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Trump says he ordered DoJ to file lawsuit to block Alaska Senate results certification
Trump justice department claims there could be ‘confusion’ about two Republicans on the ballot named Dan Sullivan
Donald Trump said he ordered the justice department to file a lawsuit to block the certification of Alaska’s US Senate results if the Republican incumbent loses, a move that voting rights experts called an “unprecedented intervention” by the federal government with control of Congress in the balance.
“I’m the one that said do it,” the president told reporters as he landed in Florida for a campaign rally on Friday.
Continue reading...How a Fake Pollster May Have Hurt Real Candidates
A pollster admitted this week to using fabricated data in its surveys that were picked up by leading publications across the U.S., claiming the stunt was an “experimental project” intended to showcase the “vulnerabilities” of the polling industry. The announcement has shaken trust in polling infrastructure ahead of the midterms, especially as some candidates say their campaigns were directly harmed by the fake numbers.
After election analysis website FiftyPlusOne submitted an inquiry to Wedgewood Polls regarding the authenticity of its data and methodologies, the pollster put out a statement across social media on Tuesday, saying it had been conducting a test to show "how easy is it for an entirely fabricated polling firm to gain traction, be taken seriously, and expose how little scrutinization exists in media and public commentary?" In the statement, which has since been deleted, the group claimed it had been “far too easy” to distribute false data and insisted it "highlights a systemic breakdown in media vetting and data aggregation.”
Wedgewood's polls had been used by multiple national outlets for their midterm reporting, including the New York Times, Newsweek, MS NOW, and POLITICO. The Times, which cited around 15 Wedgewood polls between June and October, has since removed the data from its stories. In an accompanying editor’s note, the publication detailed its checking process for polls and said Wedgewood had answered several of their inquiries prior to the data being published, including questions on the identities of the group’s pollsters, the methods used to draw their samples, and the source of their funding.
TIME's email to Wedgewood Polls bounced back.
In another fraudulent polling operation uncovered in August, a company called Median Strategies fabricated polls for races in Wisconsin, Nevada, and California and similarly called the ploy a “social experiment” in a statement to the Los Angeles Times.
“It's very troubling that we've had two examples of this within a very short period of time during election campaigns, creating a lot of problems for legitimate polling,” says Michael Traugott, a professor emeritus of political science at the University of Michigan, who previously served as president of the American Association for Public Opinion Research (AAPOR).
Here’s what to know about the Wedgewood scandal, and what it says about polling ahead of the pivotal midterm elections.
What do we know about Wedgewood Polls?
The polling group called itself a “fictional entity run by a small group of industry veterans” in its statement. Its X account, which contained many of its polls, no longer exists.
G. Elliot Morris, a pollster and journalist who works at FiftyPlusOne, tells TIME that he and his colleagues had been suspicious about a “crop of new polling firms” that have emerged and released anonymous data. An individual eventually tipped off FiftyPlusOne to Wedgewood's polling, which was “the straw that broke the camel's back for us,” he says.
FiftyPlusOne said in a public statement that Wedgewood “operated as if it were a legitimate polling firm” by collecting money from donors to carry out crowd-funded polls and share its survey data.
The poll analysis group said Wedgewood disclosed two names in connection to the company: Jack Sims and Andrew Prater. According to FiftyPlusOne, Sims had prior experience in the polling industry. SoCal Strategies, another polling firm, this week acknowledged in an email to an NPR affiliate in Michigan that Sims had worked there, but is not currently employed by the company and “has not been for a year.”
“It is not atypical for a pollster to leave an existing firm, form their own, and self-fund some surveys to build reputation; we had no reason at the time to believe this case was any different,” FiftyPlusOne said regarding Sims.
“This event does not help what is a decades-long trend of declining confidence in the polling industry,” Morris added, when asked about Sims’ involvement with Wedgewood. TIME has been unable to reach Sims for comment.
Senate candidate in Michigan says fake polling damaged her campaign
The polling scandal has sparked a flurry of reactions within the politics and government sector, with one former Senate hopeful claiming the numbers circulated by Wedgewood contributed to the collapse of her campaign.
Democratic Michigan state Sen. Mallory McMorrow recalled how a poll from Wedgewood had shown her “plummeting into single digits.”
Wedgewood released polling on June 29 that placed McMorrow as having the support of only 9% of registered voters, leaving her trailing behind Haley Stevens and Abdul El-Sayed. Other polls also showed McMorrow in third place with single digits. A Susquehanna Polling survey conducted June 9 to 14 put McMorrow at 9%, although 49% of Democratic primary voters surveyed were undecided. A July 1 Quantus Insights poll put her at under 8%. McMorrow suspended her campaign on July 5, just one month before the primaries.
In a statement posted to social media on Oct. 20, McMorrow said donations “dried up” and she was “hounded” with calls to exit the race after the circulation of the “infamous” Wedgewood poll numbers.
Advising others not to trust the polls, McMorrow said she hoped Democrats are ready to “blow up the system with campaign finance and transparency reforms.”
Jennifer Holdsworth, a Democratic strategist who ran a SuperPAC that supported McMorrow, claimed the fake poll had been “used by several donors to pull back funding at a time we were being outspent by $80 million.”
Amid widespread discussions over fabricated data, Federal Communications Commission (FCC) Chair Brendan Carr said, without naming any specific instance, that there has been a “significant surge” in fake polls and blamed news outlets for “amplifying or airing” them “without regard to basic journalistic ethics.”
Democratic FCC Commissioner Anna Gomez quipped back and shifted the onus onto fraudulent pollsters themselves.
“Pollsters who intentionally mislead the public should answer for it, but broadcasters who report publicly available polling data should never be punished when a poll is wrong,” she said in response to Carr. “FCC rules on news distortion or hoaxes do not apply to a station that reports this data in good faith.”
What impact can polls have on elections?
While documented fake polls are rare, according to experts, polling in general often plays a large role in shaping the narrative of hotly contested races.
Traugott tells TIME that polls can affect voting behavior and support for campaigns “because by and large, the average citizen is interested in backing the winner, and backing means not just votes; it also means financial contributions, contributing time and effort to the campaign, and the like.”
Research has demonstrated what is known as the “bandwagon effect,” in which voters become more likely to vote for candidates or issues that are more favored in polls. A 2023 study from the Journal of Elections, Public Opinion and Parties showed that polls discouraged support for less popular ballot items, or as the paper’s authors put it: “polls demobilize the minority and mobilize the majority.”
It also found that moderate voters, who often represent a crucial demographic of voters and can swing tight elections, were the most impressionable when it comes to being influenced by polling. “Social conformity is strongest among ideological moderates,” the study said.
This effect has been documented going back decades.
A 1996 experiment showed that participants who were shown a fabricated poll ahead of that year’s primary had a “significantly greater tendency” to vote for the candidates favored by the poll.
This phenomenon has similarly been demonstrated in prediction markets, which have been shown to sway voters almost as much as polls. A study released on Oct. 20 by policy think-tank the Brennan Center showed that voters were 10% more likely to say that a candidate would win in an election if that person had higher odds of winning based on a prediction market.
Likewise, voters were less likely to believe that a candidate would win if their odds were predicted to be lower. These prediction market odds, the study said, had a similar effect on perceived electability as traditional polls.
Traugott says that generally speaking, voters who identify with a specific party are unlikely to change their vote as a result of polling, but that they might not show up to the polls at all if they see their party’s nominee is disfavored to win.
“It's often the case in the general election that the polls have an influence in terms of turnout,” he says.
Should you trust polls?
Michael Dimock, the president of the Pew Research Center, says that in tight races, of which there are many in the upcoming midterms, “it's very easy for polls to ‘pick the wrong winner.’”
“I do think that people should be very cautious in over-interpreting pre-election polls' ability to project an election outcome, whether it's at a national level, state level, a district level, [or] local level,” he tells TIME.
One of the main reasons for this, Dimock contends, is that even if participants in a survey say who they would vote for, they still might not show up to cast their ballot. He refers to this immeasurable but important factor as “enthusiasm differences.”
“What polls are really bad at capturing is who's going to actually show up and cast a ballot. It's really hard,” Dimock adds. “Human beings are bad at predicting their [own] future behaviors.”
Criminals recruiting children to damage Greater Manchester HMOs, police believe
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Criminals are recruiting vulnerable people, including children, to damage houses suspected of being HMOs, police in Manchester believe.
In recent weeks, dozens of properties have been targeted across Greater Manchester, mostly in Salford, but also in Stockport and Tameside.
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Clean Energy Has a Lot Riding on the AI Data Center Boom
Recent weeks have brought red flag after red flag for investment in AI and the related data center boom. Bond yields soared for AI infrastructure giant CoreWeave, a sign that investors view the buildout as increasingly risky. And Meta’s reporting of negative free cash flow for the quarter to fund its infrastructure buildout while saying it might need to raise even more capital to fund its AI buildout not only hit its own stock but contributed to a price decline across the big tech companies.
These developments raised questions across Wall Street about whether too much is being spent too quickly on data center expansion. How questions over data center investment play out has big implications for how companies and investors approach the future of energy and, by extension, climate. A pullback in the pace of new data centers could ease pressure to build fossil generation, but it would also weaken the commercial case for clean energy technologies that need big, reliable buyers who are willing to pay a premium for power.
Most obviously, a dent in the AI buildout would translate directly into a slowdown in related electricity demand growth. In advanced economies like the U.S., data centers will account for 20% of that growth through 2030. By then, data centers are expected to account for 3% of global electricity demand, according to data from the International Energy Agency.
Some climate advocates may initially rejoice at slowing electricity demand. Even as renewable energy expands, grids around the world still rely on fossil fuels. In the U.S. in particular, AI demand has sparked a wave of building new natural gas infrastructure. In the short term, slowing demand would mean lower emissions from the existing system than today’s baseline assumptions and less investment in new natural gas infrastructure.
But a slowing data center build out leaves less mature technologies at risk. From advanced geothermal to new ways of producing nuclear power, big tech companies have been willing to pay more to lock in reliable power sources, thereby accelerating the market for these new efforts. Even unproven technologies like nuclear fusion have received a big financial boost from tech firms speculating that they might help solve their power challenges and give them a competitive leg up in the AI race. In a tighter financial environment with lower projected electricity demand, both the incentive and the financial capacity to make bold bets diminish.
To be clear, data centers are not the only source of growing electricity demand. New manufacturing continues to create the need for power. The need for air conditioning has ramped up in many emerging markets, particularly in the Global South, creating new grid pressures. And electric vehicles continue to take off in many markets outside the U.S.
Even as gas remains resilient, much of the power built to meet demand will come from clean sources, including and especially solar and battery storage. Solar is increasingly cost competitive. And this year’s spike in fuel prices—particularly natural gas—has made local power more appealing. But the companies behind growing sectors like air conditioning and EVs are less involved in local power systems and lack the capital to really shape them like the world’s wealthiest tech companies have. In short, clean energy will take a hit without the backing of big tech.
It’s too soon to tell whether, in the long run, this week’s AI infrastructure recalibration will be a blip in time or part of a much broader realignment. But it’s something that anyone invested in clean energy and climate should be watching closely.
Легкое чтиво, для выходных
До чего дошел прогресс
Труд физический исчез
Да и умственный заменит
Механический процесс
Позабыты хлопоты
Остановлен бег
Вкалывают роботы
Счастлив человек!
Слова из саундтрека к фильму «Приключения Электроника», очень популярный был фильм в моем детстве.
Много в последнее время за ИИ, за нейросети говорят… чуть ли не безработицу с концом света обещают. Оно конечно все может быть, только вот по моим наблюдениям искусственный интеллект (к слову название та какое, искусственный интеллект, кто бы мне объяснил, что такое интеллект естественный?), так вот, ии все больше на Интеллектуальные профессии претендует. В шахматы или го поиграть, математику посчитать, код создавать, картины или рассказы написать…, а вот штрабить или кабели воздухом или канальей тянуть как то не очень часто встречается… В продолжение этой мысли, пара-тройка юмористически-фантастических рассказов, под общим названием: Салага на производстве. Рассказики легкие, на высокую литературу не претендуют, для вечернего отдыха. Сильно строго не критикуйте.
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Should you let a chatbot manage your money?
A complaint I hear a lot from my millennial peers is that our parents didn’t teach us enough about money. I can say that personal finance wasn’t dinner table conversation in my house, and even as I went off to college and started fending for myself, it was almost a taboo subject. When it came to money, people either had it or they didn’t, and you definitely didn’t talk about it.
Maybe this is why the average American isn’t exactly fluent in personal finance, and the situation is getting worse. Americans can only answer about 50 percent of basic questions about borrowing, savings, and risk on a financial literacy survey, according to a 2026 study by TIAA, a retirement services company. About a third of those surveyed said they have a hard time making ends meet and are debt-constrained. These figures have remained relatively unchanged for nearly a decade, but dropped to their lowest level in this year’s study.
It makes sense: It’s hard to learn about money when it’s not taught in schools, and it’s such an intensely private topic. It’s also hard to make good financial decisions when you’re one of the growing number of American households struggling to pay bills, repay debts, and save for the future — and feeling very stressed about it.
In a perfect world, each of us could sit down with a financial adviser to get help — but most of us can’t afford that. Instead, average Americans tend to find personal finance tips on TikTok, in Reddit threads, by watching Suze Orman on YouTube or How to Get Rich on Netflix, or by Googling their questions.
Now we have AI tools, too.
With access to an internet’s worth of information and financial advice, ChatGPT, Claude, and other AI chatbots can answer any question you ask. They can help you put together a basic budget or, armed with your bank statements and the details of your student loans and your retirement accounts, tailor solutions to your specific situation. Or, at least, that’s the promise.
Key takeaways
- More people are turning to AI for financial advice: One in five Americans have used AI to help manage their money, and some experts think chatbots could be better than the status quo.
- AI works best if you know what questions to ask: Those with basic financial literacy will be able to make the most of these tools, while those without it — who might need help the most — could be led astray.
- Better advice comes with greater risks: Sharing your financial information with a chatbot helps personalize its recommendations. This raises privacy concerns, so you might want to delete your data when you’re done.
- Think of AI as a financially savvy friend: Chatbots can answer money questions you might be embarrassed to ask someone else and use that context to help build your budget.
AI could create a more utopian, more equitable future, where reliable, hyper-personalized advice is just a prompt or two away for people who can’t afford to meet with an expert in real life. But machines are built by people, and there’s good reason to be concerned that these machines will simply replicate the biases and disparities built into our current system.
Here’s what experts say about how to make the most of these tools — and how to think about all of the inherent risks of uploading a lot of personal financial information to a chatbot or, worse, giving an AI agent the keys to your bank account.
The steep rise, risks, and rewards of AI financial advisers
Nearly 20 percent of Americans have used AI tools like ChatGPT for financial advice, and close to 25 percent for Gen Z adults and millennials have tried it, according to an August Gallup poll. These numbers could tick up too: In October, OpenAI started rolling out a dedicated personal finance tool to its ChatGPT Free and Go users in the United States, which effectively means anyone can try it. ChatGPT Finances lets you securely connect your bank account to the tool using a platform called Plaid, and then you can basically have a conversation with the chatbot about your real-time finances down to the penny. It’s like an AI-powered Mint (RIP) that you can talk to directly.
Regardless of where you encounter a chatbot or how much data you give it, experts say these tools could genuinely help improve financial literacy. You can talk to them about money problems you’re too embarrassed to discuss with friends and family, or ask questions and receive actionable, personalized information. There are real upsides to using AI this way.
Taha Choukhmane, an associate professor of finance at the MIT Sloan School of Management, has been studying outcomes when people turn to chatbots for financial advice. In general, these AI tools give pretty good advice, and that advice gets better as people become more practiced in asking the right question, according to research he published earlier this year.
“We were actually quite surprised that relative to the status quo, the average American would be better off if they were to follow the advice,” Choukhmane told me. But he added a caveat: “Just following the advice without understanding the advice puts you at risk of not fully understanding the risk.”
But you shouldn’t trust everything a chatbot tells you and should verify everything before making any decisions.
Chatbot advice has some significant limitations. First, it’s not impartial. The finance industry has a vested interest in steering you toward certain ways of managing your money, and that typically means selling you products, like loans. If you’ve ever searched for personal finance tips on Google, you’ve probably noticed that a lot of the top results come from banks and other financial institutions. This is by design, according to John Campbell, an economics professor at Harvard. But because AI is aggregating all of that advice, the specific influences and possible bias is less obvious.
“AI is very promising for providing low-cost financial advice in plain English that will arguably be better than what many people would be able to do on their own,” said Campbell, who’s also the co-author of Fixed: Why Personal Finance Is Broken and How to Make It Work for Everyone. “One caveat is that chatbot advice is quite likely — unless a model has been carefully trained — to reflect the average of whatever’s being posted on the internet, and that may or may not be first-rate advice.”
An example Campbell gives is the question of when to start taking Social Security benefits. While you’re eligible at age 62, many economists agree that you should delay your benefits, which, based on the way the government calculates your monthly payouts, will allow you to enjoy much higher benefits later in life. But paid financial advisers can recommend starting as early as possible, since waiting longer reduces the amount of money that needs to be managed, which reduces the adviser’s fees. That means there’s a lot of conflicting advice online, much of it supplied by the finance industry. And that’s the information used to train AI models.
“The advice is much more mixed,” Campbell said. “And so if a chatbot is learning from that, it may not actually give the best advice.”
It can be hard to recognize bad advice anywhere in life, but especially when you’re talking to a machine that’s programmed to please you. A chatbot could encourage you to consider a balance transfer, to lean in on credit card rewards, or to refinance your mortgage — all decisions with upsides and downsides for you, but definite upsides for banks — without you realizing how much of its training data comes from the finance industry selling these products. What’s worse is that if you do end up taking bad advice, it might be years before you realize it. You won’t know that you refinanced a mortgage at a bad time until a better time comes along.
That leads me to the other major issue with using chatbots for money advice: People who lack financial literacy are the most likely to fall for bad advice, because they simply don’t recognize it as such. They also tend to be less wealthy, which means they can’t afford big mistakes.
One way to mitigate risk, oddly enough, is to give the AI chatbot more personal information. The more details about your situation it has to consider, the more it can offer personalized advice. More context also means better advice, according to Choukhmane.
“There’s a trade-off there,” Choukhmane said. “There’s real risk around privacy, and so I think everyone needs to find their level of control.”
Don’t assume your data is safe
Any time you’re talking to a chatbot, you’re putting a lot of trust in tech companies to take care of the personal details you’re sharing. Sending sensitive financial data is an especially risky proposition. While tech companies, including OpenAI, promise to keep your data secure, there’s already a long list of breaches, leaks, and privacy violations that serve as cautionary tales.
It’s also impossible to know what tech companies might do with your data in the future. Most of them will use it to train their models, unless you tell them not to. Some of them will use the data to serve you personalized ads.
Then there’s the risk of bad actors getting into your ChatGPT, Claude, or Gemini account. If you’ve been using a chatbot for financial advice and uploading your bank statements, a hacker could gain access to a stunning amount of information that could enable them to do all kinds of terrible things to your life.
“They could steal your identity and open accounts and then quickly bankrupt a person,” said Delicia Reynolds Hand, a senior director at Consumer Reports. “It’s not even so much the primary underlying accounts that could be at risk, it’s more just access to this information.”
The steady march of AI progress is making this situation even more complicated. The previous era of chatbots offering financial advice is quickly bleeding into a new phase of AI agents logging into bank accounts on users’ behalf. If you’re using a tool like Claude Cowork or even an AI assistant like Muse, you can give an agent the login credentials to your bank account, and because the agent is operating its own computer, it can actually make transfers, apply for loans, or really do anything in your account. This is reportedly driving major banks wild, since they don’t know which logins are coming from humans and which are agents. And because we know that AI agents are unpredictable, it’s hard to know how that access might go wrong.
Beyond data security and privacy, you should also be aware of the simple fact that nobody’s really at fault if you end up losing money using these tools. Chatbots are not regulated in the way that human financial advisers, who have a fiduciary duty to act in your best interest, are.
“If I get bad tax advice from H&R Block or a tax adviser, there are a clear set of rules and guardrails in place to manage what I can expect in that relationship and where the service they use has liability and where that liability ends,” Hand said. “Currently, that’s not the case for these tools.”
Trust but verify — and then delete your data
In the final years of the Cold War, Ronald Reagan popularized an old Russian proverb: Trust, but verify. The phrase is somewhat nonsensical, since trusting someone implies that you don’t need to verify what they’re saying, and verifying what’s said implies you didn’t trust them in the first place. But if you are willing to shoulder a bit of risk, consider approaching AI this way, especially when your life savings are on the line. I’d also suggest adding a coda to the phrase: And then delete your data.
Chatbots are not stand-ins for licensed financial advisers, he said, and you shouldn’t let them make decisions for you.
The great paradox with tools like ChatGPT and Claude is that they’re very good at generating truthy text. It sounds authoritative and looks correct — and usually is. Except when it isn’t. AI is famously unpredictable and prone to hallucinations. You can ask the same question twice and get two different answers. That doesn’t mean these tools are useless. It just means you have to approach them with caution. You have to interrogate these chatbots, challenge whatever advice they give you, and then crosscheck that with other, more reputable, sources.
“If you’ve never used these tools, I would not encourage you to just change all your financial plans based on your first conversation,” Choukhmane said. “You want to be quite careful. Start using these tools, start understanding, ask a lot of follow-up questions.”
When it comes to mitigating risk, consider giving ballpark figures for your income or spending when you’re talking to a chatbot about your finances. Uploading financial documents, even if they’re redacted, is inevitably filling a tech company’s vault with your sensitive information without necessarily knowing what will happen to it next. You should also dig into your privacy and security settings to make sure you’ve turned on multi-factor authentication and that your personal data is not being used to train AI models and gets deleted after 30 days.
Ethan Bloch, the product lead for ChatGPT for Financial Services, made a similar set of recommendations when I talked to him recently. Chatbots are not stand-ins for licensed financial advisers, he said, and you shouldn’t let them make decisions for you. They’re designed to inform and help you think through complex topics.
“You should liken it to just talking to a friend who has some very informed opinions about finance,” Bloch told me. “But if you’re actually making some large financial decision — your portfolio or retirement — you still need to seek out the help.”
Regardless of how experienced you are with chatbots or how much data you’re prepared to hand over, remember what Campbell, the Harvard professor, said. If your financial literacy isn’t great, AI chatbots can be trouble. And if you’re like most Americans, you could probably stand to learn a little bit more about things like mortgages and interest rates.
Yes, Chatbots can be a good way to brush up on financial literacy. You can ask them the money questions you’re too embarrassed to discuss with friends and family. But you can also learn about money the old-fashioned way: Reading books and websites written by reputable, independent sources. The Federal Deposit Insurance Corp. has a portal of financial education resources, including games and quizzes. Khan Academy has a course that covers everything from credit scores to college savings. There’s a personal finance subreddit that I joined years ago and read almost daily. There are budgeting apps, like YNAB which has its own huge Reddit presence.
Whatever you do to learn more about personal finance, simply doing something is progress. Talking about personal finance isn’t the taboo it once was in the US, and it’s even a required course in the vast majority of American high schools now. And while AI won’t magically solve your money problems, it can help get you started. Just be careful. Your livelihood could be on the line.
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Как я провёл второе соревнование по программированию на Эльбрусах (e2k) «Кубок СЭРПАС 2026»
Здравствуйте, друзья, меня зовут Ерохин Кирилл, я популяризатор российского программного и аппаратного обеспечения, и в этом сентябре я провёл уже второе, теперь ежегодное, соревнование по алгоритмическому программированию на отечественных аппаратных платформах «Кубок СЭРПАС 2026», собрав 112 участников со всей России, результатами которого пришлось поделиться с вами. А Хабр мне в этом поможет, ему не впервой.
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Ipswich 1-1 Nottingham Forest: Premier League – live
⚽ Updates from the 8pm BST kick-off at Portman Road
⚽ Ten things to look out for | How they stand | Mail Scott
2 min: It’s windy in Suffolk. Walton batters a clearance upfield as best he can. Forest very much playing with the wind at their back in this first half.
Most everyone takes the knee … there’s no room for racism … then Forest get the ball rolling. Or flying. Gibbs-White hoicks it straight out for a goal kick.
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Объективное сравнение платформ, где инженеры-программисты и технологические компании находят друг друга на локальном и международном удаленном рынках.
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JetBrains GameDev Day 2026: Watch the Recordings
Missed JetBrains GameDev Day 2026, or want to revisit your favorite sessions? All the recordings are now available on YouTube!
On September 28, we brought together game developers, engineers, and industry experts for a full day of technical talks, live demos, and discussions about the tools and challenges shaping modern game development.
From Epic’s open-source Lore VCS and ECS in a live game to Unreal Engine profiling, Unity UI automation, AI-driven PCG in Godot, and debugging across C# and C++, this year’s sessions stayed close to real production problems.
Whether you joined us online, attended one of our watch parties in Belgrade or Limassol, or couldn’t make it this time, you can now catch up on everything at your own pace.
Watch the full JetBrains GameDev Day 2026 playlist on YouTube
Watch the sessions
Not sure where to start? Pick your engine or role:
- Unreal Engine: Death by a Thousand Ticks and Agentic Development in Unreal Engine with JetBrains Rider
- Unity: Fix Your CI Without Leaving the IDE and How We Automated Figma-to-Unity UI Integration With AI Agents
- Godot: Prompt to Playable World
- Engine and backend programmers: Evolving a Live Game Engine and Debugging Across Two Runtimes and Two Different Languages
- Build, pipelines, and infrastructure: Splitting the Difference and Code Quality Never Changes
- Everyone, especially leads and juniors: Pass the Controller?
Pass the Controller? Communities, Data, and AI in Game Development
Anthony Tešija (Discord), Jason Weimann (Boddle Learning), Gennadii Potapov (General Arcade), and Anton Licht (Sudden Forest)
Moderated by Denis Savosin (JetBrains)
Our opening panel explored the role of communities, data, and AI in game development. When should developers listen to community feedback, and when is it better to trust their own vision? How can AI support developers without replacing critical thinking, especially for those just entering the industry?
Fix Your CI Without Leaving the IDE: TeamCity MCP and CLI (with Unity)
Artem Rokhin (JetBrains)
Discover how TeamCity’s MCP support and CLI bring continuous integration workflows directly into your development environment. Using a Unity project as an example, Artem demonstrated how to investigate build failures and interact with CI pipelines without constantly switching between tools.
Learn more: TeamCity MCP and CLI for AI agents
Evolving a Live Game Engine: Introducing ECS Without a Full Rewrite
Alex Zhorau (Meta)
How do you bring an Entity Component System into a mass-market game that’s already live without rewriting the engine? Drawing on proprietary engines and Unreal Engine, Alex walked through a staged migration, the compromises around memory layout and scripting, and why a hybrid of ECS and legacy code is often where a live game should end up, not just a stop along the way.
Code Quality Never Changes: How Static Analysis Can Help in the Age of AI
Kai Schmithusen (JetBrains)
AI-generated code brings new productivity opportunities, but also new challenges for code quality. Learn how static analysis can help teams identify issues, manage technical debt, and maintain reliable development workflows, with practical examples using Qodana.
Learn more: Qodana
Prompt to Playable World: Live-Building AI-Driven PCG with Godot, C#, and Gemini
Sharmishtha Sen (Jet Heart Labs)
Watch a procedural world generation pipeline come together using Godot, C#, and AI. Sharmishtha demonstrated how to turn natural-language prompts into playable environments while combining AI-generated configurations with traditional procedural generation techniques and Rider’s development tools.
Learn more: Godot support in JetBrains Rider
Death by a Thousand Ticks: Why Gameplay Systems Become Slow
Konrad Słoń (Fool’s Theory)
Performance problems rarely appear overnight. Through Unreal Engine examples and live profiling, Konrad explored how small engineering decisions accumulate into gameplay bottlenecks and demonstrated practical optimization workflows using Unreal Insights and Rider.
How We Automated Figma-to-Unity UI Integration With AI Agents
Alina Chupakhina (Playkot) and Olia Chupakhina (Cubic Games)
Alina and Olia shared a Figma-to-Unity pipeline that grew over years of production and now runs through an AI agent over MCP. They covered the design foundation that makes automation possible, the lessons learned from building it, and a benchmark of which UI integration tasks the agent can handle today and which still need a human.
Agentic Development in Unreal Engine with JetBrains Rider
Alexander Dzerakh (JetBrains)
Go beyond AI-assisted code generation with agentic workflows tailored to Unreal Engine. Through live demos, Alexander showed how Rider, ACP, and Unreal Engine-specific skills can help AI agents navigate engine architecture, generate C++ code, investigate crashes, and automate development tasks.
Learn more: AI agent skills for Unreal Engine in Rider
Splitting the Difference: How Epic’s Lore VCS Handles Massive Binary Files
Ben Cooke and Ryn Slack (AWS)
Most version control systems were built for code, not multi-gigabyte assets that change every day. Ben and Ryn explained how Epic Games’ open-source Lore VCS splits files into content-addressable fragments so only the changed part of an asset moves, why its edge caches can fill on demand instead of being pre-seeded, and how to deploy it yourself with the Lore module in the AWS Cloud Game Development Toolkit.
Learn more: Lore on GitHub · Lore documentation · AWS Cloud Game Development Toolkit
Debugging Across Two Runtimes and Two Different Languages
Alex Drum (Hytale)
Your client is C#, your server is C++, and the bug lives somewhere in between. Alex showed how to debug both runtimes at once with Rider and CLion, read the data crossing the protocol, and figure out which runtime is lying, and discussed why the boundary between them is the last place to look, not the first.
Keep learning and building
A huge thank you to all our speakers, hosts, and everyone who joined us online or at our watch parties in Belgrade and Limassol! Special thanks to DevGAMM x FTW! for co-hosting our Belgrade watch party. We hope these sessions give you new ideas, useful techniques, and inspiration for your own projects.
Watch all JetBrains GameDev Day 2026 recordings on YouTube
If you’d like to explore the tools featured throughout the event, check out JetBrains tools for game development.
Many of the demos ran in JetBrains Rider, which is free for non-commercial use, including learning, pet projects, and open-source contributions.
To hear about the next GameDev Day first, subscribe to the JetBrains GameDev newsletter.
See you at the next GameDev Day!
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