Octoverse 2025 Flipped GitHub: Rebuild Your Sourcing Boolean

Your site:github.com "python" "United States" string was built for a GitHub that no longer exists. Octoverse 2025 confirmed India passed the US as the largest contributor base on the platform, so if your default lead filter is still country plus language, you are now sourcing Bengaluru by accident and paying for the seats.

The flip, in one table

India overtook the United States as GitHub's largest contributor base in 2025, with 21.9M contributors to the US's 28M developers and 5.2M net-new Indian developers added in a single year. That is a contributor-count flip, not an output flip: the US still leads in total contributions and per-developer activity, but the shape of what your Boolean actually returns has already changed.

Read the last two rows carefully. On raw contributor headcount, the US still edges India 1.28 to 1. On profile-declared GitHub skill, which is what a recruiter actually sees when scanning candidates, India already leads the US almost 2 to 1. Indian engineers surface GitHub as a professional signal at a materially higher rate than Americans do, so the recruiter-visible flip is steeper than Octoverse's numbers suggest.

0.55 : 1

Why the old Boolean now demotes the people you want

The default location:United States language:python string fails on four separate axes at once, not just geography. Each one is fixable, but you have to fix all four.

1. Location is now a minority filter. GitHub itself projects India will have 57M developers by 2030, meaning one in three new devs globally will be Indian. The US share of the contributor pool is falling every quarter.
2. Public-repo X-rays sample a shrinking slice. Public repos are 63% of projects but only carry 18.5% of contributions. 81.5% of the actual work happens in private repos your Boolean cannot see.
3. Account age is polluted by Copilot Free. 80% of new developers use GitHub Copilot in their first week, and the Copilot Free launch in late 2024 pulled forward a huge wave of sign-ups. "GitHub since 2024" is now a junior signal, not a practitioner signal.
4. Python is no longer the modern lead. TypeScript overtook both Python and JavaScript as the most-used language in Octoverse 2025, growing by more than 1M contributors in a single year (a 66% YoY jump). Leading with language:python demotes the fastest-growing modern-stack cohort.

Only 31% of tech recruiters use GitHub regularly, so most of your competitors are running stale strings against a shrinking public surface. That is either an opportunity or an existential problem, depending on which side of the fix you land on.

Replace location with density

Stop filtering by country and start filtering by density. GitHub's own search qualifiers make this trivial once you know which levers to pull.

The three underused ones:

• followers:>50 (or >100 for staff-plus). Followers on GitHub are earned by shipping libraries or writing things people actually read. They are the closest thing GitHub has to a reputation score, and they cut the haystack by an order of magnitude with almost no false positives.
• repos:>10 combined with recent push dates. A working engineer touches multiple repos in a quarter. A Copilot-onboarded account touches one tutorial repo and then goes quiet.
• Contribution recency, not account age. "Contributions in the last 90 days" separates the practitioner from the resume-padder better than any join-date qualifier.

Raw contributor counts globalized, but density signals still concentrate where the tenured practitioners are. In the US that is still the SF Bay Area, LA, Austin, and Dallas per Refolk's index. In India the same signals cluster hard in Bengaluru (28% of the top-25 India sample), Delhi/NCR, and Mumbai. Filter by city, not country, on both sides.

Raw contributor counts globalized. Density signals did not.

Timezone-of-commits is the new location filter

The cleanest way to verify a developer is actually US-based is not their profile location field, which is often stale or blank, but the timezone their commits land in. Public commits carry timezone metadata, and a US-based engineer's activity clusters between roughly 14:00 and 04:00 UTC. An IST-based engineer clusters 03:00 to 18:00 UTC. The overlap is small and easy to score.

Tools that expose this without hand-parsing git logs:

• OctoHR and GitHub User Languages (Chrome extensions) surface language mix and activity on the profile page directly.
• GitHub's own contribution graph, if you hover the individual squares, gives you commit hours in the viewer's timezone. Set your account timezone to UTC once and you can eyeball provenance in seconds.
• The GitHub REST API exposes commit author timezone offsets on every commit object.

This is exactly the kind of layered filter that reads clean in plain English but takes forever to express in Boolean. It is the gap Refolk closes: you describe the engineer you want (US-based, ships in TypeScript, contributes to an LLM SDK, senior by follower count) and Refolk returns a ranked shortlist without you writing a single qualifier.

LLM-SDK repos are the new proof-of-work signal

If you want US-based engineers who can actually build with LLMs, filter by contribution to LLM infrastructure repos, not by keyword on a profile. Six of the ten fastest-growing open source repos in 2025 (by contributor count) were AI infrastructure. That is where the modern practitioners are congregating on purpose.

The list to mine:

• langchain-ai/langchain
• openai/openai-python
• vercel/ai
• run-llama/llama_index
• microsoft/autogen

Cross-reference contributors to these repos against US commit timezones, and you have built a haystack that is smaller than any location-based search would produce, but higher quality by orders of magnitude. Refolk's index shows only 2,554 US engineers with LangChain or OpenAI-API listed as a skill. That is roughly 20% of the 12,518 US engineers who list GitHub at all, and it is the scarcity moat that makes this cohort worth targeting directly.

2,554

Named companies that show up disproportionately in the US GitHub-skilled sample: Ramp, LaunchDarkly, Udemy, Google. That tells you where the poachable cohort already sits, and it tells you which alumni networks (ex-Ramp, ex-LaunchDarkly) are worth their own saved search.

Where the demand actually sits right now

Before you commit to rebuilding your Boolean, check the demand side. Most of the roles that would justify this cohort are concentrated in the same cities Refolk's index flags for US GitHub-declared engineers, which keeps the geography of your outbound and your reqs aligned.

The Boolean, rewritten

Here is the replacement string, laid out as a checklist so you can port it into whichever tool you actually use. Each line replaces a broken assumption from the old default.

1. Drop location:United States. Add city qualifiers instead: location:"San Francisco" OR location:"Austin" OR location:"New York" OR location:"Los Angeles".
2. Add density: followers:>50 repos:>10.
3. Push account age back: created:<2024-01-01 to exclude the Copilot Free wave.
4. Switch the language default: language:typescript first, language:python second.
5. Add proof-of-work: filter to contributors to langchain-ai/*, openai/openai-python, vercel/ai, run-llama/llama_index, or microsoft/autogen.
6. Verify with commit timezone (UTC-5 to UTC-8 windows), not the profile location field.

The reason recruiters keep the old string is honest: writing this new one in raw Boolean is tedious, breaks across tools, and does not survive a client's ad-hoc "actually, can you also filter for people who shipped a Rust crate" mid-search. Anchor on sustained activity and follower count, not tenure, and the string is defensible for the next 18 months. It will not survive 2030, when the India-heavy cohort is deep enough that even density filters will need geo-scoring on top, but it buys you the window.

FAQ

Does the India-overtakes-US flip mean I should stop sourcing on GitHub?

No, the opposite. Only 31% of tech recruiters use GitHub regularly, so the platform is simultaneously under-used by your competition and more informationally dense than LinkedIn for engineers. The flip means you cannot rely on country-level filters as a default lead. It does not mean the signal is gone. Density filters (followers, repo count, recent contributions) and proof-of-work filters (contributions to specific LLM-SDK repos) still isolate hireable US-based engineers cleanly, and you now have less competition on the platform than you did two years ago.

Why is TypeScript now the recommended lead filter over Python?

Because Octoverse 2025 showed TypeScript overtaking both Python and JavaScript as the most-used language on GitHub, growing by more than 1M contributors in a single year (a 66% YoY jump). Python is still enormous and still the right filter for ML and data roles, but if your search is for general backend or full-stack engineers building modern web and AI product surfaces, defaulting to Python systematically demotes the fastest-growing modern-stack cohort. Lead with TypeScript, fall back to Python.

How do I filter by commit timezone if I have never touched the GitHub API?

Use the contribution graph as a first pass. Set your GitHub account timezone to UTC in settings, then hover the individual squares on a candidate's profile: the hour distribution will cluster in whichever timezone they actually work in. For scale, the GitHub REST API returns commit author timezone offsets on every commit object, and Chrome extensions like OctoHR surface activity patterns on the profile page directly.

What is a realistic size for the US LLM-engineer haystack right now?

Roughly 2,554 US software, backend, and full-stack engineers list LangChain or OpenAI-API as a skill on their profile, per Refolk's index. That is about 20% of the 12,518 US engineers who list GitHub at all. Add contributors to langchain-ai/*, openai/openai-python, and vercel/ai who do not self-list the skill and the number grows, but it is still a small enough haystack that direct outbound to named engineers beats keyword-broadcast on job boards by a wide margin.