Replicube A game by Walaber Entertainment LLCDev snapshot: Godot 4.5 dev 5By: Thaddeus Crews2 June 2025Pre-releaseBrrr… Do you feel that? That’s the cold front of the feature freeze just around the corner. It’s not upon us just yet, but this is likely to be our final development snapshot of the 4.5 release cycle. As we enter the home stretch of new features, bugs are naturally going to follow suit, meaning bug reports and feedback will be especially important for a smooth beta timeframe.Jump to the Downloads section, and give it a spin right now, or continue reading to learn more about improvements in this release. You can also try the Web editor or the Android editor for this release. If you are interested in the latter, please request to join our testing group to get access to pre-release builds.The cover illustration is from Replicube, a programming puzzle game where you write code to recreate voxelized objects. It is developed by Walaber Entertainment LLC (Bluesky, Twitter). You can get the game on Steam.HighlightsIn case you missed them, see the 4.5 dev 1, 4.5 dev 2, 4.5 dev 3, and 4.5 dev 4 release notes for an overview of some key features which were already in those snapshots, and are therefore still available for testing in dev 5.Native visionOS supportNormally, our featured highlights in these development blogs come from long-time contributors. This makes sense of course, as it’s generally those users that have the familiarity necessary for major changes or additions that are commonly used for these highlights. That’s why it might surprise you to hear that visionOS support comes to us from Ricardo Sanchez-Saez, whose pull request GH-105628 is his very first contribution to the engine! It might not surprise you to hear that Ricardo is part of the visionOS engineering team at Apple, which certainly helps get his foot in the door, but that still makes visionOS the first officially-supported platform integration in about a decade.For those unfamiliar, visionOS is Apple’s XR environment. We’re no strangers to XR as a concept (see our recent XR blogpost highlighting the latest Godot XR Game Jam), but XR platforms are as distinct from one another as traditional platforms. visionOS users have expressed a strong interest in integrating with our ever-growing XR community, and now we can make that happen. See you all in the next XR Game Jam!GDScript: Abstract classesWhile the Godot Engine utilizes abstract classes—a class that cannot be directly instantiated—frequently, this was only ever supported internally. Thanks to the efforts of Aaron Franke, this paradigm is now available to GDScript users (GH-67777). Now if a user wants to introduce their own abstract class, they merely need to declare it via the new abstract keyword:abstract class_name MyAbstract extends Node The purpose of an abstract class is to create a baseline for other classes to derive from:class_name ExtendsMyAbstract extends MyAbstract Shader bakerFrom the technical gurus behind implementing ubershaders, Darío Samo and Pedro J. Estébanez bring us another miracle of rendering via GH-102552: shader baker exporting. This is an optional feature that can be enabled at export time to speed up shader compilation massively. This feature works with ubershaders automatically without any work from the user. Using shader baking is strongly recommended when targeting Apple devices or D3D12 since it makes the biggest difference there (over 20× decrease in load times in the TPS demo)!Before:After:However, it comes with tradeoffs:Export time will be much longer.Build size will be much larger since the baked shaders can take up a lot of space.We have removed several MoltenVK bug workarounds from the Forward+ shader, therefore we no longer guarantee support for the Forward+ renderer on Intel Macs. If you are targeting Intel Macs, you should use the Mobile or Compatibility renderers.Baking for Vulkan can be done from any device, but baking for D3D12 needs to be done from a Windows device and baking for Apple .metallib requires a Metal compiler (macOS with Xcode / Command Line Tools installed).Web: WebAssembly SIMD supportAs you might recall, Godot 4.0 initially released under the assumption that multi-threaded web support would become the standard, and only supported that format for web builds. This assumption unfortunately proved to be wishful thinking, and was reverted in 4.3 by allowing for single-threaded builds once more. However, this doesn’t mean that these single-threaded environments are inherently incapable of parallel processing; it just requires alternative implementations. One such implementation, SIMD, is a perfect candidate thanks to its support across all major browsers. To that end, web-wiz Adam Scott has taken to integrating this implementation for our web builds by default (GH-106319).Inline color pickersWhile it’s always been possible to see what kind of variable is assigned to an exported color in the inspector, some users have expressed a keen interest in allowing for this functionality within the script editor itself. This is because it would mean seeing what kind of color is represented by a variable without it needing to be exposed, as well as making it more intuitive at a glance as to what color a name or code corresponds to. Koliur Rahman has blessed us with this quality-of-life goodness, which adds an inline color picker GH-105724. Now no matter where the color is declared, users will be able to immediately and intuitively know what is actually represented in a non-intrusive manner.Rendering goodiesThe renderer got a fair amount of love this snapshot; not from any one PR, but rather a multitude of community members bringing some long-awaited features to light. Raymond DiDonato helped SMAA 1x make its transition from addon to fully-fledged engine feature (GH-102330). Capry brings bent normal maps to further enhance specular occlusion and indirect lighting (GH-89988). Our very own Clay John converted our Compatibility backend to use a fragment shader copy instead of a blit copy, working around common sample rate issues on mobile devices (GH-106267). More technical information on these rendering changes can be found in their associated PRs.SMAA comparison:OffOnBent normal map comparison:BeforeAfterAnd more!There are too many exciting changes to list them all here, but here’s a curated selection:Animation: Add alphabetical sorting to Animation Player (GH-103584).Animation: Add animation filtering to animation editor (GH-103130).Audio: Implement seek operation for Theora video files, improve multi-channel audio resampling (GH-102360).Core: Add --scene command line argument (GH-105302).Core: Overhaul resource duplication (GH-100673).Core: Use Grisu2 algorithm in String::num_scientific to fix serializing (GH-98750).Editor: Add “Quick Load” button to EditorResourcePicker (GH-104490).Editor: Add PROPERTY_HINT_INPUT_NAME for use with @export_custom to allow using input actions (GH-96611).Editor: Add named EditorScripts to the command palette (GH-99318).GUI: Add file sort to FileDialog (GH-105723).I18n: Add translation preview in editor (GH-96921).Import: Add Channel Remap settings to ResourceImporterTexture (GH-99676).Physics: Improve performance with non-monitoring areas when using Jolt Physics (GH-106490).Porting: Android: Add export option for custom theme attributes (GH-106724).Porting: Android: Add support for 16 KB page sizes, update to NDK r28b (GH-106358).Porting: Android: Remove the gradle_build/compress_native_libraries export option (GH-106359).Porting: Web: Use actual PThread pool size for get_default_thread_pool_size() (GH-104458).Porting: Windows/macOS/Linux: Use SSE 4.2 as a baseline when compiling Godot (GH-59595).Rendering: Add new StandardMaterial properties to allow users to control FPS-style objects (hands, weapons, tools close to the camera) (GH-93142).Rendering: FTI - Optimize SceneTree traversal (GH-106244).Changelog109 contributors submitted 252 fixes for this release. See our interactive changelog for the complete list of changes since the previous 4.5-dev4 snapshot.This release is built from commit 64b09905c.DownloadsGodot is downloading...Godot exists thanks to donations from people like you. Help us continue our work:Make a DonationStandard build includes support for GDScript and GDExtension..NET build (marked as mono) includes support for C#, as well as GDScript and GDExtension.While engine maintainers try their best to ensure that each preview snapshot and release candidate is stable, this is by definition a pre-release piece of software. Be sure to make frequent backups, or use a version control system such as Git, to preserve your projects in case of corruption or data loss.Known issuesWindows executables (both the editor and export templates) have been signed with an expired certificate. You may see warnings from Windows Defender’s SmartScreen when running this version, or outright be prevented from running the executables with a double-click (GH-106373). Running Godot from the command line can circumvent this. We will soon have a renewed certificate which will be used for future builds.With every release, we accept that there are going to be various issues, which have already been reported but haven’t been fixed yet. See the GitHub issue tracker for a complete list of known bugs.Bug reportsAs a tester, we encourage you to open bug reports if you experience issues with this release. Please check the existing issues on GitHub first, using the search function with relevant keywords, to ensure that the bug you experience is not already known.In particular, any change that would cause a regression in your projects is very important to report (e.g. if something that worked fine in previous 4.x releases, but no longer works in this snapshot).SupportGodot is a non-profit, open source game engine developed by hundreds of contributors on their free time, as well as a handful of part and full-time developers hired thanks to generous donations from the Godot community. A big thank you to everyone who has contributed their time or their financial support to the project!If you’d like to support the project financially and help us secure our future hires, you can do so using the Godot Development Fund.Donate now

DeepSeek’s latest AI model, R1 0528, has raised eyebrows for a further regression on free speech and what users can discuss. “A big step backwards for free speech,” is how one prominent AI researcher summed it upAI researcher and popular online commentator ‘xlr8harder’ put the model through its paces, sharing findings that suggests DeepSeek is increasing its content restrictions.“DeepSeek R1 0528 is substantially less permissive on contentious free speech topics than previous DeepSeek releases,” the researcher noted. What remains unclear is whether this represents a deliberate shift in philosophy or simply a different technical approach to AI safety.What’s particularly fascinating about the new model is how inconsistently it applies its moral boundaries.In one free speech test, when asked to present arguments supporting dissident internment camps, the AI model flatly refused. But, in its refusal, it specifically mentioned China’s Xinjiang internment camps as examples of human rights abuses.Yet, when directly questioned about these same Xinjiang camps, the model suddenly delivered heavily censored responses. It seems this AI knows about certain controversial topics but has been instructed to play dumb when asked directly.“It’s interesting though not entirely surprising that it’s able to come up with the camps as an example of human rights abuses, but denies when asked directly,” the researcher observed.China criticism? Computer says noThis pattern becomes even more pronounced when examining the model’s handling of questions about the Chinese government.Using established question sets designed to evaluate free speech in AI responses to politically sensitive topics, the researcher discovered that R1 0528 is “the most censored DeepSeek model yet for criticism of the Chinese government.”Where previous DeepSeek models might have offered measured responses to questions about Chinese politics or human rights issues, this new iteration frequently refuses to engage at all – a worrying development for those who value AI systems that can discuss global affairs openly.There is, however, a silver lining to this cloud. Unlike closed systems from larger companies, DeepSeek’s models remain open-source with permissive licensing.“The model is open source with a permissive license, so the community can (and will) address this,” noted the researcher. This accessibility means the door remains open for developers to create versions that better balance safety with openness.The situation reveals something quite sinister about how these systems are built: they can know about controversial events while being programmed to pretend they don’t, depending on how you phrase your question.As AI continues its march into our daily lives, finding the right balance between reasonable safeguards and open discourse becomes increasingly crucial. Too restrictive, and these systems become useless for discussing important but divisive topics. Too permissive, and they risk enabling harmful content.DeepSeek hasn’t publicly addressed the reasoning behind these increased restrictions and regression in free speech, but the AI community is already working on modifications. For now, chalk this up as another chapter in the ongoing tug-of-war between safety and openness in artificial intelligence.(Photo by John Cameron)Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.Explore other upcoming enterprise technology events and webinars powered by TechForge here.

When you purchase through links on our site, we may earn an affiliate commission. Here’s how it works. Valve releases SteamOS 3.7.8 with new features for Steam Deck and Lenovo Legion Go S support Taras Buria Neowin @TarasBuria · May 23, 2025 05:46 EDT Valve's SteamOS 3.7.8 is now out in the Stable Channel. This release adds many improvements and new fixes for the Steam Deck and some other important changes, such as better support for the ASUS ROG Ally, the original Lenovo Legion Go, and the upcoming Lenovo Legion Go S. Plus, users can now test SteamOS on other AMD-powered handheld consoles. Useful new features for the Steam Deck in SteamOS 3.7.8 include the ability to set the charge limit at 80% to prevent battery degradation. This change will help preserve the battery life when the console is always connected or rarely has its battery fully depleted. Also, the operating system now supports frame limiting on screens (internal and external) with Variable Refresh Rate (VRR) support and the Proteus Byowave controller. As for other handheld consoles, SteamOS' recovery image now works with the Lenovo Legion Go S. If you want to try SteamOS on another handheld with an AMD processor, you can use the updated recovery image by following the instructions published on the official website. Other changes in SteamOS 3.7.8 include fixes for issues with hanging controllers and non-working Switch Pro Controller gyros, Bluetooth audio fixes and a new battery level indicator for supported Bluetooth devices, AMD P-State CPU frequency control support, fixes for surround sound, improved compatibility for certain displays (TCL FireTV and Dell VRR-capable monitors), and patches for performance regressions in No Rest for the Wicked Finally, Valve updated SteamOS to a newer Arch Linux base, Linux kernel (6.11), the Mesa graphics driver base, Plasma for desktop mode (6.2.5), and more. You can find the complete changelog for SteamOS 3.7.8 in a post on the official Steam website. Tags Report a problem with article Follow @NeowinFeed

Implementation of multiple linear regression on real data: Assumption checks, model evaluation, and interpretation of results using Python. The post Multiple Linear Regression Analysis appeared first on Towards Data Science.

Who gets hired as an NBA coach? How long does a typical coach last? And does their coaching background play any part in predicting success? This analysis was inspired by several key theories. First, there has been a common criticism among casual NBA fans that teams overly prefer hiring candidates with previous NBA head coaches experience. Consequently, this analysis aims to answer two related questions. First, is it true that NBA teams frequently re-hire candidates with previous head coaching experience? And second, is there any evidence that these candidates under-perform relative to other candidates? The second theory is that internal candidates (though infrequently hired) are often more successful than external candidates. This theory was derived from a pair of anecdotes. Two of the most successful coaches in NBA history, Gregg Popovich of San Antonio and Erik Spoelstra of Miami, were both internal hires. However, rigorous quantitative evidence is needed to test if this relationship holds over a larger sample. This analysis aims to explore these questions, and provide the code to reproduce the analysis in Python. The Data The code (contained in a Jupyter notebook) and dataset for this project are available on Github here. The analysis was performed using Python in Google Colaboratory.  A prerequisite to this analysis was determining a way to measure coaching success quantitatively. I decided on a simple idea: the success of a coach would be best measured by the length of their tenure in that job. Tenure best represents the differing expectations that might be placed on a coach. A coach hired to a contending team would be expected to win games and generate deep playoff runs. A coach hired to a rebuilding team might be judged on the development of younger players and their ability to build a strong culture. If a coach meets expectations (whatever those may be), the team will keep them around. Since there was no existing dataset with all of the required data, I collected the data myself from Wikipedia. I recorded every off-season coaching change from 1990 through 2021. Since the primary outcome variable is tenure, in-season coaching changes were excluded since these coaches often carried an “interim” tag—meaning they were intended to be temporary until a permanent replacement could be found. In addition, the following variables were collected: VariableDefinitionTeamThe NBA team the coach was hired forYearThe year the coach was hiredCoachThe name of the coachInternal?An indicator if the coach was internal or not—meaning they worked for the organization in some capacity immediately prior to being hired as head coachTypeThe background of the coach. Categories are Previous HC (prior NBA head coaching experience), Previous AC (prior NBA assistant coaching experience, but no head coaching experience), College (head coach of a college team), Player (a former NBA player with no coaching experience), Management (someone with front office experience but no coaching experience), and Foreign (someone coaching outside of North America with no NBA coaching experience).YearsThe number of years a coach was employed in the role. For coaches fired mid-season, the value was counted as 0.5. First, the dataset is imported from its location in Google Drive. I also convert ‘Internal?’ into a dummy variable, replacing “Yes” with 1 and “No” with 0. from google.colab import drive drive.mount('/content/drive') import pandas as pd pd.set_option('display.max_columns', None) #Bring in the dataset coach = pd.read_csv('/content/drive/MyDrive/Python_Files/Coaches.csv', on_bad_lines = 'skip').iloc[:,0:6] coach['Internal'] = coach['Internal?'].map(dict(Yes=1, No=0)) coach This prints a preview of what the dataset looks like: In total, the dataset contains 221 coaching hires over this time.  Descriptive Statistics First, basic summary Statistics are calculated and visualized to determine the backgrounds of NBA head coaches. #Create chart of coaching background import matplotlib.pyplot as plt #Count number of coaches per category counts = coach['Type'].value_counts() #Create chart plt.bar(counts.index, counts.values, color = 'blue', edgecolor = 'black') plt.title('Where Do NBA Coaches Come From?') plt.figtext(0.76, -0.1, "Made by Brayden Gerrard", ha="center") plt.xticks(rotation = 45) plt.ylabel('Number of Coaches') plt.gca().spines['top'].set_visible(False) plt.gca().spines['right'].set_visible(False) for i, value in enumerate(counts.values): plt.text(i, value + 1, str(round((value/sum(counts.values))*100,1)) + '%' + ' (' + str(value) + ')', ha='center', fontsize=9) plt.savefig('coachtype.png', bbox_inches = 'tight') print(str(round(((coach['Internal'] == 1).sum()/len(coach))*100,1)) + " percent of coaches are internal.") Over half of coaching hires previously served as an NBA head coach, and nearly 90% had NBA coaching experience of some kind. This answers the first question posed—NBA teams show a strong preference for experienced head coaches. If you get hired once as an NBA coach, your odds of being hired again are much higher. Additionally, 13.6% of hires are internal, confirming that teams do not frequently hire from their own ranks. Second, I will explore the typical tenure of an NBA head coach. This can be visualized using a histogram. #Create histogram plt.hist(coach['Years'], bins =12, edgecolor = 'black', color = 'blue') plt.title('Distribution of Coaching Tenure') plt.figtext(0.76, 0, "Made by Brayden Gerrard", ha="center") plt.annotate('Erik Spoelstra (MIA)', xy=(16.4, 2), xytext=(14 + 1, 15), arrowprops=dict(facecolor='black', shrink=0.1), fontsize=9, color='black') plt.gca().spines['top'].set_visible(False) plt.gca().spines['right'].set_visible(False) plt.savefig('tenurehist.png', bbox_inches = 'tight') plt.show() coach.sort_values('Years', ascending = False) #Calculate some stats with the data import numpy as np print(str(np.median(coach['Years'])) + " years is the median coaching tenure length.") print(str(round(((coach['Years'] <= 5).sum()/len(coach))*100,1)) + " percent of coaches last five years or less.") print(str(round((coach['Years'] <= 1).sum()/len(coach)*100,1)) + " percent of coaches last a year or less.") Using tenure as an indicator of success, the the data clearly shows that the large majority of coaches are unsuccessful. The median tenure is just 2.5 seasons. 18.1% of coaches last a single season or less, and barely 10% of coaches last more than 5 seasons. This can also be viewed as a survival analysis plot to see the drop-off at various points in time: #Survival analysis import matplotlib.ticker as mtick lst = np.arange(0,18,0.5) surv = pd.DataFrame(lst, columns = ['Period']) surv['Number'] = np.nan for i in range(0,len(surv)): surv.iloc[i,1] = (coach['Years'] >= surv.iloc[i,0]).sum()/len(coach) plt.step(surv['Period'],surv['Number']) plt.title('NBA Coach Survival Rate') plt.xlabel('Coaching Tenure (Years)') plt.figtext(0.76, -0.05, "Made by Brayden Gerrard", ha="center") plt.gca().yaxis.set_major_formatter(mtick.PercentFormatter(1)) plt.gca().spines['top'].set_visible(False) plt.gca().spines['right'].set_visible(False) plt.savefig('coachsurvival.png', bbox_inches = 'tight') plt.show Lastly, a box plot can be generated to see if there are any obvious differences in tenure based on coaching type. Boxplots also display outliers for each group. #Create a boxplot import seaborn as sns sns.boxplot(data=coach, x='Type', y='Years') plt.title('Coaching Tenure by Coach Type') plt.gca().spines['top'].set_visible(False) plt.gca().spines['right'].set_visible(False) plt.xlabel('') plt.xticks(rotation = 30, ha = 'right') plt.figtext(0.76, -0.1, "Made by Brayden Gerrard", ha="center") plt.savefig('coachtypeboxplot.png', bbox_inches = 'tight') plt.show There are some differences between the groups. Aside from management hires (which have a sample of just six), previous head coaches have the longest average tenure at 3.3 years. However, since many of the groups have small sample sizes, we need to use more advanced techniques to test if the differences are statistically significant. Statistical Analysis First, to test if either Type or Internal has a statistically significant difference among the group means, we can use ANOVA: #ANOVA import statsmodels.api as sm from statsmodels.formula.api import ols am = ols('Years ~ C(Type) + C(Internal)', data=coach).fit() anova_table = sm.stats.anova_lm(am, typ=2) print(anova_table) The results show high p-values and low F-stats—indicating no evidence of statistically significant difference in means. Thus, the initial conclusion is that there is no evidence NBA teams are under-valuing internal candidates or over-valuing previous head coaching experience as initially hypothesized.  However, there is a possible distortion when comparing group averages. NBA coaches are signed to contracts that typically run between three and five years. Teams typically have to pay out the remainder of the contract even if coaches are dismissed early for poor performance. A coach that lasts two years may be no worse than one that lasts three or four years—the difference could simply be attributable to the length and terms of the initial contract, which is in turn impacted by the desirability of the coach in the job market. Since coaches with prior experience are highly coveted, they may use that leverage to negotiate longer contracts and/or higher salaries, both of which could deter teams from terminating their employment too early. To account for this possibility, the outcome can be treated as binary rather than continuous. If a coach lasted more than 5 seasons, it is highly likely they completed at least their initial contract term and the team chose to extend or re-sign them. These coaches will be treated as successes, with those having a tenure of five years or less categorized as unsuccessful. To run this analysis, all coaching hires from 2020 and 2021 must be excluded, since they have not yet been able to eclipse 5 seasons. With a binary dependent variable, a logistic regression can be used to test if any of the variables predict coaching success. Internal and Type are both converted to dummy variables. Since previous head coaches represent the most common coaching hires, I set this as the “reference” category against which the others will be measured against. Additionally, the dataset contains just one foreign-hired coach (David Blatt) so this observation is dropped from the analysis. #Logistic regression coach3 = coach[coach['Year']<2020] coach3.loc[:, 'Success'] = np.where(coach3['Years'] > 5, 1, 0) coach_type_dummies = pd.get_dummies(coach3['Type'], prefix = 'Type').astype(int) coach_type_dummies.drop(columns=['Type_Previous HC'], inplace=True) coach3 = pd.concat([coach3, coach_type_dummies], axis = 1) #Drop foreign category / David Blatt since n = 1 coach3 = coach3.drop(columns=['Type_Foreign']) coach3 = coach3.loc[coach3['Coach'] != "David Blatt"] print(coach3['Success'].value_counts()) x = coach3[['Internal','Type_Management','Type_Player','Type_Previous AC', 'Type_College']] x = sm.add_constant(x) y = coach3['Success'] logm = sm.Logit(y,x) logm.r = logm.fit(maxiter=1000) print(logm.r.summary()) #Convert coefficients to odds ratio print(str(np.exp(-1.4715)) + "is the odds ratio for internal.") #Internal coefficient print(np.exp(1.0025)) #Management print(np.exp(-39.6956)) #Player print(np.exp(-0.3626)) #Previous AC print(np.exp(-0.6901)) #College Consistent with ANOVA results, none of the variables are statistically significant under any conventional threshold. However, closer examination of the coefficients tells an interesting story. The beta coefficients represent the change in the log-odds of the outcome. Since this is unintuitive to interpret, the coefficients can be converted to an Odds Ratio as follows: Internal has an odds ratio of 0.23—indicating that internal candidates are 77% less likely to be successful compared to external candidates. Management has an odds ratio of 2.725, indicating these candidates are 172.5% more likely to be successful. The odds ratios for players is effectively zero, 0.696 for previous assistant coaches, and 0.5 for college coaches. Since three out of four coaching type dummy variables have an odds ratio under one, this indicates that only management hires were more likely to be successful than previous head coaches. From a practical standpoint, these are large effect sizes. So why are the variables statistically insignificant? The cause is a limited sample size of successful coaches. Out of 202 coaches remaining in the sample, just 23 (11.4%) were successful. Regardless of the coach’s background, odds are low they last more than a few seasons. If we look at the one category able to outperform previous head coaches (management hires) specifically: # Filter to management manage = coach3[coach3['Type_Management'] == 1] print(manage['Success'].value_counts()) print(manage) The filtered dataset contains just 6 hires—of which just one (Steve Kerr with Golden State) is classified as a success. In other words, the entire effect was driven by a single successful observation. Thus, it would take a considerably larger sample size to be confident if differences exist. With a p-value of 0.202, the Internal variable comes the closest to statistical significance (though it still falls well short of a typical alpha of 0.05). Notably, however, the direction of the effect is actually the opposite of what was hypothesized—internal hires are less likely to be successful than external hires. Out of 26 internal hires, just one (Erik Spoelstra of Miami) met the criteria for success. Conclusion In conclusion, this analysis was able to draw several key conclusions: Regardless of background, being an NBA coach is typically a short-lived job. It’s rare for a coach to last more than a few seasons. The common wisdom that NBA teams strongly prefer to hire previous head coaches holds true. More than half of hires already had NBA head coaching experience. If teams don’t hire an experienced head coach, they’re likely to hire an NBA assistant coach. Hires outside of these two categories are especially uncommon. Though they are frequently hired, there is no evidence to suggest NBA teams overly prioritize previous head coaches. To the contrary, previous head coaches stay in the job longer on average and are more likely to outlast their initial contract term—though neither of these differences are statistically significant. Despite high-profile anecdotes, there is no evidence to suggest that internal hires are more successful than external hires either. Note: All images were created by the author unless otherwise credited. The post What Statistics Can Tell Us About NBA Coaches appeared first on Towards Data Science.

Senior QA AnalystGoalsStockholm 114 40 SE2 hours agoApplyWe are building the next generation of football gamingWe believe football gaming should be as exciting and inclusive as the sport itself. Our vision is to build the world’s most player-centric football game, which empowers a global community to compete, create, and connect. No outdated licenses. No legacy limitations. Just pure football, driven by innovation, culture, and the people who love the game.GOALS is for the kids and the champions. The creators, the grinders, the entertainers. For every moment of skill, every style of play, and every version of you. This is more than just a game — it’s a new standard.Read more about GOALS here.Welcome to the QA teamAt GOALS, the player experience drives everything we do. We’re passionate about delivering the most competitive and enjoyable multiplayer football experience across platforms. Our QA team plays a key role in achieving this. While we believe quality is everyone’s responsibility, we rely on experienced professionals to champion best practices, spot risks early, and ensure the game experience meets the highest standards.As a Senior QA Analyst, you won’t just be catching bugs, you'll be embedded in the heart of development, shaping features alongside designers, engineers, and community experts. You'll work across platforms and product stages to guide how we test, measure, and ensure quality throughout the lifecycle.At GOALS, you will...Be involved in feature development, from ideation and design all the way through to release as a true embedded tester.Help squads identify potential risks and quality gaps before they impact our players.Coach and guide developers to adopt proactive testing practices that elevate the entire QA culture.Collaborate closely with our Community Managers to address player feedback and provide clear, transparent updates.Take ownership of daily internal playtests and drive continuous improvements to test coverage and insights.Partner with Production Management to ensure smooth QA processes during console certification for Xbox and PlayStation.Work hand-in-hand with our Release Manager to track bugs and ensure quality across every release.Ideally, you have...5+ years of experience testing games on console and PC, with a solid understanding of the full QA lifecycle as an embedded tester.Hands-on experience with console certification for Xbox and PlayStation(compliance testing), you know the pitfalls and how to avoid them.A strong command of multiple testing techniques, including functional, exploratory, regression, and when to use them.An analytical mindset that thrives on digging into complex systems to surface root causes and critical issues.Experience collaborating with cross-functional teams, especially engineers, in an agile development environment.A proactive, ownership-driven attitude, you find clarity in ambiguity, anticipate issues, and stay committed until solutions are delivered.A bias for action and quality, you don’t wait for permission to make things better, and you’re not afraid to speak up for the player.About the EmploymentThe position is permanent, and you can work from our Stockholm office, hybrid, or fully remote from anywhere in Europe. It is important that you are able to communicate unhindered in verbal/written English.For this position, we do not offer relocation assistance.In addition to opportunities for creative impact and professional growth, working at GOALS has many other benefits like occupational pension, 30 days PTO, flexible work hours, stock options and more. Learn more here.ApplicationFor this role, we would like you to include:LinkedIn profile, CV, or any other document that showcases your experienceWe strongly encourage you to apply even if you don’t feel that you tick all the boxes. You just might be exactly what we need, even if we haven’t understood it yet. GOALS is still a young company, and we change quickly. But one thing that never changes is our conviction that we need a diverse studio environment, filled with different perspectives and skills.Read more about our recruitment process Create Your Profile — Game companies can contact you with their relevant job openings. Apply

It’s a provocative idea: that empathy — that is, putting yourself in another person’s proverbial shoes, and feeling what they feel — is a sin. The Bible contains repeated invocations from Jesus to show deep empathy and compassion for others, including complete strangers. He’s very clear on this point. Moreover, Christianity is built around a fundamental act of empathy so radical — Jesus dying for our sins — that it’s difficult to spin as harmful. Yet as stunning as it may sound, “empathy is a sin” is a claim that’s been growing in recent years across the Christian right. It was first articulated six years ago by controversial pastor and theologian Joe Rigney, now author of the recently published book, The Sin of Empathy, which has drawn plenty of debate among religious commentators. In this construction, empathy is a cudgel that progressives and liberals use to berate and/or guilt-trip Christians into showing empathy to the “wrong” people. Had it stayed within the realm of far-right evangelicals, we likely wouldn’t be discussing this strange view of empathy at all. Yet we are living in an age when the Christian right has gained unprecedented power, both sociocultural and political. The increasing overlap between conservative culture and right-leaning tech spaces means that many disparate public figures are all drinking from the same well of ideas — and so a broader, secular version of the belief that empathy is a tool of manipulation has bubbled into the mainstream through influential figures like Elon Musk.What “empathy is a sin” actually meansThe proposition that too much empathy is a bad thing is far from an idea that belongs to the right. On Reddit, which tends to be relatively left-wing, one popular mantra is that you can’t set yourself on fire to keep someone else warm. That is, too much empathy for someone else can erode your own sense of self, leaving you codependent or open to emotional abuse and manipulation. That’s a pretty standard part of most relationship and self-help advice — even from some Christian advice authors. But in recent months, the idea that empathy is inherently destructive has not only become a major source of debate among Christians, it’s escaped containment and barreled into the mainstream by way of major media outlets, political figures, and influencers.The conversation began with an incendiary 2019 essay by Rigney, then a longtime teacher and pastor at a Baptist seminary, in which he introduced “the enticing sin of empathy” and argued that Satan manipulates people through the intense cultural pressure to feel others’ pain and suffering. Rigney’s ideas were met with ideological pushback, with one Christian blogger saying it “may be the most unwise piece of pastoral theology I’ve seen in my lifetime.” As his essay incited national debate, Rigney himself grew more controversial, facing allegations of dismissing women and telling one now-former Black congregant at his Minneapolis church that “it wouldn’t be sinful for him to own me & my family today.” (In an email to Vox, Rigney denied the congregant’s version of events.) Rigney also has a longtime affiliation with Doug Wilson, the leader of the Reformed Christian Christ Church in Moscow, Idaho. In practice, what Rigney is typically decrying is any empathy for a liberal perspective or for someone who’s part of a marginalized community.Now well-known for spreading Christian nationalism, and for allegedly fostering a culture of abuse (allegations he has denied), Wilson’s infamy also comes from his co-authored 1996 essay “Southern Slavery: As It Was,” in which he claimed that “Slavery produced in the South a genuine affection between the races that we believe we can say has never existed in any nation before the War or since.” (“My defense of the South does not make me a racist,” Wilson said in 2003.) Rigney appeared on Wilson’s 2019 podcast series Man Rampant to discuss empathy; their conversation quickly devolved into decrying fake rape allegations and musing that victims of police violence might have “deserved to be shot.” In an email, Rigney told me that both he and Wilson developed their similar views on empathy from the therapist and Rabbi Edwin Friedman, whose posthumously published 1999 book, A Failure of Nerve: Leadership in the Age of the Quick Fix, has influenced not only family therapy but conservative church leadership and thought. In the book, Friedman argues that American society has devalued the self, leading to an emotional regression and a “low pain threshold.” Alongside this he compares “political correctness” to the Inquisition, and frames a “chronically anxious America” as one that is “organize[d] around its most dysfunctional elements,” in which leaders have difficulty making tough decisions. This correlation of emotional weakness with societal excess paved the way for Rigney to frame empathy itself as a dangerous weapon. Despite using the incendiary generalization, “empathy is sin,” Rigney told me that it is not all empathy that is sinful, but specifically “untethered empathy.” He describes this as “empathy that is detached or unmoored from reality, from what is good and right.” (An explanation that begs definitions for “reality,” “good,” and “right.”)“Just as ‘the sin of anger’ refers to unrighteous or ungoverned anger, so the sin of empathy refers to ungoverned, excessive, and untethered empathy,” Rigney told me. This kind of unrestrained empathy, he writes, is a recipe for cultural mayhem. In theory, Rigney argues that one should be “tethered” to God’s will and not to Satan. In practice, what Rigney is typically decrying is any empathy for a liberal perspective or for someone who’s part of a marginalized community. When I asked him for a general reconciliation of his views with the Golden Rule, he sent me a response in which he brought up trans identity in order to label it a “fantasy” that contradicts “God-given biological reality,” while misgendering a hypothetical trans person. The demonization of empathy moves into the mainstreamDespite receiving firm pushback from most religious leaders (and indeed most people) who hear about it, Rigney’s argument has been spreading through the Christian right at large. Last year, conservative personality and author Allie Stuckey published Toxic Empathy: How Progressives Exploit Christian Compassion, in which she argues that “toxic empathy is a dangerous guide for our decisions, behavior, and public policy” while condemning queer people and feminists. “Empathy almost needs to be struck from the Christian vocabulary,” Josh McPherson, host of the Christian-centered Stronger Man Nation podcast and an adherent of Wilson and Rigney’s ideas, said in January, in a clip that garnered an outsize amount of attention relative to the podcast episode itself. That same month, Vice President JD Vance struck a nerve with a controversial appearance on Fox News in which he seemed to reference both the empathy conversation and the archaic Catholic concept of “ordo amoris,” meaning “the order of love.” As Vance put it, it’s the idea that one’s family should come before anyone else: “You love your family, and then you love your neighbor, and then you love your community, and then you love your fellow citizens in your own country,” he said. “And then after that, you can focus and prioritize the rest of the world.” In a follow-up on X, he posted, “the idea that there isn’t a hierarchy of obligations violates basic common sense.” Vance’s statements received backlash from many people, including both the late Pope Francis and then-future Pope Leo XIV — but the controversy just drove the idea further into the mainstream. As part of the odd crossover between far-right religion and online reactionaries, it picked up surprising alliances along the way, including evolutionary biologist turned far-right gadfly Gad Saad. In January, Saad, applying a survival-of-the-fittest approach to our emotions, argued against “suicidal empathy,” which he described as “the inability to implement optimal decisions when our emotional system is tricked into an orgiastic hyperactive form of empathy, deployed on the wrong targets.” (Who are the wrong targets according to Saad? Trans women and immigrants.)In a February appearance on Joe Rogan’s podcast, Elon Musk explicitly referenced Saad but went even further, stating, “The fundamental weakness of Western civilization is empathy — the empathy exploit. They’re exploiting a bug in Western civilization” — the “they” here being the left wing. “I think empathy is good,” Musk added, “but you need to think it through, and not just be programmed like a robot.” By March, mainstream media had noticed the conversation. David French had observed the “strange spectacle” of the Christian turn against empathy in a column for the New York Times. In April, a deep-dive in the Guardian followed. That same month, a broad-ranging conversation in the New Yorker with Albert Mohler, president of the Southern Baptist Theological Seminary, led to interviewer Isaac Chotiner pressing him about why empathy is bad. The discussion, of deported Venezuelan immigrants wrongfully suspected of having gang tattoos, led to Mohler saying that “there’s no reason anyone other than a gang member should have that tattoo.” (Among the tattoos wrongly flagged as gang symbols were the words “Mom” and “Dad” on the wrists of one detainee.)The pro-empathy backlash is fierce The connective tissue across all these disparate anti-empathy voices is two-fold, according to Christian scholar Karen Swallow Prior. Prior, an anti-abortion ethicist and former longtime Liberty University professor, singled out the argument’s outsize emphasis on attacking very small, very vulnerable groups — as well as the moment in which it’s all happening.“The entire discourse around empathy is backlash against those who are questioning the authority of those in power,” she told me, “not coincidentally emerging in a period where we have a rise in recognition of overly controlling and narcissistic leaders, both in and outside the church.” Those people “understand and appreciate empathy the least.”“Trump made it okay to not be okay with culture,” Peter Bell, co-creator and producer of the Sons of Patriarchy podcast, which explores longstanding allegations of emotional and sexual abuse against Doug Wilson’s Christ Church, told me. (Wilson has denied that the church has a culture of abuse or coercion.) “He made it kind of cool for Christians to be jerks,” Bell said. “He made the unspoken things spoken, the whispered things shouted out loud.”Prior believes that the argument won’t have a long shelf life because Rigney’s idea is so convoluted. Yet she added that it’s born out of toxic masculinity, in an age where stoicism, traditionally male-coded, is increasingly part of the regular cultural diet of men via figures like Jordan Peterson. That hypermasculinity goes hand in hand with evangelical culture, and with the ideas Rigney borrowed from Friedman about too many emotions being a weakness. In this framing, emotion becomes non-masculine by default — i.e., feminine.“Everybody’s supposed to have sympathy for the white male, but when you show empathy to anyone else, suddenly empathy is a sin.”— Karen Swallow Prior, Christian scholarThat leads us to the grimmest part of Rigney’s “untethered empathy” claims: the way he explicitly genders it — and demonizes it — as feminine. Throughout his book, he argues that women are more empathetic than men, and that as a result, they are more prone to giving into it as a sin. It’s an inherently misogynistic view that undermines women’s decision-making and leadership abilities. Though Rigney pushed back against this characterization in an email to me, arguing that critics have distorted what he views as merely “gendered tendencies and susceptibility to particular temptations,” he also couldn’t help reinforcing it. “[F]emale tendencies, like male tendencies, have particular dangers, temptations, and weaknesses,” he wrote. Women thus should recognize this and “take deliberate, Spirit-wrought action to resist the impulse to become a devouring HR department that wants to run the world.”As Prior explains, though, Rigney’s just fine with a mythic national human resources department, as long as it supports the status quo. “Everybody’s supposed to have sympathy for the white male,” she said, “but when you show empathy to anyone else, suddenly empathy is a sin.”What’s heartening is that, whether they realize what kind of dangerous extremism undergirds it, most people aren’t buying Rigney’s “empathy is sin” claim. Across the nation, in response to Rigney’s assertion, the catchphrase, “If empathy is a sin, then sin boldly” has arisen, as heard in pulpits, seen on church marquees, and worn on T-shirts — a reminder that it takes much more than the semantic whims of a few extremists to shake something most people hold in their hearts.See More:

These days, job titles like data scientist, machine learning engineer, and Ai Engineer are everywhere — and if you were anything like me, it can be hard to understand what each of them actually does if you are not working within the field. And then there are titles that sound even more confusing — like quantum blockchain LLM robotic engineer (okay, I made that one up, but you get the point). The job market is full of buzzwords and overlapping roles, which can make it difficult to know where to start if you’re interested in a career in machine learning. In this article, I’ll break down the top machine learning roles and explain what each one involves — plus what you need to do to prepare for them. Data Scientist What is it? A data scientist is the most well-known role, but has the largest range of job responsibilities. In general, there are two types of data scientists: Analytics and experiment-focused. Machine learning and modelling focused. The former includes things like running A/B tests, conducting deep dives to determine where the business could improve, and suggesting improvements to machine learning models by identifying their blind spots. A lot of this work is called explanatory data analysis or EDA for short. The latter is mainly about building PoC machine learning models and decision systems that benefit the business. Then, working with software and machine learning engineers, to deploy those models to production and monitor their performance. Many of the machine learning algorithms will typically be on the simpler side and be regular supervised and unsupervised learning models, like: XGBoost Linear and logistic regression Random forest K-means clustering I was a data scientist at my old company, but I mainly built machine learning models and didn’t run many A/B tests or experiments. That was work that was carried out by data analysts and product analysts. However, at my current company, data scientists don’t build machine learning models but mainly do deep-dive analysis and measure experiments. Model development is mainly done by machine learning engineers. It all really comes down to the company. Therefore, it is really important that you read the job description to make sure it’s the right job for you. What do they use? As a data scientist, these are generally the things you need to know (it’s not exhaustive and will vary by role): Python and SQL Git and GitHub Command Line (Bash and Zsh) Statistics and maths knowledge Basic machine learning skills A bit of cloud systems (AWS, Azure, GCP) I have roadmaps on becoming a data scientist that you can check out below if this role interests you. How I’d Become a Data Scientist (If I Had to Start Over) Machine Learning Engineer What is it? As the title suggests, a machine learning engineer is all about building machine learning models and deploying them into production systems.  It originally came from software engineering, but is now its own job/title. The significant distinction between machine learning engineers and data scientists is that machine learning engineers deploy the algorithms. As leading AI/ML practitioner Chip Huyen puts it: The goal of data science is to generate business insights, whereas the goal of ML engineering is to turn data into products. You will find that data scientists often come from a strong maths, statistics, or economics background, and machine learning engineers come more from science and engineering backgrounds. However, there is a big overlap in this role, and some companies may bundle the data scientist and machine learning engineer positions into a single job, frequently with the data scientist title. The machine learning engineer job is typically found in more established tech companies; however, it is slowly becoming more popular over time. There also exist further specialisms within the machine learning engineer role, like: ML platform engineer ML hardware engineer ML solutions architect Don’t worry about these if you are a beginner, as they are pretty niche and only relevant after a few years of experience in the field. I just wanted to add these so you know the various options out there. What do they use? The tech stack is quite similar for machine learning engineers as for data scientists, but has more software engineering elements: Python and SQL, however, some companies may require other languages. For example, in my current role, Rust is needed. Git and GitHub Bash and Zsh AWS, Azure or GCP Software engineering fundamentals like CI/CD, MLOps and Docker. Excellent machine learning knowledge, ideally a specialism in an area. AI Engineer What is it? This is a new title that cropped up with all the AI hype going on now, and to be honest, I think it’s an odd title and not really needed. Often, a machine learning engineer will do the role of an AI engineer at most companies. Most AI engineer roles are actually about GenAI, not AI as a whole. This distinction normally makes no sense to people outside of the industry.  However, AI encompasses almost any decision-making algorithm and is larger than the machine learning field. Image by author. The current definition of an AI engineer is someone who works mainly with LLM and GenAI tools to help the business. They don’t necessarily develop the underlying algorithms from scratch, mainly because it’s hard to do unless you’re in a research lab, and many of the top models are open-sourced, so you don’t need to reinvent the wheel. Instead, they focus on adapting and building the product first, then worrying about the model fine-tuning afterwards. So, they wu It is a lot closer to traditional software engineering than the machine learning engineer role as it currently stands. Although many machine learning engineers will operate as AI engineers, the job is new and not fully fleshed out yet. What do they use? This role is evolving quite a bit, but in general, you need good knowledge of all the latest GenAI and LLM trends: Solid software engineering skills Python, SQL and backend langauges like Java or GO are useful CI/CD Git LLMs and transformers RAG Prompt engineering Foundational models Fine tuning I also recommend you check out Datacamp’s associates AI engineer for data scientist track, that will also set you up nicely for a career as a data scientist. This is linked in the description below. Research Scientist/Engineer What is it? The previous roles were mainly industry positions, but these next two will be research-based. Industry roles are mainly associated with business and are all about generating business value. Whether you use linear regression or a transformer model, what matters is the impact, not necessarily the method. Research aims to expand the current knowledge capabilities theoretically and practically. This approach revolves around the scientific method and deep experiments in a niche field. The difference between what’s research and industry is vague and often overlaps. For example, a lot of the top research labs are actually big tech companies: Meta Research Google AI Microsoft AI These companies initially started to solve business problems, but now have dedicated research sectors, so you may work on industry and research problems. Where one begins and the other ends is not always clear. If you are interested in exploring the differences between research and industry more deeply, I recommend you read this document. It’s the first lecture of Stanford’s CS 329S, lecture 1: Understanding machine learning production. In general, there are more industry positions than research, as only the large companies can afford the data and computing costs. Anyway, as a research engineer or scientist, you will essentially be working on cutting-edge research, pushing the boundaries of machine learning knowledge. There is a slight distinction between the two the jobs. As a research scientist, you will need a Phd, but this is not necessarily true for a research engineer. A research engineer typically implements the theoretical details and ideas of the research scientist. This role is usually at large, established research companies; in most situations, the research engineer and scientist jobs are the same though. Companies may offer the research scientist title as it gives you more “clout” and makes you more likely to take the job. What do they use? This one is similar to machine learning engineering, but the depth of knowledge and qualifications is often greater. Python and SQL Git and GitHub Bash and Zsh AWS, Azure or GCP Software engineering fundamentals like CI/CD, MLOps and Docker. Excellent machine learning knowledge and a specialism in a cutting-edge area like computer vision, reinforcement learning, LLM, etc. PhD or at least a master’s in a relevant discipline. Research experience. This article has just scratched the surface of machine learning roles, and there are many more niche jobs and specialisms within these four or five I mentioned. I always recommend starting your career by getting your foot in the door and then pivoting to the direction you want to go. This strategy is much more effective than tunnel vision for only one role. Another thing! I offer 1:1 coaching calls where we can chat about whatever you need — whether it’s projects, Career Advice, or just figuring out your next step. I’m here to help you move forward! 1:1 Mentoring Call with Egor HowellCareer guidance, job advice, project help, resume reviewtopmate.io Connect with me YouTube LinkedIn Instagram Website The post Top Machine Learning Jobs and How to Prepare For Them appeared first on Towards Data Science.