Updated 2026-08-15 ยท period n/a to n/a
Stack Pairing Atlas
This atlas shows which technologies are co-mentioned in the same hiring posts. It reports co-mentions, conditional probability, and lift, not a claim that every pair belongs to one role.
Technology co-mentions
A co-mention means the same top-level HN post mentions both technologies. It is useful for stack planning, but it is not yet role-level proof.
| Pair | Co-mentions | Lift | P(A|B) | P(B|A) | Salary n |
|---|---|---|---|---|---|
| Artificial Intelligence + TypeScript | 41 | 1.27 | 70.7% | 30.8% | 0 |
| Artificial Intelligence + Python | 36 | 1.03 | 57.1% | 27.1% | 0 |
| Artificial Intelligence + Full Stack | 35 | 1.01 | 56.5% | 26.3% | 0 |
| React + TypeScript | 35 | 2.83 | 60.3% | 68.6% | 0 |
| Artificial Intelligence + React | 34 | 1.2 | 66.7% | 25.6% | 0 |
| Artificial Intelligence + Backend | 31 | 0.98 | 54.4% | 23.3% | 0 |
| Full Stack + TypeScript | 29 | 1.93 | 50% | 46.8% | 0 |
| Artificial Intelligence + PostgreSQL | 28 | 1.07 | 59.6% | 21.1% | 0 |
| Full Stack + React | 28 | 2.12 | 54.9% | 45.2% | 0 |
| PostgreSQL + TypeScript | 28 | 2.45 | 48.3% | 59.6% | 0 |
| Backend + TypeScript | 27 | 1.95 | 46.6% | 47.4% | 0 |
| Python + TypeScript | 27 | 1.77 | 46.6% | 42.9% | 0 |
| Artificial Intelligence + Machine Learning | 26 | 1.2 | 66.7% | 19.6% | 0 |
| PostgreSQL + React | 25 | 2.49 | 49% | 53.2% | 0 |
| Backend + Full Stack | 24 | 1.62 | 38.7% | 42.1% | 0 |
| Backend + PostgreSQL | 23 | 2.05 | 48.9% | 40.4% | 0 |
| Machine Learning + Python | 23 | 2.24 | 36.5% | 59% | 0 |
| Artificial Intelligence + LLM | 22 | 1.58 | 88% | 16.5% | 0 |
| Backend + Python | 22 | 1.46 | 34.9% | 38.6% | 0 |
| Backend + React | 22 | 1.81 | 43.1% | 38.6% | 0 |
| Full Stack + PostgreSQL | 22 | 1.8 | 46.8% | 35.5% | 0 |
| PostgreSQL + Python | 22 | 1.78 | 34.9% | 46.8% | 0 |
| AWS + PostgreSQL | 21 | 2.89 | 44.7% | 56.8% | 0 |
| Full Stack + Python | 21 | 1.28 | 33.3% | 33.9% | 0 |
| AWS + Artificial Intelligence | 20 | 0.97 | 15% | 54.1% | 0 |
| AWS + Python | 20 | 2.05 | 31.8% | 54.1% | 0 |
| AWS + TypeScript | 19 | 2.12 | 32.8% | 51.3% | 0 |
| AWS + Full Stack | 18 | 1.88 | 29% | 48.7% | 0 |
| Artificial Intelligence + Kubernetes | 18 | 1.24 | 69.2% | 13.5% | 0 |
| Backend + Frontend | 18 | 3.02 | 72% | 31.6% | 0 |
| Node.js + TypeScript | 18 | 3.09 | 31% | 75% | 0 |
| Python + React | 18 | 1.34 | 35.3% | 28.6% | 0 |
| AWS + React | 17 | 2.15 | 33.3% | 46% | 0 |
| Artificial Intelligence + Frontend | 17 | 1.22 | 68% | 12.8% | 0 |
| Artificial Intelligence + Node.js | 17 | 1.27 | 70.8% | 12.8% | 0 |
| Frontend + Full Stack | 17 | 2.62 | 27.4% | 68% | 0 |
| AWS + Backend | 16 | 1.81 | 28.1% | 43.2% | 0 |
| Full Stack + Machine Learning | 16 | 1.58 | 41% | 25.8% | 0 |
| Frontend + TypeScript | 15 | 2.47 | 25.9% | 60% | 0 |
| Kubernetes + PostgreSQL | 15 | 2.93 | 31.9% | 57.7% | 0 |
| Kubernetes + Python | 15 | 2.19 | 23.8% | 57.7% | 0 |
| Backend + Node.js | 14 | 2.45 | 58.3% | 24.6% | 0 |
| Node.js + React | 14 | 2.73 | 27.5% | 58.3% | 0 |
| Frontend + React | 13 | 2.44 | 25.5% | 52% | 0 |
| AWS + Kubernetes | 12 | 2.98 | 46.2% | 32.4% | 0 |
| AWS + Node.js | 12 | 3.23 | 50% | 32.4% | 0 |
| Artificial Intelligence + Golang | 12 | 0.83 | 46.2% | 9% | 0 |
| Full Stack + Node.js | 12 | 1.93 | 50% | 19.4% | 0 |
| Node.js + PostgreSQL | 12 | 2.54 | 25.5% | 50% | 0 |
| Full Stack + Next.js | 11 | 3.53 | 91.7% | 17.7% | 0 |
| Kubernetes + React | 11 | 1.98 | 21.6% | 42.3% | 0 |
| Kubernetes + TypeScript | 11 | 1.74 | 19% | 42.3% | 0 |
| Next.js + TypeScript | 11 | 3.78 | 19% | 91.7% | 0 |
| API + Full Stack | 10 | 1.93 | 16.1% | 50% | 0 |
| Backend + Golang | 10 | 1.61 | 38.5% | 17.5% | 0 |
| Docker + PostgreSQL | 10 | 2.99 | 21.3% | 58.8% | 0 |
| Docker + Python | 10 | 2.23 | 15.9% | 58.8% | 0 |
| Golang + React | 10 | 1.8 | 19.6% | 38.5% | 0 |
| Golang + TypeScript | 10 | 1.58 | 17.2% | 38.5% | 0 |
| Python + Terraform | 10 | 2.71 | 71.4% | 15.9% | 0 |
Methodology and data dictionary
Source: public Hacker News Who is Hiring top-level comments. Demand means share of classified hiring posts in this corpus, not the whole labor market and not confirmed hires. Raw comment text is stored privately for reproducible parsing, but public exports contain derived fields, HN IDs where relevant, and source URLs only.
Sample gates: hide salary medians below 20 salary observations, keep weak change claims marked as insufficient sample, and show counts next to every table. The taxonomy and parser versions are included in every export row.
CSV checksum: 1964d80ec6939213eec466694fd942cd6aa89d5fa06a0f60413df7c475901775