In physical cosmology and astronomy, dark energy is a proposed form of energy that affects the universe on its largest scales. Its primary effect is to drive the accelerating expansion of the universe. It also slows the rate of structure formation. Assuming that the lambda-CDM model of cosmology is correct, dark energy dominates the universe, contributing 68% of the total mass-energy in the present-day observable universe while dark matter and ordinary (baryonic) matter contribute 27% and 5%, respectively, and other components such as neutrinos and photons are nearly negligible. Dark energy's density is very low: 7×10−30 g/cm3 (6×10−10 J/m3 in mass-energy), much lower than the density of ordinary matter or dark matter within galaxies. However, it dominates the universe's mass–energy content because it is uniform across space.
The first observational evidence for dark energy's existence came from measurements of supernovae. Type Ia supernovae have constant luminosity, which means they can be used to accurately measure distances. Comparing this distance to the redshift (which measures the speed at which the supernova is receding) shows that the universe's expansion is accelerating. Prior to this observation, scientists thought that the gravitational attraction of matter and energy in the universe would cause the universe's expansion to slow over time. Since the discovery of accelerating expansion, several independent lines of evidence have been discovered that support the existence of dark energy.
The exact nature of dark energy remains a mystery, and many possible explanations have been theorized. The main candidates are a cosmological constant (representing a constant energy density filling space homogeneously) and scalar fields (dynamic quantities having energy densities that vary in time and space) such as quintessence or moduli. A cosmological constant would remain constant across time and space, while scalar fields can vary. Yet other possibilities are interacting dark energy (see the section Dark energy § Theories of dark energy), an observational effect, cosmological coupling, and shockwave cosmology (see the section § Alternatives to dark energy).

== History of discovery and previous speculation ==

=== Einstein's cosmological constant ===
The "cosmological constant" is the simplest candidate for dark energy. It is a constant term that can be added to the Einstein field equations of general relativity. If considered as a "source term" in the field equation, it can be viewed as equivalent to the mass of empty space (which conceptually could be either positive or negative), or "vacuum energy".
The cosmological constant was first proposed by Einstein as a mechanism to obtain a solution to the gravitational field equation that would lead to a static universe, effectively using dark energy to balance gravity. Einstein gave the cosmological constant the symbol Λ (capital lambda). Einstein stated that the cosmological constant required that 'empty space takes the role of gravitating negative masses that are distributed all over the interstellar space'.
The mechanism was an example of fine-tuning, and it was later realized that Einstein's static universe would not be stable: local inhomogeneities would ultimately lead to either the runaway expansion or contraction of the universe. The equilibrium is unstable: if the universe expands slightly, then the expansion releases vacuum energy, which causes yet more expansion. Likewise, a universe that contracts slightly will continue contracting. According to Einstein, "empty space" can possess its own energy. Because this energy is a property of space itself, it would not be diluted as space expands. As more space comes into existence, more of this energy-of-space would appear, thereby causing accelerated expansion. These sorts of disturbances are inevitable, due to the uneven distribution of matter throughout the universe. Further, observations made by Edwin Hubble in 1929 showed that the universe appears to be expanding and is not static. Einstein reportedly referred to his failure to predict the idea of a dynamic universe, in contrast to a static universe, as his greatest blunder.

=== Inflationary dark energy ===
Alan Guth and Alexei Starobinsky proposed in 1980 that a negative pressure field, similar in concept to dark energy, could drive cosmic inflation in the very early universe. Inflation postulates that some repulsive force, qualitatively similar to dark energy, resulted in an enormous and exponential expansion of the universe during its earliest stages. Such expansion is an essential feature of most current models of the Big Bang. However, inflation must have occurred at a much higher energy density than the dark energy we observe today, and inflation is thought to have completely ended when the universe was just a fraction of a second old. It is unclear what relation, if any, exists between dark energy and inflation. Even after inflationary models became accepted, the cosmological constant was thought to be irrelevant to the current universe.

=== Late-time dark energy ===
Nearly all inflation models predict that the total (matter+energy) density of the universe should be very close to the critical density. During the 1980s, most cosmological research focused on models with critical density in matter only, usually 95% cold dark matter (CDM) and 5% ordinary matter (baryons). These models were found to be successful at forming realistic galaxies and clusters, but some problems appeared in the late 1980s: in particular, the model required a value for the Hubble constant lower than preferred by observations, and the model under-predicted observations of large-scale galaxy clustering. These difficulties became stronger after the discovery of anisotropy in the cosmic microwave background by the COBE spacecraft in 1992, and several modified CDM models came under active study through the mid-1990s: these included the Lambda-CDM model and a mixed cold/hot dark matter model. The first direct evidence for dark energy came from supernova observations in 1998 of accelerated expansion in Riess et al. and in Perlmutter et al., and the Lambda-CDM model then became the leading model. Soon after, dark energy was supported by independent observations: in 2000, the BOOMERanG and Maxima cosmic microwave background experiments observed the first acoustic peak in the cosmic microwave background, showing that the total (matter+energy) density is close to 100% of critical density. Then in 2001, the 2dF Galaxy Redshift Survey gave strong evidence that the matter density is around 30% of critical. The large difference between these two supports a smooth component of dark energy making up the difference. Much more precise measurements from WMAP in 2003–2010 have continued to support the standard model and give more accurate measurements of the key parameters.
The term "dark energy" was coined by cosmologist Michael S. Turner in 1998 for a paper written with Saul Perlmutter and Martin White.

== Nature ==
The nature of dark energy is more hypothetical than that of dark matter, and many things about it remain in the realm of speculation. Dark energy is thought to be very homogeneous and not dense, and is not known to interact through any of the fundamental forces other than gravity. Since it is rarefied and un-massive—roughly 10−27 kg/m3—it is unlikely to be detectable in laboratory experiments. The reason dark energy can have such a profound effect on the universe, making up 68% of universal density in spite of being so dilute, is that it is believed to uniformly fill otherwise empty space.
The vacuum energy, that is, the particle–antiparticle pairs generated and mutually annihilated within a time frame in accord with Heisenberg's uncertainty principle in the energy–time formulation, has been often invoked as the main contribution to dark energy. The mass–energy equivalence postulated by general relativity implies tha


The multiverse is the hypothetical set of all universes. Together, these universes are presumed to comprise everything that exists: the entirety of space, time, matter, energy, information, and the physical laws and constants that describe them. The different universes within the multiverse are called "parallel universes", "flat universes", "other universes", "alternate universes", "multiple universes", "plane universes", "parent and child universes", "many universes", or "many worlds". One common assumption is that the multiverse is a "patchwork quilt of separate universes all bound by the same laws of physics."
The concept of multiple universes, or a multiverse, has been discussed throughout history. It has evolved and has been debated in various fields, including cosmology, physics, and philosophy. Some physicists have argued that the multiverse is a philosophical notion rather than a scientific hypothesis, as it cannot be empirically falsified. In recent years, there have been proponents and skeptics of multiverse theories within the physics community. Although some scientists have analyzed data in search of evidence for other universes, no statistically significant evidence has been found. Critics argue that the multiverse concept lacks testability and falsifiability, which are essential for scientific inquiry, and that it raises unresolved metaphysical issues.
Max Tegmark and Brian Greene have proposed different classification schemes for multiverses and universes. Tegmark's four-level classification consists of Level I: an extension of our universe, Level II: universes with different physical constants, Level III: many-worlds interpretation of quantum mechanics, and Level IV: ultimate ensemble. Brian Greene's nine types of multiverses include quilted, inflationary, brane, cyclic, landscape, quantum, holographic, simulated, and ultimate. The ideas explore various dimensions of space, physical laws, and mathematical structures to explain the existence and interactions of multiple universes. Some other multiverse concepts include twin-world models, cyclic theories, M-theory, and black-hole cosmology.
The anthropic principle suggests that the existence of a multitude of universes, each with different physical laws, could explain the asserted appearance of fine-tuning of our own universe for conscious life. The weak anthropic principle posits that we exist in one of the few universes that support life. Debates around Occam's razor and the simplicity of the multiverse versus a single universe arise, with proponents like Max Tegmark arguing that the multiverse is simpler and more elegant. The many-worlds interpretation of quantum mechanics and modal realism, the belief that all possible worlds exist and are as real as our world, are also subjects of debate in the context of the anthropic principle.

== History of the concept ==
According to some, the idea of infinite worlds was first suggested by the pre-Socratic Greek philosopher Anaximander in the sixth century BCE. However, there is debate as to whether he believed in multiple worlds, and if he did, whether those worlds were co-existent or successive.
Ancient Indian thinkers, both  Hindu and  Buddhist, postulated multiple worlds/realms/universes.
The first figures to whom historians can definitively attribute the concept of innumerable worlds are the Ancient Greek Atomists, beginning with Leucippus and Democritus in the 5th century BCE, followed by Epicurus (341–270 BCE) and the Roman Epicurean Lucretius (1st century BCE). In the third century BCE, the philosopher Chrysippus suggested that the world eternally expired and regenerated, effectively suggesting the existence of multiple universes across time. The concept of multiple universes became more defined in the Middle Ages. In the Renaissance, Giordano Bruno (1548–1600) expressed the concept of infinite worlds.
The American philosopher and psychologist William James used the term "multiverse" in 1895, but in a different context.
The concept first appeared in the modern scientific context in the course of the debate between Boltzmann and Zermelo in 1895.
In Dublin in 1952, Erwin Schrödinger gave a lecture in which he jocularly warned his audience that what he was about to say might "seem lunatic". He said that when his equations seemed to describe several different histories, these were "not alternatives, but all really happen simultaneously". This sort of duality is called  "superposition".

== Search for evidence ==
In the 1990s, after recent works of fiction about the concept gained popularity, scientific discussions about the multiverse and journal articles about it gained prominence.
Around 2010, scientists such as Stephen M. Feeney analyzed Wilkinson Microwave Anisotropy Probe (WMAP) data and claimed to find evidence suggesting that this universe collided with other (parallel) universes in the distant past. However, a more thorough analysis of data from the WMAP and from the Planck satellite, which has a resolution three times higher than WMAP, did not reveal any statistically significant evidence of such a bubble universe collision. In addition, there was no evidence of any gravitational pull of other universes on ours.

In 2015, an astrophysicist may have found evidence of alternate or parallel universes by looking back in time to a time immediately after the Big Bang, although it is still a matter of debate among physicists. Dr. Ranga-Ram Chary, after analyzing the cosmic radiation spectrum, found a signal 4,500 times brighter than it should have been, based on the number of protons and electrons scientists believe existed in the very early universe. This signal—an emission line that arose from the formation of atoms during the era of recombination—is more consistent with a universe whose ratio of matter particles to photons is about 65 times greater than our own. There is a 30% chance that this signal is noise, and not really a signal at all; however, it is also possible that it exists because a parallel universe dumped some of its matter particles into our universe. If additional protons and electrons had been added to our universe during recombination, more atoms would have formed, more photons would have been emitted during their formation, and the signature line that arose from all of these emissions would be greatly enhanced. Chary said:Many other regions beyond our observable universe would exist with each such region governed by a different set of physical parameters than the ones we have measured for our universe.
Chary also noted:Unusual claims like evidence for alternate universes require a very high burden of proof.
The signature that Chary has isolated may be a consequence of incoming light from distant galaxies, or even from clouds of dust surrounding our own galaxy.

== Proponents and skeptics ==
Modern proponents of one or more of the multiverse hypotheses include Lee Smolin, Don Page, Brian Greene, Max Tegmark, Alan Guth, Andrei Linde, Michio Kaku, David Deutsch, Leonard Susskind, Alexander Vilenkin, Yasunori Nomura, Raj Pathria, Laura Mersini-Houghton, Neil deGrasse Tyson, Sean Carroll and Stephen Hawking.
Scientists who are generally skeptical of the concept of a multiverse or popular multiverse hypotheses include Sabine Hossenfelder, David Gross, Paul Steinhardt, Anna Ijjas, Abraham Loeb, David Spergel, Neil Turok, Viatcheslav Mukhanov, Michael S. Turner, Roger Penrose, George Ellis, Joe Silk, Carlo Rovelli, Adam Frank, Marcelo Gleiser, Jim Baggott and Paul Davies.

== Arguments against multiverse hypotheses ==
In his 2003 New York Times opinion piece, "A Brief History of the Multiverse", author and cosmologist Paul Davies offered a variety of arguments that multiverse hypotheses are non-scientific: For a start, how is the existence of the other universes to be tested? To be sure, all cosmologists accept that there are some regions of the universe that lie beyond the reach of our telescopes, but somewhere on th


Present-day climate change includes both global warming—the ongoing increase in global average temperature—and its wider effects on Earth's climate system. In a broader sense, climate change also includes previous long-term changes to Earth's climate. The modern-day rise in global temperatures is driven by human activities, especially fossil fuel (coal, oil and natural gas) burning since the Industrial Revolution. Fossil fuel use, deforestation, and some agricultural and industrial practices release greenhouse gases. These gases absorb some of the heat that the Earth radiates after it warms from sunlight, warming the lower atmosphere. Earth's atmosphere now has roughly 50% more carbon dioxide, the main gas driving global warming, than it did at the end of the pre-industrial era, reaching levels not seen for millions of years.
Climate change has an increasingly large impact on the environment. Heat waves and wildfires are becoming more common. Amplified warming in the Arctic has contributed to thawing permafrost, retreat of glaciers and sea ice decline. Higher temperatures are also causing more intense storms, droughts, and other weather extremes. Rapid environmental change in mountains, coral reefs, and the Arctic is forcing many species to relocate or become extinct. Even if efforts to minimize future warming are successful, some effects will continue for centuries. These include ocean heating, ocean acidification and sea level rise.
Climate change threatens people with increased flooding, extreme heat, increased food and water scarcity, more disease, and economic loss. Human migration and conflict can also be a result. The World Health Organization calls climate change one of the biggest threats to global health in the 21st century. Societies and ecosystems will experience more severe risks without action to limit warming. Adapting to climate change through efforts like flood control measures or drought-resistant crops partially reduces climate change risks, although some limits to adaptation have already been reached. Poorer communities are responsible for a small share of global emissions, yet have the least ability to adapt and are most vulnerable to climate change.

Many climate change impacts have been observed in the first decades of the 21st century, with 2024 the warmest on record at +1.60 °C (2.88 °F) since regular tracking began in 1850. Additional warming will increase these impacts and can trigger tipping points, such as melting all of the Greenland ice sheet. Under the 2015 Paris Agreement, nations collectively agreed to keep warming "well under 2 °C". However, with pledges made under the Agreement, global warming would still reach about 2.8 °C (5.0 °F) by the end of the century.
There is widespread support for climate action worldwide, and most countries aim to stop emitting carbon dioxide. Fossil fuels can be phased out by ending their subsidies, conserving energy and switching to energy sources that do not produce significant carbon pollution. These energy sources include wind, solar, hydro, and nuclear power. Cleanly generated electricity can replace fossil fuels for powering transportation, heating buildings, and running industrial processes. Carbon can also be removed from the atmosphere, for instance by increasing forest cover and farming with methods that store carbon in soil.

== Terminology ==
Before the 1980s, it was unclear whether the warming effect of increased greenhouse gases was stronger than the cooling effect of airborne particulates in air pollution. Scientists used the term inadvertent climate modification to refer to human impacts on the climate at this time. In the 1980s, the terms global warming and climate change became more common, often being used interchangeably. Scientifically, global warming refers only to increased global average surface temperature, while climate change describes both global warming and its effects on Earth's climate system, such as precipitation changes.
Climate change can also be used more broadly to include changes to the climate that have happened throughout Earth's history as result of natural processes. The term anthropogenic climate change is sometimes used to describe climate change resulting from human activities.
Global warming—used as early as 1975—became the more popular term after NASA climate scientist James Hansen used it in his 1988 testimony in the U.S. Senate. Since the 2000s, usage of climate change has increased. Various scientists, politicians and media may use the terms climate crisis or climate emergency to talk about climate change, and may use the term global heating instead of global warming.

== Global temperature rise ==

=== Temperatures prior to present-day global warming ===

Over the last few million years the climate cycled through ice ages. One of the hotter periods was the Last Interglacial, around 125,000 years ago, where temperatures were between 0.5 °C and 1.5 °C warmer than before the start of global warming. This period saw sea levels 5 to 10 metres higher than today. The most recent glacial maximum 20,000 years ago was some 5–7 °C colder. This period has sea levels that were over 125 metres (410 ft) lower than today.
Temperatures stabilized in the current interglacial period beginning 11,700 years ago. This period also saw the start of agriculture. Historical patterns of warming and cooling, like the Medieval Warm Period and the Little Ice Age, did not occur at the same time across different regions. Temperatures may have reached as high as those of the late 20th century in a limited set of regions. Climate information for that period comes from climate proxies, such as trees and ice cores.

=== Warming since the Industrial Revolution ===

Around 1850 thermometer records began to provide global coverage.
Between the 18th century and 1970 there was little net warming, as the warming impact of greenhouse gas emissions was offset by cooling from sulfur dioxide emissions. Sulfur dioxide causes acid rain, but it also produces sulfate aerosols in the atmosphere, which reflect sunlight and cause global dimming. After 1970, the increasing accumulation of greenhouse gases and controls on sulfur pollution led to a marked increase in temperature.

Ongoing changes in climate have had no precedent for several thousand years. Multiple datasets all show worldwide increases in surface temperature, at a rate of around 0.2 °C per decade. The 2014–2023 decade warmed to an average 1.19 °C [1.06–1.30 °C] compared to the pre-industrial baseline (1850–1900). Not every single year was warmer than the last: internal climate variability processes can make any year 0.2 °C warmer or colder than the average. From 1998 to 2013, negative phases of two such processes, Pacific Decadal Oscillation (PDO) and Atlantic Multidecadal Oscillation (AMO) caused a short slower period of warming called the "global warming hiatus". After the "hiatus", the opposite occurred, with 2024 well above the recent average at more than +1.5 °C. This is why the temperature change is defined in terms of a 20-year average, which reduces the noise of hot and cold years and decadal climate patterns, and detects the long-term signal.
A wide range of other observations reinforce the evidence of warming. The upper atmosphere is cooling, because greenhouse gases are trapping heat near the Earth's surface, and so less heat is radiating into space. Warming reduces average snow cover and forces the retreat of glaciers. At the same time, warming also causes greater evaporation from the oceans, leading to more atmospheric humidity, and more and heavier precipitation. Plants are flowering earlier in spring, and thousands of animal species have been permanently moving to cooler areas.

==== Differences by region ====
Different regions of the world warm at different rates. The pattern is independent of where greenhouse gases are emitted, because the gases persist long enough to diffuse across the planet. S


Renewable energy (also called green energy) is energy made from renewable natural resources that are replenished on a human timescale. The most widely used renewable energy types are solar energy, wind power, and hydropower. Bioenergy and geothermal power are also significant in some countries. Renewable energy installations can be large or small and are suited for both urban and rural areas. Renewable energy is often deployed together with further electrification. This has several benefits: electricity can move heat and vehicles efficiently and is clean at the point of consumption. Variable renewable energy sources are those that have a fluctuating nature, such as wind power and solar power. In contrast, controllable renewable energy sources include dammed hydroelectricity, bioenergy, or geothermal power.
Renewable energy systems have rapidly become more efficient and cheaper over the past 40 years. A large majority of newly installed worldwide electricity capacity is now renewable. Renewable energy sources, such as solar and wind power, have seen significant cost reductions over the past decade, making them more competitive with traditional fossil fuels. In some geographic localities, photovoltaic solar or onshore wind is the cheapest new-build electricity. From 2011 to 2021, renewable energy grew from 20% to 28% of the global electricity supply. Power from the sun and wind accounted for most of this increase, growing from a combined 2% to 10%. Use of fossil energy shrank from 68% to 62%. In 2025, renewables accounted for than 30% of global electricity generation and are projected to reach over 45% by 2030. Many countries already have renewables contributing more than 20% of their total energy supply, with some generating over half or even all their electricity from renewable sources.
The main motivation to use renewable energy instead of fossil fuels is to reduce greenhouse gas emissions, which cause climate change. In general, renewable energy sources pollute much less than fossil fuels. Renewables also cause much less air pollution than fossil fuels, improving public health, and are less noisy. The International Energy Agency estimates that to achieve net zero emissions by 2050, 90% of global electricity will need to be generated by renewables. The current pace of renewable expansion remains far from this required rate globally, including in major economies with high financial capacities such as the G7 and the EU.
The deployment of renewable energy still faces obstacles, especially fossil fuel subsidies, lobbying by incumbent power providers, and local opposition to the use of land for renewable installations. Like all mining, the extraction of minerals required for many renewable energy technologies also results in environmental damage.
Some also consider nuclear power a renewable power source, although this is controversial, as nuclear energy requires mining uranium, a nonrenewable resource.

== Overview ==

=== Definition ===
Renewable energy is usually understood as energy harnessed from continuously occurring natural phenomena. The International Energy Agency defines it as "energy derived from natural processes that are replenished at a faster rate than they are consumed". Solar power, wind power, hydroelectricity, geothermal energy, and biomass are widely agreed to be the main types of renewable energy. Renewable energy often displaces conventional fuels in four areas: electricity generation, hot water/space heating, transportation, and rural (off-grid) energy services.
Although almost all forms of renewable energy cause much fewer carbon emissions than fossil fuels, the term is not synonymous with low-carbon energy. Some non-renewable sources of energy, such as nuclear power,generate almost no emissions, while some renewable energy sources can be very carbon-intensive, such as the burning of biomass if it is not offset by planting new plants. Renewable energy is also distinct from sustainable energy, a more abstract concept that seeks to group energy sources based on their overall permanent impact on future generations of humans. For example, biomass is often associated with unsustainable deforestation.

=== Role in addressing climate change ===
As part of the global effort to limit climate change, most countries have committed to net zero greenhouse gas emissions. In practice, this means phasing out fossil fuels and replacing them with low-emissions energy sources. This much needed process, coined as "low-carbon substitutions" in contrast to other transition processes including energy additions, needs to be accelerated multiple times in order to successfully mitigate climate change. At the 2023 United Nations Climate Change Conference, around three-quarters of the world's countries set a goal of tripling renewable energy capacity by 2030. The European Union aimed in 2022 to generate 40% of its electricity from renewables by the same year, and by 2025, 47% of EU electricity generation  was by renewables.

=== Other benefits ===

Renewable energy is more evenly distributed around the world than fossil fuels, which are concentrated in a limited number of countries. It also brings health benefits by reducing air pollution caused by the burning of fossil fuels. The potential worldwide savings in health care costs have been estimated at trillions of dollars annually.

=== Intermittency ===

The two most important forms of renewable energy, solar and wind, are intermittent energy sources: they are not available constantly, resulting in lower capacity factors. In contrast, fossil fuel power plants, nuclear power plants and hydropower are usually able to produce precisely the amount of energy an electricity grid requires at a given time. Solar energy can only be captured during the day, and ideally in cloudless conditions. Wind power generation can vary significantly not only day-to-day, but even month-to-month. This poses a challenge when transitioning away from fossil fuels: energy demand will often be higher or lower than what renewables can provide.
In the medium-term, this variability may require keeping some gas-fired power plants or other dispatchable generation on standby until there is enough energy storage, demand response, grid improvement, or base load power from non-intermittent sources. In the long-term, energy storage is an important way of dealing with intermittency. Using diversified renewable energy sources and smart grids can also help flatten supply and demand.
Sector coupling of the power generation sector with other sectors may increase flexibility: for example the transport sector can be coupled by charging electric vehicles and sending electricity from vehicle to grid. Similarly the industry sector can be coupled by hydrogen produced by electrolysis, and the buildings sector by thermal energy storage for space heating and cooling.
Building overcapacity for wind and solar generation can help ensure sufficient electricity production even during poor weather. In optimal weather, it may be necessary to curtail energy generation if it is not possible to use or store excess electricity.

==== Electrical energy storage ====

Electrical energy storage is a collection of methods used to store electrical energy. Electrical energy is stored during times when production (especially from intermittent sources such as wind power, tidal power, solar power) exceeds consumption, and returned to the grid when production falls below consumption. Pumped-storage hydroelectricity accounts for more than 85% of all grid power storage. Batteries are increasingly being deployed for storage and grid ancillary services and for domestic storage. Green hydrogen is a more economical means of long-term renewable energy storage, in terms of capital expenditures compared to pumped hydroelectric or batteries.

==== Energy supply security ====
Two main renewable energy sources - solar power and wind power - are usually deployed in distributed generat


Nuclear energy may refer to:

Nuclear power, the use of sustained nuclear fission or nuclear fusion to generate heat and electricity
Nuclear reactor, the device used to sustain a controlled fission nuclear chain reaction
Nuclear binding energy, the energy needed to fuse or split a nucleus of an atom
Nuclear potential energy, the potential energy of the particles inside an atomic nucleus
Nuclear Energy (sculpture), a bronze sculpture by Henry Moore in the University of Chicago


Economics () is a social science that studies the production, distribution, and consumption of goods and services.
Economics focuses on the behaviour and interactions of economic agents and how economies work. Microeconomics analyses what is viewed as the basic elements of economies, including individual agents and markets, their interactions, and the outcomes of those interactions. Individual agents may include households, firms, buyers, and sellers. Macroeconomics analyses economies as systems where production, distribution, consumption, savings, and investment expenditure interact; and the factors of production affecting them, such as: labour, capital, land, and enterprise, inflation, economic growth, and public policies that impact these elements. It also seeks to analyse and describe the global economy.
Other broad distinctions within economics include those between positive economics, describing "what is", and normative economics, advocating "what ought to be"; between economic theory and applied economics; between rational and behavioural economics; and between mainstream economics and heterodox economics.
Economic analysis can be applied throughout society, including business, finance, cybersecurity, health care, engineering and government. It is also applied to such diverse subjects as crime, education, the family, feminism, law, philosophy, politics, religion, social institutions, war, science, and the environment.

== Definitions of economics ==

The earlier term for the discipline was "political economy", but since the late 19th century, it has commonly been called "economics". The term is ultimately derived from Ancient Greek οἰκονομία (oikonomia), a term for the "way (nomos) to run a household (oikos)", or in other words the know-how of an οἰκονομικός (oikonomikos), or "household or homestead manager". Derived terms such as "economy" can therefore often mean "frugal" or "thrifty". By extension then, "political economy" was the way to manage a polis or state.
There are a variety of modern definitions of economics; some reflect evolving views of the subject or different views among economists. Scottish philosopher Adam Smith (1776) defined what was then called political economy as "an inquiry into the nature and causes of the wealth of nations", in particular as:

a branch of the science of a statesman or legislator [with the twofold objectives of providing] a plentiful revenue or subsistence for the people ... [and] to supply the state or commonwealth with a revenue for the public services.
Jean-Baptiste Say (1803), distinguishing the subject matter from its public-policy uses, defined it as the science of production, distribution, and consumption of wealth. On the satirical side, Thomas Carlyle (1849) coined "the dismal science" as an epithet for classical economics, in this context, commonly linked to the pessimistic analysis of Malthus (1798). John Stuart Mill (1844) delimited the subject matter further:

The science which traces the laws of such of the phenomena of society as arise from the combined operations of mankind for the production of wealth, in so far as those phenomena are not modified by the pursuit of any other object.
Alfred Marshall provided a still widely cited definition in his textbook Principles of Economics (1890) that extended analysis beyond wealth and from the societal to the microeconomic level:

Economics is a study of man in the ordinary business of life. It enquires how he gets his income and how he uses it. Thus, it is on the one side, the study of wealth and on the other and more important side, a part of the study of man.
Lionel Robbins (1932) developed implications of what has been termed "[p]erhaps the most commonly accepted current definition of the subject":

Economics is the science which studies human behaviour as a relationship between ends and scarce means which have alternative uses.
Robbins described the definition as not classificatory in "pick[ing] out certain kinds of behaviour" but rather analytical in "focus[ing] attention on a particular aspect of behaviour, the form imposed by the influence of scarcity." He affirmed that previous economists have usually centred their studies on the analysis of wealth: how wealth is created (production), distributed, and consumed; and how wealth can grow. But he said that economics can be used to study other things, such as war, that are outside its usual focus. This is because war has as its goal winning it (as a sought-after end), generates both costs and benefits, and uses resources (human life and other costs) to attain the goal. If the war is not winnable or if the expected costs outweigh the benefits, the deciding actors (assuming they are rational) may never go to war (a decision) but rather explore other alternatives. Economics cannot be defined as the science that studies wealth, war, crime, education, and any other field to which economic analysis can be applied, but rather as the science that studies a particular common aspect of each of those subjects (they all use scarce resources to attain a sought-after end).
Some subsequent comments criticised the definition as overly broad, failing to limit its scope to market analysis. From the 1960s, however, such comments abated as the economic theory of maximizing behaviour and rational-choice modelling expanded the domain of the subject to areas previously treated in other fields. There are other criticisms as well, such as in scarcity not accounting for the macroeconomics of high unemployment.
Gary Becker, a contributor to the expansion of economics into new areas, described the approach he favoured as "combin[ing the] assumptions of maximizing behaviour, stable preferences, and market equilibrium, used relentlessly and unflinchingly." One commentary characterises the remark as making economics an approach rather than a subject matter but with great specificity as to the "choice process and the type of social interaction that [such] analysis involves." The same source reviews a range of definitions found in principles of economics textbooks and concludes that the lack of agreement need not affect the subject-matter the texts treat. Among economists more generally, it argues that a particular definition presented may reflect the direction toward which the author believes economics is evolving, or should evolve.
Many economists including Nobel Prize winners James M. Buchanan and Ronald Coase reject the method-based definition of Robbins and continue to prefer definitions like those of Say, in terms of its subject matter. Ha-Joon Chang has for example argued that the definition of Robbins would make economics very peculiar because all other sciences define themselves in terms of the area of inquiry or object of inquiry rather than the methodology. In the biology department, it is not said that all biology should be studied with DNA analysis. People study living organisms in many different ways: some perform DNA analysis, others analyse anatomy, and still others build game-theoretic models of animal behaviour. But they are all called biology because they all study living organisms. According to Ha Joon Chang, this view that the economy can and should be studied in only one way (for example by studying only rational choices), and going even one step further and basically redefining economics as a theory of everything, is peculiar.

== History of economic thought ==

=== From antiquity through the physiocrats ===

Questions regarding distribution of resources are found throughout the writings of the Boeotian poet Hesiod and several economic historians have described him as the "first economist". However, the Greek word oikos was used for issues regarding how to manage a household (which was understood to be the landowner, his family, and his slaves) rather than to refer to some normative societal system of distribution of resources, which is a far more recent phenomenon. Although Xenophon, the author


Finance refers to monetary resources as well as the discipline that studies such resources, with related concepts such as money, currency, assets and liabilities. As a subject of study, it is related to the fields of economics (the study of the production, distribution, and consumption of goods and services) and business administration (the management of an organization's resources to achieve its goals). Based on the scope of financial activities in financial systems, the discipline can be divided into personal, corporate, and public finance.
In these financial systems, assets are bought, sold, or traded as financial instruments, such as currencies, loans, bonds, shares, stocks, options, futures, swaps, etc. Assets can also be banked, invested, and insured to maximize value and minimize loss. In practice, risks are always present in any financial action and entities.
Due to its wide scope, a broad range of subfields exists within finance. Asset-, money-, risk- and investment management aim to maximize value and minimize volatility. Financial analysis assesses the viability, stability, and profitability of an action or entity. Some fields are multidisciplinary, such as mathematical finance, financial law, financial economics, financial engineering and financial technology. These fields are the foundation of business and accounting. In some cases, theories in finance can be tested using the scientific method, covered by experimental finance.
The early history of finance parallels the early history of money, which is prehistoric. Ancient and medieval civilizations incorporated basic functions of finance, such as banking, trading and accounting, into their economies. In the late 19th century, the global financial system was formed.
In the middle of the 20th century, finance emerged as a distinct academic discipline, separate from economics. The earliest doctoral programs in finance were established in the 1960s and 1970s. Today, finance is also widely studied through career-focused undergraduate and master's level programs.

== The financial system ==

As outlined, the financial system consists of the flows of capital that take place between individuals and households (personal finance), governments (public finance), and businesses (corporate finance). "Finance" thus studies the process of channeling money from savers and investors to entities that need it. Savers and investors have money available which could earn interest or dividends if put to productive use. Individuals, companies, and governments must obtain money from an external source, such as loans or credit, when they lack sufficient funds to run their operations.
In general, an entity whose income exceeds its expenditure can lend or invest the surplus with the aim of earning a fair return. Correspondingly, an entity where income is less than expenditure can raise capital usually in one of two ways: (i) by borrowing in the form of a loan (private individuals), or by selling government or corporate bonds; (ii) by a corporation selling equity, also called stock or shares (which may take various forms: preferred stock or common stock). The owners of both bonds and stock may be institutional investors—financial institutions such as investment banks and pension funds—or private individuals, called private investors or retail investors. (See Financial market participants.)
The lending is often indirect, through a financial intermediary such as a bank, or via the purchase of notes or bonds (corporate bonds, government bonds, or mutual bonds) in the bond market. The lender receives interest, the borrower pays a higher interest than the lender receives, and the financial intermediary earns the difference for arranging the loan.
A bank aggregates the activities of many borrowers and lenders. Banks accept deposits from individuals and businesses, paying interest on these funds. The bank then lends these deposits to borrowers, facilitating transactions between borrowers and lenders of various sizes and enabling efficient financial coordination. Investing typically entails the purchase of stock, either individual securities or via a mutual fund, for example. Stocks are usually sold by corporations to investors so as to raise required capital in the form of "equity financing", as distinct from the debt financing described above. The financial intermediaries here are the investment banks (which find the initial investors and facilitate the listing of the securities, typically shares and bonds), the securities exchanges (which allow their trade thereafter), and the various investment service providers (including mutual funds, pension funds, wealth managers, and stock brokers, typically servicing retail investors).
Inter-institutional trade and investment, and fund-management at this scale, is referred to as "wholesale finance".
Institutions here extend the products offered, with related trading, to include bespoke options, swaps, and structured products, as well as specialized financing; this "financial engineering" is inherently mathematical, and these institutions are then the major employers of quantitative analysts (or "quants", see below). In these institutions, risk management, regulatory capital, and compliance play major roles.

== Areas of finance ==
As outlined, finance broadly comprises three areas: personal finance, corporate finance, and public finance. These, in turn, overlap and employ various activities and sub-disciplines—chiefly investments, risk management, and quantitative finance.

=== Personal finance ===

Personal finance refers to the practice of budgeting to ensure enough funds are available to meet basic needs, while ensuring there is only a reasonable level of risk to lose said capital. Personal finance may involve paying for education, financing durable goods such as real estate and cars, buying insurance, investing, and saving for retirement. Personal finance may also involve paying for a loan or other debt obligations. The main areas of personal finance are considered to be income, spending, saving, investing, and protection. The following steps, as outlined by the Financial Planning Standards Board, suggest that an individual will understand a potentially secure personal finance plan after:

Purchasing insurance to ensure protection against unforeseen personal events;
Understanding the effects of tax policies, subsidies, or penalties on the management of personal finances;
Understanding the effects of credit on individual financial standing;
Developing a savings plan or financing for large purchases (auto, education, home);
Planning a secure financial future in an environment of economic instability;
Pursuing a checking or a savings account;
Preparing for retirement or other long term expenses.

=== Corporate finance ===

Corporate finance deals with the actions that managers take to increase the value of the firm to the shareholders, the sources of funding and the capital structure of corporations, and the tools and analysis used to allocate financial resources. While corporate finance is in principle different from managerial finance, which studies the financial management of all firms rather than corporations alone, the concepts are applicable to the financial problems of all firms, and this area is then often referred to as "business finance".
Typically, "corporate finance" relates to the long term objective of maximizing the value of the entity's assets, its stock, and its return to shareholders, while also balancing risk and profitability. This entails three primary areas:

Capital budgeting: selecting which projects to invest in—here, accurately determining value is crucial, as judgements about asset values can be "make or break".
Dividend policy: the use of "excess" funds—these are to be reinvested in the business or returned to shareholders.
Capital structure: deciding on the mix of funding to be used—here attempting to find the optimal capital mix re debt-commitme


Cryptocurrency is a type of digital asset that uses distributed ledger, or blockchain, technology to enable a secure transaction.
Individual coin ownership records are stored in a digital ledger or blockchain, which is a computerized database that uses a consensus mechanism to secure transaction records, control the creation of additional coins, and verify the transfer of coin ownership. The two most common consensus mechanisms are proof of work and proof of stake. Despite the name, which has come to describe many of the fungible blockchain tokens that have been created, cryptocurrencies are not considered to be currencies in the traditional sense, and varying legal treatments have been applied to them in various jurisdictions, including classification as commodities, securities, and currencies. Cryptocurrencies are generally viewed as a distinct asset class in practice.

== History ==

In 1983, American cryptographer David Chaum conceived of a type of cryptographic electronic money called ecash. Later, in 1995, he implemented it through Digicash, an early form of cryptographic electronic payments. Digicash required user software in order to withdraw notes from a bank and designate specific encrypted keys before they could be sent to a recipient. This allowed the digital currency to be untraceable by a third party.
In 1996, the National Security Agency published a paper entitled How to Make a Mint: The Cryptography of Anonymous Electronic Cash, describing a cryptocurrency system. The paper was first published in an MIT mailing list (October 1996) and later (April 1997) in The American Law Review.
In 1998, Wei Dai described "b-money," an anonymous, distributed electronic cash system. Shortly thereafter, Nick Szabo described bit gold. Like bitcoin and other later cryptocurrencies, bit gold was proposed as a digital currency system in which users would generate money by completing proof-of-work functions whose solutions were cryptographically chained and published.
In January 2009, bitcoin was created by pseudonymous developer Satoshi Nakamoto. It used SHA-256, a cryptographic hash function, in its proof-of-work scheme. In April 2011, Namecoin was created as an attempt at forming a decentralized DNS. In October 2011, Litecoin was released, which used scrypt as its hash function instead of SHA-256. Peercoin, created in August 2012, used a hybrid of proof-of-work and proof-of-stake.
Cryptocurrency has undergone several periods of growth and retraction, including several bubbles and market crashes, such as in 2011, 2013–2014/15, 2017–2018, and 2021–2023.
In August 2014, the UK announced its Treasury had commissioned a study of cryptocurrencies and what role, if any, they could play in the UK economy. The study was also to report on whether regulation should be considered. Its final report was published in 2018, and it issued a consultation on cryptoassets and stablecoins in January 2021.
In June 2021, El Salvador became the first country to accept bitcoin as legal tender, after the Legislative Assembly had voted 62–22 to pass a bill submitted by President Nayib Bukele classifying the cryptocurrency as such.
In August 2021, Cuba followed with Resolution 215 to recognize and regulate cryptocurrencies such as bitcoin.
In September 2021, the government of China, the single largest market for cryptocurrency, declared all cryptocurrency transactions illegal. This completed a crackdown on cryptocurrency that had previously banned the operation of intermediaries and miners within China.
In September 2022, the world's second largest cryptocurrency at that time, Ethereum, transitioned its consensus mechanism from proof-of-work (PoW) to proof-of-stake (PoS) in an upgrade process known as "the Merge".  According to the Ethereum Founder, the upgrade would cut both Ethereum's energy use and carbon-dioxide emissions by 99.9%.
On 11 November 2022, FTX Trading Ltd., a cryptocurrency exchange, which also operated a crypto hedge fund, and had been valued at $18 billion, filed for bankruptcy. The financial impact of the collapse extended beyond the immediate FTX customer base, as reported, while, at a Reuters conference, financial industry executives said that "regulators must step in to protect crypto investors." Technology analyst Avivah Litan commented on the cryptocurrency ecosystem that "everything...needs to improve dramatically in terms of user experience, controls, safety, customer service."
An October 2024 survey from the Pew Research Center found that 63% of adults in the U.S. "have little to no confidence that current ways to invest in, trade or use cryptocurrencies are reliable and safe." The survey also reported that 17% of U.S. adults had directly interacted with cryptocurrency, which was statistically unchanged from 2021.

== Terminology ==

The word "cryptocurrency", in its modern sense, was added to the Oxford English Dictionary in September 2018, with its earliest usage noted as being on Twitter in September 2009; its diminutive, crypto, was entered in 2022.       In March 2018 cryptocurrency was added to the Merriam-Webster Dictionary.

=== Altcoins ===

After the early innovation of bitcoin in 2008 and the early network effect gained by bitcoin, tokens, cryptocurrencies, and other digital assets that were not bitcoin became collectively known during the 2010s as alternative cryptocurrencies, or "altcoins". Sometimes the term "alt coins" was used, or disparagingly, "shitcoins". Paul Vigna of The Wall Street Journal described altcoins in 2020 as "alternative versions of Bitcoin" given its role as the model protocol for cryptocurrency designers. A Polytechnic University of Catalonia thesis in 2021 used a broader description, including not only alternative versions of bitcoin but every cryptocurrency other than bitcoin. As of early 2020, there were more than 5,000 cryptocurrencies.

Altcoins often have underlying differences when compared to bitcoin. For example, Litecoin aims to process a block every 2.5 minutes, rather than bitcoin's 10 minutes which allows Litecoin to confirm transactions faster than bitcoin. Another example is Ethereum, which has smart contract functionality that allows decentralized applications to be run on its blockchain. Ethereum was the most used blockchain in 2020, according to Bloomberg News. In 2016, it had the largest "following" of any altcoin, according to the New York Times.
Significant market price rallies across multiple altcoin markets are often referred to as an "altseason".

==== Stablecoins ====
Stablecoins are cryptocurrencies designed to maintain a stable level of purchasing power. Notably, these designs are not foolproof, as a number of stablecoins have crashed or lost their peg. For example, on 11 May 2022, Terra's stablecoin UST fell from $1 to 26 cents. The subsequent failure of Terraform Labs resulted in the loss of nearly $40B invested in the Terra and Luna coins. In September 2022, South Korean prosecutors requested the issuance of an Interpol Red Notice against the company's founder, Do Kwon. In Hong Kong, the expected regulatory framework for stablecoins in 2023/24 is being shaped and includes a few considerations.

==== Memecoins ====

Memecoins are a category of cryptocurrencies that originated from Internet memes or jokes. One example is Dogecoin, a memecoin featuring the Shiba Inu dog from the Doge meme. Donald Trump and his wife Melania are the subject of their own meme coins, both of which declined in value soon after launch.
Memecoins are known for extreme volatility; for example, the record-high value for a Dogecoin was 73 cents, but that had plunged to 13 cents by mid-2024. Scams are prolific among memecoins.

== Physical crypto ==

Physical cryptocurrency coins have been made as promotional items and some have become collectibles. Some of these have a private key embedded in them to access crypto worth a few dollars. There have also been attempts to issue bitcoin "bank notes".
The term "physic


Trade is the act by which people swap somthing of value (such as products, resources, money) to obtain another valuable item. It involves the transfer of goods and services from one person or entity (seller) to another (buyer), often in exchange for money. Economists refer to a system or network that allows trade as a market.
Traders generally negotiate through a medium of credit or exchange, such as money. Though some economists characterize barter (i.e. trading things without the use of money) as an early form of trade, money was invented before written history began. Letters of credit, paper money, and non-physical money have greatly simplified and promoted trade as buying can be separated from selling, or earning. Trade between two traders is called bilateral trade, while trade involving more than two traders is called multilateral trade.
In one modern view, trade exists due to specialization and the division of labor, a predominant form of economic activity in which individuals and groups concentrate on a small aspect of production, but use their output in trade for other products and needs. Trade exists between regions because different regions may have a comparative advantage (perceived or real) in the production of some trade-able goods – including the production of scarce or limited natural resources elsewhere. For example, different regions' sizes may encourage mass production. In such circumstances, trading at market price between locations can benefit both locations. Different types of traders may specialize in trading different kinds of goods; for example, the spice trade and grain trade have both historically been important in the development of a global, international economy. Retail trade consists of the sale of goods or merchandise from a fixed location, online or by mail, in small or individual lots for direct consumption or use by the purchaser. Wholesale trade is the traffic in goods that are sold as merchandise to retailers, industrial, commercial, institutional, or other professional business users, or to other wholesalers and related subordinated services.
Historically, openness to free trade substantially increased in some areas from 1815 until the outbreak of World War I in 1914. Trade openness increased again during the 1920s but collapsed (in particular in Europe and North America) during the Great Depression of the 1930s. Trade openness increased substantially again from the 1950s onward (albeit with a slowdown during the oil crisis of the 1970s). Economists and economic historians contend that current levels of trade openness are the highest they have ever been.

== Etymology ==
Trade is from Middle English trade ("path, course of conduct"), introduced into English by Hanseatic merchants, from Middle Low German trade ("track, course"), from Old Saxon trada ("spoor, track"), from Proto-Germanic *tradō ("track, way"), and cognate with Old English tredan ("to tread").
Commerce is derived from the Latin commercium, from cum "together" and merx, "merchandise."

== History ==

=== Prehistory ===
Trade originated from human communication in prehistoric times. Prehistoric peoples exchanged goods and services with each other in a gift economy before the innovation of modern-day currency.  Recent research finds evidence that early humans developed trade networks for obsidian 15,000 years ago as well as ostrich egg shell beads 50,000 years ago.
In the Mediterranean region, the earliest contact between cultures involved members of the species Homo sapiens, principally using the Danube river, at a time beginning 35,000–30,000 BP.

There is evidence of the exchange of obsidian and flint during the Stone Age. Trade in obsidian is believed to have taken place in New Guinea from 17,000 BCE.

The earliest use of obsidian in the Near East dates to the Lower and Middle paleolithic.
Robert Carr Bosanquet investigated trade in the Stone Age by excavations in 1901. The first clear archaeological evidence of trade in manufactured goods is found in south west Asia.
Archaeological evidence of obsidian use provides data on how this material was increasingly the preferred choice rather than chert from the late Mesolithic to Neolithic, requiring exchange as deposits of obsidian are rare in the Mediterranean region.
Obsidian provided the material to make cutting utensils or tools, although since other more easily obtainable materials were available, use was exclusive to the higher status of the tribe using "the rich man's flint". Obsidian has held its value relative to flint.
Early traders traded Obsidian at distances of 900 kilometres within the Mediterranean region.
Trade in the Mediterranean during the Neolithic of Europe was greatest in this material. Networks were in existence at around 12,000 BCE Anatolia was the source primarily for trade with the Levant, Iran and Egypt according to Zarins study of 1990. Melos and Lipari sources produced among the most widespread trading in the Mediterranean region as known to archaeology.
The Sari-i-Sang mine in the mountains of Afghanistan was the largest source for trade of lapis lazuli. The material was most largely traded during the Kassite period of Babylonia beginning 1595 BCE.
Adam Smith traces the origins of commerce to the very start of transactions in prehistoric times. Apart from traditional self-sufficiency, trading became a principal faculty for prehistoric people, who bartered what they had for goods and services from each other. Anthropologists have found no evidence of barter systems that did not exist alongside systems of credit.

=== Ancient history ===

==== Mediterranean and Near East ====
The  earliest evidence of writing is deeply bound up with trade, as a system of clay tokens used for accounting – found in the Upper Euphrates valley in Syria dated to the 10th millennium BCE – is one of the earliest surviving versions of writing.
Ebla was a prominent trading-center during the third millennium BCE, with a network reaching into Anatolia and north Mesopotamia.

Materials used for making jewelry were traded with Egypt from 3000 BCE. Long-range trade routes first appeared in the 3rd millennium BCE, when Sumerians in Mesopotamia traded with the Harappan civilization of the Indus Valley. The Phoenicians were noted maritime traders, traveling across the Mediterranean Sea and as far north as Britain for sources of tin to manufacture bronze. For this purpose they established trading  colonies — which the Greeks called emporia. Along the coasts of the Mediterranean, researchers have found a positive relationship between how well-connected a coastal location was and the local prevalence of archaeological sites from the Iron Age. This suggests that a location's trade potential was an important determinant of human settlements.
The complaint tablet to Ea-nāṣir, dated 1750 BCE, documents the tribulations of a copper merchant at the time.
From the beginning of Greek civilization until the fall of the Roman Empire in the 5th century CE, a financially lucrative trade brought valuable spice to Europe from the far east, including India and China. Roman commerce allowed its empire to flourish and endure, notable by importing grain to Italy from Sicily
and from Egypt.
The latter Roman Republic and the Pax Romana of the Roman empire produced a stable and secure transportation-network that enabled the shipment of trade goods without fear of significant piracy, as Rome had become the sole effective sea-power in the Mediterranean with the conquest of Egypt and the near east.
In ancient Greece Hermes was the god of trade (commerce) and weights and measures. In ancient Rome, Mercurius was the god of merchants, whose festival was celebrated by traders on the 25th day of the fifth month. The concept of free trade was an antithesis to the will and economic direction of the sovereigns of the ancient Greek states. Free trade between states was stifled by the need for strict internal controls (via taxation) to


Go Analysis Framework: modular static analysis by go team


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Show HN: CheapSecurity – Lightweight, Self-Hosted CCTV for Linux SBCs


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DeepSeek pause fundraise after comments on compute gap to US leaked (transcript) [pdf]


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I got tired of Googling basic course info, so I made a free directory of every US course
Google filters for golf course results are terrible so I built a better way to browse courses using OSM as the backbone.... https://golfcoursebrowser.com/
It's a work in progress and mostly US for now, but I want to expand to the rest of North America, the EU, and the rest of the world ASAP.
It's free, no ads, no login, no bs. If you spot anything wrong (bad info, a missing course, wrong scorecard), you can flag it right on the course page. I actually read those and fix them.
Still a lot of missing info, but I'm filling out more and more each day. The goal is the most complete, and current golf course directory in the world, verified and maintained by actual golfers. I think is attainable in the next few months.
The base layer is OpenStreetMap, cross-checked against course websites and enriched with scorecards, USGA rating/slope, and public/private status. User corrections from golfers who know their home course have beaten every commercial data source we check against.


Alien World Chemistry Found Inside Meteorite That Struck New Jersey Home


Show HN: Managing on-premise servers without Kubernetes


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Ask HN: What are the most promising RL fields for a new master student?

i am an incoming msc student thinking about a research direction in reinforcement learning. which rl subfields do you think have the most potential right now? i am particularly interested in emboddied ai and bcis.


Running a 28.9M parameter LLM on an $8 microcontroller


DskDitto: Ultra-fast, parallel duplicate-file detector


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Show HN: Brolly, a plain-text weather forecast site

The UK MET office recently redesigned their site, adding a lot of additional whitespace, scrolling, and animations. This significantly reduced its usability for me, and left me wanting an ‘at a glance’ weather site.
I made https://brolly.sh, a minimalist, plain text weather forecasting site. You can use it to view weather from around the world, with: 7 day forecast; Previous day log (so you can confirm it definitely was cooler / hotter / wetter / drier yesterday!); Hourly rain, wind, temperature, conditions; Hourly UV, air quality and pollen, including pollen type specific forecasts within the EU / UK; Location search and last 5 locations; Location specific units.
I mostly made the site for myself, if anyone else also benefits from it that’s an added advantage.
You can check out the weather in York, UK at https://brolly.sh/forecast/RWFP2qW8, or search for a location at https://brolly.sh
The site is deliberately styled as a single long scrollable column, to work on mobile phones. You can view it on desktop too, there's just a lot of horizontal padding. I naturally took a lot of inspiration from plaintextsports.com. Despite not being a sports fan, I love its aesthetic. But, you'll hopefully see that this site isn't a rip off, and has its own deliberate look and feel.
Visualisations are really important to showing information at a glance. I spent a lot of time designing the different visualisations and making them work with only characters. My favourite is the hourly heat map used for pollen count.
It's also frustrating to have an interactive site, where you can't share a page with a friend and have them see what you're seeing. To solve this, all page state (i.e. location, selected day, expanded / collapsed sections), is stored in the URL. You can share or bookmark the specific view, and know that you'll always be able to come back to it.
The site uses PocketBase. It’s written in Go and plain HTML/JavaScript/CSS. All pages are backend rendered, with light JavaScript to handle re-loading content without page jumps when using interactive features like next / previous day navigation. Weather forecasts are fetched from open-meteo.com, which has a very generous free tier. However, I also built a custom LRU cache on top of PocketBase’s SQLite DB to cache forecasts for 5 minutes, and avoid putting unnecessary pressure on the open-meteo API.


Show HN: I made some transistor animations

Hi HN,
I made some animations of the most important kinds of transistors using my semiconductor simulation, details of which are on the page. I tried to make the visuals as realistic as possible while also aiming for clarity. If you want to go beyond the charge carriers and look at, for example, the electric field, you can do so in the simulation software. The desktop software also has less common devices like IBGTs and SCRs that have similar animations.
The last thread about my software was posted here about a year ago: https://news.ycombinator.com/item?id=43942279


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Show HN: Writemark, a dependency free web component for inline Markdown editing

I like writing Markdown, but do not like writing it inside a plain textarea.
I wanted something I could use anywhere by dropping in a single web component:
```<p><writemark-editor name="body"></writemark-editor><p>```
That became Writemark.
It renders Markdown while you write, but Markdown remains the value you read, store, and submit. It also has source, split, and preview modes, along with slash commands, tables, task lists, code blocks, native form support, and an API for adding your own controls. There are no runtime dependencies and no required framework or built in toolbar.
It is fully vibecoded. The process was very iterative. I knocked something out, tried using it, found bugs, fixed them, and repeated that cycle until I had something I liked writing in and that performed reasonably well.
It did not begin as an experiment about AI generated software -- just did not want to use textareas anymore. I like Markdown, and I wanted one component that I could use anywhere without bringing along an entire editor framework.
It is still very young. The parser is handwritten, the component is essentially one large JavaScript file, and I am certain there are edge cases waiting to be found. If you find one, I am happy to fix it.
I have tried to give it a decent safety net. It currently has 951 Playwright checks across Chromium, Firefox, and WebKit, along with hostile input cases, sanitizer fuzzing, and differential tests against CommonMark.
I built this because I wanted it for myself, but I think it turned into something kind of cool. I hope some of you enjoy it. I would love hearing what you think, especially if you try it and manage to break something.
- Github: https://github.com/Brostoffed/writemark
- NPM: https://www.npmjs.com/package/writemark-editor


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Show HN: SpinWin – A macOS menu bar app to visually rotate or spin any window

SpinWin is an open source menu bar app to visually rotate any individual window. You can rotate / spin as many as you want one by one.
I was scrolling reels when one came on where the content creator suggested "turning the phone upside down" for a portion. I was sitting at my mac, and decided to see if I could find something that did this. To my surprise, nothing exists, so I decided to build it.
Turns out macOS doesn't provide any public API to do anything like this, so I wound up using the Accessibility API to move the target window offscreen, where it keeps rendering. Then we capture it's contents with ScreenCaptureKit, and display that on a transparent, borderless overlay window which has a CALayer to rotate it.
It's written in Swift, open source, MIT license, and signed/notarized.
Have fun - would love to hear your comments.


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Ask HN: Are you using Rust on embedded devices yet? If not, why?

I started dabbling with ESP32-based MCUs from Waveshare with Rust, and I'm quite impressed with the state of things (esp-rs, embassy, probe-rs etc). Note that I haven't really done embedded in any other languages, so I don't really have anything to compare it with.
One thing I did notice was that creating beautiful, interactive user interfaces on MCUs with displays is a little harder, libs like embedded-graphics don't look that great.
This made me wonder, are people using Rust in their professional embedded work?


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Show HN: Echo – Fable-level results at 1/3 the cost using open-weight models

I’ve been building Echo (https://echo.tracerml.ai/), an experiment in making one AI system out of a pool of open-weight models rather than choosing a single model and using it for every task.
It started with a simple experiment. I took a group of models, including GLM-5.2, Kimi K2.7 and others, and ran them on the same evaluations. Then I measured what would happen if, for each problem, you somehow knew in advance which models would be useful and how their outputs should be combined.
That hypothetical system performed substantially better than any individual model in the pool. Of course, it is not something you can actually deploy because it relies on knowing which decisions were good after seeing the result. Echo is my attempt to recover some of that advantage without having that information in advance.
For each request, Echo decides how much computation to allocate, which models should participate, and how their work should be combined. Some prompts may only need a relatively small amount of inference, while others benefit from multiple models working on different parts of the problem.
One thing that surprised me while building it was how complementary the models are. A model that is clearly weaker overall can still be extremely useful on particular problems or as part of a combination.
On my first evaluation mix, Echo consistently performed better than the best individual model in its pool. It also reached roughly the same aggregate result as Fable, which I used as one of the stronger comparison systems, at around one third of the inference cost.
There are still some cases where Echo makes the wrong allocation or combination decision. I’m currently spending a lot of time understanding those failures, as well as testing whether the same approach holds up on coding and agentic tasks where measuring the quality of each decision becomes much harder.
I built a chat interface (echo.tracerml.ai) and an OpenAI-compatible API (https://echo.tracerml.ai/docs/api) so the system can be tested outside the evaluation setup.
Here is a short/high level video on how it works: https://www.youtube.com/watch?v=lJFJSvOdXhg
I wrote up the evaluation methodology, individual model results, costs and current limitations here: https://echo.tracerml.ai/eval
I would love for you to try it! Especially if you hit any weird failure cases or places where the allocation looks unintuitive.


PyPI Blog: Releases now reject new files after 14 days


Building a Tiny 3D Renderer for a Tiny Handheld


Claude Code Cut Their System Prompt by 80%. Does That Work for Small Models Too?


The case for MUDs in modern times (2018)


What if the RAM/GPU shortage is deliberate?


Engineering management after the cost of code collapsed


My security camera shipped a GitHub admin token in its login page


Startup founders urge U.S. government not to shut off Chinese open weight AI


Government orders GitHub to remove Bluetooth-based chat app Bitchat: Jack Dorsey


Show HN: Inflect TTS v2+ONNX, 9M/4M text-to-speech models running in the browser


Opus 5 is currently #1 on Artificial Analysis Intelligence Leaderboard


Introduction to Microsoft Excel 1990 with Jan Brehm


Agatha Christie's Disappearance: Amnesia, Suicide, or Despair?


Show HN: Pg_stat_ch, a Postgres extension to export every metric to ClickHouse


Digital Intelligence: Why Humanity Created What It Does Not Understand


Show HN: What 180k words look like as a temporal knowledge graph (Oz series)

The graph is free to explore and requires no registration.
SynapTale builds a model of a story as a temporal graph made up of nodes (entities) and edges (their actions and relationships).
The graph is not a visualization of the wiki. The wiki, timelines, relationship histories, and analytics are projections of the graph.
The current demo contains 232 entities, 1,852 edges, and a snapshot of the story’s state at every chapter. By chapter 100, it still remembers a promise made in chapter 8 and turns the story into a set of source-verifiable facts.
The most interesting things can be found in the graph itself and in the Analytics tab. A few things I found:
1. The character with the highest kill count is the Tin Woodman—the same character who cries over a beetle he accidentally crushed. Dorothy comes second, with three killing events.
2. Dorothy never deceives anyone during the first 100 chapters of the series.
3. The Scarecrow’s debt to the stork has remained active for 92 chapters, starting in chapter 8.
4. The Cowardly Lion ranks third by number of threats.
5. The first 100 chapters contain 60 secrets and 254 dialogue events.
Technical details
1. Five different multi-agent pipelines combining LLMs and NLP: a prescan, ontology construction, chapter-by-chapter graph extraction, retrospective validation over spans of dozens of chapters, and a linguistic prescan for speech profiles and linguistic edges.
2. A living story needs a living graph. It has to account for time, because entities and the relationships between them evolve. A simple is_active field is not enough.
I ended up with three types of edges:
event: an instantaneous action;
identity: a fact;
state: a persistent action whose termination requires justification and a supporting quote from the text.
The vast majority of edges are events and end in the same chapter in which they began. This allows the system to scale well, since only a minority of state and identity edges remain continuously active.
3. Ontology. You cannot simply ask an LLM to extract entities and relationships into a graph. With every chapter, even the smartest model will keep inventing unimportant fields, creating new aliases for existing fields, and representing the same fields inconsistently.
Before extracting the graph, the system therefore performs an ontology scan across the entire story. It captures story-specific entity and edge types, along with their fields and descriptions.
4. Epistemics. Events are only one part of a story. It is also important to understand how information is distributed, which is difficult to represent using event edges alone.
I addressed this by introducing a new node type: epistemic nodes, which capture different entities’ perspectives on the same fact. Subtle hints can still be missed, the system is not yet perfect in this area.


Watching Go's new garbage collector move through the heap


Show HN: ZWPlayer – Free player with interactive annotations and subtitle search


SpaceX Starship Flight 13 livestream [video]


Registration of new accounts to the Arch AUR reopened with hardened requirements


Why Sony can't bring back its classic Walkman models


Tile's security is so bad it's a feature for stalkers


Book Corners: Community map of neighborhood book exchange spots


Scanwheel is a drum style mechanical television you can build yourself


Agentic test processes, LLM benchmarks, and other notes on agentic coding


Be skeptical of OpenAI's rogue hacker agent story


The PImpl idiom and the C++26 std:indirect type


Teardown: The BMW / Harman IDC23H Infotainment Unit (B423)


ESP32-C6 Power Consumption: Arduino vs. Zephyr vs. ESP-IDF Comparison


Show HN: Hubo – two agents that implement and review code until they agree


An OpenAI model left notes about how to evade containment; we need more details


Kimi K3 exploited the latest Redis server


Google slapped with $1B fine under landmark EU digital law


Bond market anxiety is growing over AI capex budgets


Resonance: Microphysics Meets the Cosmological Constant


Show HN: Rudoc – a 4.5MB Rust document converter

I built Rudoc because I wanted a smaller tool for simple file convertion workflow. (Pandoc was so heavy for me) I once wanted to try Pandoc, but after I downloaded it from GitHub, I found that it is around 70 MB. It also needed a lot of dependencies.
Rudoc is not a substitue of Pandoc, it supports limited set of formats (only txt, md, html, typ, docx, pdf, pptx, xml, json) and doesn't support advanced formatting. The most common workflow is just turn a markdown file to a html. PDF generation can work without Typst, but PDF input conversion requires Typst as a backend. It has no other runtime dependencies and can run as a single binary, by the way.
It is pretty easy to use, just use the format of
rudoc input.file output.file
One sample usage is like this:
```powershell
rudoc OhPDF_ROADMAP.md.txt r.md
[rudoc] OhPDF_ROADMAP.md.txt → r.md  (txt→md)
[rudoc] done in 52ms  (6286 bytes)
rudoc r.md r.html
[rudoc] r.md → r.html  (md→html)
[rudoc] done in 4ms  (7363 bytes)
```
Some complex formats (such as pptx) still have limited compatibility. I planned to add standard error messages and better format compatibilities. I'd be pleased to see feedback or issues on my project.
QA:
Why not Pandoc?
Pandoc is a great tool, but sometimes we only need a convenient little one to handle common conversion tasks.
How compatible is it to docx/pptx?
Currently it supports basic text conversion, and I'm trying to make it be able to keep advanced formatting.


Registration Open for OS/2 and ArcaOS Warpstock 2026 in San Antonio, Texas, USA


Betlang – A programming language detection model in 50kb


Programming language file extensions that match ISO 3166-1 alpha-2 country codes


IRGC claims it destroyed Amazon's Bahrain data center


Show HN: Palmier Pro – Open-source macOS video editor built for AI

Hi HN, we are Marcos and Harrison, cofounders of Palmier (https://palmier.io). We are building Palmier Pro, an open source macOS video editor, with built-in AI generation and a local MCP server that connects to your agent. Here are a few demos:
- Making some AI transitions: https://www.youtube.com/watch?v=hbM_-eR1GX4
- Multicam editing with Codex: https://www.youtube.com/watch?v=SjS2q2LT1q8
- Cutting long form clips into shorts: https://www.youtube.com/watch?v=PR66eN2ouuQ
We built Palmier Pro as an internal tool when we were making AI launch videos for other startups. The main problem it solved in the beginning was the back-and-forth between AI generation platform and video editor. The iteration loop was awkward: AI videos → download → import to editor → edit → realize we need to change the AI video → repeat. So we built a minimal video editor where we could let Claude generate AI videos inside the editor.
As we gave more and more tools to the agent, we wanted to push to see what else agents can do in the video editing space. So today, your Claude/Codex can:
- Manage projects inside Palmier Pro
- Import media from a public URL or filesystem to the project, and organize them in folders
- Search media (by embedding footages using SigLIP2 running locally)
- Edit the timeline (tracks/clips/keyframes operations)
- Generate images, videos, sound effects, captions, music
- Export videos
There are two ways for LLMs to interact with the editor: by connecting to the local MCP server, or using the in-app chat. Both use the same tools and APIs exposed by the video editor.
We have seen people using MCP server to connect to their own workflow to automate massive-scale video editing (e.g. given this same podcast style, replicate it with other footages that I have). We have also seen people using the in-app chat where it lives closer to the editor UI, with lower latency for faster iteration.
We don't believe that AI is going to replace human creativity (nor should it), but where it can really help is in automating the gruntwork in video editing. Most work in the editing workflow is more mechanical than creative. Our vision is to build a video editor where AI can enable more individuals to create.
Throughout our experiments, AI is not very good at creative editing, but given a pattern (transcription-based, beat-based), it can do a decent job at rough cut.
We built Palmier Pro in Swift as an MVP because we wanted better performance and minimal dependencies (no nodejs/webview), with some native macOS APIs like SpeechAnalyzer and CoreML for us to run some models locally. We use SpeechAnalyzer for local transcriber, SigLip2 to embed video frames, beat_this for beat detection, Silero VAD for silence detection, all running locally. The tradeoff is that we don't support Linux or Windows at the moment.
Palmier Pro is open source and free to try out (macOS 26 only at the moment, though - we'll support more platforms over time, but for now are focusing on iterating the core product).
No login required except for the AI generation features, which route requests to our backend. We are offering free credits on sign up so you can try out the AI generation as well.
We'd love to hear your feedback!


Interlock: Proving a secret moved, instead of guessing that it might


No Stack Overflow, No Autocomplete: What Coding Felt Like in the 80s


GNU Hurd gets 9pfs, OpenNTPD, dynamic /dev/ entries, and more


Kimi K3 after a week of real project use: what works, what doesn't


Canada remains anti-nuke, but its potential path to the bomb is getting shorter


Juice – non-essential visual, audio, and haptic effects to enhance UX


I scanned my AI agent framework for destructive/consequential actions, and wow


LG to ban residential proxies from smart TV apps


India's 'Cockroach' Protesters Declare Rare Victory over Government


Western Europe, State Formation, and Genetic Pacification


Show HN: I simulated closing the Strait of Hormuz on real oil trade data

OP here: I created this visualization tool as the byproduct of a supply chain class I taught at Columbia. The pedagogical exercise grew into a full blown visualization and paper about global oil trade.
The model:
The mechanics are the same as the financial network Eisenberg-Noe: Instead of banks, every country consumes oil interconnected via bilateral trading. Shocks propagate throughout the network, depleting oil reserves when bottleneck nodes (such as the Strait of Hormuz) are blocked.
Insights:
The interesting part is the mechanics of how the crisis unfolds: for example, France receives 0 oil from Hormuz directly, yet their reserves are depleted faster because other countries reactively increase their safety oil stock, increasing oil price, making stockouts more expensive for everyone.
The model also gives price dynamics which are interesting on their own: the price increase is not immediate, it follows sequentially as countries reserves deplete.
Some caveats:
1. For producer nodes, depletion means their export slack is reduced/exhausted.
2. No sanctioned trade (UN Comtrade data)
Technical Details:
The visualization is 600 lines of flask plus js frontend (LLM assisted visualization with ground-truth matching the original numerical exercise of the paper)
Paper with proofs/theory:
https://arxiv.org/abs/2607.17491


GrapheneOS duress PIN could land a man in prison


AI isn't killing consulting. It's killing time as a proxy for value


DuckPGQ – A DuckDB community extension for graph workloads


The front end framework for correctness: built on Effect, architected like Elm


The perils of parsing type inference declarations in C


Some more things about Django I've been enjoying


Some combinatorial applications of spacefilling curves


AI Chatbots Know How to Make Deadly Biological Weapons. Some Will Teach You


Robots programmed by kids to speak their Indigenous languages


MIT to become hotbed of AI video surveillance


I Tried Building a Real App with AI. It Took a Year


Latest Airbus single aisle aircraft innovations


Wildfire Forces Evacuation of NASA's Deep Space Network Complex in Spain


Apple seeks U.S. approval to buy memory chips from blacklisted CXMT


3D-Aware VLMs with Implicit and Explicit Geometries

Despite rapid progress, most existing vision-language models (VLMs) built from 2D visual inputs often struggle when handling various 3D tasks that require fine-grained spatial understanding and reasoning. To bridge this gap, we present VLM-IE3D, a unified framework that enhances the 3D spatial awareness of VLMs by equipping them with both implicit and explicit 3D geometries learned from RGB videos. Our VLM-IE3D introduces Implicit Geometry Tokens (IGTs) that capture high-level geometric priors from input videos, as well as complementary Explicit Geometry Tokens (EGTs) that encode detailed geometric structures from reconstructed 3D attributes. On top of that, VLM-IE3D comes with a 3D-aware adapter that effectively fuses the two types of geometric representations with 2D visual cues. This RGB-only design injects strong 3D inductive biases for fine-grained spatial understanding and reasoning without requiring any additional 3D inputs. Extensive experiments show that VLM-IE3D achieves superior performance consistently across various 3D tasks including 3D video detection, 3D visual grounding, 3D dense captioning, and spatial reasoning. Code and models are available at https://github.com/Vegetebird/VLM-IE3D.


GraphVid: Interactive Graph-Controllable Video Generation

Controllable video generation remains challenging due to the difficulty of specifying precise multi-object interactions using text prompts or motion-control inputs that primarily constrain pixel movement. In practice, trajectory-based control often requires users to draw accurate tracks for multiple objects, which scales poorly with scene complexity and becomes ambiguous under occlusion or overlap. To enable flexible yet precise multi-subject control, we introduce $\textbf{GraphVid}$, a graph-conditioned image-to-video generation model that enables interactive control through structured interaction graphs. We further curate $\textbf{GraphVid-Bench}$, a large-scale interaction-centric video dataset with structured relational annotations to enable training of interaction-aware video generation models. Despite using substantially less training data and fewer trainable parameters than prior motion-control methods, GraphVid delivers strong controllability and video quality. Compared with Motion-I2V, GraphVid reduces FID by up to 39.9% and FVD by 37.6%, while improving PSNR (9.87=>15.98) and SSIM (0.38=>0.61). Our results highlight the potential of structured semantic interfaces as a powerful paradigm for controllable video generation.


Barzilai-Borwein Fails Superlinear Convergence on an Open Set of Quadratics for Every Dimension $n\geq 4$

Barzilai--Borwein (BB) method has shown strong practical performance in continuous optimization, yet its convergence dynamics remains poorly understood. In particular, a central unresolved question is whether BB converges superlinearly for almost every strictly convex quadratic problem and initialization. We provide a negative answer to this question. Specifically, for every finite dimension $n\geq4$, we construct a nonempty open, hence positive-Lebesgue-measure, family of strictly convex quadratic problems and initial points for which the long Barzilai--Borwein method (BB1) converges but cannot converge root-superlinearly. More precisely, with the explicit constants $ρ_{\min}=10^{-6},ρ_{\max}=0.61$, every spectral component of the gradient is bounded above and below by the corresponding geometric sequence. Consequently, the gradient norm and the energy norm of the error satisfy two-sided geometric estimates with the same rates, while the objective gap satisfies the corresponding estimates with squared rates. In particular, all three quantities are bounded below by geometric sequences, ruling out superlinear convergence. The construction is highly nontrivial, based on a computer-assisted proof of a nonresonant, attracting seven-cycle of the projectivized BB dynamics in dimension four.


Synthetic data generation framework for quality control automation in gravure printing

Quality control in printing, particularly in rotogravure printing, still depends on slow, costly, and subjective manual inspection. Automated surface defect detection is critical for maintaining high-quality standards in rotogravure printing. Deep learning models give prospects for automation. However, training robust deep learning models, such as YOLO or Vision Transformers, is heavily hindered by the extreme scarcity of real-world industrial defects images. To overcome this limitation, this paper introduces a novel synthetic data generation framework tailored for rotogravure printing quality control. The proposed pipeline automatically generates high-fidelity images of specific printing defects (creases, streaks, misregistration, etc.) and outputs corresponding bounding boxes and annotations. To validate the framework, a synthetic dataset of 7533 images was generated and used to train the state-of-the-art object-detection model RFDETR. Experimental results demonstrate that the model trained on our synthetic data achieves a Mean Average Precision (mAP) of 80.9\% on real industrial testing samples. This framework provides a zero-cost, rapid-deployment solution for automating defect inspection in printing lines without requiring massive manual data collection.


Beyond Sufficiency: Time Series Explanation with Counterfactual Necessity

Faithful explanations of time-series classifiers should identify subsequences that are not only sufficient to preserve a black-box model's prediction, but also necessary for maintaining it. However, existing sufficiency-oriented methods can assign high importance to spurious subsequences that support the prediction without being essential to the model's decision. We introduce \textbf{TimePNS}, a necessity-aware framework for time-series explanation. Inspired by Pearl's counterfactual notion of necessity, TimePNS assesses whether a temporal factor is necessary by intervening on it and measuring whether the original prediction is disrupted. The framework adopts a two-stage design. Stage I learns an identifiable causal generative process together with a sufficiency-oriented explanation mask. Stage II performs counterfactual interventions on temporal factors to derive necessity signals, which supervise a temporal gate that refines the initial explanation by suppressing non-essential components and emphasizing counterfactually necessary ones. Experiments on synthetic and real-world time-series benchmarks show that TimePNS more accurately identifies decision-critical subsequences and consistently improves sufficiency-necessity trade-offs over strong baselines.


Unsupervised Consensus-Based Anomaly Detection for Spatiotemporal Malaria Incidence in Ghana

A consensus anomaly detection framework was applied to monthly malaria surveillance data from Ghana (2014-2023) to identify atypical transmission patterns. Anomalies were highly structured in space and time. Ashanti and Northern Regions accounted for most recurrent anomalies, with persistent hotspots at Tamale, Kumasi, and Accra. A key finding was the spatial distinction between anomaly burden (cumulative cases during anomalous periods) and anomaly frequency (persistence of unusual behaviour). Tamale had the highest burden during anomalies, whereas the highest anomaly rates clustered in Ashanti districts, showing that high-burden areas are not necessarily those with the most frequent anomalous transmission. Anomalous months formed a statistically distinct group, with much higher case counts (Cohen's $d = 3.252$) and large seasonal deviations ($d > 1.2$) compared with normal months. Malaria burden alone provides an incomplete picture of transmission dynamics. By distinguishing where malaria is most prevalent from where transmission behaves most unusually, this framework can strengthen surveillance, prioritise investigations, and support targeted control strategies.