OpenAI 2026 hackathon

Gesto

Gesto turns your hand gestures into personalised Mac shortcuts for opening apps, controlling media, switching Chrome tabs, taking screenshots, and more.

Solo project by CodesByNeeraj Lakshmanan · 0 likes · 0 comments

Archive position — measured, not model output

0 likes on Devpost

2,264 of the 7,856 archived projects have more likes, and 5,592 share exactly 0 — so this project's #4,303 place in the like-ranked listing is a tie-break inside that group, not a ranking.

Projects (log scale)

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Likes on Devpost. ▲ marks this project's group.

Show the figures
LikesProjectsShare of archive
05,59271.2%
11,75822.4%
22853.6%
3–41321.7%
5–9751.0%
10+140.2%
Devpost like counts for all 7,856 archived projects, captured when this archive was built.

Executive Summary

What the company appears to be

Gesto is a self-reported personal project that turns hand gestures into Mac shortcuts using computer vision and machine learning. The author describes it as a gesture-based automation tool for macOS, built with Python and open-source libraries.

What changed

The project was submitted to the OpenAI 2026 hackathon on Devpost. It is described as a personal prototype with no commercial traction or revenue evidence.

Single most important open question

Is there any evidence of user adoption, market demand, or product-market fit beyond the author’s own development and submission?

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What The Product Actually Is

The description states that Gesto:

  • Turns hand gestures into Mac shortcuts.
  • Allows users to train custom gestures and map them to actions like opening apps, controlling media, switching tabs, taking screenshots, and locking the Mac.
  • Uses MediaPipe for landmark extraction, a K-nearest-neighbours classifier for gesture recognition, and scikit-learn for ML.
  • Is built with Python, CustomTkinter, OpenCV, MediaPipe, and scikit-learn.
  • Runs locally on macOS, stores models and mappings under ~/.gesto/.
  • Is packaged as a downloadable .app file.

Confidence Low. The product is described as a prototype or personal project, not a commercial offering.

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Positioning & Claim Evolution

The author states:

  • Gesto was inspired by an open-source project that turned laptop space into virtual buttons.
  • It aims to make everyday Mac tasks more accessible through natural hand gestures.
  • It supports user-trained gestures instead of fixed labels.
  • It is privacy-focused, processing data locally.

Confidence Low. These are claims about intent and design, not evidence of traction or market positioning.

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Target Customer & ICP

The description does not state:

  • Who the target customer is.
  • Whether there’s a defined ideal customer profile (ICP).
  • Any segmentation or persona development.

Confidence Not evidenced.

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Business Model & Pricing Evidence

The description does not mention:

  • A pricing model.
  • Revenue streams.
  • Monetisation strategy.
  • Subscription, licensing, or transactional models.

Confidence Not evidenced.

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Technical & Delivery Signals

The author states:

  • The app uses MediaPipe for landmark detection and scikit-learn for classification.
  • It is built with Python, CustomTkinter, OpenCV, and other open-source tools.
  • It runs locally on macOS.
  • It handles camera permissions, confidence thresholds, and retraining.
  • It supports system-level actions via local controls.
  • It is distributed as a downloadable .app file.

Confidence Medium. Technical details are provided but not validated or independently verified.

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Traction & Maturity Signals

The description does not state:

  • Any user base or adoption metrics.
  • Customer feedback or usage data.
  • Product maturity beyond prototype stage.
  • Revenue, ARR, or funding information.

Confidence Not evidenced.

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Competitive Context

The description does not mention:

  • Competitors.
  • Market landscape.
  • How Gesto compares to existing tools or platforms.

Confidence Not evidenced.

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Key Risks & Red Flags

Inferences based on the description:

  • The project is a solo effort (team size: 1), which may limit scalability and long-term maintenance.
  • It relies on macOS permissions, which can be fragile and require frequent user interaction or app signing.
  • It uses open-source tools and local processing, which may not scale well for broader adoption.
  • There is no evidence of commercial viability or monetisation strategy.

Confidence Medium. These are inferences from the self-reported nature of the project.

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Diligence Questions To Ask The Founders

  1. What problem are you solving, and how do you know users care about it?
  2. How many people have tried Gesto, and what feedback have you received?
  3. Are there any plans to monetize or commercialize this tool?
  4. What are the technical limitations of the current implementation that might prevent broader adoption?
  5. How do you plan to handle macOS permission issues at scale?
  6. Have you considered integrating with other platforms beyond macOS?

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Investment/Partnership Verdict

The description states that Gesto is a personal project submitted to a hackathon, built by one person using open-source tools. There is no evidence of:

  • Revenue or customer traction.
  • A defined business model.
  • Market demand or competitive positioning.

Confidence Very low. This is not a commercial product with demonstrated market fit or scalability.

Verdict Not evidenced as a viable investment or partnership opportunity at this stage.

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Source

Submitted to the OpenAI 2026 hackathon on Devpost. Project home on DevPost.

The analysis above was generated by a language model from the project's own one-line description. It is not independent research and contains no verified traction, revenue or customer data.