# Filming yourself shooting hoops, earning 1000 stars: a CV PhD's demo repo sustained by a weekly LinkedIn video · GitHub open-source project

> Source: https://stringers.io/en/github/gh_jeremyipark__vision-demos.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.
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> The author appears on camera himself and posts one easy-to-understand sports AI video on LinkedIn each week (the best one drew 11,645 engagements). All the traffic flows into the same repo. In essence it is an acquisition funnel for the company's API.

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

jeremyipark/vision-demos · jeremyipark

# Filming yourself shooting hoops, earning 1000 stars: a CV PhD's demo repo sustained by a weekly LinkedIn video

**What it is** A set of sports analysis demos: film yourself doing pull-ups, push-ups, deadlifts, climbing or shooting hoops on your phone, and AI counts reps and judges whether the form is correct. All models run through VLM Run's API.

**Why it took off** The author appears on camera himself and posts one easy-to-grasp sports AI video a week on LinkedIn (up to 11,645 engagements). All traffic is funneled into the same repo; it's essentially a lead-generation entry point for the company's API.

**B** **How you could follow** Move to Xiaohongshu and Bilibili with a Chinese version, switching to sports everyone in China practices, like jump rope, fitness tests, or badminton. Appear on camera yourself, post one a week, and keep all demos in one repo.

Grade B: the path is clear and the Chinese market is still empty, but the stars came almost entirely from off-platform videos and an audience the author had already built. Newcomers don't have that starting point, and growth stops when posting stops; the scale is only around a thousand. Spreading in small circles

Data (updated daily) · Repo created 2026-09-08 · 1,117 stars · 7 days +200

GitHub · stars

Data 2026-10-09 → 2026-11-08 (31 days, 30 observations). Points before 10-28 come from deeply / archive backfill, not every day has one

Nov 2 was a Sunday. Jeremy Park bought a basketball in North Carolina and shot at a court all afternoon. The next day he posted a video on LinkedIn: on screen, AI tracks the ball and judges whether it went in, and three makes in a row lit up a little flame. The caption opens with "I made a computer vision tool for basketball shooting practice!" and ends with a GitHub link. The post got 4,984 reactions and 158 comments (LinkedIn, 11-06), and two days later his repo jeremyipark/vision-demos gained 105 stars in one day. Below, here's what the repo is, where the stars came from, and whether copying this path is worth it.

### What it actually is

A Python repo called vision-demos, with a one-line description: "Fun real-world computer vision demos!" Inside are sports analysis demos, each one taking a phone video as input:

| demo | What it does |
|---|---|
| pull_ups / chin_ups | Counts pull-ups, times each rep, and judges whether each one is full or half range |
| push_ups | Counts push-ups, checks whether each rep's depth is enough and whether the back stays straight |
| deadlift | Counts deadlifts and judges whether the back stayed straight or rounded on each rep, with a probability |
| rock_climbing / rock_climbing_3d | Picks out the holds on a climbing wall, records which holds were used and in what order; the 3D version uses the iPhone's LiDAR to measure the route in meters |
| running | Cadence, ground contact time per step, and knee angle at landing |
| basketball | Judges whether each shot goes in, and draws a top-down shot chart with shooting percentage |
| dance_sync | Two people dance the same routine and it scores their sync |

Every demo produces one number that's instantly understandable: reps, full or not, shooting percentage. The models are all off-the-shelf with no training: SAM 3.1 segments objects, ViTPose+ estimates human pose, and the deadlift one adds a Gemma 4 for judgment. There's also a Gradio web version that runs locally, where you upload a video to analyze.

The key line in the README: "No API key needed (anonymous, rate-limited tier); paste a VLM Run key for higher limits." All models are called through the VLM Run Gateway. Jeremy Park is the founding DevRel at VLM Run, a vision model API company; the Gateway had just launched on the Hugging Face blog in Sep 4, and the repo was created 4 days later. Put plainly, it's a polished entry point to the company's API.

### Where the stars came from

The repo was created in Sep 8, and by Oct 9 had 689 stars; by Nov 6 it had 1,048 (GitHub, 11-06; the live page now shows over 1,090). Most came in the first month, and each new demo added a smaller bump afterward:

| Date | What was posted | Reception |
|---|---|---|
| 09-10 | Running, pull-ups | LinkedIn 2,812 reactions |
| 09-15 | Climbing | LinkedIn 2,793 |
| 09-24 | Climbing 3D (iPhone LiDAR) | LinkedIn 3,326; Show HN 5 points |
| 09-30 | Deadlift | Show HN 8 pts |
| 10-07 | Pull-ups (full/half range scoring) | LinkedIn 11,645, the biggest one so far |
| 10-26 | Pushups + web version | LinkedIn 1,103; X 6,884 views; repo +42 two days later |
| 11-03 | basketball | LinkedIn 4,984 / 158 comments; 11-05 repo +105 |

(LinkedIn post dates inferred from post IDs; reaction counts are the public numbers at time of scraping)

**The main channel is LinkedIn.** Each X post gets five or six thousand views, and each Hacker News post gets five to eight points. That's nowhere near the thousands and tens of thousands of interactions on LinkedIn. Forks cluster on 9-09, 9-16, 9-25, 10-08, 11-04 through 11-06, matching the posting days one for one. The fork-to-star ratio sits around 15%, above a typical new repo, which suggests people are actually cloning it and running it.

**The second channel is r/computervision on Reddit.** Titles follow one template: "I made a computer vision tool for …!" He also goes into other people's threads about squat counting and basketball detection and comments that he has an open-source repo with lots of fitness examples.

**It doesn't rely on GitHub clout.** The author's GitHub account was registered 2019 years ago, has only this one public repo, and has 88 followers. It has never appeared on the GitHub trending charts.

**The groundwork came first.** Before joining VLM Run, he was already posting videos like this on LinkedIn: a deadlift form-check video with a rounded back got 3,117 reactions in February, and a climbing video in April got 475. The audience was already there. The company just absorbed this person and this kind of content into an acquisition channel. The CEO also comments on every post to boost it.

Another number worth noting: a post with 11,645 interactions brought in only about one or two hundred stars (inferred; there is no day-by-day data before September). LinkedIn has big reach, but the conversion rate to stars is low.

### Who else does the same thing

| Project | Scale | How it differs |
|---|---|---|
| roboflow/sports | About 5,500 stars | The same DevRel playbook from a company: broadcast-style sports footage (soccer, basketball) with its own trained detection models |
| roboflow/supervision, ultralytics | 50k to 60k stars | General-purpose toolkit; fitness counting is just one feature, and it doesn't do content |
| chonyy/AI-basketball-analysis | About 1,300 stars | An 2020-year-old project that only does shooting and has stopped updating |
| yo-WASSUP/Good-GYM, Good-Badminton | 440 / 1,147 star | Fitness counting and badminton line-calling built by Chinese developers, local-model desktop apps |
| vlm-run official example library | 554 / 312 star | Same company, documentation extraction demos, no traction; the personal demo repo has nearly twice their combined stars in two months |

(GitHub,11-06)

No project copies it directly, just a few renamed forks. Its code isn't better than the Roboflow or Ultralytics approaches. It wins on three things: the subject is ordinary sports people train for themselves; the author appears on camera and tells personal stories ("I used to play ball, then I went to dance"); and it ships roughly one post a week, 9 demos in two months, all in one repo, so stars don't get split up.

### What it lacks

- **Stars follow the posts.** In weeks with no post, it gains only three or five stars a day. When posting stops, growth stops.

- **The audience is borrowed.** Most of the several thousand reactions on LinkedIn come from the followers he built up starting in February, plus the company's boosting. A newcomer copying this would not have that head start on their first post.

- **The ceiling is low.** Two months, about ten videos, roughly a thousand stars. That's good for GitHub but not a breakout.

### If it were you

**Verdict** : B. Worth a try. The playbook is clear, but how many stars you get depends on whether you already have an audience on some platform and can keep putting out a video every week.

**Who it suits** : developers who know a bit about vision models and already train at the gym, climb, or play ball; people who already have a few hundred to a few thousand followers on Xiaohongshu, Bilibili, Jike, or LinkedIn; and people who want a portfolio that can be seen and demonstrated, for job hunting or personal branding.

**Who it doesn't suit** : people who don't want to be on camera or don't want to shoot videos every week; people who only want to write code and wait for stars to arrive on their own. Almost all of this repo's stars come from off-site videos. The code itself doesn't spread on its own.

**How to do it** , ranked by how worthwhile:

1. **Switch platforms and make a Chinese version.** Almost no one in the Chinese-speaking world uses this format: film yourself training, get real-time AI scoring, post the videos to Xiaohongshu, Bilibili, and Douyin, and use the repo as the single destination. Swap in subjects more people in China train: rope-skipping counts, Baduanjin, badminton, synchronized scoring for square dancing, and physical fitness tests (pull-ups, sit-ups, standing long jump). Rope-skipping and fitness tests are the kind of thing parents and students will share.

2. **Fill in what it doesn't do well.** Its demos run as scripts on a computer, and the pushup demo isn't in the web version yet. Build a version that works when opened on a phone, or a fully local-model version with no dependency on any API. Developers will be more willing to star that.

3. **Put all demos in one repo.** Don't open a separate repo for each demo, or stars get spread thin. Point every new video at the same link.

**How to get users** : each video covers one movement and gives one number ("I did 23 pushups, 22 with a straight back"), the author appears on camera, and the title uses a fixed template. After posting, comment on related topics to send people over. Show HN and X bring little traffic, so don't count on them.

**When to get in early** : the "no training, just call an API" combos of this generation, like SAM 3.1 and ViTPose+, are still fresh, so building within the next six months is still in time (inferred). Once fitness apps make AI counting standard, the novelty of this kind of demo will be gone.

**What would show you're wrong** : ① the repo keeps gaining dozens of stars a day for a month after it stops updating, which means the code itself is pulling search traffic; then you don't need videos, and the focus should be on doing everything fully and well; ② someone has already made the same Chinese version on Xiaohongshu or Bilibili and it took off; then you're too late and should pick a different subject; ③ you post four or five videos in a row and each brings fewer than twenty or thirty stars, meaning the platform's audience doesn't click through to GitHub; then switch platforms or switch to building an app.

### Data and sources

GitHub: jeremyipark/vision-demos repo page, README, fork list, and daily stars (10-16 to 11-06); star counts for similar repos (11-06). LinkedIn: public pages of Jeremy Park's 9 demo posts (reaction counts are the numbers at time of scraping). X: @jeremyparkphd, the basketball and pushup posts. Hacker News: two Show HN posts, deadlift and climbing 3D. Hugging Face blog: the VLM Run Gateway launch post (09-04). Reddit post scores were not retrieved. Anywhere it says "inferred" is an inference.

[GitHub repo ↗](https://github.com/jeremyipark/vision-demos)

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

Competitors

| Name | Where | Launched | Scale | How it differs from this one |
|---|---|---|---|---|
| roboflow/sports + Roboflow blog | Open source | 2024-05 | 5,475 stars | Same CV-company DevRel playbook, but it's broadcast-style sports footage with self-trained detection models; vision-demos is the author filming his own training, with more everyday subjects |
| roboflow/supervision | Open source | 2022-11 | 51,501 stars | General-purpose toolkit, not a scenario demo |
| Ultralytics AI Gym | Open source | 2022-09 (library) | ultralytics 63,160 stars | Fitness counting is just one feature of a YOLO library, and it doesn't do content distribution |
| chonyy/AI-basketball-analysis | Open source | 2020-04 | 1,304 stars | Single shooting scenario, old project that has stopped updating |
| yo-WASSUP/Good-GYM, Good-Badminton | Open source | 2025 / 2026-06 | 440 / 1,147 stars | Local-model desktop app, single sport |
| HomeCourt / Hooper / ClimbAI | iOS | Years | Commercial apps | A finished product aimed at athletes; vision-demos is aimed at developers and comes as code plus video |
| vlm-run/vlmrun-hub, vlmrun-cookbook | Open source | 2024 | 554 / 312 stars | The company's official examples are about document extraction with no reach; the personal demo repo passed them within two months |

Where users come from

- **Verified** **LinkedIn, posted by the author himself** : 9 demo posts with 475–11,645 interactions (pull-ups 11,645, basketball 4,984/158 comments, climbing 3D 3,326), each with a repo link; posting dates line up with fork/star peaks

- **Verified** **Reddit r/computervision** : posts with the uniform title "I made a computer vision tool for …!" (1wb2xc6, 1wcx27z, 1xmzpgd), plus comments on others' threads that send people to the repo

- **Verified** **X** : each demo is cross-posted to X, the basketball post got 5,703 views and the pushup post 6,884 views, and the company CEO commented underneath

- **Verified** **Hacker News Show HN** : the deadlift post got 8 points and the climbing 3D post 5 points, bringing in almost no traffic

- **Verified** **Reposts and aggregators** : the Instagram account dealonai reposted the climbing 3D reel; CSDN published a Chinese-language write-up (10-24); Robotics_Notebooks 10-28 listed it; DeepWiki and GitDiagram indexed it

- **Verified** **Company resources** : the author is the DevRel lead and co-founder of VLM Run; the repo was created 4 days after the Gateway launch (09-04), and the README directs users to enter a VLM Run key

## jeremyipark/vision-demos: why it took off

> Research day 2026-11-06. Data: our github tables (repo_profile / repo_day), our hackernews table; GitHub API (commits, fork list); Jina/Exa/Brave search and page reads (LinkedIn, X, YouTube, Instagram). Reddit post bodies and scores were blocked by 403 and were not read.

### One line

A CV PhD and the DevRel co-founder of VLM Run (a vision-model gateway startup) turned "film yourself doing pull-ups / climbing / deadlifts / basketball on your phone, and AI automatically counts reps and scores you" into a series of short video demos. **Each week he posts one on LinkedIn titled "I made a computer vision tool for X!"** , with a link to this repo at the end. The stars came from LinkedIn (1k–11k interactions per post) and r/computervision. The repo itself is a user-acquisition funnel for VLM Run Gateway: every demo's model goes through VLM Run Gateway, and the README says that without a key you're on anonymous rate limits, while pasting a VLM Run key raises the limit.

### Key facts

- The repo was created on 2026-09-08, and the first demo (dance_sync) shipped the same day. VLM Run Gateway had just launched (2026-09-04) on the Hugging Face blog and Show HN (5 points). The repo was opened 4 days after the Gateway went live as supporting content.

- Stars: first observation in our database was 2026-10-09 = 689 stars (stars_first); 10-16 831; 11-04 930; 11-05 1035 (single-day +105); 11-06 1048 (in the repo); live GitHub count 1095 stars, 179 forks. gain_7d 134, prev_7d 58.

- **Most stars came in the repo's first month** (0→689 took 31 days), and each new demo added a small increase after that.

- The fork-to-star ratio is 15.6%, above the usual level for new repos (about 11%), which suggests people are actually cloning and running it. Forks cluster by date on 09-09, 09-11, 09-16, 09-25, 10-01, 10-08, 11-04 to 06, **matching the posting day of each new demo one for one** (GitHub forks API).

- The author's account was registered 2019 years ago, has only this one public repo, and has 88 GitHub followers. The stars didn't come from clout inside GitHub.

- The GitHub stargazers list endpoint returned empty results for every repo in this check (including the official vlm-run repos), so the individual starring accounts can't be inspected. Fake-star signals: none matched the suspect list; the fork ratio is high, and there are real issues (rowing machine requests, reproduction problems, how to track multiple people in frame). Judged to be organic stars.

### Where the growth came from (timeline aligned)

| Date | demo | Posts | Engagement (at crawl time) |
|---|---|---|---|
| 2026-02-12 | Deadlift back rounding (pre-database) | LinkedIn | 3,117 reactions |
| 2026-04-24 | Climbing (pre-database) | LinkedIn | 475 |
| 09-08 | dance_sync | r/computervision "I just created this vision-demos repo so that I can share these projects" | Not found |
| 09-10 | running / chin_ups | LinkedIn 2,812; r/computervision post |  |
| 09-15 | rock_climbing | LinkedIn 2,793 |  |
| 09-24 | rock_climbing_3d(iPhone LiDAR) | LinkedIn 3,326; X; Show HN (5 pts); Instagram repost account dealonai 9-25 reposts |  |
| 09-30 | deadlift | LinkedIn; X; Show HN 8 pts |  |
| 10-07 | pull_ups | LinkedIn **11,645** (largest post) |  |
| 10-26 | push_ups + Gradio web version | LinkedIn 1,103;X 6,884 views | 10-28 hoops +42 |
| 11-03 | basketball | LinkedIn **4,984 reactions / 158 comments** ; X 5,703 views; r/computervision | 11-05 hoops +105 |
| 11-05 | basketball trajectory / swish | LinkedIn 2,099;X |  |

LinkedIn post dates are inferred from activity IDs (the high bits are a millisecond timestamp). Engagement counts come from public pages read via r.jina.ai.

Conclusion:

1. **LinkedIn is the main channel** . Each X post gets only 5–7k views and each HN post 5–8 points, far below the thousands to tens of thousands of LinkedIn reactions. LinkedIn's CV/AI crowd (engineers, recruiters, students) eats up short videos of "a PhD using AI to analyze his own workouts," and the company's CEO Sudeep Pillai also boosts each post by commenting under it.

2. **r/computervision is the second channel** , with titles uniformly reading "I made a computer vision tool for …!" The author also drives traffic by commenting under other people's posts (an iOS squat game, a basketball RF-DETR pipeline) with "I have an open-source repo with lots of fitness examples." That is a textbook DevRel playbook.

3. Secondary spread: an Instagram AI repost account (dealonai) reshared a 3D climbing video; CSDN/GitCode has Chinese-language write-ups 10-24; ImChong/Robotics_Notebooks 10-28 lists it; DeepWiki and GitDiagram index it automatically; a "GithubStarMate" account reached out via issue to offer promotion.

4. No evidence of paid promotion, and it has not hit GitHub trending either (the repo was under 60 days old, so it isn't eligible under the rules).

5. **Groundwork came first** : the author was already posting sports CV demos on LinkedIn in 2026 (February and April), before joining VLM Run, so he had an audience ready. VLM Run just folded this person and this content format into a company acquisition channel.

### Competitors / similar

| Name | Where | Timeline | Scale | Where they differ |
|---|---|---|---|---|
| roboflow/sports + Roboflow blog | Open source / web | 2024-05 | 5,475 stars | Roboflow uses the same DevRel playbook (football and basketball analytics, with Piotr Skalski posting videos on X/LinkedIn), but it leans toward broadcast game footage and self-trained YOLO/RF-DETR. vision-demos is "filming and training yourself," with a more personal persona. |
| roboflow/supervision | Open source | 2022-11 | 51,501 stars | A general-purpose CV toolkit, not a demo collection |
| ultralytics (AI Gym solution) | Open source | 2022-09 | 63,160 stars | YOLO pose has built-in fitness counting as one library feature, not content |
| chonyy/AI-basketball-analysis | Open source | 2020-04 | 1,304 stars | An old project covering only shooting, no longer maintained |
| avishah3/AI-Basketball-Shot-Detection-Tracker | Open source | 2023-07 | 285 stars | YOLOv8 shot detection, single feature |
| yo-WASSUP/Good-GYM / Good-Badminton | Open source | 2025 / 2026-06 | 440 / 1,147 stars | Chinese developers' fitness counters and badminton line-calling, with local models and desktop apps |
| HomeCourt, Hooper, ClimbAI and similar | iOS / web | Years | Commercial apps | Finished apps for athletes; vision-demos is code and videos for developers |
| vlm-run/vlmrun-hub and vlmrun-cookbook (the company's official libraries) | Open source | 2024 | 554 / 312 stars | The official examples cover document extraction, which is dry. The personal demo repo passed nearly double the combined stars of the company's repos within two months. |

No copycat projects found that mimic this repo specifically; only some renamed forks (such as xulytiengviet/mapvision and kbsartain/vision-demos, which keeps only running).

### Differentiation

- **Subject matter** : not a generic "what CV can do," but sports ordinary people actually practice (pull-ups, push-ups, deadlifts, climbing, shooting, dance). Each demo has one metric you can understand at a glance (reps, full/partial, whether the back rounds, FG%, sync score). The videos are good content on their own.

- **Persona** : the author appears on camera himself, telling personal stories like "I used to play ball, then I started dancing." That is naturally shareable on LinkedIn.

- **New model combination** : SAM 3.1 + ViTPose+ + Gemma 4, zero training, no weight downloads, everything through one API. This is the selling point VLM Run wants, and it also lets demos iterate very fast (9 demos in two months).

- **Steady rhythm** : roughly one new demo a week, each one bringing a wave of stars to the repo.

These differences (especially subject matter + on-camera host + weekly cadence) are why it took off. The code itself is no stronger than the Roboflow or Ultralytics approaches.

### What to borrow

- DevRel acquisition: wrap the product API in "everyday scenario demos anyone can understand," post the videos on LinkedIn and Reddit with a uniform title template, use the repo as the single landing point, and leave a conversion hook in the README: "anonymous free quota → add a key for more."

- One repo holds all the demos, so stars accumulate in one place instead of being split across a repo per demo.

- LinkedIn distributes AI/CV video demos far more than X or HN does, and few people in the Chinese-speaking community are doing it there.

### Uncertain

- Reddit post scores were not retrieved (403). Stargazer details could not be pulled from GitHub, so it is impossible to confirm which channel the stars mainly came from. We can only align by date and infer.

- We have no daily star counts for September (before our library began tracking), so it is unclear which post drove the first month's surge. Fork dates show peaks on 09-09, 09-16 and 09-25.

- There is no public data on how many paid users or signups VLM Run Gateway gained from this.

### Sources

- [github.com/jeremyipark/vision-demos](https://github.com/jeremyipark/vision-demos) (README, commit history, issues)

- LinkedIn posts: [www.linkedin.com/posts/jeremyipark_computervision-ai-ml-acti…](https://www.linkedin.com/posts/jeremyipark_computervision-ai-ml-activity-7523437196230623232-JPUn) (basketball, 4,984), …7513718492974694401 (pull-ups, 11,645), …7509034566980923392 (climbing 3D, 3,326), …7505763952740478976, …7503931655175323648, …7520618006222213120, …7524216924822478848, …7427791268254109696, …7453521895401938944

- X: [x.com/jeremyparkphd/status/2117672620283863070](https://x.com/jeremyparkphd/status/2117672620283863070) (5,703 views), [x.com/jeremyparkphd/status/2114852863566602517](https://x.com/jeremyparkphd/status/2114852863566602517) (6,884 views), [x.com/jeremyparkphd/status/2103269593024434382](https://x.com/jeremyparkphd/status/2103269593024434382)

- Reddit: [www.reddit.com/r/computervision/comments/1xmzpgd/](https://www.reddit.com/r/computervision/comments/1xmzpgd/), /1wb2xc6/, /1wcx27z/ (search snippets)

- HN (our hackernews library): 49924301 (8 pts), 49849064 (5 pts), 49568379 (VLM Run Gateway Show HN, 5 pts)

- VLM Run Gateway launch: [huggingface.co/blog/vlm-run/introducing-gateway](https://huggingface.co/blog/vlm-run/introducing-gateway) (2026-09-04)

- YouTube @jeremyparkphd (30 subscribers, 8 videos); Instagram dealonai.official repost reel DdtQbhMkZgp

- CSDN Chinese write-up: [blog.csdn.net/gitblog_00123/article/details/157416781](https://blog.csdn.net/gitblog_00123/article/details/157416781) (2026-10-24)

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