mistakes" thread. It was a raw release announcement for v0.13.0 — we just shipped a Job Market feature that pulls 2M+ jobs from 40+ ATS platforms (Lever, Greenhouse, Ashby, Workday) directly into the app. Click "Track Job" and AI auto-extracts keywords and requirements in the background.

Lesson: real product progress beats thought leadership when you're this early.

What didn't work

- Paid directories are a trap at this stage (TAAFT wants $347, Toolify $99)

- LinkedIn reach dropped 50%+ in the second half of the week

— new account honeymoon is over

- Reddit karma ≠ Reddit traffic. Comments build presence but don't drive clicks yet

What's next

The next big feature is an auto-apply agent.

The vision: paste your resume once, wake up to interviews. Half-excited, half-terrified to ship that one. Also going to stop cold-posting into the void and actually engage in communities (hello 👋).

Demo if you're curious: https://youtu.be/8HZkLP9gfcU

Try it (free): https://nextloom.ai

Release notes: https://press.nextloom.ai/v0.13.0-job-market

Question for fellow builders: When your week 1 numbers are small (like mine), how do you stay grounded vs. start panic-iterating? Would love to hear how others got through the 0-to-100 valley.