# Great Developers: What 2.5M Profiles Actually Look Like

> What great developers look like in public data — medians, outliers and the signals that matter when you are hiring, drawn from 2.5 million profiles.

- Canonical: https://www.realgreatdevs.com/blog/great-developers
- Target query: great developers
- Funnel: top
- Published: 2026-08-05
- Tags: data study, hiring, engineering

"Great developers" is a phrase people use when they mean "I will know it when I see it." That is fine for a gut check after an interview. It is useless as a sourcing filter.

So we looked at the opposite question: what does the public record of 2.5 million developers actually show about activity, geography and skill mix — and which of those numbers are worth using when you hire?

This is a companion to [What Makes a Great Developer?](/blog/what-makes-a-great-developer). That piece challenges the usual myths. This one is the field guide: what the population looks like, so your bar is calibrated to reality.

<Callout title="About the data">
Figures come from an index of public GitHub activity covering 2.5 million developer profiles, of which 1,115,277 have a resolvable country. Data as of August 2026.
</Callout>

## The middle of the market is quieter than LinkedIn suggests

Across the largest skill populations, a typical developer has roughly:

- **24 public repositories**
- **42 contributions in the last year**
- **8 followers**

That is the median, not the left tail. If your mental model of a "great developer" requires a green contribution graph every day, you have defined greatness as "works in the open" — which correlates with employer policy and career stage more than with ability.

| Skill | Median contributions | Median repos | Median followers |
| --- | --- | --- | --- |
| JavaScript | 42 | 24 | 8 |
| Python | 44 | 22 | 8 |
| TypeScript | 69 | 27 | 9 |
| Java | 39 | 25 | 8 |
| C++ | 43 | 28 | 10 |

TypeScript sits higher on contributions (69) than most languages. That is selection, not proof of superiority: people who adopt TypeScript deliberately tend to be more engaged with tooling. Always compare within a population.

## Where the great ones actually cluster

The largest skill populations are not where scarcity lives:

1. JavaScript — 1,336,934
2. HTML — 1,202,938
3. Python — 1,153,995
4. TypeScript — 749,904
5. Java — 743,885

Rust (127,090), Kotlin (123,406) and Swift (105,978) are an order of magnitude smaller. A "great JavaScript developer" search is a ranking problem. A "great Rust developer" search is a response-rate problem. Treating them the same stalls the hire.

Geography is similarly skewed. Of located developers:

| Country | Developers |
| --- | --- |
| United States | 251,534 |
| India | 128,786 |
| China | 82,597 |
| Brazil | 65,199 |
| Germany | 37,907 |

The US alone is about 23% of located profiles. Colombia sits ahead of France. Bangladesh is close to Japan. If you only search the markets you already know, you are fishing where everyone else is fishing.

Browse the full breakdown on the [skill](/hire-developers) and [country](/hire-developers-in/united-states) hubs.

## Signals that separate useful profiles from noise

**Recency over volume.** Forty contributions spread across twelve months beats four hundred in one burst two years ago.

**Depth over breadth.** Sustained work in two languages is more legible than fourteen tutorial repos.

**Relative scores over absolute ones.** Sixty contributions is unremarkable in TypeScript and strong in Java.

**Public disagreement in issues.** How someone behaves in another maintainer's repository is the best free collaboration signal available.

None of these prove greatness. They narrow two million people to a shortlist worth talking to — which is the only thing public data is good for.

## What the data cannot tell you

How someone reasons about a problem they have not seen. How they handle disagreement in a team. Whether they can explain a technical decision to a non-engineer. Those still require a conversation.

Any tool — including ours — that claims otherwise is overselling.

## Practical takeaway

Calibrate your filters to the population you are hiring from. Widen geography before you lower the bar. Read repositories, not graphs. And when you find someone who looks strong on paper, talk to them — the public record is the start of the evaluation, not the end.

Every figure here comes from the same index you can search. Start with the [free skill pages](/hire-developers), or go deeper with the [full data study](/blog/what-makes-a-great-developer).