Everything / Vision and robotics / World models
World models Emerging
On TutorHero's Everything Index, World models is emerging on GitHub: 0.05 of every 10,000 new public repos over the last three months (51 a month), 6x vs a year ago. The 12-month forecast says rising (+75%). By the end of 2032, lines in the same state ended between 0.02x and 2.5x of where they started (median 0.53x).
Since 2019, and the range to 2032
New repos per 10,000 (3-month average, log scale)12-month forecastRange to 2032: middle half and 90% of past similar lines
The range is not a forecast of this line: it is what GitHub lines in the same state (more than doubled its share of new repos in a year) did 1 to 5 years later, 2019 to 2026, applied to today's level. Past 5 years it is stretched, not measured. Checked out of time (learned before 2022, scored after), 71% of outcomes landed inside the 90% range, so read it as wide. Open it in the Everything chart.
Week by week
New public repos with the world-model tag each week (Monday to Sunday, UTC). The outlined bars, last week included, are still filling in, as tags are often added days after a repo is created, so their height will grow a little. "Against the field" divides the change by what the typical tag did, so a wave that lifts every tag does not count. Hover for the numbers.
Built vs talked about
Each line is its own share (of all new GitHub repos, all Hacker News stories, all English Wikipedia views, all new stars on the repos we track), shown as a share of its own peak, so the timing and shape compare even though the units differ. Hover for the counts. Tap a name to hide or show a line.
What it means
- Worth learning now: World models is emerging, 6x share of new GitHub repos vs a year ago.
- Start by reading deeplethe/utopia (+12.1k stars in 3 months) and vllm-project/vllm-omni (+647 stars in 3 months).
- People to follow: worldbench, REAL Lab, OpenWAM.
- Builders and attention both rising: new repos 6x, attention +13% in a year.
- 92.8 new stars per new repo over 3 months, vs 8.50 for the typical trend: more demand per project than most fields.
- The 12-month forecast for its share: +75% (Rising).
- Talk and building in step: Hacker News +40% vs new repos 6x in a year, close to the typical trend.
- History: of past GitHub lines that more than doubled its share of new repos in a year, 61% had a smaller share 3 to 5 years later and 8% at least doubled.
- Attention: 0.35% of new stars on the repos we track, +13% in a year.
- Hacker News: 8 stories a month, +40% share in a year.
- Not investment advice: this measures what people build on GitHub, not any company.
- Most-starred tools here lately: deeplethe/utopia (vouched), vllm-project/vllm-omni (vouched).
- The field is emerging, so expect new tools and fast changes; pin versions.
Repos carrying it now
People building it
worldbenchSurveys and benchmarks for world models and autonomous driving.Influence 30
REAL LabResearch on reasoning, embodied, agentic, and lifelong evolving AI.Influence 44
OpenWAMAn Open, Modular Exploration Towards Systematic World–Action Model PretrainingInfluence 4More in Vision and robotics
Questions
Is World models growing on GitHub?
Yes. Its share of all new public GitHub repos is 6x vs a year ago and +33% over the last three months (Sep 2026). Phase: Emerging.
How many new World models repos are created each month?
About 51 new public repos a month carry the world-model tag (three-month average to Sep 2026), or 0.05 of every 10,000 new repos.
What will World models look like in 2032?
Nobody knows, and we say how much. Of past GitHub lines that more than doubled its share of new repos in a year, the middle half had between 0.13x and 1.26x of their share 3 to 5 years later, and 61% ended smaller.
Is World models more talked about than built?
Talk and building in step. Over the year its share of Hacker News stories moved +40% while its share of new GitHub repos moved 6x. New repos grew faster than talk for most AI trends this year, so the label compares it with the typical trend: about 1/1.2 of the talk per builder of the median trend.
Not investment advice. Measurements of what people build and star on GitHub, a statistical forecast, base rates and a model's reading of them. Nothing here is about any company's revenue, valuation or stock.