Foodsi

Foodsi is a Polish mobile application that connects customers with restaurants, convenience stores, bakeries and cafes that have a surplus of food, allowing its users to buy the surplus at a reduced price. The service launched in 2019 in Warsaw and has expanded to other major cities in Poland. In 2023, a new feature was introduced in the app, allowing users to buy packages not only with self-pickup but also with delivery. The products range has also been expanded to include unsold magazines, cosmetics or plants. == History == The company was created in 2019 in Poland by Mateusz Kowalczyk and Jakub Fryszczyn. During studies in their home country and abroad, when they made a living working in restaurants and bakeries, they recognized the problem and the scale of food waste. They launched the application by themselves, having previously raised PLN 100,000 on their own for the purpose. Initially, Foodsi was an Android-only app, but over time, an IOS version was developed. In 2022, the startup raised PLN 6 million in a seed round from VC companies including CofounderZone and Status Starter, as well as private investors such as founders of Pyszne.pl. As of December 2023, it claimed more than 5000 businesses, serving over 1,5 million users, have saved nearly 3 million bags of food. == Purpose == Foodsi aims to significantly reduce food waste, which contributes to the Sustainable Development Goals. The application bridges the gap between the customers who are looking for shopping deals and the companies that want to reduce surplus products but are unable to sell them at a normal price. This allows the customers to buy unsold products for as little as 30% of the normal price. The company claims that every 4 out of 5 packages are sold on average. As of 2019 Foodsi employed more than 30 people. By 2024 it was more than 50. For now, Foodsi operates in major Polish cities such as Warsaw, Kraków, Trójmiasto, Wrocław, Poznań etc. However, in the upcoming years, Foodsi plans to expand to other countries. == Use == To start selling surplus, a company must leave Foodsi its contact information to register in the system. Registration in the app is completely free of charge. Then, companies offer available packages anticipating what won’t be sold and post them in the app along with the price so that users can buy them and pick them up. Companies can put their packages in the app at any time during the day. Users can pick up packages from bakeries, grocery stores, restaurants, but also florists and beauty stores. Foodsi charges a small commission on each package from the cooperating companies. If a user wants to start ordering packages from Foodsi, he or she needs to install the app on their mobile phone (Android or IOS) and register an account. The app displays a list of restaurants and other venues available in a specific region set by the user's location. Customers can see the price, address, distance and time range for package pickup. Packages are usually in the form of so-called 'surprise-packages', meaning that customers do not know specifically what kind of food/product will be inside. Some restaurants offer a choice of different package sizes. Prices are up to 70% lower than those of the original products. Customers have to show up at the restaurant to pick up the package using their phone at a time specified in the app. == Awards == Auler All-Stars 2025 - 3rd place Deloitte Technology Fast 50 - 2025 Central Europe Executive Club - Innowacja Roku: Żywność i Rolnictwo - Wyróżnienie (2025) Stena Circular Economy Award - Lider Gospodarki Obiegu Zamkniętego (2025) - wyróżnienie w kategorii start-up wdrażający GOZ na rynku polskim 255th place in the international poll FoodTech 500 2025 Finalist for the EY Entrepreneur Of The Year™ 2025 Wpływowi 2024 - Laureat w kategorii “Zrównoważony rozwój” Supplier of the Year 2024 - XXII Food & Business Forum Supplier of the Year 2024 - VII Sweets & Coffee Forum Innovative Leader 2024 - Leader in Food / Food-Tech Category - Executive Summit “Orzeł Innowacji - Start-up z potencjałem Polska-Świat” (Rzeczpospolita, 2024) 102nd place in the international poll FoodTech 500 2024 Auler 2023 Startup of the Year 2023 according to money.pl Start(up) w zrównoważoną przyszłość Kongresu Kompas ESG 2023 Marka Godna Zaufania according to My Company Polska 2023 184th place in the international poll FoodTech 500 2023 In 2023, Foodsi co-founder Mateusz Kowalczyk was recognized by Forbes magazine and included in its "30 before 30" list.

Confusion matrix

In machine learning, a confusion matrix, also known as error matrix, is a specific table layout that allows visualization of the performance of an algorithm, typically a supervised learning one. In unsupervised learning it is usually called a matching matrix. The term is used specifically in the problem of statistical classification. Each row of the matrix represents the instances in an actual class while each column represents the instances in a predicted class, or vice versa – both variants are found in the literature. The diagonal of the matrix therefore represents all instances that are correctly predicted. The name stems from the fact that it makes it easy to identify whether the system is confusing two classes (i.e., commonly mislabeling one class as another). The confusion matrix has its origins in human perceptual studies of auditory stimuli. It was adapted for machine learning studies and used by Frank Rosenblatt, among other early researchers, to compare human and machine classifications of visual (and later auditory) stimuli. It is a special kind of contingency table, with two dimensions ("actual" and "predicted"), and identical sets of "classes" in both dimensions (each combination of dimension and class is a variable in the contingency table). == Example == Given a sample of 12 individuals, 8 that have been diagnosed with cancer and 4 that are cancer-free, where individuals with cancer belong to class 1 (positive) and non-cancer individuals belong to class 0 (negative), we can display that data as follows: Assume that we have a classifier that distinguishes between individuals with and without cancer in some way, we can take the 12 individuals and run them through the classifier. The classifier then makes 9 accurate predictions and misses 3: 2 individuals with cancer wrongly predicted as being cancer-free (sample 1 and 2), and 1 person without cancer that is wrongly predicted to have cancer (sample 9). Notice, that if we compare the actual classification set to the predicted classification set, there are 4 different outcomes that could result in any particular column: The actual classification is positive and the predicted classification is positive (1,1). This is called a true positive result because the positive sample was correctly identified by the classifier. The actual classification is positive and the predicted classification is negative (1,0). This is called a false negative result because the positive sample is incorrectly identified by the classifier as being negative. The actual classification is negative and the predicted classification is positive (0,1). This is called a false positive result because the negative sample is incorrectly identified by the classifier as being positive. The actual classification is negative and the predicted classification is negative (0,0). This is called a true negative result because the negative sample gets correctly identified by the classifier. We can then perform the comparison between actual and predicted classifications and add this information to the table, making correct results appear in green so they are more easily identifiable. The template for any binary confusion matrix uses the four kinds of results discussed above (true positives, false negatives, false positives, and true negatives) along with the positive and negative classifications. The four outcomes can be formulated in a 2×2 confusion matrix, as follows: The color convention of the three data tables above were picked to match this confusion matrix, in order to easily differentiate the data. Now, we can simply total up each type of result, substitute into the template, and create a confusion matrix that will concisely summarize the results of testing the classifier: In this confusion matrix, of the 8 samples with cancer, the system judged that 2 were cancer-free, and of the 4 samples without cancer, it predicted that 1 did have cancer. All correct predictions are located in the diagonal of the table (highlighted in green), so it is easy to visually inspect the table for prediction errors, as values outside the diagonal will represent them. By summing up the 2 rows of the confusion matrix, one can also deduce the total number of positive (P) and negative (N) samples in the original dataset, i.e. P = T P + F N {\displaystyle P=TP+FN} and N = F P + T N {\displaystyle N=FP+TN} . == Table of confusion == In predictive analytics, a table of confusion (sometimes also called a confusion matrix) is a table with two rows and two columns that reports the number of true positives, false negatives, false positives, and true negatives. This allows more detailed analysis than simply observing the proportion of correct classifications (accuracy). Accuracy will yield misleading results if the data set is unbalanced; that is, when the numbers of observations in different classes vary greatly. For example, if there were 95 cancer samples and only 5 non-cancer samples in the data, a particular classifier might classify all the observations as having cancer. The overall accuracy would be 95%, but in more detail the classifier would have a 100% recognition rate (sensitivity) for the cancer class but a 0% recognition rate for the non-cancer class. F1 score is even more unreliable in such cases, and here would yield over 97.4%, whereas informedness removes such bias and yields 0 as the probability of an informed decision for any form of guessing (here always guessing cancer). According to Davide Chicco and Giuseppe Jurman, the most informative metric to evaluate a confusion matrix is the Matthews correlation coefficient (MCC). Other metrics can be included in a confusion matrix, each of them having their significance and use. Some researchers have argued that the confusion matrix, and the metrics derived from it, do not truly reflect a model's knowledge. In particular, the confusion matrix cannot show whether correct predictions were reached through sound reasoning or merely by chance (a problem known in philosophy as epistemic luck). It also does not capture situations where the facts used to make a prediction later change or turn out to be wrong (defeasibility). This means that while the confusion matrix is a useful tool for measuring classification performance, it may give an incomplete picture of a model’s true reliability. == Confusion matrices with more than two categories == Confusion matrix is not limited to binary classification and can be used in multi-class classifiers as well. The confusion matrices discussed above have only two conditions: positive and negative. For example, the table below summarizes communication of a whistled language between two speakers, with zero values omitted for clarity. == Confusion matrices in multi-label and soft-label classification == Confusion matrices are not limited to single-label classification (where only one class is present) or hard-label settings (where classes are either fully present, 1, or absent, 0). They can also be extended to Multi-label classification (where multiple classes can be predicted at once) and soft-label classification (where classes can be partially present). One such extension is the Transport-based Confusion Matrix (TCM), which builds on the theory of optimal transport and the principle of maximum entropy. TCM applies to single-label, multi-label, and soft-label settings. It retains the familiar structure of the standard confusion matrix: a square matrix sized by the number of classes, with diagonal entries indicating correct predictions and off-diagonal entries indicating confusion. In the single-label case, TCM is identical to the standard confusion matrix. TCM follows the same reasoning as the standard confusion matrix: if class A is overestimated (its predicted value is greater than its label value) and class B is underestimated (its predicted value is less than its label value), A is considered confused with B, and the entry (B, A) is increased. If a class is both predicted and present, it is correctly identified, and the diagonal entry (A, A) increases. Optimal transport and maximum entropy are used to determine the extent to which these entries are updated. TCM enables clearer comparison between predictions and labels in complex classification tasks, while maintaining a consistent matrix format across settings.

Rifts (role-playing game)

Rifts is a multi-genre role-playing game created by Kevin Siembieda in August 1990 and published continuously by Palladium Books since then. It takes place in a post-apocalyptic future, deriving elements from cyberpunk, science fiction, fantasy, horror, western, mythology and many other genres. Rifts serves as a cross-over environment for a variety of other Palladium games with different universes connected through "rifts" on Earth that lead to different spaces, times, and realities that Palladium calls the "Rifts Megaverse". Rifts describes itself as an "advanced" role-playing game and not an introduction for those new to the concept. Palladium continues to publish books for the Rifts series, with about 80 books published between 1990 and 2011. Rifts Ultimate Edition was released in August 2005 and designed to update the game with Palladium's incremental changes to its system, changes in the game world, and additional information and character types. The web site is quick to point out that this is not a second edition but an improvement and expansion of the original role playing game. == Background == The RPG had the tentative title Boomers, named after the original name for the Glitter Boy power armor until Kevin Siembieda changed the name after finding out it was in use for Bubblegum Crisis. == Setting == The Rifts world is Earth, but hundreds of years into the future. Ley lines, lines of magic energy, criss-cross the earth forming supernatural geographic areas such as the Bermuda Triangle. Points where Ley Lines intersect, called a nexus, are places of powerful magic, such as the Pyramids of Giza and Stonehenge. If a Ley Line nexus energy surges or is purposely activated, the fabric of space and time can be torn, creating a rift - a hole in space-time leading to another place, time, or dimension. Ley lines contain magical energy called Potential Psychic Energy (PPE), which is found in various places, objects, and animals and is particularly strong in children. An adult's level of PPE can vary based on other factors. PPE also allows Psionics which uses energy known as Inner Strength Points or ISP. Psychic phenomenon (more commonly called psionics) can also vary from individuals, ranging from none at all to Master level abilities. Psychic abilities can manifest in virtually any way imaginable. Some psychics develop differently, such as psi-stalkers; human mutants that feed on psychic energy. === Earth === Rifts begins with two future-historical premises: first, a golden age of humanity occurs, with tremendous advances in science, technology, military, and society. Humanity as a whole is at peace as a majority of Earth's nations decide to cease world war and begin to share ideas and technology freely. Much of the Solar System is conquered, humanity's wars will end, and harmony will reign. This golden age is followed by an unknown cause near the winter solstice and a rare planetary alignment, causing a disaster that cascades into tremendous destruction via a ripple effect. The cataclysm begins with unprecedented storms, earthquakes, tsunamis, and volcanic eruptions, which kill millions of people. The Ley Line networks that crisscross the globe are energized, causing rifts to open both on Earth and throughout the Megaverse. For hundreds of years after the holocaust, many creatures, both mythical beasts and aliens, come through the Rifts to wreak havoc. The old world gone, a new Dark Age dawns and humanity's shrinking population is reduced, due to catastrophe and domestic failure, immeasurably. This period is covered in Palladium's Rifts Chaos Earth spin-off series. Rifts initially takes place in 101 P.A. (equivalent to the year 2387) 289 years after this event. The "Post-Apocalypse" calendar was established by the formation of the Coalition States in 2286. By this time, most of the disasters have quieted down, though Earth is still bathed in PPE. The planet's mystical energy has attracted aliens from other dimensions, who continue to arrive through the Rifts both accidentally and deliberately. The humanoid creatures that arrive on Earth are referred to as Dimensional Beings (called D-Bees). Some resemble familiar fantasy races, such as elves and dwarfs, while others were created specifically for the game setting. Non-humanoid creatures have also arrived, including monstrous creatures and mystical demons. To cope with these natural, supernatural, and alien menaces, the human race has adapted in a variety of ways, many of them borrowed from the technological developments of the lost Golden Age. Powered armor suits and giant vehicles are frequently used to combat the dangers of Rifts, but more invasive augmentation is common. This has three basic categories: "Juicers" augment themselves chemically, the "Borgs" augment themselves mechanically, and "Crazies" use performance-enhancing brain implants. All such augmentations boost strength, speed, endurance, and dexterity to superhuman levels. However, all come at great cost. Chemicals cause the body to wear out faster, decreasing life span to a few years. Mechanical Borg augmentation causes a loss of humanity when those with multiple limb and organ replacements become more machine than human. Brain implants cause mental instability ranging from mild phobias to crippling neurosis or psychosis. ==== North America ==== The strongest power in North America is the Coalition States (CS), which is based in the arcological city of Chi-Town and lays claim to northern Illinois, all of Iowa, the Texas Panhandle, Missouri, and the eastern half of Ontario, Canada. The second greatest power is Free Quebec, a former Coalition State that seceded following a civil war with the other Coalition States. Mexico is ruled by a group of vampire kingdoms, who treat humans as little more than food. North of the Rio Grande, west of Texas and roaming most of the American Southwest are large nomadic bands/tribes of bandits who collectively form the Pecos Empire, consisting of El Paso, Los Alamos, and Houstown. Much of the western United States has more or less willingly reverted to a mix of modern and past technology akin to the Wild West. The Royal Canadian Mounted Police managed to survive the great cataclysm, though Canada itself did not. The Mounties have become an independent law enforcement force called the Tundra Rangers, patrolling the northern wilderness. The Midwest, both upper and central, is home to most of North America's population. The Manistique Imperium and Northern Gun in Michigan's Upper Peninsula, both Coalition allies, are among the largest weapons manufacturing areas on the continent. New Lazlo is one of the largest cities in Michigan's southern portion. Chillicothe in Missouri is a large supplier of Coalition food processing and growing. Missouri's southern half, home to the city-states of Whykin (Poplar Bluff) and Kingsdale (West Plains) are in constant opposition to the CS and claim independence. Arkansas is home to the independent CS ally El Dorado. Southern Illinois and the Ohio Valley is home to the Federation of Magic. Also in the Ohio Valley is Psyscape, a city-state founded by psychics. Tolkeen was a major city in the former Minneapolis region in early Rifts books; the city welcomed users of magic. A military campaign made by the Coalition States (which is the primary event of 109 PA) resulted in the magic-user kingdom being wiped off the map. In the Northeast, the city-state of Lazlo, named after supernatural researcher and writer Victor Lazlo, was built upon the ruins of Toronto. This major center of civilization is well known as a melting pot of humans, D-Bees and other beings, and is the home of Techno-Wizardry. Mad Haven is the name given to the ruins of Manhattan; tectonic forces during the cataclysm have moved it into the coast, creating a peninsula. It is seen by most denizens of Rifts Earth as a refuge of demons and madness. ==== South America ==== The return of Atlantis caused the Amazon River basin to flood most of western South America, giving it the nickname The Land of a Thousand Islands. The Empire of the Sun, consisting of Cuzco, Nazca, Arequipa and Lima, created a wide range of technology and magic, including magic derived from the Nazca lines. In Argentina, the Silver River Republics of Cordoba (the South American Chi-Town), Santiago (one of the most tolerant human nations on Rifts Earth), Achilles (a nation founded by mutants), and New Babylon, a nation where humans and aliens coexist) have thrived and created nations whose strength rivals that of the CS. In Bolivia, freed Human and D-Bees formed the Megaversal Legion: a mercenary company with one of the highest levels of technology on Rifts Earth. ==== Europe ==== England has become a vast wilderness again, broken up by the occasional giant Millennium Tree or feudal kingdom, complete with a New Camelot and a new King Arthur, partially being manipulated by an alien intelligence disguised as Merlin. Also the magic of

IJCAI Computers and Thought Award

The IJCAI Computers and Thought Award is presented every two years by the International Joint Conference on Artificial Intelligence (IJCAI), recognizing outstanding young scientists in artificial intelligence. It was originally funded with royalties received from the book Computers and Thought (edited by Edward Feigenbaum and Julian Feldman), and is currently funded by IJCAI. It is considered to be "the premier award for artificial intelligence researchers under the age of 35". == Laureates == Terry Winograd (1971) Patrick Winston (1973) Chuck Rieger (1975) Douglas Lenat (1977) David Marr (1979) Gerald Sussman (1981) Tom Mitchell (1983) Hector Levesque (1985) Johan de Kleer (1987) Henry Kautz (1989) Rodney Brooks (1991) Martha E. Pollack (1991) Hiroaki Kitano (1993) Sarit Kraus (1995) Stuart Russell (1995) Leslie Kaelbling (1997) Nicholas Jennings (1999) Daphne Koller (2001) Tuomas Sandholm (2003) Peter Stone (2007) Carlos Guestrin (2009) Andrew Ng (2009) Vincent Conitzer (2011) Malte Helmert (2011) Kristen Grauman (2013) Ariel D. Procaccia (2015) Percy Liang (2016) for his contributions to both the approach of semantic parsing for natural language understanding and better methods for learning latent-variable models, sometimes with weak supervision, in machine learning. Devi Parikh (2017) Stefano Ermon (2018) Guy Van den Broeck (2019) for his contributions to statistical and relational artificial intelligence, and the study of tractability in learning and reasoning. Piotr Skowron (2020) for his contributions to computational social choice, and to the theory of committee elections. Fei Fang (2021) for her contributions to integrating machine learning with game theory and the use of these novel techniques to tackle societal challenges such as more effective deployment of security resources, enhancing environmental sustainability, and reducing food insecurity. Bo Li (2022) for her contributions to uncovering the underlying connections among robustness, privacy, and generalization in AI, showing how different models are vulnerable to malicious attacks, and how to eliminate these vulnerabilities using mathematical tools that provide robustness guarantees for learning models and privacy protection. Pin-Yu Chen (2023) for his contributions to consolidating properties of trust, robustness and safety into rigorous algorithmic procedures and computable metrics for improving AI systems. Nisarg Shah (2024) for his contributions to AI and society, in particular foundational work on the theory of algorithmic fairness using principles from social choice theory. Aditya Grover (2025) for his foundational contributions uniting deep generative models, representation learning, and reinforcement learning, and for their applications in advancing scientific reasoning.

Argument Interchange Format

The Argument Interchange Format (AIF) is an international effort to develop a representational mechanism for exchanging argument resources between research groups, tools, and domains using a semantically rich language. AIF traces its history back to a 2005 colloquium in Budapest. The result of the work in Budapest was first published as a draft description in 2006. Building on this foundation, further work then used the AIF to build foundations for the Argument Web. AIF-RDF is the extended ontology represented in the Resource Description Framework Schema (RDFS) semantic language. The Argument Interchange Format introduces a small set of ontological concepts that aim to capture a common understanding of argument -- one that works in multiple domains (both domains of argumentation and also domains of academic research), so that data can be shared and re-used across different projects in different areas. These ontological concepts are: Information (I-nodes) Applications of Rules of Inference (RA-nodes) Applications of Rules of Conflict (CA-nodes) Applications of Rules of Preference (PA-nodes) extended by: Schematic Forms (F-nodes) that are instantiated by RA, CA and PA nodes The AIF has reifications in a variety of development environments and implementation languages including MySQL database schema RDF Prolog JSON as well as translations to visual languages such as DOT and SVG. AIF data can be accessed online at AIFdb.

Learning curve (machine learning)

In machine learning (ML), a learning curve (or training curve) is a graphical representation that shows how a model's performance on a training set (and usually a validation set) changes with the number of training iterations (epochs) or the amount of training data. Typically, the number of training epochs or training set size is plotted on the x-axis, and the value of the loss function (and possibly some other metric such as the cross-validation score) on the y-axis. Synonyms include error curve, experience curve, improvement curve and generalization curve. More abstractly, learning curves plot the difference between learning effort and predictive performance, where "learning effort" usually means the number of training samples, and "predictive performance" means accuracy on testing samples. Learning curves have many useful purposes in ML, including: choosing model parameters during design, adjusting optimization to improve convergence, and diagnosing problems such as overfitting (or underfitting). Learning curves can also be tools for determining how much a model benefits from adding more training data, and whether the model suffers more from a variance error or a bias error. If both the validation score and the training score converge to a certain value, then the model will no longer significantly benefit from more training data. == Formal definition == When creating a function to approximate the distribution of some data, it is necessary to define a loss function L ( f θ ( X ) , Y ) {\displaystyle L(f_{\theta }(X),Y)} to measure how good the model output is (e.g., accuracy for classification tasks or mean squared error for regression). We then define an optimization process which finds model parameters θ {\displaystyle \theta } such that L ( f θ ( X ) , Y ) {\displaystyle L(f_{\theta }(X),Y)} is minimized, referred to as θ ∗ {\displaystyle \theta ^{}} . === Training curve for amount of data === If the training data is { x 1 , x 2 , … , x n } , { y 1 , y 2 , … y n } {\displaystyle \{x_{1},x_{2},\dots ,x_{n}\},\{y_{1},y_{2},\dots y_{n}\}} and the validation data is { x 1 ′ , x 2 ′ , … x m ′ } , { y 1 ′ , y 2 ′ , … y m ′ } {\displaystyle \{x_{1}',x_{2}',\dots x_{m}'\},\{y_{1}',y_{2}',\dots y_{m}'\}} , a learning curve is the plot of the two curves i ↦ L ( f θ ∗ ( X i , Y i ) ( X i ) , Y i ) {\displaystyle i\mapsto L(f_{\theta ^{}(X_{i},Y_{i})}(X_{i}),Y_{i})} i ↦ L ( f θ ∗ ( X i , Y i ) ( X i ′ ) , Y i ′ ) {\displaystyle i\mapsto L(f_{\theta ^{}(X_{i},Y_{i})}(X_{i}'),Y_{i}')} where X i = { x 1 , x 2 , … x i } {\displaystyle X_{i}=\{x_{1},x_{2},\dots x_{i}\}} === Training curve for number of iterations === Many optimization algorithms are iterative, repeating the same step (such as backpropagation) until the process converges to an optimal value. Gradient descent is one such algorithm. If θ i ∗ {\displaystyle \theta _{i}^{}} is the approximation of the optimal θ {\displaystyle \theta } after i {\displaystyle i} steps, a learning curve is the plot of i ↦ L ( f θ i ∗ ( X , Y ) ( X ) , Y ) {\displaystyle i\mapsto L(f_{\theta _{i}^{}(X,Y)}(X),Y)} i ↦ L ( f θ i ∗ ( X , Y ) ( X ′ ) , Y ′ ) {\displaystyle i\mapsto L(f_{\theta _{i}^{}(X,Y)}(X'),Y')}

A Very Fatal Murder

A Very Fatal Murder is a podcast produced by the satirical publication The Onion. A parody of true crime podcasts, A Very Fatal Murder is hosted by fictional New York City reporter David Pascall, who travels to the small town Bluff Springs, Nebraska to investigate the murder of prom queen Hayley Price. Pascall is voiced by David Sidorov, who also wrote for the podcast. The podcast premiered on January 23, 2018, and consists of 7 episodes. Season 2 was released in its entirety on May 11, 2019. == Production == A Very Fatal Murder satirizes popular true crime podcasts such as Serial, S-Town, and My Favorite Murder. According to head writer Katy Yeiser, the podcast is not meant as a take down of any particular podcast, but rather an ode to the genre. == Synopsis == The podcast follows fictional investigative reporter David Pascall (voiced by David Sidorov) who is searching for the perfect murder to create an award-winning podcast about. He is assisted by ETHL (the Extremely Timely Homicide Locator), an MIT-created computer programmed to find "the most interesting, violent, culturally relevant murder cases in America". == Episodes == === Season 1 === === Season 2 === == Reception == The podcast received mostly positive reviews, and was largely praised for attacking true-crime tropes such as the "hot dead girl" and the romanticization of small-town America. === Awards ===