Candidates have no visibility into how many rounds remain, typical wait times, or what comes next at specific companies, causing anxiety and poor decision-making (e.g., accepting worse offers while waiting).
Candidate-contributed database of interview pipelines per company/role: number of rounds, time between stages, offer timeline, ghosting rates. Users log their own process in real-time and get benchmarked against historical data (e.g., 'Apple DevOps typically has 5-6 rounds, avg 8 weeks total').
Freemium — free basic timelines, paid tier for detailed analytics, salary benchmarks, and recruiter response-rate predictions
The pain is visceral and universal among tech job seekers. The Reddit thread you cited is one of thousands — 'what happens next?' anxiety is the #1 emotional burden of interviewing. People make $50K+ career decisions (accepting worse offers) because they lack this information. The pain is acute but episodic (only when job searching), which slightly limits intensity.
TAM is meaningful but bounded. ~3-4M tech workers in the US actively interview in a given year, global tech job seekers maybe 15-20M. At $10-15/month for a 3-month job search, the addressable market is ~$500M-$1B if you capture a significant share. However, the active user base churns completely every 3-6 months (people stop using it once hired), which compresses effective TAM.
This is the weakest link. Job seekers are notoriously reluctant to pay for tools — they're often between jobs or anxious about money. Glassdoor's freemium conversion is extremely low. Levels.fyi and Blind are free. The comparison is salary negotiation tools (where ROI is obvious: $10K+ salary bump) vs. anxiety reduction (harder to quantify). A $10-15/month price point during active search is plausible but conversion rates will be low. B2B pivot (selling aggregate data to recruiters/employers) is the real monetization path.
Very buildable as MVP. Core is a structured form (company, role, stage, date, outcome) feeding into a database with aggregation queries. No ML required for V1. Stack: Next.js + Postgres + simple analytics dashboard. A solo dev can build a functional MVP in 3-4 weeks. The hard part isn't tech — it's getting data contributions.
This is the strongest signal. Nobody does structured, real-time, crowdsourced interview pipeline tracking. Glassdoor has freeform text reviews. Blind has noisy threads. Job trackers are personal-only. The specific combination of structured stage data + timestamps + aggregation + benchmarking is a clear whitespace. The risk is Glassdoor adding this feature (they have the data infrastructure and user base), but large companies move slowly.
This is structurally challenged for subscriptions. Users need it for 2-4 months during active job search, then churn completely. Annual retention will be terrible. You'd need to expand into ongoing career intelligence (salary tracking, market conditions, promotion timelines) to justify year-round subscriptions. The B2B side (selling to recruiters, employers benchmarking their process) has much better recurring potential.
- +Clear whitespace — no one does structured interview pipeline tracking with aggregated metrics
- +Intense, emotionally-charged pain point with strong community validation (Reddit, Blind posts)
- +Technically simple MVP — a solo dev can ship in 3-4 weeks
- +Natural viral loop — candidates share pipeline data and invite others interviewing at the same companies
- +B2B pivot potential is strong — recruiters and employers would pay for aggregate hiring funnel benchmarks
- !Cold-start problem is severe — useless without data, can't get data without users. You need a seeding strategy (scrape Reddit/Blind, manually compile top 50 companies, or partner with a bootcamp/community)
- !High churn by design — users leave once they land a job, requiring constant acquisition
- !Willingness to pay on B2C side is weak — free alternatives exist for 'good enough' unstructured data
- !Glassdoor could ship this as a feature in 6 months if the idea gains traction — you're building in their shadow
- !Data quality/verification is hard — fake or inaccurate submissions poison the dataset, but too much friction kills contributions
Employer review platform with an interview reviews section where candidates submit freeform descriptions of their interview experience, difficulty rating, and outcome
Compensation data platform for tech workers with some interview process overviews and negotiation coaching. Known for verified, detailed salary data at FAANG and Big Tech.
Anonymous professional network verified by work email. Users discuss interviews, comp, layoffs, and hiring freezes in forum-style threads. Very popular among FAANG/tech workers.
Personal Kanban-style job application trackers where candidates manage their own job search pipeline — track applications, stages, follow-ups, and deadlines.
Anonymous mock interview platform where candidates practice with engineers from top companies. Provides some data on interview pass rates by company.
Week 1-2: Build structured interview logging form (company, role, stage name, date, outcome, wait time) and a simple company/role search page showing aggregated pipeline timelines. Week 3: Seed database by manually extracting structured data from 200+ Reddit and Blind posts for top 30 tech companies. Week 4: Add comparison view ('You are here in the process — here's what typically comes next and how long it takes'). Launch on r/cscareerquestions, Blind, and HackerNews. Do NOT build auth, payments, or analytics dashboards for V1 — validate that people will contribute data first.
Free: View basic pipeline timelines (# of rounds, avg total duration) for top companies. Paid ($12/month): Detailed stage-by-stage analytics, ghosting probability per stage, wait-time percentiles, salary correlation data, and 'what to expect next' predictions for your active interviews. B2B ($500-2K/month): Sell aggregate hiring funnel benchmarks to recruiting teams ('your process takes 40% longer than competitors for the same role'). Long-term: Become the 'Glassdoor for hiring process transparency' and get acquired or expand into recruiter-side tools.
8-14 weeks. First 4 weeks building and seeding. Weeks 5-8 growing organically via community launches (Reddit, HN, Blind). B2C paid conversions likely start around week 10-12 once you have enough data density for the premium tier to feel valuable. B2B revenue (the real money) is 4-6 months out — you need enough aggregate data to sell meaningful benchmarks to employers.
- “what's gonna happen next? will I be called for another onsite interview”
- “Took them 8 weeks after FIVE ROUNDS to get back to me”
- “each one had ~7 rounds and EVERY single time between hearing if I'd be progressing or not, I started getting in my head”
- “no real rhyme or reason behind any of it”