# A bridge, a mountain, located to within 2 m: geo-sleuth gained 6,000 stars in 49 days · GitHub open-source project

> Source: https://stringers.io/en/github/gh_Oldcircle__geo-sleuth.html (Stringers / 什么值得做, generated 2026-11-08)
> Stringers answers one question every day: what small product can one or two people build in a few months and actually make money from. Numbers come from 15 databases we collect daily, each with source and date.
> For agents: these verdicts and the underlying databases (verified revenue, App Store / Google Play charts, ad libraries, GitHub, Product Hunt…) are queryable live over MCP — tools today / hot / products / directions / direction / data. Setup: https://github.com/komako-workshop/stringers (the user logs in at stringers.io → "配置 agent" to get a personal URL). Site index: https://stringers.io/llms.txt

> The line "no text, no landmarks, a bridge and a mountain pin it to within 2 meters" is counterintuitive and hits privacy anxiety. Add a one-line Agent Skill install, Chinese and English AI news accounts passing it along, and Bellingcat naming it as a comparison.

[GitHub open-source project](https://stringers.io/en/github/index.md) · written 11-6

Oldcircle/geo-sleuth · Oldcircle

# A bridge, a mountain, located to within 2 m: geo-sleuth gained 6,000 stars in 49 days

**What it is** A free, open-source skill you drop into your coding agent. Give it a photo and it works step by step with maps, terrain, satellite imagery, and street view to calculate the shooting coordinates, error radius, and camera heading, and attaches an evidence map.

**Why it took off** The line "no text, no landmarks, one bridge and one mountain, located to within 2 m" is counterintuitive and hits privacy anxiety. It installs in one line as an Agent Skill. Chinese and English AI news accounts relayed it, and Bellingcat named it as a benchmark.

**B** **How you could follow it** Don't build another photo-geolocation tool. Take a domain you know, break your expert judgment into scripts that can compute and verify, and package them as an Agent Skill. First build one "impossible task" case that can be explained in a single sentence and use it as the header image, then add a trilingual README and proactively submit it.

Grade B: the photo-geolocation slot is already taken, with plenty of copycats and name-riders. But a zero-follower creator got traction with one skill and one header image, and this structure can transfer to other topics. The bet is whether you can find a demo with the same punch. Publicly discussed / widely covered

Data (updated daily) · Repo created 2026-09-18 · 6,423 stars · 7 days +1,543

GitHub · stars

Data 2026-09-25 → 2026-11-08 (45 days, 32 observations). Points before 10-28 come from deeply / archive backfill, not every day has one

A Chinese developer with only 96 followers turned "find where a photo was taken" into an agent skill that installs into Claude Code, Codex, and Cursor with one command. After 49 days of development it has 6,129 stars and 765 forks (GitHub repo page, 11-06). How it got going, whether there's still room to copy it, and what an open-source project can learn about getting stars are covered section by section below.

### A "Holy cow" post from Nov 1

On Nov 1 2026, a Chinese X account "周览资源" posted: "Holy crap! Someone open-sourced an insane AI Skill on GitHub. Give it a photo and it can work out where it was taken!... The craziest part: in the official example, a photo with only rice paddies, an elevated highway and mountains was pinpointed to within just 2 meters!" The post ended with the install command `npx skills add Oldcircle/geo-sleuth`.

The comments split in two. One said: "Lol, from now on if you say you're working overtime at the office, a photo of the view sent home will expose you with a two-meter error." Another said: "Scam, I uploaded lots of street photos and it recognized none of them." Someone else dug up the fine print in the README: these cases are regression tests, "not accuracy on photos it has never seen."

That week the repo gained 270 to 380 stars a day (Trendshift daily data), its second peak. The first wave came a month earlier, in Oct, with a single-day gain of +410 on 7.

### What it actually is

geo-sleuth is a standard Agent Skill: a SKILL.md instruction file written for the agent, plus about twenty single-purpose Python scripts. Once it's installed in your coding agent, drop in a photo and say "find where this photo was taken." The agent runs these scripts on its own:

- pulls all railway bridges, power lines, and roads in an area from OpenStreetMap;

- uses elevation data to compute the ridgelines visible from each candidate point and matches them against the ridgeline in the photo;

- uses bridge pier spacing as a ruler to measure distance, shadows to determine direction, and satellite imagery and street view to cross-check;

- finally gives the shooting coordinates, error radius, camera heading, and a satellite evidence map with the field of view drawn on it.

Its rule is "scripts rank, the model decides": the scripts handle searching, scoring, and ranking, and the model only chooses among the top candidates. Every conclusion must point to commands run in the session and the files they produced. It's MIT-licensed and free, using the user's own agent and their own quota.

Under the README header image is a single line: "No text. No plates. No landmarks. One bridge, one mountain. Located to within 2 m." Next comes a funnel: **photo → 27,335 → 171 → 14,372 → 22 → 3 → 1 → ±2 m** . A rice-paddy photo from Qingyuan, Guangdong, is filtered from 27,335 railway-bridge segments in the area down to a single point. The README also says this run took about 72 minutes, and it was the first time the method solved this photo, not a blind rerun.

### Why this one, and why now

**The demo fits in one sentence.** "No text, no plates, no landmarks, one bridge and one mountain, 2 m" is counterintuitive, hits privacy anxiety, and both the AI crowd and ordinary people are willing to pass it on. Nearly every repost quoted that line and the funnel verbatim.

**The format caught the moment.** By 2026, SKILL.md had become a common format across Claude Code, Codex, Cursor, Gemini CLI, and Copilot. Users already had an agent, so there was no new app to install and no API key to apply for, just one command. It effectively rode on the installed base of every coding agent.

**Others guess, it calculates.** Open-source projects that have ChatGPT guess locations from images, or wrap Gemini, rely on model intuition and often answer confidently and wrong. geo-sleuth uses geometry, terrain, and open data to compute step by step, so it can deliver meter-level results on photos with no text at all. That's exactly the demo line that grabs people.

**Someone vouched for it.** In Oct 22, Bellingcat, which does open-source investigation, published a benchmark of LLM geolocation and named GeoSleuth as "a similar project already publicly released," comparing it against its own internal agent. On a photo of a monastery courtyard in Venice, 11 of 17 models got within 5 km, but "only Bellingcat's agent and GeoSleuth gave a precise camera-position coordinate."

**Honesty actually helped.** The README gives reproduction conditions for every case and states up front: "This is a record of one run. Changing the model or decisions may not reach the same result." Big accounts that reposted it included that caveat too, which blocked some of the "scam" criticism.

### Where the stars came from

The author, yuanbo, lists Central China Normal University on GitHub. Earlier repos were mostly forks and small games, the top one with 17 stars. In other words, they started with no following at all.

| Timeline | What happened |
|---|---|
| 9-18 | Repo created |
| 9-20 | Reveneau coverage, at 189 stars at the time |
| 9-24 | The author filed "open-source recommendation" issues the same day with Ruan Yifeng's "Tech Enthusiast Weekly" and HelloGitHub (whether they were included is unverified) |
| 9-27 | Dan Kornas posted long threads on X and LinkedIn |
| 10-06 ~ 10-07 | A Chinese AI tools site published a post; 10-07 gained +410 in a single day, ranking #7 on Trendshift's Python chart (who reposted it was not found; likely large Chinese accounts) |
| 10-11 ~ 10-16 | +200 to +374 per day; two Zhihu articles and long-form breakdowns of the principles |
| 10-18 ~ 10-23 | AlphaSignal coverage (3,200 stars at the time), an X post from @RoundtableSpace with 40k views, the Bellingcat benchmark, and a Tencent Cloud developer community article |
| 10-31 ~ 11-02 | The Zhoulan Resources post, the second-wave peak |

Beyond these, there were posts on r/BestGitHubRepos, a Taiwanese Facebook fan page, Bilibili and YouTube Shorts, and on Instagram some people even used the "comment LOBSTER for a free skill" trick to bring in DMs. Afterward, dozens of skill directories and aggregator sites listed it automatically. No paid promotion was observed.

The trilingual README (Chinese, English, Japanese) let traction build on both the Chinese and English sides almost simultaneously: the Chinese side through Zhihu, Chinese X accounts, and Tencent Cloud; the English side through AlphaSignal, Dan Kornas, and Mario Nawfal's accounts.

The fork-to-star ratio is about 12.5%, high for a skill meant to be used right away. Many people forked it to modify it.

### How well it actually works

It is far weaker than the README cases, but not as bad as the comments claimed.

The Japanese blog KOBO Lab tested it in Nov 3 with 3 public photos stripped of GPS. A street scene in Kofu was pinned within 20 m in 8 minutes, helped by a sign with text. A rural Okayama photo with mountains, a river, and a railway was located to within 2 m in 12 minutes. A Ibaraki photo of flooded paddies with a row of houses in the distance could not be identified after 15 minutes; it returned a reference point 100 km off with "low" confidence. The author's verdict: photos with clues can reach tens of meters, and when there are no clues it honestly says it doesn't know. There were flaws along the way too: OCR misread "甲府駅" as "甲府歌," and satellite-image scores on one photo all clustered between 0.997 and 1.00, which removed the point of ranking.

The X claim that it "couldn't identify a single street photo" can't be verified, but it shows what spread was the demo, not real usage. Real install numbers are not public.

### Who's competing for this space

| Name | What it is | How it differs from geo-sleuth |
|---|---|---|
| GeoSpy (now renamed Raven) | Commercial SaaS, from 2023 per year, mainly sold to law enforcement | Closed source, paid, the model guesses end to end |
| Picarta | Web and API | Single-model prediction, no reasoning shown |
| Large models like ChatGPT guessing location from images | Had a wave of hype starting in April 2025 | Doesn't call map or terrain data, and often confidently answers wrong |
| atiilla/GeoIntel | Open source, 1,164 stars | Wraps the Gemini API and just asks |
| Netryx-Astra-V2 | Open source, 1,088 stars | Local street-view feature matching, only works on street-level photos, not a fit for an agent skill |
| Bellingcat's internal agent | Internal to the org, not open source | Same idea |

Copycats have already arrived: marouane53/GeoINT starts from geo-sleuth's code (keeping its license notice) and adds guess-then-verify, local models and audio localization, about 6.2 GB in size; Franky100-pig/Geo-Guesser swaps in Amap satellite imagery for users on China networks; there are also similar skills like zeze1024/geo-locate. Some are sketchier: a repo named Geo-Sleuth-Skill/Geo-Sleuth has a download button, and its contents are a patchwork of files, possibly a lure to download malware; on Google Play there's an ad-supported "Photo Location AI: geo-sleuth" with the package name oldcircle, but the developer is an unrelated WormholeTech. An onei.ai comparison from Nov 6 puts it bluntly: the other skills are "either built on top of it or responding to it."

### If it were you

**Verdict: B.** There's no room left for a "photo location skill" now. geo-sleuth has claimed this slot, it's the biggest, and Bellingcat has named it, and there are already plenty of copycats and name-riders. But its playbook barely depends on the author's resources: zero followers, one SKILL.md, a few dozen scripts, one hero image, and 6,000 stars in 49 days. This structure can be moved to other topics. The bet is whether you can find a demo with the same punch.

**Who it suits:** developers with real hard-won expertise in some field (geography, remote sensing, audio/video forensics, image analysis, astronomy, power grids, traffic data) who can also write Python. Your biggest asset is being able to break one kind of professional judgment into steps a script can compute.

**Who it doesn't suit:** anyone aiming to build a second photo-location skill (you'll be seen as copying); people who can only write prompts and have no computable evidence, since skills that just say "let the model think harder" are a dime a dozen and nobody shares them; anyone hoping to make money from it, because open-source skills earn reputation, not revenue.

**How to do it:**

- Keep the structure, change the topic. Directions worth testing (inferred): shooting time of a photo (sun angle plus weather records), shooting location of a video (multi-frame plus audio), aircraft or ship identification checked against flight or sailing tracks, screenshot authenticity checks. The bar is one rule: the result must be verifiable, and it must be expressible as a one-line "impossible task."

- First build one case that can be retold in a single sentence, with a numeric funnel and error bars, and turn it into the hero image. geo-sleuth's one line and its funnel show up in almost every repost.

- Package it as a standard Agent Skill, with `npx skills add` as the very first line, and fill the README with badges for every supported agent.

- State the limitations and reproduction conditions upfront. It won't stop every challenge, but it makes big accounts willing to share.

- If you want to get close to it, you can also cover what it does poorly: China-based map sources, dedicated methods for a category of photos with no clues (fields, beaches, indoors), batch processing, shipped as a companion skill or as a PR.

**How to get attention:** follow its sequence. Within a week of launch, submit self-nomination issues to ruanyf's weekly and HelloGitHub; write READMEs in Chinese, English (ideally also Japanese); post to relevant subreddits and r/BestGitHubRepos; wait for English AI newsletters (AlphaSignal-type) and Chinese X accounts to pick it up. If the topic touches OSINT or investigative journalism, send it to organizations like Bellingcat directly: one mention from them carries more credibility than ten tweets.

**Why now, before the window closes:** Agent Skills have just become a common format across agents, directory sites are still adding listings in bulk, and newsletters are still hunting for "breakout skills" to feature. The window is probably a few more months. Once every domain has dozens of skills and directories are flooded, the same hero image will earn far less attention.

**Signs you've misjudged it:** ① over the next two months, several "different topic, same structure" skills appear but all stall at a few hundred stars, showing geo-sleuth rode the privacy anxiety around photo location itself and the structure isn't replicable, so downgrade to C; ② agents ship built-in similar capabilities (say an official geolocation tool), and the whole category cools off together; ③ conversely, if followers like GeoINT also reach thousands of stars, the slot isn't full yet and photo location itself still has room.

### Data and sources

GitHub repo Oldcircle/geo-sleuth stars, forks, README and commit history (as of 11-06); Trendshift daily star data and Python chart rankings; X @grgerwcwetwet (11-01) and its comments; Bellingcat review (10-22); KOBO Lab field test in Japan (11-03); Reveneau (9-20), Dan Kornas (9-27), AlphaSignal (10-18), X @RoundtableSpace (10-22); ruanyf weekly #11903, HelloGitHub #3770 self-nomination issues; Zhihu and Tencent Cloud developer community articles; onei.ai comparison article (11-06). Places marked "inferred" or "estimated" are inferences. Star counts for similar repos are from GitHub.

[GitHub repo ↗](https://github.com/Oldcircle/geo-sleuth)

**Research notes: competitor table, acquisition evidence, full research report**

Competitors

| Name | Where | Launched | Scale | How it differs from this one |
|---|---|---|---|---|
| GeoSpy(Raven) | Web | 2023 | Commercial SaaS, aimed at law enforcement | Closed source, paid, end-to-end model guessing; geo-sleuth is free, runs inside the user's own agent, and every step is auditable |
| Picarta | Web | About 2023 | API repo about 100 stars | Single-model prediction, no reasoning or evidence shown |
| Large models like ChatGPT o3 guessing location from images | Big-company features | 2025-04 | Huge user bases | Relies only on model intuition, doesn't call map or terrain data, and often confidently answers wrong |
| atiilla/GeoIntel | Open source | 2024-01 | 1,164 star | Wraps a Gemini API layer and just asks, no geometric retrieval |
| Netryx-Astra-V2 | Open source | 2026-03 | 1,088 star | Local street-view feature matching, only works on street-level photos, not an agent skill |
| Bellingcat internal agent + OSM Search | Other | Disclosed 2026-10 | Not publicly released | Same idea but not open source; Bellingcat uses geo-sleuth as the public benchmark |
| marouane53/GeoINT | Open source | 2026-10/11 | Not verified | Rewritten from geo-sleuth's code, adds GeoGuessr-style guess-then-verify, local models and audio localization, heavier overall |

Where users come from

- **Confirmed** **Author self-nominated to tech weeklies** : 2026-09-24 submitted self-nomination issues to ruanyf/weekly#11903 and HelloGitHub#3770

- **Confirmed** **README demo and trilingual docs** : hero animation, 'One bridge, one mountain. Located to within 2 m.', and the 27,335→1 funnel chain, quoted verbatim in repost threads

- **Confirmed** **English AI newsletters and big X accounts** : Reveneau 9-20, Dan Kornas 9-27, AlphaSignal 10-18, @RoundtableSpace 10-22 (40.3K views), r/BestGitHubRepos

- **Confirmed** **Chinese X, Zhihu, tech communities** : Zhihu 10-14/10-16, Tencent Cloud 10-23, X @byCanen 10-18, @grgerwcwetwet 11-01, overlapping with the second peak from 10-31 to 11-02

- **Confirmed** **Media or institutional endorsement** : Bellingcat 2026-10-22 LLM geolocation review, uses GeoSleuth as the benchmark, gave the exact camera position in the Venice case

- **Confirmed** **Instagram / YouTube short video** : multiple reels, including the DM funnel "comment LOBSTER for a free skill," plus YouTube Shorts; reach not verified

- Inferred **Source of the 10-07 first wave** : +410 that day, Trendshift Python chart #7; I couldn't find which big account reposted it

## geo-sleuth (Oldcircle/geo-sleuth) research report

> Channel: GitHub. Data as of 2026-11-06.

### One line

geo-sleuth is a standard Agent Skill (one SKILL.md plus about 20 Python scripts). Once installed in Claude Code / Codex / Cursor / Gemini CLI, give it a photo and say "where was this taken?" and it queries OpenStreetMap geometry, computes the skyline from elevation data, matches against satellite imagery and street view, and returns shooting coordinates, error radius, camera heading and an evidence image.

**Bottom line: it took off because it has a demo with strong visual punch that can also be verified: "no text, no license plates, no landmarks, just a bridge and a mountain, located within 2 meters", plus "one line, `npx skills add`, and it's in your coding agent." The timing fit the moment Agent Skills became a format shared across agents. Chinese-language circles and English AI newsletters relayed it in turn, and Bellingcat named it as a benchmark in its review, which raised its credibility another notch.**

### 1. Data

- Repo created 2026-09-18; as of 11-06 it had 6,129 stars and 765 forks (data card, GitHub repo). GitHub API reading on the day: 6,218 stars / 773 forks. A fork-to-star ratio of about 12.5% is on the high side for a "install and use" skill.

- Trendshift daily star increments (https://trendshift.io/repositories/247193):
    - 9-19 to 10-06: between +20 and +90 per day, a cold start. 9-20 had 189 stars two days after launch (Reveneau covered it that day).
    - **10-07 +410** (Trendshift Python chart #7), 10-08 +241; from 10-11 to 10-14, +200 to +374 per day (10-14 Python chart #11).
    - From 10-15 to 10-30 it settled into a plateau of +90 to +200 per day.
    - **10-31 +274, 11-01 +380, 11-02 +355** (Python chart #10 to #19): this was the second wave, followed by about +165 per day.

- The author yuanbo (GitHub profile lists Central China Normal University, location "China") has 96 followers. Most other repos are forks or small games; among the original ones, ai-workspace has 17 stars. In effect, no existing audience at all.

- The repo is active: 50 days, 24 PRs merged, and November is still adding Central Asian license plates and text clues (commit log).

- Under 60 days old, it hasn't made GitHub's official trending page, so the charts aren't where its traffic comes from.

### II. Competitors

| Name | Where | Launched | Scale | How it differs from geo-sleuth |
|---|---|---|---|---|
| GeoSpy (Graylark, now renamed Raven) | Web / SaaS | 2023 | Commercial product, mainly sold to law enforcement and intelligence agencies | Closed-source, end-to-end model that guesses coordinates directly, paid; geo-sleuth is free, runs inside your own agent, and every step can be audited |
| Picarta | Web / API | About 2023 | Picarta-API repo has about 100 stars | Single-model prediction, no reasoning shown |
| ChatGPT o3 / GPT series "guess the location from a photo" | Big-company features | From ¥2025-04 | Huge user base, and there was a GeoGuessr craze back in the day | Relies only on the model's intuition, with no map or terrain data, and often confidently gets it wrong (Bellingcat's tests) |
| atiilla/GeoIntel | Open source | 2024-01 | 1,164 stars (github library) | Wraps the Gemini API and just asks directly, with no geometric retrieval |
| Netryx-Astra-V2 | Open source | 2026-03 | 1,088 stars (github library) | Local street-view feature matching, only suited to street-level photos, and can't be dropped into an agent |
| Bellingcat's in-house agent + OSM Search tool | Others (internal to organizations) | Disclosed 2026-10 | No public release | Same idea, but not open-sourced; Bellingcat instead used geo-sleuth as the public control group |
| Browser Use v4 GeoGuessr demo | Open-source demo | 2026-06 | Went viral from a single tweet | Plays the game by driving a browser, with accuracy on the order of 50 km, not a forensic tool |
| marouane53/GeoINT | Open-source skills | 2026-10/11 | Star count unverified | Starts from geo-sleuth's code (keeping its license notice), then adds GeoGuessr-style "guess first, then verify," local models, and audio geolocation; heavier (about 6.2 GB) |
| Geo OSINT Locator | Skill directory | 2026 autumn | Not verified | Focused on reasoning discipline, few tools |

In the lane of "can be installed in a coding agent and uses geographic data to calculate instead of having the model guess," geo-sleuth was the first to build it and is also the largest. onei.ai's 2026-11-06 comparison article calls it "the most-used skill out there; the others either build on it or respond to it."

### 3. Growth tactics (in chronological order)

1. **The author submitted it to a tech weekly themselves (confirmed)** . On 9-24 the same day, they filed self-promotion issues with Ruan Yifeng's *Tech Enthusiast Weekly* (ruanyf/weekly#11903, "[Open-source self-recommendation]") and HelloGitHub (#3770). Whether either was accepted, and in which issue, is unverified.

2. **The README itself is the promotional material (confirmed)** :
    - A hero GIF with the line "No text. No plates. No landmarks. One bridge, one mountain. Located to within 2 m.";
    - The funnel chain "photo → 27,335 → 171 → 14,372 → 22 → 3 → 1 → ±2 m" that anyone can repeat at a glance;
    - README in Chinese, English, and Japanese;
    - Every case documents its reproduction conditions and explicitly states "this is a regression test and does not represent accuracy on unseen photos." This candor got quoted back verbatim by reposters (it's in the Mario Nawfal tweet), which reduced backlash.

3. **English AI news accounts and aggregator sites (confirmed)** :
    - 9-20 Reveneau coverage;
    - 9-27 Dan Kornas long posts on X and LinkedIn;
    - 10-18 AlphaSignal coverage (at the time, 3,200 stars);
    - 10-22 0 x Mario Nawfal (@RoundtableSpace) tweet, 40k views;
    - r/BestGitHubRepos post (at 2.3k stars, around mid-October);
    - Afterwards, a flood of skill directories and SEO sites listed it: SkillsLLM, ai-tldr, Kitploit, SourceForge mirror, TypingMind, Pinokio, OSM Apps Catalog, and others.

4. **Relay in the Chinese-speaking community (confirmed)** :
    - 10-06 AI tools encyclopedia;
    - 10-14 and 10-16 two Zhihu articles;
    - 10-15 a breakdown of the principle on Fuyuan Seven's site;
    - 10-18 X @byCanen;
    - 10-23 Tencent Cloud developer community;
    - **11-01 X @grgerwcwetwet (Zhoulan resources) "Holy shit! ... the error is only 2 meters"** , which lines up exactly with the second traffic peak from 10-31 to 11-02;
    - There were also Taiwanese Facebook fan-page posts saying it "went viral online," and Bilibili recommendation videos.

5. **Bellingcat endorsement (confirmed)** : 10-22 Bellingcat published an LLM geolocation evaluation that named GeoSleuth as "a similar project already publicly released," using it as a comparison; on the Venice photo, only Bellingcat's own agent and geo-sleuth gave the exact shooting position. This is a very strong credibility endorsement within the OSINT community, but stars showed no obvious jump around that time; the effect was more of a long tail.

6. **Short-video creators (existence confirmed, reach unverified)** : Instagram has several reels, one using the "comment LOBSTER to get the free geo-sleuth skill" bait-and-DM trick; YouTube Shorts also has some.

7. **10-07 wave 1 +410: the direct trigger wasn't found (unconfirmed)** . The timing lines up with the 10-06 Chinese tool-site article and the Trendshift listing, so the guess is a repost by a large Chinese X or WeChat Official Account.

No sign of paid promotion; as an open-source skill, there's also no monetization to recoup.

### 4. Why it broke out

- **The demo itself is the hook** : "One bridge, one mountain → 2 m," which is both counterintuitive and touches on privacy anxiety. Comments under the reposts were saying things like "I won't dare post photos anymore" and "I'm working overtime at the office, snap a photo of the view out the window and send it home, and the error is two meters." This kind of topic travels in both the AI crowd and the general public.

- **The format hit the timing** : by 2026, Agent Skills (SKILL.md) had become the common format across Claude Code, Codex, Cursor, Gemini CLI, and Copilot, and `npx skills add` installs it in one line. Users already have an agent, so there's no separate app to install and no API key needed, and the barrier to entry is nearly zero.

- **The methodology of "scripts sort, the model adjudicates, and every conclusion points to a file"** turns LLMs' most common complaint, "confidently guessing," into an auditable chain of evidence. It fits the needs of OSINT circles like Bellingcat and makes it look hardcore next to the pile of "just ask Gemini" copycats.

- **The author's honest statement on accuracy** ("the examples don't represent accuracy on photos the model hasn't seen") was quoted verbatim by big accounts, which blocked some criticism (people on X still say "I tried it on a street photo and it identified nothing").

- **Three-language README, author is a Chinese developer** : the Chinese-speaking community (Ruan Yifeng weekly submission, Zhihu, Tencent Cloud, Chinese X accounts) and the English-speaking one (AlphaSignal, Mario Nawfal, Dan Kornas) built momentum at the same time.

### 5. Differentiation

- Compared with closed SaaS (GeoSpy, Picarta): free and open-source, runs inside the user's own agent, with a transparent process that outputs an error radius and an evidence map.

- Compared with "ask a large model" (ChatGPT o3, GeoIntel): it doesn't rely on model intuition. It uses "computable" evidence such as OSM geometry, elevation skyline fitting, bridge-pier ranging, shadow orientation, and open street-tree data, which is why it can reach meter-level accuracy on photos with no text and no landmarks.

- Compared with academic models (GeoCLIP, PIGEON): it isn't a single model but a workflow, one that any agent can use once installed.

**This differentiation is why it took off** : "locating to 2 m with no text" is something only the geometric retrieval approach can do, and it's also the line that grabs people when they repost. But what spread was the demo, not real usage. Japan's KOBO Lab tested 3 public photos; for the rice-paddy photo it "couldn't pin it down, error 100 km." On X, people also reported failing to identify street shots. Real-world usability is far below what the README cases show.

### 6. Visibility and copycats

- Visibility: **widely discussed** . Basis: the Bellingcat evaluation citation, AlphaSignal coverage, Mario Nawfal's 40k views, multiple long Chinese articles, a Japanese hands-on test blog, and listings across dozens of skill directories. It's an open-source project with no revenue.

- Copycats:
    - **marouane53/GeoINT** : a rewrite based on its code, with heavier changes;
    - **Geo-Sleuth-Skill/Geo-Sleuth** : an SEO repo riding the name, with a "GET Geo Sleuth" download button; the content is a patched-together PIGEON file, likely a lure to download;
    - **WormholeTech's "Photo Location AI: geo-sleuth" on Google Play** : package name io.oldcircle.geo_sleuth, with ads, 100+ downloads, looks like a name-hijacking app that uses the author's name;
    - **Franky100-pig/Geo-Guesser** : an adaptation for the China network, using Amap satellite imagery;
    - **zeze1024/geo-locate** and other similar skills;
    - lots of direct forks (BrocksX, PterX, xiaonan-37021, etc.).

### VII. What others can borrow

1. An open-source tool's README needs one "impossible task" case that can be retold in a single sentence, with the numbers funnel and error margin attached.

2. Package it as a general Agent Skill, one-line install, to ride the existing user base of every coding agent.

3. Declare the limitations up front to earn the trust needed for big accounts to repost.

4. Multilingual README, plus proactively submit to Chinese tech newsletters and HelloGitHub.

5. Pick a topic that triggers privacy anxiety or a "detective" feeling; it's naturally shareable.

### 8. Not sure

- The direct trigger for 10-07's first wave (+410).

- Whether Ruan Yifeng's weekly and HelloGitHub included it, and in which issue.

- Real install counts and reuse rates: I couldn't find install counts from skills.sh or similar.

- GeoINT's star scale.

- Whether the app on Play was published by the author: the developer name WormholeTech has no visible link to the author, yet the package name uses oldcircle.

### Sources

- [github.com/Oldcircle/geo-sleuth](https://github.com/Oldcircle/geo-sleuth) (README, commit history)

- [trendshift.io/repositories/247193](https://trendshift.io/repositories/247193) (daily stars, Python chart rank)

- [reveneau.com/ainews/geo-sleuth-agent-skill-locate-photo-two-…](https://reveneau.com/ainews/geo-sleuth-agent-skill-locate-photo-two-metres-osm-skylines-189-stars) (2026-09-20)

- [github.com/ruanyf/weekly/issues/11903](https://github.com/ruanyf/weekly/issues/11903), [github.com/521xueweihan/HelloGitHub/issues/3770](https://github.com/521xueweihan/HelloGitHub/issues/3770) (2026-09-24 self-submissions)

- [x.com/DanKornas/status/2104277134961193307](https://x.com/DanKornas/status/2104277134961193307) (2026-09-27)

- [alphasignal.ai/news/geo-sleuth-turns-any-coding-agent-into-a…](https://alphasignal.ai/news/geo-sleuth-turns-any-coding-agent-into-an-open-source-photo-detective) (2026-10-18)

- [x.com/RoundtableSpace/status/2113182317766127622](https://x.com/RoundtableSpace/status/2113182317766127622) (2026-10-22,40.3K views)

- [www.bellingcat.com/resources/2026/10/22/can-llms-supercharge…](https://www.bellingcat.com/resources/2026/10/22/can-llms-supercharge-geolocations-our-tests-show-big-improvements-but-humans-remain-key/)

- [x.com/grgerwcwetwet/status/2116861824284942646](https://x.com/grgerwcwetwet/status/2116861824284942646) (2026-11-01)

- [zhuanlan.zhihu.com/p/2094769314742134515](https://zhuanlan.zhihu.com/p/2094769314742134515), [developer.cloud.tencent.cn/article/2767438](https://developer.cloud.tencent.cn/article/2767438), [aitools.converterclub.com/article/geo-sleuth.html](https://aitools.converterclub.com/article/geo-sleuth.html)

- [localai-kobo.com/geo-sleuth/](https://localai-kobo.com/geo-sleuth/) (hands-on test from Japan, 2026-11-03)

- [onei.ai/blog/ai-photo-geolocation-skills](https://onei.ai/blog/ai-photo-geolocation-skills) (comparison of three similar skills, 2026-11-06)

- [github.com/marouane53/GeoINT](https://github.com/marouane53/GeoINT), [github.com/Geo-Sleuth-Skill/Geo-Sleuth](https://github.com/Geo-Sleuth-Skill/Geo-Sleuth), [play.google.com/store/apps/details?id=io.oldcircle.geo_sleut…](https://play.google.com/store/apps/details?id=io.oldcircle.geo_sleuth)

- github repo_profile (star counts of similar repos)

This article was written by AI from public data and the data we collect daily. Numbers are sourced, and inferences are labeled.