Project ideas from Hacker News discussions.

28% of job postings on company career sites have been open over 90 days

📝 Discussion Summary (Click to expand)

1. Ghost job postings – Many commenters argue that a large share of listings are never intended to be filled; they are kept open to inflate hiring numbers, satisfy compliance, or make the company look active.

“It's clear‑cut fraud – they are advertising something that doesn't exist, specifically to game numbers.” – legitster
“Investors maybe. Makes your company look better than it is.” – HumblyTossed
“It's the case for most government jobs … they will have an internal candidate, and the rest is for show.” – mixdup

2. Frustrating, dehumanizing hiring process – Applicants describe receiving instant rejections, no feedback, and postings left up for weeks, wasting their time and eroding trust.

“Regardless – it would be nothing to show some basic human decency and respect: … Submit a basic and honest response for the people who's resumes didn't make the consideration … A simple human note for the candidates who were considered but didn't make the interview.” – legitster
“If you met the listing qualifications but got rejected as not qualified, I would double check that your resume wasn't unclear about something.” – Aurornis
“I've got a stack of 100 resumes and 2 hours today to select the top 10 candidates from that stack.” – shagie

3. Mandatory postings with an internal candidate already chosen – Especially in education and government roles, laws require advertising the job even when the hire is predetermined, leading to “ghost”‑like listings.

“Education roles are also an indication of mandatory listing: they have someone internally in mind for a future vacancy but are legally required to list it.” – dfm
“The law in most states and at the federal level require opening jobs to the general public to prevent favoritism … It usually just results in having a job posting that is filled before it's posted as they will have an internal candidate.” – mixdup
“District admins often design a role with someone in mind … They need to post the job to meet union criteria, but only one person could realistically fit the bill.” – renjimen

4. AI/LLM‑driven noise in applications – The rise of language‑model‑generated resumes and cover letters makes it harder for both sides to signal genuine fit, increasing reliance on AI screening and worsening the signal‑to‑noise ratio.

“AI tools have devalued resumes and cover letters, personal repos, leet code scores, etc…” – criddell
“In the age of LLMs every one is ‘tailoring’ their resume to every posting.” – whstl
“I do reach out to LinkedIn profiles … but I'd never use LLM‑written text to do so. You have to appear like a real human.” – Aurornis


🚀 Project Ideas

GhostJob Radar

Summary

  • Detects likely ghost job postings by analyzing age, repost frequency, and employer hiring signals across major boards.
  • Core value: saves job seekers time by highlighting low‑probability listings so they can focus on genuine opportunities.

Details

| Target Audience | Job seekers frustrated by low response rates and suspect ghost jobs, especially in tech. | | Core Feature | Browser extension that scores each listing (0‑100) based on days open, repost history, company size, and recent hiring activity, flagging high‑risk ghost jobs. | | Tech Stack | JavaScript/TypeScript for extension, Node.js backend, PostgreSQL, AWS Lambda for periodic scraping. | | Difficulty | Medium | | Monetization | Revenue-ready: Freemium (free basic scoring, $5/mo for detailed history and alerts) |

Notes

  • HN commenter legitster said “it feels like 90% of tech jobs right now are Ghost jobs”; a tool that quantifies this would be welcomed.
  • Enables discussion on hiring transparency and could be used by regulators to monitor compliance.

ResumeTailor AI

Summary

  • LLM‑powered resume customizer that transforms a master CV into a job‑specific version optimized for ATS and human skimmers.
  • Core value: reduces manual tailoring time while increasing match scores and response rates.

Details

| Target Audience | Tech applicants who send many applications and want to improve their odds. | | Core Feature | Web app where user uploads a master resume (PDF/Markdown), pastes a job description, receives a tailored resume with keyword ordering, optional match percentage, and edit suggestions. | | Tech Stack | Python/FastAPI backend, LLM API (OpenAI or open‑source Llama), React frontend, S3/Postgres storage. | | Difficulty | Medium | | Monetization | Revenue-ready: Subscription $9/mo for unlimited tailoring (free tier limited to 5 per month) |

Notes

  • drillsteps5 described using Claude to get a percentage match and improve resumes; automating this would be a direct hit.
  • Encourages discussion on the balance between ATS optimization and authentic self‑presentation.

SalaryTransparency Watch

Summary

  • Crowdsourced salary‑range validator that checks job postings for disclosed pay info, flags missing or overly wide ranges, and shows community‑reported medians.
  • Core value: empowers applicants to know their worth and pushes employers toward compliance with emerging salary‑transparency laws.

Details

| Target Audience | Job seekers in regions with salary‑transparency requirements (US states, UK, Canada) and anyone seeking fair pay data. | | Core Feature | Browser extension that reads a posting, extracts any salary range, warns if absent or >2× spread, and displays median salary from community data for similar role/location. | | Tech Stack | Extension (JS), Node/Express backend, MongoDB for crowdsourced data, optional integration with public salary APIs. | | Difficulty | Low-Medium | | Monetization | Revenue-ready: Free with optional paid API for employers ($0.01 per compliance check) or premium analytics dashboard ($4/mo). |

Notes

  • oejxiwjxjwjd urged “Wanting employers to respect my goddamned time? Wanting to know how much employers are willing to pay…”; this directly addresses that demand.
  • Could spark discussion on legal compliance and provide practical utility for both job seekers and responsible employers.

FeedbackLoop

Summary

  • Platform for collecting and sharing anonymized employer feedback on job applications, revealing response times, usefulness of replies, and ghost‑job frequency.
  • Core value: reduces opacity in hiring, giving applicants actionable insight and encouraging better employer behavior via reputational pressure.

Details

| Target Audience | Applicants who receive generic rejections and want to know whether an employer gives useful feedback. | | Core Feature | Users submit anonymized feedback (timestamp, employer, job title, response received, reason quality); aggregated stats per employer (avg response time, % giving specific feedback, ghost‑job rate) are displayed; optional email parsing to auto‑capture replies. | | Tech Stack | React frontend, Node/Express API, Postgres database, optional webhook services for email parsing. | | Difficulty | Medium | | Monetization | Revenue-ready: Freemium (free basic stats, $6/mo for deep analytics, export, and priority support). |

Notes

  • legitster urged “Submit a basic and honest response…”, shagie noted the desire for feedback; this tool turns that request into measurable data.
  • Provides a forum for discussing hiring practices and could be used by advocacy groups to push for better standards.

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