有人做了个 AI FOMO 指数，用一周新闻对比基线衡量 AI 热度
Show HN: I built an index to measure AI FOMO
一项名为 AI FOMO 的指数通过对比一周新闻与近期基线，衡量 AI 相关话题的热度变化，当前观测得分为 43/100。该指数覆盖 14,696 条语料记录，其中 Hacker News 占 80%，敏感性检验下不同设定得分在 38 至 52 之间。方法版本 v1.1.0，属探索性研究，未经独立验证，语义标注不参与指数权重。
Can the changing volume and attention around AI be described with a transparent, reproducible index? This study compares a week of collected news with its recent baseline. It measures activity in selected sources, not intelligence, impact, or how far anyone is falling behind.
- Corpus records
- 14,696
- Source streams
- 20
- Daily reconstructions
- 1,431
- Data observed through
- 5 Oct 2026
Sensitivity check
How much does the method matter?
Recompute this observation with equal weights, omit each signal in turn, then omit each eligible source from both current and reference windows.
These specified alternatives produce scores from 38 to 52. The range is a sensitivity diagnostic, not a confidence interval. It does not test every modeling choice.
SpecificationScore / 100
Published weights
43
Equal signal weights
43
Without news velocity
46
Without lab activity
39
Without community attention
43
Without topic spike
45
Inspect source exclusions
- Without Agility Robotics43
- Without Anthropic41
- Without Black Forest Labs44
- Without Boston Dynamics43
- Without DeepMind44
- Without Google AI44
- Without Hacker News52
- Without Hugging Face38
- Without Mistral42
- Without OpenAI48
- Without Runway43
- Without Stability AI43
- Without Wayve43
- Without World Labs41
The remaining corpus is re-baselined for each exclusion. Removing a source changes the population being described.
The observed corpus
A lens with visible limits.
Hacker News accounts for 80% of stored records. The corpus favors English-language, developer-facing coverage. Publisher posts, community attention and technical progress are different things.
120 of 124 records in the current window belong to eligible sources. Eligibility uses declared coverage dates; it is not proof of complete collection. Historical points use retrospectively collected data.
Inspect the underlying headlines →
| Source | All records | 7 days |
|---|---|---|
| Hacker News | 11,735 | 87 |
| OpenAI | 1,032 | 13 |
| Hugging Face | 761 | 7 |
| Anthropic | 260 | 2 |
| DeepMind | 190 | 2 |
| Stability AI | 115 | 0 |
| MidjourneyExcluded: short coverage | 94 | 1 |
| Mistral | 83 | 1 |
| Boston Dynamics | 72 | 1 |
| Google AI | 58 | 2 |
| Agility Robotics | 57 | 3 |
| QwenExcluded: short coverage | 44 | 0 |
| Wayve | 37 | 0 |
| SunoExcluded: short coverage | 36 | 2 |
| Black Forest Labs | 30 | 0 |
| FigureExcluded: short coverage | 24 | 1 |
| Runway | 24 | 1 |
| World Labs | 18 | 1 |
| DeepSeekExcluded: short coverage | 14 | 0 |
| PikaExcluded: short coverage | 12 | 0 |
Semantic annotations
Model annotations · Not human-validated
Beyond counting.
Testing semantic judgments.
Jev / System One annotates whether a headline concerns AI and whether it reports a concrete change. Each decision retains its probability distribution, model version and input fingerprint.
14,696 current corpus records have model annotations in this snapshot. 6 carry provider-consistency flags. These annotations have zero index weight.
Headline judgments cannot establish real-world impact. Human annotation, held-out evaluation and calibration checks come before any proposal to change the index.
Read the experimental protocol →
Reproduce & contribute
A study you can inspect.
Method v1.1.0 · exploratory, not independently validated. Contributions can challenge source selection, audit relevance, propose a rubric or compare alternative specifications.
Dataset SHA-256 · data/index.json
051464a66bec2f505192d80ca65d0772faf159fcfc5a624264d124ed281e47d4
For citation, record the method version, observation date and repository commit. The snapshot includes fingerprints of data and implementation files. No DOI or peer-review status is claimed.
来源：Hacker News · AI · aifomoindex.com