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AI Is Now Tackling Mathematics and Physics Problems Humans Couldn’t Solve
A visual exploration of AI’s expanding role in fundamental science, illustrating mathematical disproofs and physics equation discovery. Image Credit: DeepMind, Lean, and MIT.Dear Cherubs, we’ve spent years asking whether AI can do our homework. The more interesting question now is whether it can help humanity solve problems that have been sitting on the scientific naughty step for decades.
The answer is increasingly yes — although “AI solved physics” would be somewhat premature. What is happening is more subtle, and arguably more exciting: AI is beginning to discover equations, construct proofs, challenge mathematical assumptions and find solutions that researchers had not previously considered.
MATHEMATICS IS GETTING INTERESTING
Mathematics provides an unusually brutal test for AI. A calculation can be checked. A proof either works or it doesn’t. There is very little room for the classic scientific equivalent of “trust me, bro.”
In 2024, Google DeepMind reported that AlphaProof and AlphaGeometry solved four of six International Mathematical Olympiad problems, reaching the level of a silver-medal contestant. AlphaProof also generated formal proofs that could be checked using Lean, a system designed to verify mathematical reasoning.
Then came something considerably bigger.
In May 2026, OpenAI reported that an AI model had produced a disproof of the Erdős unit-distance conjecture, an approximately 80-year-old problem in discrete geometry. External mathematicians checked the proof, and the result produced a construction that improves on the previously believed limit.
OpenAI subsequently reported ten AI-generated mathematical results that either resolved or substantially advanced long-standing open problems across geometry, group theory, complexity theory, coding theory, quantum information and lattice problems. These remain results requiring serious mathematical scrutiny, but they demonstrate something beyond solving textbook exercises: AI can contribute to frontier research.
PHYSICS: CAN AI DISCOVER THE EQUATION?
Physics presents a different challenge. Scientists don’t merely want predictions; they want rules that explain why something happens.
That is where symbolic regression becomes interesting. MIT researchers developed AI Feynman, a system designed to discover mathematical equations directly from numerical data. It successfully recovered all 100 equations in a benchmark drawn from the Feynman Lectures and improved performance dramatically on a more difficult physics dataset.
The significance is easy to miss. Give a conventional machine-learning model enough data and it may become extremely good at predicting an outcome. Give a symbolic-discovery system data, however, and the goal is different: find the compact mathematical relationship hiding underneath.
AI Feynman even demonstrated recovery of Newton’s gravitational equation from numerical data. That isn’t a new law of nature — the equation was already known — but it shows that machines can reconstruct physical relationships from observations rather than simply being handed the formula.
And that distinction matters.
We should not yet claim that AI has discovered a new fundamental law of physics. No machine has independently replaced Einstein or rewritten the Standard Model. But researchers are actively developing systems that search for governing equations and hidden relationships in physical data.
The bigger story may therefore be the transition from AI as calculator to AI as scientific collaborator.
It can search possibilities humans would never have time to enumerate, challenge assumptions that researchers take for granted and sometimes produce mathematical objects worth investigating.
The machine hasn’t taken over the laboratory.
But it has started knocking on the door.
OpenAI — Ten advances in mathematics and theoretical computer science — https://openai.com/index/ten-advances-in-mathematics/
OpenAI — An OpenAI model has disproved a central conjecture in discrete geometry — https://openai.com/index/model-disproves-discrete-geometry-conjecture/
Google DeepMind — AI achieves silver-medal standard solving International Mathematical Olympiad problems — https://deepmind.google/blog/ai-solves-imo-problems-at-silver-medal-level/
MIT / Science Advances — AI Feynman: A physics-inspired method for symbolic regression — https://pmc.ncbi.nlm.nih.gov/articles/PMC7159912/
OpenAI — How GPT-5 helped mathematician Ernest Ryu solve a 40-year-old open problem — https://openai.com/index/gpt-5-mathematical-discovery/
Thisclaimer — https://thisclaimer.com
The Thisclaimer logo blends a classic warning symbol with a brain icon to represent critical thinking, curiosity, and thoughtful disclaimers #aiBreakthroughs #aiMathematics #aiPhysics #artificialIntelligence #futureOfScience #mathematicalProofs #mathematics #physics #scientificDiscovery #symbolicRegression -
AI Is Now Tackling Mathematics and Physics Problems Humans Couldn’t Solve
A visual exploration of AI’s expanding role in fundamental science, illustrating mathematical disproofs and physics equation discovery. Image Credit: DeepMind, Lean, and MIT.Dear Cherubs, we’ve spent years asking whether AI can do our homework. The more interesting question now is whether it can help humanity solve problems that have been sitting on the scientific naughty step for decades.
The answer is increasingly yes — although “AI solved physics” would be somewhat premature. What is happening is more subtle, and arguably more exciting: AI is beginning to discover equations, construct proofs, challenge mathematical assumptions and find solutions that researchers had not previously considered.
MATHEMATICS IS GETTING INTERESTING
Mathematics provides an unusually brutal test for AI. A calculation can be checked. A proof either works or it doesn’t. There is very little room for the classic scientific equivalent of “trust me, bro.”
In 2024, Google DeepMind reported that AlphaProof and AlphaGeometry solved four of six International Mathematical Olympiad problems, reaching the level of a silver-medal contestant. AlphaProof also generated formal proofs that could be checked using Lean, a system designed to verify mathematical reasoning.
Then came something considerably bigger.
In May 2026, OpenAI reported that an AI model had produced a disproof of the Erdős unit-distance conjecture, an approximately 80-year-old problem in discrete geometry. External mathematicians checked the proof, and the result produced a construction that improves on the previously believed limit.
OpenAI subsequently reported ten AI-generated mathematical results that either resolved or substantially advanced long-standing open problems across geometry, group theory, complexity theory, coding theory, quantum information and lattice problems. These remain results requiring serious mathematical scrutiny, but they demonstrate something beyond solving textbook exercises: AI can contribute to frontier research.
PHYSICS: CAN AI DISCOVER THE EQUATION?
Physics presents a different challenge. Scientists don’t merely want predictions; they want rules that explain why something happens.
That is where symbolic regression becomes interesting. MIT researchers developed AI Feynman, a system designed to discover mathematical equations directly from numerical data. It successfully recovered all 100 equations in a benchmark drawn from the Feynman Lectures and improved performance dramatically on a more difficult physics dataset.
The significance is easy to miss. Give a conventional machine-learning model enough data and it may become extremely good at predicting an outcome. Give a symbolic-discovery system data, however, and the goal is different: find the compact mathematical relationship hiding underneath.
AI Feynman even demonstrated recovery of Newton’s gravitational equation from numerical data. That isn’t a new law of nature — the equation was already known — but it shows that machines can reconstruct physical relationships from observations rather than simply being handed the formula.
And that distinction matters.
We should not yet claim that AI has discovered a new fundamental law of physics. No machine has independently replaced Einstein or rewritten the Standard Model. But researchers are actively developing systems that search for governing equations and hidden relationships in physical data.
The bigger story may therefore be the transition from AI as calculator to AI as scientific collaborator.
It can search possibilities humans would never have time to enumerate, challenge assumptions that researchers take for granted and sometimes produce mathematical objects worth investigating.
The machine hasn’t taken over the laboratory.
But it has started knocking on the door.
OpenAI — Ten advances in mathematics and theoretical computer science — https://openai.com/index/ten-advances-in-mathematics/
OpenAI — An OpenAI model has disproved a central conjecture in discrete geometry — https://openai.com/index/model-disproves-discrete-geometry-conjecture/
Google DeepMind — AI achieves silver-medal standard solving International Mathematical Olympiad problems — https://deepmind.google/blog/ai-solves-imo-problems-at-silver-medal-level/
MIT / Science Advances — AI Feynman: A physics-inspired method for symbolic regression — https://pmc.ncbi.nlm.nih.gov/articles/PMC7159912/
OpenAI — How GPT-5 helped mathematician Ernest Ryu solve a 40-year-old open problem — https://openai.com/index/gpt-5-mathematical-discovery/
Thisclaimer — https://thisclaimer.com
The Thisclaimer logo blends a classic warning symbol with a brain icon to represent critical thinking, curiosity, and thoughtful disclaimers #aiBreakthroughs #aiMathematics #aiPhysics #artificialIntelligence #futureOfScience #mathematicalProofs #mathematics #physics #scientificDiscovery #symbolicRegression -
AI Is Now Tackling Mathematics and Physics Problems Humans Couldn’t Solve
A visual exploration of AI’s expanding role in fundamental science, illustrating mathematical disproofs and physics equation discovery. Image Credit: DeepMind, Lean, and MIT.Dear Cherubs, we’ve spent years asking whether AI can do our homework. The more interesting question now is whether it can help humanity solve problems that have been sitting on the scientific naughty step for decades.
The answer is increasingly yes — although “AI solved physics” would be somewhat premature. What is happening is more subtle, and arguably more exciting: AI is beginning to discover equations, construct proofs, challenge mathematical assumptions and find solutions that researchers had not previously considered.
MATHEMATICS IS GETTING INTERESTING
Mathematics provides an unusually brutal test for AI. A calculation can be checked. A proof either works or it doesn’t. There is very little room for the classic scientific equivalent of “trust me, bro.”
In 2024, Google DeepMind reported that AlphaProof and AlphaGeometry solved four of six International Mathematical Olympiad problems, reaching the level of a silver-medal contestant. AlphaProof also generated formal proofs that could be checked using Lean, a system designed to verify mathematical reasoning.
Then came something considerably bigger.
In May 2026, OpenAI reported that an AI model had produced a disproof of the Erdős unit-distance conjecture, an approximately 80-year-old problem in discrete geometry. External mathematicians checked the proof, and the result produced a construction that improves on the previously believed limit.
OpenAI subsequently reported ten AI-generated mathematical results that either resolved or substantially advanced long-standing open problems across geometry, group theory, complexity theory, coding theory, quantum information and lattice problems. These remain results requiring serious mathematical scrutiny, but they demonstrate something beyond solving textbook exercises: AI can contribute to frontier research.
PHYSICS: CAN AI DISCOVER THE EQUATION?
Physics presents a different challenge. Scientists don’t merely want predictions; they want rules that explain why something happens.
That is where symbolic regression becomes interesting. MIT researchers developed AI Feynman, a system designed to discover mathematical equations directly from numerical data. It successfully recovered all 100 equations in a benchmark drawn from the Feynman Lectures and improved performance dramatically on a more difficult physics dataset.
The significance is easy to miss. Give a conventional machine-learning model enough data and it may become extremely good at predicting an outcome. Give a symbolic-discovery system data, however, and the goal is different: find the compact mathematical relationship hiding underneath.
AI Feynman even demonstrated recovery of Newton’s gravitational equation from numerical data. That isn’t a new law of nature — the equation was already known — but it shows that machines can reconstruct physical relationships from observations rather than simply being handed the formula.
And that distinction matters.
We should not yet claim that AI has discovered a new fundamental law of physics. No machine has independently replaced Einstein or rewritten the Standard Model. But researchers are actively developing systems that search for governing equations and hidden relationships in physical data.
The bigger story may therefore be the transition from AI as calculator to AI as scientific collaborator.
It can search possibilities humans would never have time to enumerate, challenge assumptions that researchers take for granted and sometimes produce mathematical objects worth investigating.
The machine hasn’t taken over the laboratory.
But it has started knocking on the door.
OpenAI — Ten advances in mathematics and theoretical computer science — https://openai.com/index/ten-advances-in-mathematics/
OpenAI — An OpenAI model has disproved a central conjecture in discrete geometry — https://openai.com/index/model-disproves-discrete-geometry-conjecture/
Google DeepMind — AI achieves silver-medal standard solving International Mathematical Olympiad problems — https://deepmind.google/blog/ai-solves-imo-problems-at-silver-medal-level/
MIT / Science Advances — AI Feynman: A physics-inspired method for symbolic regression — https://pmc.ncbi.nlm.nih.gov/articles/PMC7159912/
OpenAI — How GPT-5 helped mathematician Ernest Ryu solve a 40-year-old open problem — https://openai.com/index/gpt-5-mathematical-discovery/
Thisclaimer — https://thisclaimer.com
The Thisclaimer logo blends a classic warning symbol with a brain icon to represent critical thinking, curiosity, and thoughtful disclaimers #aiBreakthroughs #aiMathematics #aiPhysics #artificialIntelligence #futureOfScience #mathematicalProofs #mathematics #physics #scientificDiscovery #symbolicRegression -
AI Is Now Tackling Mathematics and Physics Problems Humans Couldn’t Solve
A visual exploration of AI’s expanding role in fundamental science, illustrating mathematical disproofs and physics equation discovery. Image Credit: DeepMind, Lean, and MIT.Dear Cherubs, we’ve spent years asking whether AI can do our homework. The more interesting question now is whether it can help humanity solve problems that have been sitting on the scientific naughty step for decades.
The answer is increasingly yes — although “AI solved physics” would be somewhat premature. What is happening is more subtle, and arguably more exciting: AI is beginning to discover equations, construct proofs, challenge mathematical assumptions and find solutions that researchers had not previously considered.
MATHEMATICS IS GETTING INTERESTING
Mathematics provides an unusually brutal test for AI. A calculation can be checked. A proof either works or it doesn’t. There is very little room for the classic scientific equivalent of “trust me, bro.”
In 2024, Google DeepMind reported that AlphaProof and AlphaGeometry solved four of six International Mathematical Olympiad problems, reaching the level of a silver-medal contestant. AlphaProof also generated formal proofs that could be checked using Lean, a system designed to verify mathematical reasoning.
Then came something considerably bigger.
In May 2026, OpenAI reported that an AI model had produced a disproof of the Erdős unit-distance conjecture, an approximately 80-year-old problem in discrete geometry. External mathematicians checked the proof, and the result produced a construction that improves on the previously believed limit.
OpenAI subsequently reported ten AI-generated mathematical results that either resolved or substantially advanced long-standing open problems across geometry, group theory, complexity theory, coding theory, quantum information and lattice problems. These remain results requiring serious mathematical scrutiny, but they demonstrate something beyond solving textbook exercises: AI can contribute to frontier research.
PHYSICS: CAN AI DISCOVER THE EQUATION?
Physics presents a different challenge. Scientists don’t merely want predictions; they want rules that explain why something happens.
That is where symbolic regression becomes interesting. MIT researchers developed AI Feynman, a system designed to discover mathematical equations directly from numerical data. It successfully recovered all 100 equations in a benchmark drawn from the Feynman Lectures and improved performance dramatically on a more difficult physics dataset.
The significance is easy to miss. Give a conventional machine-learning model enough data and it may become extremely good at predicting an outcome. Give a symbolic-discovery system data, however, and the goal is different: find the compact mathematical relationship hiding underneath.
AI Feynman even demonstrated recovery of Newton’s gravitational equation from numerical data. That isn’t a new law of nature — the equation was already known — but it shows that machines can reconstruct physical relationships from observations rather than simply being handed the formula.
And that distinction matters.
We should not yet claim that AI has discovered a new fundamental law of physics. No machine has independently replaced Einstein or rewritten the Standard Model. But researchers are actively developing systems that search for governing equations and hidden relationships in physical data.
The bigger story may therefore be the transition from AI as calculator to AI as scientific collaborator.
It can search possibilities humans would never have time to enumerate, challenge assumptions that researchers take for granted and sometimes produce mathematical objects worth investigating.
The machine hasn’t taken over the laboratory.
But it has started knocking on the door.
OpenAI — Ten advances in mathematics and theoretical computer science — https://openai.com/index/ten-advances-in-mathematics/
OpenAI — An OpenAI model has disproved a central conjecture in discrete geometry — https://openai.com/index/model-disproves-discrete-geometry-conjecture/
Google DeepMind — AI achieves silver-medal standard solving International Mathematical Olympiad problems — https://deepmind.google/blog/ai-solves-imo-problems-at-silver-medal-level/
MIT / Science Advances — AI Feynman: A physics-inspired method for symbolic regression — https://pmc.ncbi.nlm.nih.gov/articles/PMC7159912/
OpenAI — How GPT-5 helped mathematician Ernest Ryu solve a 40-year-old open problem — https://openai.com/index/gpt-5-mathematical-discovery/
Thisclaimer — https://thisclaimer.com
The Thisclaimer logo blends a classic warning symbol with a brain icon to represent critical thinking, curiosity, and thoughtful disclaimers #aiBreakthroughs #aiMathematics #aiPhysics #artificialIntelligence #futureOfScience #mathematicalProofs #mathematics #physics #scientificDiscovery #symbolicRegression -
From Ancient Conjurers to Houdini: The Magicians Who Changed Magic Forever
A visual journey through the history of magic—from ancient roots and 17th-century texts to Victorian stage illusions, Houdini’s daring escapes, and modern mentalism.Dear Cherubs, magic has been fooling human beings for a very long time. Long before Las Vegas stages, television specials and suspiciously elegant card tricks, performers were already discovering that the human eye and brain are wonderfully easy to confuse.
THE ANCIENT ART OF DECEPTION
One of the earliest surviving books devoted to conjuring is Hocus Pocus Junior, published in London in 1635. Held by the Library of Congress, it describes tricks involving sleight of hand and other forms of legerdemain, showing that practical magic was already being documented centuries ago.
Earlier traditions are harder to separate from folklore, religion and genuine claims of supernatural power. Ancient Egyptian and classical sources contain stories of wonder-workers and seemingly impossible feats, although historians cannot always determine whether particular figures were real performers or literary creations.
By the eighteenth century, professional conjurers were increasingly treating magic as entertainment rather than simply mystery. The nineteenth century then delivered the big plot twist: magic became modern theatre.
Jean-Eugène Robert-Houdin is central to that transformation. The French illusionist presented magic in an elegant theatrical setting and helped establish the image of the magician as a sophisticated performer rather than a mysterious occult figure.
His influence reached one young performer who would borrow part of his name: Harry Houdini.
THE GOLDEN AGE AND BEYOND
Houdini became the world’s most famous escape artist, turning handcuffs, locked containers and dangerous escapes into headline-making entertainment. According to the Library of Congress, his career moved from early sleight-of-hand work toward escapology after impresario Martin Beck encouraged him to concentrate on escapes in 1899.
Houdini’s genius was not simply escaping restraints. It was creating an event around the escape. He understood publicity, suspense and audience psychology almost as well as he understood locks. Frankly, being very good at getting out of boxes probably helped too.
The Golden Age of Magic produced many other influential performers, including John Nevil Maskelyne, David Devant, Harry Kellar and Howard Thurston, whose elaborate stage productions helped establish large-scale illusion as a major form of popular entertainment.
Then came Dai Vernon, known as “The Professor,” whose influence on close-up magic was enormous. Vernon represented a different philosophy: the magic could happen inches from the spectator without enormous machinery or theatrical spectacle. Cards, coins, cups and apparently ordinary objects became enough.
Mentalism developed along another path, with figures such as Theodore Annemann becoming highly influential through prediction, apparent mind reading and psychological presentation.
Later performers pushed the boundaries again. David Copperfield turned illusion into cinematic spectacle; Penn & Teller mixed magic with comedy and theatrical deception; David Blaine helped popularise intimate street magic; and Derren Brown brought modern mentalism, suggestion and psychological theatre to huge television and stage audiences.
There is no objective “greatest magician ever.” Houdini dominated escapology, Vernon became a towering figure in close-up magic, Robert-Houdin transformed theatrical presentation, and others changed entirely different branches of the art.
That may actually be the point. Magic has survived for centuries because it keeps reinventing the same basic promise: “Watch closely. You still won’t see it.”
As noted by thisclaimer.com, the fascination isn’t merely with the trick itself, but with the strange gap between what we see, what we expect and what we believe happened.
Sources:
The Thisclaimer logo blends a classic warning symbol with a brain icon to represent critical thinking, curiosity, and thoughtful disclaimers #closeUpMagic #conjuring #daiVernon #famousMagicians #harryHoudini #illusionists #magicHistory #mentalism #robertHoudin #stageMagic
Library of Congress — https://www.loc.gov/loc/lcib/970224/houdini.html
Library of Congress — https://guides.loc.gov/chronicling-america-harry-houdini
Library of Congress — https://blogs.loc.gov/loc/2021/07/its-magic-ye-olde-hocus-pocus/
Library of Congress — https://www.loc.gov/resource/rbc0001.2008houdini10760/?r=-0.176%2C0.503%2C1.328%2C0.677%2C0&sp=43&st=image
Smithsonian Archives of American Art — https://www.aaa.si.edu/download_pdf_transcript/ajax?record_id=edanmdm-AAADCD_oh_395670
Thisclaimer — https://thisclaimer.com/
YouTube — https://www.youtube.com/@thisclaimer?sub_confirmation=1 -
More than 400 euros for a "suicide prevention pack". Sounds expensive, but it seems to be working so far.
#thoughts #blog #medicine #pillss #health #mentalhealth #life #depression
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More than 400 euros for a "suicide prevention pack". Sounds expensive, but it seems to be working so far.
#thoughts #blog #medicine #pillss #health #mentalhealth #life #depression
-
More than 400 euros for a "suicide prevention pack". Sounds expensive, but it seems to be working so far.
#thoughts #blog #medicine #pillss #health #mentalhealth #life #depression
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Raided medical funds for other needs. ~200 short for neurologist and skills rehab THIS WEEK. Truly, every bit and boost matters. 🙏🥹
Grab #Tabletop #IndieGames:
https://www.drivethrurpg.com/en/publisher/7970/thought-punks?affiliate_id=741284Drop #MutualAid support:
https://ko-fi.com/revcasey
https://paypal.me/tpipc
(credit/debit option): https://bit.ly/payrevRead the previews & sponsor my new #TTRPG: https://dice.camp/@thoughtpunks/117090213182499055
-
Raided medical funds for other needs. ~200 short for neurologist and skills rehab THIS WEEK. Truly, every bit and boost matters. 🙏🥹
Grab #Tabletop #IndieGames:
https://www.drivethrurpg.com/en/publisher/7970/thought-punks?affiliate_id=741284Drop #MutualAid support:
https://ko-fi.com/revcasey
https://paypal.me/tpipc
(credit/debit option): https://bit.ly/payrevRead the previews & sponsor my new #TTRPG: https://dice.camp/@thoughtpunks/117090213182499055
-
Raided medical funds for other needs. ~200 short for neurologist and skills rehab THIS WEEK. Truly, every bit and boost matters. 🙏🥹
Grab #Tabletop #IndieGames:
https://www.drivethrurpg.com/en/publisher/7970/thought-punks?affiliate_id=741284Drop #MutualAid support:
https://ko-fi.com/revcasey
https://paypal.me/tpipc
(credit/debit option): https://bit.ly/payrevRead the previews & sponsor my new #TTRPG: https://dice.camp/@thoughtpunks/117090213182499055
-
Raided medical funds for other needs. ~200 short for neurologist and skills rehab THIS WEEK. Truly, every bit and boost matters. 🙏🥹
Grab #Tabletop #IndieGames:
https://www.drivethrurpg.com/en/publisher/7970/thought-punks?affiliate_id=741284Drop #MutualAid support:
https://ko-fi.com/revcasey
https://paypal.me/tpipc
(credit/debit option): https://bit.ly/payrevRead the previews & sponsor my new #TTRPG: https://dice.camp/@thoughtpunks/117090213182499055
-
Raided medical funds for other needs. ~200 short for neurologist and skills rehab THIS WEEK. Truly, every bit and boost matters. 🙏🥹
Grab #Tabletop #IndieGames:
https://www.drivethrurpg.com/en/publisher/7970/thought-punks?affiliate_id=741284Drop #MutualAid support:
https://ko-fi.com/revcasey
https://paypal.me/tpipc
(credit/debit option): https://bit.ly/payrevRead the previews & sponsor my new #TTRPG: https://dice.camp/@thoughtpunks/117090213182499055
-
What Has AI Actually Solved? The Biggest Problems Where Machines Found Real Answers
Exploring the biggest problems where machines found real answers. This visualization highlights specific breakthroughs in protein folding, antibiotic discovery, materials science, and mathematics. Image generated by AI.Dear Cherubs, AI has been promised as everything from an office assistant to the machine that will apparently fix civilisation before lunch. Strip away the hype, though, and something genuinely remarkable has happened: AI has already helped solve, or substantially crack, several problems that humans had struggled with for decades.
The important bit is “helped”. AI has not cured cancer, solved climate change or discovered the meaning of life while we were making coffee. But in science, mathematics, medicine and engineering, it has produced results that have survived contact with reality.
PROBLEMS THAT GOT REAL ANSWERS
Perhaps the most famous example is protein structure. For decades, predicting how a protein folds into its three-dimensional shape was a major biological challenge. DeepMind’s AlphaFold transformed the field by predicting protein structures at unprecedented scale, contributing to work that earned Demis Hassabis and John Jumper the 2024 Nobel Prize in Chemistry.
Then there are antibiotics. MIT researchers used machine learning to identify halicin, a previously known compound with powerful antibacterial activity, including against several drug-resistant bacteria. Later work identified abaucin, a new antibiotic candidate targeting Acinetobacter baumannii, with activity demonstrated in laboratory experiments and a mouse infection model. MIT’s Antibiotics-AI project now reports nine novel antibiotics discovered or designed with AI and more than 70 billion molecules screened computationally.
Materials science has produced another eye-watering number. Google DeepMind’s GNoME system predicted around 2.2 million crystal structures, including 380,000 predicted to be particularly stable. Researchers independently produced 736 of those predicted structures experimentally, turning some computer-generated possibilities into actual materials.
Mathematics has also taken a hit from the robot army. In 2024, DeepMind reported that AlphaProof and AlphaGeometry solved four of six International Mathematical Olympiad problems, reaching silver-medal-level performance. AlphaProof also produced formal proofs for its solutions, rather than merely throwing suspiciously confident numbers at the wall.
FROM ANSWERS TO DISCOVERY
The more interesting development may be that AI is increasingly being used not simply to answer questions, but to generate new solutions.
AlphaEvolve, introduced by Google DeepMind in 2025, combines large language models with automated evaluation and evolutionary search to discover and improve algorithms. Reported applications include faster matrix multiplication, improvements to computing infrastructure and solutions to mathematical problems. By 2026, Google reported further work involving DNA sequencing, disaster prediction, power-grid simulations and scientific computing.
That changes the game slightly. Traditional science usually looks something like: human has idea, human tests idea, human gets annoyed, human tries another idea.
AI can potentially run the cycle at enormous scale: generate thousands of hypotheses, test them computationally, discard the rubbish and send the promising candidates to humans or automated laboratories.
And that is probably the real story.
AI has not “solved everything”. It has started solving pieces of problems that once seemed stubbornly resistant to computation, while opening entirely new ways of searching through possibilities.
The next breakthrough may therefore not be one spectacular answer.
It may be the machine that keeps finding the next question.
Sources list:
Nobel Prize — The Nobel Prize in Chemistry 2024 — https://www.nobelprize.org/prizes/chemistry/2024/summary/
Google DeepMind — Millions of new materials discovered with deep learning — https://deepmind.google/blog/millions-of-new-materials-discovered-with-deep-learning/
Google DeepMind — AlphaEvolve: A Gemini-powered coding agent for designing advanced algorithms — https://deepmind.google/blog/alphaevolve-a-gemini-powered-coding-agent-for-designing-advanced-algorithms/
Google DeepMind — AI achieves silver-medal standard solving International Mathematical Olympiad problems — https://deepmind.google/blog/ai-solves-imo-problems-at-silver-medal-level/
Google — AlphaEvolve, 1 year later: Impact on science, technology — https://blog.google/innovation-and-ai/infrastructure-and-cloud/google-cloud/alphaevolve-updates/
MIT News — Artificial intelligence yields new antibiotic — https://news.mit.edu/2020/artificial-intelligence-identifies-new-antibiotic-0220
MIT Jameel Clinic — Antibiotic identified by AI — https://jclinic.mit.edu/antibiotic-identified-by-ai/
MIT Collins Lab — Antibiotics-AI Project — https://www.collinslab.mit.edu/antibiotics-ai-project
Thisclaimer — https://thisclaimer.com
The Thisclaimer logo blends a classic warning symbol with a brain icon to represent critical thinking, curiosity, and thoughtful disclaimers #aiBreakthroughs #aiMedicine #aiScience #algorithmDiscovery #alphafold #antibiotics #artificialIntelligence #materialsScience #mathematics #scientificDiscovery -
What Has AI Actually Solved? The Biggest Problems Where Machines Found Real Answers
Exploring the biggest problems where machines found real answers. This visualization highlights specific breakthroughs in protein folding, antibiotic discovery, materials science, and mathematics. Image generated by AI.Dear Cherubs, AI has been promised as everything from an office assistant to the machine that will apparently fix civilisation before lunch. Strip away the hype, though, and something genuinely remarkable has happened: AI has already helped solve, or substantially crack, several problems that humans had struggled with for decades.
The important bit is “helped”. AI has not cured cancer, solved climate change or discovered the meaning of life while we were making coffee. But in science, mathematics, medicine and engineering, it has produced results that have survived contact with reality.
PROBLEMS THAT GOT REAL ANSWERS
Perhaps the most famous example is protein structure. For decades, predicting how a protein folds into its three-dimensional shape was a major biological challenge. DeepMind’s AlphaFold transformed the field by predicting protein structures at unprecedented scale, contributing to work that earned Demis Hassabis and John Jumper the 2024 Nobel Prize in Chemistry.
Then there are antibiotics. MIT researchers used machine learning to identify halicin, a previously known compound with powerful antibacterial activity, including against several drug-resistant bacteria. Later work identified abaucin, a new antibiotic candidate targeting Acinetobacter baumannii, with activity demonstrated in laboratory experiments and a mouse infection model. MIT’s Antibiotics-AI project now reports nine novel antibiotics discovered or designed with AI and more than 70 billion molecules screened computationally.
Materials science has produced another eye-watering number. Google DeepMind’s GNoME system predicted around 2.2 million crystal structures, including 380,000 predicted to be particularly stable. Researchers independently produced 736 of those predicted structures experimentally, turning some computer-generated possibilities into actual materials.
Mathematics has also taken a hit from the robot army. In 2024, DeepMind reported that AlphaProof and AlphaGeometry solved four of six International Mathematical Olympiad problems, reaching silver-medal-level performance. AlphaProof also produced formal proofs for its solutions, rather than merely throwing suspiciously confident numbers at the wall.
FROM ANSWERS TO DISCOVERY
The more interesting development may be that AI is increasingly being used not simply to answer questions, but to generate new solutions.
AlphaEvolve, introduced by Google DeepMind in 2025, combines large language models with automated evaluation and evolutionary search to discover and improve algorithms. Reported applications include faster matrix multiplication, improvements to computing infrastructure and solutions to mathematical problems. By 2026, Google reported further work involving DNA sequencing, disaster prediction, power-grid simulations and scientific computing.
That changes the game slightly. Traditional science usually looks something like: human has idea, human tests idea, human gets annoyed, human tries another idea.
AI can potentially run the cycle at enormous scale: generate thousands of hypotheses, test them computationally, discard the rubbish and send the promising candidates to humans or automated laboratories.
And that is probably the real story.
AI has not “solved everything”. It has started solving pieces of problems that once seemed stubbornly resistant to computation, while opening entirely new ways of searching through possibilities.
The next breakthrough may therefore not be one spectacular answer.
It may be the machine that keeps finding the next question.
Sources list:
Nobel Prize — The Nobel Prize in Chemistry 2024 — https://www.nobelprize.org/prizes/chemistry/2024/summary/
Google DeepMind — Millions of new materials discovered with deep learning — https://deepmind.google/blog/millions-of-new-materials-discovered-with-deep-learning/
Google DeepMind — AlphaEvolve: A Gemini-powered coding agent for designing advanced algorithms — https://deepmind.google/blog/alphaevolve-a-gemini-powered-coding-agent-for-designing-advanced-algorithms/
Google DeepMind — AI achieves silver-medal standard solving International Mathematical Olympiad problems — https://deepmind.google/blog/ai-solves-imo-problems-at-silver-medal-level/
Google — AlphaEvolve, 1 year later: Impact on science, technology — https://blog.google/innovation-and-ai/infrastructure-and-cloud/google-cloud/alphaevolve-updates/
MIT News — Artificial intelligence yields new antibiotic — https://news.mit.edu/2020/artificial-intelligence-identifies-new-antibiotic-0220
MIT Jameel Clinic — Antibiotic identified by AI — https://jclinic.mit.edu/antibiotic-identified-by-ai/
MIT Collins Lab — Antibiotics-AI Project — https://www.collinslab.mit.edu/antibiotics-ai-project
Thisclaimer — https://thisclaimer.com
The Thisclaimer logo blends a classic warning symbol with a brain icon to represent critical thinking, curiosity, and thoughtful disclaimers #AI #aiBreakthroughs #aiMedicine #aiScience #algorithmDiscovery #alphafold #antibiotics #artificialIntelligence #artificialIntelligence #chatgpt #materialsScience #mathematics #philosophy #scientificDiscovery #technology -
What Has AI Actually Solved? The Biggest Problems Where Machines Found Real Answers
Exploring the biggest problems where machines found real answers. This visualization highlights specific breakthroughs in protein folding, antibiotic discovery, materials science, and mathematics. Image generated by AI.Dear Cherubs, AI has been promised as everything from an office assistant to the machine that will apparently fix civilisation before lunch. Strip away the hype, though, and something genuinely remarkable has happened: AI has already helped solve, or substantially crack, several problems that humans had struggled with for decades.
The important bit is “helped”. AI has not cured cancer, solved climate change or discovered the meaning of life while we were making coffee. But in science, mathematics, medicine and engineering, it has produced results that have survived contact with reality.
PROBLEMS THAT GOT REAL ANSWERS
Perhaps the most famous example is protein structure. For decades, predicting how a protein folds into its three-dimensional shape was a major biological challenge. DeepMind’s AlphaFold transformed the field by predicting protein structures at unprecedented scale, contributing to work that earned Demis Hassabis and John Jumper the 2024 Nobel Prize in Chemistry.
Then there are antibiotics. MIT researchers used machine learning to identify halicin, a previously known compound with powerful antibacterial activity, including against several drug-resistant bacteria. Later work identified abaucin, a new antibiotic candidate targeting Acinetobacter baumannii, with activity demonstrated in laboratory experiments and a mouse infection model. MIT’s Antibiotics-AI project now reports nine novel antibiotics discovered or designed with AI and more than 70 billion molecules screened computationally.
Materials science has produced another eye-watering number. Google DeepMind’s GNoME system predicted around 2.2 million crystal structures, including 380,000 predicted to be particularly stable. Researchers independently produced 736 of those predicted structures experimentally, turning some computer-generated possibilities into actual materials.
Mathematics has also taken a hit from the robot army. In 2024, DeepMind reported that AlphaProof and AlphaGeometry solved four of six International Mathematical Olympiad problems, reaching silver-medal-level performance. AlphaProof also produced formal proofs for its solutions, rather than merely throwing suspiciously confident numbers at the wall.
FROM ANSWERS TO DISCOVERY
The more interesting development may be that AI is increasingly being used not simply to answer questions, but to generate new solutions.
AlphaEvolve, introduced by Google DeepMind in 2025, combines large language models with automated evaluation and evolutionary search to discover and improve algorithms. Reported applications include faster matrix multiplication, improvements to computing infrastructure and solutions to mathematical problems. By 2026, Google reported further work involving DNA sequencing, disaster prediction, power-grid simulations and scientific computing.
That changes the game slightly. Traditional science usually looks something like: human has idea, human tests idea, human gets annoyed, human tries another idea.
AI can potentially run the cycle at enormous scale: generate thousands of hypotheses, test them computationally, discard the rubbish and send the promising candidates to humans or automated laboratories.
And that is probably the real story.
AI has not “solved everything”. It has started solving pieces of problems that once seemed stubbornly resistant to computation, while opening entirely new ways of searching through possibilities.
The next breakthrough may therefore not be one spectacular answer.
It may be the machine that keeps finding the next question.
Sources list:
Nobel Prize — The Nobel Prize in Chemistry 2024 — https://www.nobelprize.org/prizes/chemistry/2024/summary/
Google DeepMind — Millions of new materials discovered with deep learning — https://deepmind.google/blog/millions-of-new-materials-discovered-with-deep-learning/
Google DeepMind — AlphaEvolve: A Gemini-powered coding agent for designing advanced algorithms — https://deepmind.google/blog/alphaevolve-a-gemini-powered-coding-agent-for-designing-advanced-algorithms/
Google DeepMind — AI achieves silver-medal standard solving International Mathematical Olympiad problems — https://deepmind.google/blog/ai-solves-imo-problems-at-silver-medal-level/
Google — AlphaEvolve, 1 year later: Impact on science, technology — https://blog.google/innovation-and-ai/infrastructure-and-cloud/google-cloud/alphaevolve-updates/
MIT News — Artificial intelligence yields new antibiotic — https://news.mit.edu/2020/artificial-intelligence-identifies-new-antibiotic-0220
MIT Jameel Clinic — Antibiotic identified by AI — https://jclinic.mit.edu/antibiotic-identified-by-ai/
MIT Collins Lab — Antibiotics-AI Project — https://www.collinslab.mit.edu/antibiotics-ai-project
Thisclaimer — https://thisclaimer.com
The Thisclaimer logo blends a classic warning symbol with a brain icon to represent critical thinking, curiosity, and thoughtful disclaimers #AI #aiBreakthroughs #aiMedicine #aiScience #algorithmDiscovery #alphafold #antibiotics #artificialIntelligence #artificialIntelligence #chatgpt #materialsScience #mathematics #philosophy #scientificDiscovery #technology -
What Has AI Actually Solved? The Biggest Problems Where Machines Found Real Answers
Exploring the biggest problems where machines found real answers. This visualization highlights specific breakthroughs in protein folding, antibiotic discovery, materials science, and mathematics. Image generated by AI.Dear Cherubs, AI has been promised as everything from an office assistant to the machine that will apparently fix civilisation before lunch. Strip away the hype, though, and something genuinely remarkable has happened: AI has already helped solve, or substantially crack, several problems that humans had struggled with for decades.
The important bit is “helped”. AI has not cured cancer, solved climate change or discovered the meaning of life while we were making coffee. But in science, mathematics, medicine and engineering, it has produced results that have survived contact with reality.
PROBLEMS THAT GOT REAL ANSWERS
Perhaps the most famous example is protein structure. For decades, predicting how a protein folds into its three-dimensional shape was a major biological challenge. DeepMind’s AlphaFold transformed the field by predicting protein structures at unprecedented scale, contributing to work that earned Demis Hassabis and John Jumper the 2024 Nobel Prize in Chemistry.
Then there are antibiotics. MIT researchers used machine learning to identify halicin, a previously known compound with powerful antibacterial activity, including against several drug-resistant bacteria. Later work identified abaucin, a new antibiotic candidate targeting Acinetobacter baumannii, with activity demonstrated in laboratory experiments and a mouse infection model. MIT’s Antibiotics-AI project now reports nine novel antibiotics discovered or designed with AI and more than 70 billion molecules screened computationally.
Materials science has produced another eye-watering number. Google DeepMind’s GNoME system predicted around 2.2 million crystal structures, including 380,000 predicted to be particularly stable. Researchers independently produced 736 of those predicted structures experimentally, turning some computer-generated possibilities into actual materials.
Mathematics has also taken a hit from the robot army. In 2024, DeepMind reported that AlphaProof and AlphaGeometry solved four of six International Mathematical Olympiad problems, reaching silver-medal-level performance. AlphaProof also produced formal proofs for its solutions, rather than merely throwing suspiciously confident numbers at the wall.
FROM ANSWERS TO DISCOVERY
The more interesting development may be that AI is increasingly being used not simply to answer questions, but to generate new solutions.
AlphaEvolve, introduced by Google DeepMind in 2025, combines large language models with automated evaluation and evolutionary search to discover and improve algorithms. Reported applications include faster matrix multiplication, improvements to computing infrastructure and solutions to mathematical problems. By 2026, Google reported further work involving DNA sequencing, disaster prediction, power-grid simulations and scientific computing.
That changes the game slightly. Traditional science usually looks something like: human has idea, human tests idea, human gets annoyed, human tries another idea.
AI can potentially run the cycle at enormous scale: generate thousands of hypotheses, test them computationally, discard the rubbish and send the promising candidates to humans or automated laboratories.
And that is probably the real story.
AI has not “solved everything”. It has started solving pieces of problems that once seemed stubbornly resistant to computation, while opening entirely new ways of searching through possibilities.
The next breakthrough may therefore not be one spectacular answer.
It may be the machine that keeps finding the next question.
Sources list:
Nobel Prize — The Nobel Prize in Chemistry 2024 — https://www.nobelprize.org/prizes/chemistry/2024/summary/
Google DeepMind — Millions of new materials discovered with deep learning — https://deepmind.google/blog/millions-of-new-materials-discovered-with-deep-learning/
Google DeepMind — AlphaEvolve: A Gemini-powered coding agent for designing advanced algorithms — https://deepmind.google/blog/alphaevolve-a-gemini-powered-coding-agent-for-designing-advanced-algorithms/
Google DeepMind — AI achieves silver-medal standard solving International Mathematical Olympiad problems — https://deepmind.google/blog/ai-solves-imo-problems-at-silver-medal-level/
Google — AlphaEvolve, 1 year later: Impact on science, technology — https://blog.google/innovation-and-ai/infrastructure-and-cloud/google-cloud/alphaevolve-updates/
MIT News — Artificial intelligence yields new antibiotic — https://news.mit.edu/2020/artificial-intelligence-identifies-new-antibiotic-0220
MIT Jameel Clinic — Antibiotic identified by AI — https://jclinic.mit.edu/antibiotic-identified-by-ai/
MIT Collins Lab — Antibiotics-AI Project — https://www.collinslab.mit.edu/antibiotics-ai-project
Thisclaimer — https://thisclaimer.com
The Thisclaimer logo blends a classic warning symbol with a brain icon to represent critical thinking, curiosity, and thoughtful disclaimers #AI #aiBreakthroughs #aiMedicine #aiScience #algorithmDiscovery #alphafold #antibiotics #artificialIntelligence #artificialIntelligence #chatgpt #materialsScience #mathematics #philosophy #scientificDiscovery #technology -
What Has AI Actually Solved? The Biggest Problems Where Machines Found Real Answers
Exploring the biggest problems where machines found real answers. This visualization highlights specific breakthroughs in protein folding, antibiotic discovery, materials science, and mathematics. Image generated by AI.Dear Cherubs, AI has been promised as everything from an office assistant to the machine that will apparently fix civilisation before lunch. Strip away the hype, though, and something genuinely remarkable has happened: AI has already helped solve, or substantially crack, several problems that humans had struggled with for decades.
The important bit is “helped”. AI has not cured cancer, solved climate change or discovered the meaning of life while we were making coffee. But in science, mathematics, medicine and engineering, it has produced results that have survived contact with reality.
PROBLEMS THAT GOT REAL ANSWERS
Perhaps the most famous example is protein structure. For decades, predicting how a protein folds into its three-dimensional shape was a major biological challenge. DeepMind’s AlphaFold transformed the field by predicting protein structures at unprecedented scale, contributing to work that earned Demis Hassabis and John Jumper the 2024 Nobel Prize in Chemistry.
Then there are antibiotics. MIT researchers used machine learning to identify halicin, a previously known compound with powerful antibacterial activity, including against several drug-resistant bacteria. Later work identified abaucin, a new antibiotic candidate targeting Acinetobacter baumannii, with activity demonstrated in laboratory experiments and a mouse infection model. MIT’s Antibiotics-AI project now reports nine novel antibiotics discovered or designed with AI and more than 70 billion molecules screened computationally.
Materials science has produced another eye-watering number. Google DeepMind’s GNoME system predicted around 2.2 million crystal structures, including 380,000 predicted to be particularly stable. Researchers independently produced 736 of those predicted structures experimentally, turning some computer-generated possibilities into actual materials.
Mathematics has also taken a hit from the robot army. In 2024, DeepMind reported that AlphaProof and AlphaGeometry solved four of six International Mathematical Olympiad problems, reaching silver-medal-level performance. AlphaProof also produced formal proofs for its solutions, rather than merely throwing suspiciously confident numbers at the wall.
FROM ANSWERS TO DISCOVERY
The more interesting development may be that AI is increasingly being used not simply to answer questions, but to generate new solutions.
AlphaEvolve, introduced by Google DeepMind in 2025, combines large language models with automated evaluation and evolutionary search to discover and improve algorithms. Reported applications include faster matrix multiplication, improvements to computing infrastructure and solutions to mathematical problems. By 2026, Google reported further work involving DNA sequencing, disaster prediction, power-grid simulations and scientific computing.
That changes the game slightly. Traditional science usually looks something like: human has idea, human tests idea, human gets annoyed, human tries another idea.
AI can potentially run the cycle at enormous scale: generate thousands of hypotheses, test them computationally, discard the rubbish and send the promising candidates to humans or automated laboratories.
And that is probably the real story.
AI has not “solved everything”. It has started solving pieces of problems that once seemed stubbornly resistant to computation, while opening entirely new ways of searching through possibilities.
The next breakthrough may therefore not be one spectacular answer.
It may be the machine that keeps finding the next question.
Sources list:
Nobel Prize — The Nobel Prize in Chemistry 2024 — https://www.nobelprize.org/prizes/chemistry/2024/summary/
Google DeepMind — Millions of new materials discovered with deep learning — https://deepmind.google/blog/millions-of-new-materials-discovered-with-deep-learning/
Google DeepMind — AlphaEvolve: A Gemini-powered coding agent for designing advanced algorithms — https://deepmind.google/blog/alphaevolve-a-gemini-powered-coding-agent-for-designing-advanced-algorithms/
Google DeepMind — AI achieves silver-medal standard solving International Mathematical Olympiad problems — https://deepmind.google/blog/ai-solves-imo-problems-at-silver-medal-level/
Google — AlphaEvolve, 1 year later: Impact on science, technology — https://blog.google/innovation-and-ai/infrastructure-and-cloud/google-cloud/alphaevolve-updates/
MIT News — Artificial intelligence yields new antibiotic — https://news.mit.edu/2020/artificial-intelligence-identifies-new-antibiotic-0220
MIT Jameel Clinic — Antibiotic identified by AI — https://jclinic.mit.edu/antibiotic-identified-by-ai/
MIT Collins Lab — Antibiotics-AI Project — https://www.collinslab.mit.edu/antibiotics-ai-project
Thisclaimer — https://thisclaimer.com
The Thisclaimer logo blends a classic warning symbol with a brain icon to represent critical thinking, curiosity, and thoughtful disclaimers #AI #aiBreakthroughs #aiMedicine #aiScience #algorithmDiscovery #alphafold #antibiotics #artificialIntelligence #artificialIntelligence #chatgpt #materialsScience #mathematics #philosophy #scientificDiscovery #technology -
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The Basque 100 Mountains Challenge: A Century-Old Test of Endurance
Txindoki rising above Lazkaomendi, Gipuzkoa — a classic landscape of the Basque mountains. Photo: Andersalinas, Wikimedia Commons, CC BY 3.0.Dear Cherubs, imagine being told that your hiking bucket list is not 10 beautiful peaks, not 20, but 100 different mountains — and that you have to choose them from an official catalogue. Welcome to the Basque Country’s wonderfully stubborn tradition of the Concurso de los Cien Montes.
THE CHALLENGE
The Concurso de los Cien Montes, or Hundred Mountains Contest, is organised by the Euskal Mendizale Federazioa, the Basque Mountaineering Federation. The basic idea is beautifully simple: complete 100 ascents of 100 different mountains included in the official Catálogo de Cimas de Euskal Herria.
The catalogue is considerably larger than the challenge itself. The 2018 edition contained 597 recognised summits, covering the historical territories of Araba, Bizkaia, Gipuzkoa, Nafarroa and Iparraldea. The selection is not simply a list of the tallest peaks; it also includes distinctive or strategically important mountains and aims to represent the territory geographically.
So, no, you cannot simply climb the same spectacular summit 100 times and call it a day. Nice try.
The tradition has deep roots. According to Pyrenaica, the competition became an official federation initiative in 1949, incorporating earlier hundred-mountain challenges that had already been organised by Basque mountaineering clubs during the 1920s. The aim was not merely collecting summits but encouraging people to discover the mountains, landscapes, communities and culture of Euskal Herria.
There are rules, naturally. The historical regulations required 100 different mountains, with the competition completed over a minimum of five and maximum of ten calendar years. Participants also had to meet federation requirements, while individual ascents had to reach the summit itself.
A CENTURY OF SUMMITS
The achievement has become more than a personal checklist. Completing the official challenge leads to recognition as a Finalista Centenario and entry into the Hermandad de Montañeros Centenarios, an honorary community connected to the federation.
And people are still doing it. In November 2025, the federation reported that 53 mountaineers had completed their first Hundred Mountains competition that year. The Hermandad had reached 4,939 registered mountaineers and 7,591 recorded histories.
The tradition is also being dragged, rather sensibly, into the digital age. In 2026, the federation introduced EMF Mendiak, an official app covering more than 1,500 summits and allowing federated hikers to register ascents digitally.
There is an even bigger challenge emerging. The federation has been developing a new catalogue and a 600-summit initiative. The current 2018 catalogue contains 597 peaks, with Auñamendi, Gurdieta and Kanbilu added to create the round figure of 600.
That neatly captures what makes the tradition interesting: it is competitive without being particularly glamorous, methodical without requiring a stopwatch, and deeply local without being exclusive in spirit.
For broader background on Basque history and culture, thisclaimer.com also offers an accessible introduction to the region and its long cultural continuity.
A hundred mountains, hundreds of possible routes, and enough mud, rain and questionable weather to keep things interesting. Honestly, that sounds less like a competition and more like a very Basque way of keeping a lifelong relationship with the mountains.
Sources list:
Euskal Mendizale Federazioa — Concurso de los Cien Montes and Hermandad Centenaria: EMF — Hermandad Centenaria
Euskal Mendizale Federazioa — 600 Gailurrak initiative and 2018 catalogue: EMF — 600 Gailurrak
Euskal Mendizale Federazioa — 2025 Hermandad figures: EMF — 100 Montes recognition 2025
Euskal Mendizale Federazioa — EMF Mendiak app: EMF — EMF Mendiak
Pyrenaica — History of the Concurso de los Cien Montes: Pyrenaica — Editorial on the 100 Mountains Contest
Euskariana — Bibliographic record for the official Catálogo de Cimas de Euskal Herria: Euskariana — Catálogo oficial de cimas de Euskal Herria
thisclaimer.com — broader background on Basque history and culture: Thisclaimer — The Basques: Europe’s Ancient Survivors
The Thisclaimer logo blends a classic warning symbol with a brain icon to represent critical thinking, curiosity, and thoughtful disclaimers #basqueCountry #basqueCulture #basqueMountains #cienMontes #ehunMendiak #euskalHerria #hiking #mountainChallenges #mountaineering #trekking -
Hey, TTRPG & #IndieGame friends on desktop:
Like #retro #arcade games? Like #biopunk and survival horror? Have I got the unexpected flavor combination for you in Phage Keeper!
Run walls to repair/reinforce. Jump escaped beasts. Survive the time. Face the end of humanity.
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Hey, TTRPG & #IndieGame friends on desktop:
Like #retro #arcade games? Like #biopunk and survival horror? Have I got the unexpected flavor combination for you in Phage Keeper!
Run walls to repair/reinforce. Jump escaped beasts. Survive the time. Face the end of humanity.
-
Hey, TTRPG & #IndieGame friends on desktop:
Like #retro #arcade games? Like #biopunk and survival horror? Have I got the unexpected flavor combination for you in Phage Keeper!
Run walls to repair/reinforce. Jump escaped beasts. Survive the time. Face the end of humanity.
