IdeaWell

Project ideas from Hacker News discussions.

Michelangelo's first painting, created when he was 12 or 13

Original Article

Hacker News Discussion

📝 Discussion Summary (Click to expand)

Based on the Hacker News discussion surrounding Michelangelo's painting The Torment of St. Anthony, here are the four most prevalent themes:

1. Artistic Attribution and Provenance

Many users expressed skepticism about the painting's authenticity and the strength of the evidence provided in the article. They questioned the certainty of attributing the work specifically to Michelangelo, noting the financial incentives involved in art attribution.

  • "How can they possibly know that for sure? It seems massively unlikely. We don't have any really reliable records from that time." — saberience
  • "It seems like we do know the year it was painted fairly reliably, but we don't know that it was Michelangelo specifically that painted it (the article exudes more confidence that I would give based on the inherent uncertainty of these identifications)." — zeroonetwothree
  • "I sure could find some experts for hire to drive up the price of my cultural artifact." — mxfh

2. The Nature of the Work (Original vs. Copy)

A central point of discussion was whether the painting was an original creation or a copy of an existing engraving by Martin Schongauer. Most commenters concluded it was a master study or copy, which contextualizes the complexity of the composition and the skill required for a 12-year-old.

  • "It isn't an original work, but actually a painted version of a famous engraving by Martin Schongauer." — mcgannon2007
  • "This painting is a masterstudy of Schongauer's engraving 'Saint Anthony Tormented by Demons'." — LegitShady
  • "One thing is to invent such a picture, the other is to copy it almost 1:1 and add some touch, which was the case." — ojciecczas

3. The Role of Talent vs. Training

The discussion frequently turned to the age-old debate of innate genius versus rigorous early training. While some argued Michelangelo was a prodigy, others emphasized that his skill was the result of dedicated apprenticeship and practice from a very young age, not just natural talent.

  • "Anyone can do this level of work - they just need to actually learn it. It doesn't require someone be born with talent." — speff
  • "Sorry, that's like saying with enough math practice, any kid could perform at the level of young Terry Tao... Some people are just intrinsically talented at certain things, and no amount of hard work... will get them to that level." — MontyCarloHall
  • "At that time kids spent their lives training under other masters. By this time he's been painting and assisting full time for many years already." — lawn

4. Historical Context and Religious Content

Users analyzed the painting's subject matter—the torment of St. Anthony—through the lens of Renaissance religious education and cultural norms. They noted that such imagery was common in a pre-secularized age and reflected the theological struggles of the time, as well as the adolescent fascination with the macabre.

  • "In this context, Man's fallen state predisposes him toward sin... The image would then be received as quite inspiring, perhaps helping to inspire and concentrate the viewer's own efforts to resist temptation." — lo_zamoyski
  • "Teenage boys love badass, edgy stuff. And what's badass and edgy in Catholicism? Demons!" — GuB-42
  • "Demons look like that in Medieval and Renaissance paintings. 'Red dude with horns' didn't become the standard depiction of demons until much later." — Maken

🚀 Project Ideas

AI Artwork Copyist & Critic

Summary

  • [A tool that analyzes user-uploaded images of artwork (paintings, sketches, etc.) to identify and contextualize them. It can find the original source material, show related works, and perform a "pentimenti" analysis to distinguish between masterful copies and original creative works, addressing user skepticism about art attribution.]
  • [Provides instant art historical context, comparison overlays, and provenance analysis for any artwork, turning vague online discussions into educational deep dives.]

Details

Key Value
Target Audience Art enthusiasts, students, casual museum-goers, and HN commenters debating art attribution and history.
Core Feature Image recognition to identify art pieces, overlay comparison tools to highlight differences between copies and originals, and a contextual engine to fetch related works (e.g., Schongauer's engraving vs. Michelangelo's copy).
Tech Stack Python (OpenCV, PyTorch), Image Recognition APIs, Vector DB for art metadata, React frontend.
Difficulty Medium
Monetization Hobby (Free tier for casual users; Revenue-ready: Freemium model with premium features like high-res comparisons, API access for researchers, or exportable reports.)

Notes

  • [HN commenters frequently debate the lineage of art (e.g., "It's a copy of Schongauer's engraving" vs. "It's an original work"). Users like basch and tlb specifically discuss pentimenti (correction marks) to determine if a work is a copy or an original. This tool would automate that verification process.]
  • [High practical utility for verifying art claims in real-time discussions, distinguishing between "homework assignments" (as dabluecaboose noted) and original genius, and educating users on the difference between copying and creation.]
  • [Highly relevant to the thread's focus on distinguishing Michelangelo's early copy from the original engraving by Schongauer.]

Apprentice: Skill Trajectory Visualizer

Summary

  • [A platform that maps the learning trajectory of historical prodigies to modern skill acquisition, visualizing the "years of practice" required to reach specific proficiency levels. It debunks the "natural talent" myth by showing the structured apprenticeship systems of the past (e.g., Michelangelo under Ghirlandaio).]
  • [Reframes the "prodigy" narrative by contextualizing early mastery within rigorous training environments, providing a roadmap for modern learners to replicate historical success.]

Details

Key Value
Target Audience Aspiring artists, parents, educators, and HN users interested in talent development vs. hard work debates.
Core Feature Interactive timelines comparing historical training regimens (e.g., Renaissance apprenticeships) with modern education. Users can input their current skill level to see the projected path to mastery.
Tech Stack React/D3.js for visualizations, Node.js backend, PostgreSQL for historical data.
Difficulty Low
Monetization Hobby (Free educational tool. Revenue-ready: "Premium Pathways" subscription for personalized learning plans and mentorship matching.)

Notes

  • [Addresses the fierce debate in the thread about whether Michelangelo's skill was innate talent (MontyCarloHall) or the result of intense early training (speff, roadside_picnic). Users noted he ignored grammar school to copy paintings and was apprenticed young.]
  • [Practical utility for demystifying high-level skill acquisition. It combats "learned helplessness" (as speff mentions) by showing that high skill is a function of structured practice, not magic.]
  • [Supports the sentiment that "anyone can do this level of work... they just need to actually learn it" by visualizing the learning curve.]

Schongauer vs. Michelangelo: Style Transfer Analyzer

Summary

  • [An AI-powered tool that allows users to upload an image of a painting (like Michelangelo's "Torment of Saint Anthony") and automatically applies the style of the original source material (Schongauer's engraving) or vice versa. It highlights the compositional differences and technical additions made by the copying artist.]
  • [Demonstrates the evolution of artistic skill by separating the "copy" from the "interpretation," allowing users to see exactly what Michelangelo added to Schongauer's composition (color, light, specific details).]

Details

Key Value
Target Audience Digital artists, art historians, and curious HN users wanting to understand the mechanics of copying vs. originality.
Core Feature Neural Style Transfer specifically tuned for fine art analysis. Side-by-side view showing the source engraving and the painted copy with heatmaps indicating deviations.
Tech Stack TensorFlow/Keras for style transfer models, WebGL for client-side rendering, FastAPI.
Difficulty Medium
Monetization Hobby (Open source tool). Revenue-ready: Licensing the engine to museums for educational kiosks or online courses.

Notes

  • [Directly addresses the thread's comparison of Schongauer's engraving to Michelangelo's painting. Users like mcgannon2007 linked the original engraving, and LegitShady noted it was a "masterstudy" rather than a 1:1 copy.]
  • [Practical utility for artists learning to study masters; it visualizes the "master study" process mentioned in the thread, showing how copying leads to developing a personal style.]
  • [Appeals to the technical curiosity of HN users who enjoy seeing how tools can deconstruct artistic processes.]

Provenance Verifier

Summary

  • [A decentralized ledger or database application designed to track the provenance and attribution of art, specifically addressing the skepticism surrounding art attribution (e.g., "Press X to Doubt" comments). It aggregates historical records, material analysis data, and expert opinions to generate a "confidence score" for art attribution.]
  • [Adds transparency to the art world by allowing users to see the evidence behind art claims, such as the Bonsanti research mentioned in the thread, and compare it against counter-arguments.]

Details

Key Value
Target Audience Collectors, investors, skeptics, and HN users wary of art world hype (e.g., debbiedowner referencing "The Lost Leonardo").
Core Feature Aggregates data from museums, auction houses, and academic papers. Uses a scoring algorithm to rate the likelihood of attribution (e.g., "Is this really by Michelangelo?").
Tech Stack Blockchain or IPFS for immutable records, Python for data scraping/analysis, React frontend.
Difficulty High
Monetization Revenue-ready: Subscription for detailed reports, API access for auction houses, and verification fees for collectors.

Notes

  • [Addresses the skepticism voiced by users like saberience ("How can they possibly know that for sure?") and zeroonetwothree ("We don't know that it was Michelangelo specifically").]
  • [High potential for discussion; it turns art history debates into data-driven discussions. It validates the concerns raised by mxfh regarding the incentives of museums to attribute works to famous artists to drive up value.]
  • [Practical utility in reducing fraud and misinformation in the art market, a common topic on HN.]

Read Later