‘Fallout’ Season 2 is full of game details. Here’s a handy guide.

Tech-news December 24, 2025

Reactivate those Pip-Boys and set your Spotify listening age to 87, as Prime Video’s adaptation of post-apocalyptic game franchise Fallout is back with a second season. And of course, we’re in for more Easter eggs, needle drops, and details from Bethesda’s games that you might recognise from your travels through the Wasteland.

For Season 1, showrunners Geneva Robertson-Dworet and Graham Wagner recruited production designer Howard Cummings and set decorator Regina Graves to bring the retro-futurist and post-apocalyptic details of the Fallout games to the screen, from those instantly familiar “Please stand by” screens to the famous T-60 power armor. In Season 2, there’s more Sugar Bombs, irradiated enemies, and Vault-Tec facilities where that came from.

Fallout Season 2 includes key locations from the games including Fallout: New Vegas

Let’s gooooo.
Credit: Courtesy of Prime

The Fallout TV series covers a lot of ground within the post-apocalyptic Wasteland featured in multiple Fallout games. In Season 1, the characters came across the games’ Red Rocket gas stations, Super Duper Marts, and a thrown-together town akin to the game’s cities of Megaton, Rivet City, New Reno, and Diamond City. But in Season 2, it’s all about New Vegas, a crumbling, post-apocalyptic version of the City of Lights which forms the core setting for the third Fallout game.

While Episodes 1 and 2 don’t reach New Vegas, it’s always on the horizon, with Lucy (Ella Purnell) and Cooper Howard/The Ghoul (Walton Goggins) following her father’s trail there. But in the very first scene of the season, the pair are in quite a predicament within the The Great Khan raider hideout, a location in Fallout: New Vegas located in the Mojave Wasteland. For the Fallout show, the gang’s base is actually the Dino Dee-lite Motel, another location from Fallout: New Vegas, which features that giant T-rex.

Lucy’s sniper spot is straight out of “Fallout: New Vegas.”
Credit: Bethesda

Vault-wise, this season’s underground locations are Vaults 31, 32, and 33, all with their own predicaments and mysteries. But there are also plenty of empty vaults to explore like the game — and in episode 1, Lucy and Coop find Vault 24, a vault that was actually cut from Fallout: New Vegas (meaning the TV series has a blank backstory slate and fills it with brainwashed Americans and the game’s dreaded radscorpions). Right near the entrance to this vault, the pair walk through the Starlight Drive-In, an iconic location from Fallout 4. You’ll see on the cinema’s marquee that the very last movie to be shown here was A Man and His Dog 3, starring none other than Coop.

Fallout brings the armor, weapons, and gadgets of the games to life.

Back into the vault with Woody Thomas (Zach Cherry).
Credit: Lorenzo Sisti / Prime

One of the most impressive elements of the Fallout series is the impeccable production design, especially on details like armor, weapons, and gadgets — but not the games’ signature aim-support V.A.T.S. system (Vault-Tec Assisted Targeting System). In Season 2, there’s just as much to delight fans new and longtime.

For one, every Vault Dweller including Lucy still wears the franchise’s signature Pip-Boy on their wrist, a wearable computer that’s used in Season 1 for its inbuilt map and navigation, Geiger counter, torch, and more. This season, the devices take on additional uses; Lucy uses hers to open Vault 24 in episode 1. The Pip-Boys also feature the game’s ubiquitous Vault Boy, Vault-Tec’s thumbs-up mascot, who we learned last season is based on Cooper.

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Frances Turner (Barb Howard) has her own Pip Boy.
Credit: Lorenzo Sisti / Prime

Armor-wise, the Brotherhood of Steel’s preferred T-60 power armor from Fallout 4 is back in all its lumbering glory and requisite armor bays, along with plenty of raider leathers and vault jumpsuits — as well as a few small sightings of armor resembling that of the New California Republic (NCR) in episode 2.

That iconic power armor in “Fallout 4.”
Credit: Bethesda

Weapon-wise, while Season 1 featured game selections from automatic turrets to miniguns and the Junk Jet from Fallout 4, Season 2 starts with a bang and Fallout: New Vegas‘ grenade launcher. One of the funniest weapons inclusions of episode 2 is the two members of the Brotherhood casually dicking around with a plasma grenade, which gamers will know will make short work of everyone in the vicinity.

Fallout relies on aid just like the game.

You’re gonna need food, drugs, and friends.
Credit: Courtesy of Prime.

You can’t survive the Fallout games without aid items, and neither can the characters in the series. Season 1 featured the franchise’s love for Atomic Age foods made for Vault-Dwellers like YumYum Deviled Eggs, Insta-Mash, Nuka-Cola, and Sugar Bombs, the latter of which is a cereal shaped like tiny nuclear bombs — and it plays an important role at the end of episode 1. Notably, the flea soup Lucy drinks in episode 1 is not from the game and is entirely the messed up creation of the showrunners.

Aid goes beyond food in Fallout, however, with stimulants and anti-radiation meds the tip of the drugberg. The first season included crucial-in game items like RadAway (to clear radiation poisoning), Jet (a chemical stimulant or “chem” used regularly by Cooper), and injections called “stimpaks” that instantly heal — in episode 2, Lucy assists a person in Vault 24 with one and leaves Coop to heal painfully and slowly on his own.

Plus, it’s no fun traversing the Wasteland alone, and in the Fallout games, you can bring your friends along for company (and the perks and storylines you’ll unlock). In Season 1, the series included companions like the Mister Handy robot butlers, one of whom was voiced by Matt Berry, and introduced a glorious dog named CX404, otherwise known by the terrible name of Dogmeat. The pooch is back for Season 2.

Fallout’s enemies are right out of the game.

Watch your back.
Credit: Lorenzo Sisti / Prime

Enemies abound in the Wasteland, and the Fallout series has already featured plenty of them, from Raiders (gangs of outlaws) to Fiends (cannibals). Creature-wise, Season 1 included Radroaches (irradiated cockroaches), Yao Guai (mutant bears), Feral Ghouls (zombie versions of the mutated humans), and a giant anglerfish.

In Season 2, we’ve only really seen one band of Raiders — the Great Khans of Fallout: New Vegas — however the sinister forces within the vaults (and the past?) seem more of a threat at this point. And in episode 2, Lucy and Coop face the formidable radscorpions, irradiated beasts ready to wound and poison you.

One famous foe missing from the Fallout TV series? The game’s fierce and omnipresent Super Mutants; you can spot one for a second on a “Wanted!” poster in Season 1, episode 6. Our fingers are crossed.

Perhaps all this detail makes you want to play the games for the first time — or all over again?

Fallout Season 2 premieres Dec. 16 at 9 p.m. ET on Prime Video, with a new episode every week.

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In 2026, you cannot snatch AI coding tools out of developers’ vice-grip hands, researchers have discovered.

But while AI is undoubtedly helping coders produce code faster, it may not be producing better code, other researchers warn. And that could cause problems down the road for them.

Specifically, in February 2026, respected AI research lab METR published a surprising revelation: most developers won’t work, even on a limited number of tasks, without AI anymore.

METR had hoped to provide an update to some groundbreaking research published a few months earlier, in 2025, on AI coding productivity. In it, researchers measured how much time open source developers took to do tasks by hand versus with AI.

While developers in that study reported that AI was making them more productive, they were shocked to learn it actually slowed them down. Sure, it generated code faster, but then they spent extra time finding and fixing errors, steering the AI and waiting on it to complete tasks.

When METR set out to repeat the experiment to measure advances in AI and coder proficiency, they couldn’t.

Devs weren’t willing to participate “because they do not wish to work without AI” even just for the study, the researchers confessed.

Instead, METR published a survey in May that allowed technical employees to self-report their AI productivity gains. Not surprisingly, they perceived that AI made them twice as valuable to their organizations.

But recent headlines about the wild expense of so-called tokenmaxxing, coupled with a smattering of recent research, make such self-perceptions dubious.

Tokenmaxxing, or using the number of tokens a person uses as a proxy for productivity with AI, has been the trend of 2026 so far. And it may already be over.

Amazon shut down its internal token-tracking leaderboard called Kirorank after employees were gaming it by using AI agents excessively, and running up costs, the Financial Times reported this week. The employees proved that AI use does not automatically translate to increased productivity.

Uber blew through its 2026 AI budget within the first four months of the year, The Information reported. COO Andrew Macdonald recently said on a podcast that such spending hadn’t led to a measurable increase in projects or productivity.

AI-generated code also doesn’t necessarily reduce ongoing code maintenance needs, and may even increase it, programmer and author James Shore elegantly argued in a blog post that went viral on Hacker News.

“You write code twice as quick now? Better hope you’ve halved your maintenance costs,” he wrote. “Otherwise, you’re screwed. You’re trading a temporary speed boost for permanent indenture.”

There’s other evidence that AI can increases code maintenance woes.

A viral tweet from Aiswarya Sankar, founder and CEO of reliability engineering agent startup Entelligence AI, proclaims that companies are spending 44% of their tokens on bug fixes that their AI generated. Code reviewing tool company Code Rabbit says it analyzed open source pull requests and found that AI produced 1.7x more problems than human code.

Those are, admittedly, self-serving stats from those trying to sell AI code reviewing tools.

Yet independent researchers have also found such issues. Researchers from the respected Singapore Management University published a report in April warning that “AI-generated code can introduce long-term maintenance costs into real software projects.”

Given that programmers love their AI assistants, what’s the solution?

Well, those who want to sell you AI coding agents say devs can just use AI coding agents to do the bone wearing tasks of fixing code as fast as AI spits it out. That’s what Cognition founder CEO Scott Wu suggests, maker of AI coding agent Devin.

But even he admits that, while Devin can work independently, he’d currently rate its skill between a junior and mid-level programmer, depending on the task. This is not a hand-it-off and forget it solution.

The SMU researchers suggest a more human approach. Programmers should know what tasks AI does and doesn’t do well as deeply as they know their favorite coding languages. They need strong quality assurance systems designed for AI and they are stuck with carefully reviewing the AI’s work as if it was a junior dev.

Meanwhile, the researchers say (and Wu agrees), humans should still be doing the big-picture work like software architecture and security design.

When you purchase through links in our articles, we may earn a small commission. This doesn’t affect our editorial independence.

#Coders #refusing #work #AIand #bite #TechCrunch">

Coders are refusing to work without AI — and that could come back to bite them | TechCrunch
In 2026, you cannot snatch AI coding tools out of developers’ vice-grip hands, researchers have discovered.

But while AI is undoubtedly helping coders produce code faster, it may not be producing better code, other researchers warn. And that could cause problems down the road for them.

Specifically, in February 2026, respected AI research lab METR published a surprising revelation: most developers won’t work, even on a limited number of tasks, without AI anymore.

METR had hoped to provide an update to some groundbreaking research published a few months earlier, in 2025, on AI coding productivity. In it, researchers measured how much time open source developers took to do tasks by hand versus with AI.

While developers in that study reported that AI was making them more productive, they were shocked to learn it actually slowed them down. Sure, it generated code faster, but then they spent extra time finding and fixing errors, steering the AI and waiting on it to complete tasks.

When METR set out to repeat the experiment to measure advances in AI and coder proficiency, they couldn’t.

Devs weren’t willing to participate “because they do not wish to work without AI” even just for the study, the researchers confessed.

Instead, METR published a survey in May that allowed technical employees to self-report their AI productivity gains. Not surprisingly, they perceived that AI made them twice as valuable to their organizations.

But recent headlines about the wild expense of so-called tokenmaxxing, coupled with a smattering of recent research, make such self-perceptions dubious.

Tokenmaxxing, or using the number of tokens a person uses as a proxy for productivity with AI, has been the trend of 2026 so far. And it may already be over.

Amazon shut down its internal token-tracking leaderboard called Kirorank after employees were gaming it by using AI agents excessively, and running up costs, the Financial Times reported this week. The employees proved that AI use does not automatically translate to increased productivity.

Uber blew through its 2026 AI budget within the first four months of the year, The Information reported. COO Andrew Macdonald recently said on a podcast that such spending hadn’t led to a measurable increase in projects or productivity.

AI-generated code also doesn’t necessarily reduce ongoing code maintenance needs, and may even increase it, programmer and author James Shore elegantly argued in a blog post that went viral on Hacker News.

“You write code twice as quick now? Better hope you’ve halved your maintenance costs,” he wrote. “Otherwise, you’re screwed. You’re trading a temporary speed boost for permanent indenture.”

There’s other evidence that AI can increases code maintenance woes.

A viral tweet from Aiswarya Sankar, founder and CEO of reliability engineering agent startup Entelligence AI, proclaims that companies are spending 44% of their tokens on bug fixes that their AI generated. Code reviewing tool company Code Rabbit says it analyzed open source pull requests and found that AI produced 1.7x more problems than human code.

Those are, admittedly, self-serving stats from those trying to sell AI code reviewing tools.

Yet independent researchers have also found such issues. Researchers from the respected Singapore Management University published a report in April warning that “AI-generated code can introduce long-term maintenance costs into real software projects.”

Given that programmers love their AI assistants, what’s the solution?

Well, those who want to sell you AI coding agents say devs can just use AI coding agents to do the bone wearing tasks of fixing code as fast as AI spits it out. That’s what Cognition founder CEO Scott Wu suggests, maker of AI coding agent Devin.

But even he admits that, while Devin can work independently, he’d currently rate its skill between a junior and mid-level programmer, depending on the task. This is not a hand-it-off and forget it solution.

The SMU researchers suggest a more human approach. Programmers should know what tasks AI does and doesn’t do well as deeply as they know their favorite coding languages. They need strong quality assurance systems designed for AI and they are stuck with carefully reviewing the AI’s work as if it was a junior dev.

Meanwhile, the researchers say (and Wu agrees), humans should still be doing the big-picture work like software architecture and security design.
When you purchase through links in our articles, we may earn a small commission. This doesn’t affect our editorial independence.#Coders #refusing #work #AIand #bite #TechCrunch

mwasimuddin125 0

In 2026, you cannot snatch AI coding tools out of developers’ vice-grip hands, researchers have discovered.

But while AI is undoubtedly helping coders produce code faster, it may not be producing better code, other researchers warn. And that could cause problems down the road for them.

Specifically, in February 2026, respected AI research lab METR published a surprising revelation: most developers won’t work, even on a limited number of tasks, without AI anymore.

METR had hoped to provide an update to some groundbreaking research published a few months earlier, in 2025, on AI coding productivity. In it, researchers measured how much time open source developers took to do tasks by hand versus with AI.

While developers in that study reported that AI was making them more productive, they were shocked to learn it actually slowed them down. Sure, it generated code faster, but then they spent extra time finding and fixing errors, steering the AI and waiting on it to complete tasks.

When METR set out to repeat the experiment to measure advances in AI and coder proficiency, they couldn’t.

Devs weren’t willing to participate “because they do not wish to work without AI” even just for the study, the researchers confessed.

Instead, METR published a survey in May that allowed technical employees to self-report their AI productivity gains. Not surprisingly, they perceived that AI made them twice as valuable to their organizations.

But recent headlines about the wild expense of so-called tokenmaxxing, coupled with a smattering of recent research, make such self-perceptions dubious.

Tokenmaxxing, or using the number of tokens a person uses as a proxy for productivity with AI, has been the trend of 2026 so far. And it may already be over.

Amazon shut down its internal token-tracking leaderboard called Kirorank after employees were gaming it by using AI agents excessively, and running up costs, the Financial Times reported this week. The employees proved that AI use does not automatically translate to increased productivity.

Uber blew through its 2026 AI budget within the first four months of the year, The Information reported. COO Andrew Macdonald recently said on a podcast that such spending hadn’t led to a measurable increase in projects or productivity.

AI-generated code also doesn’t necessarily reduce ongoing code maintenance needs, and may even increase it, programmer and author James Shore elegantly argued in a blog post that went viral on Hacker News.

“You write code twice as quick now? Better hope you’ve halved your maintenance costs,” he wrote. “Otherwise, you’re screwed. You’re trading a temporary speed boost for permanent indenture.”

There’s other evidence that AI can increases code maintenance woes.

A viral tweet from Aiswarya Sankar, founder and CEO of reliability engineering agent startup Entelligence AI, proclaims that companies are spending 44% of their tokens on bug fixes that their AI generated. Code reviewing tool company Code Rabbit says it analyzed open source pull requests and found that AI produced 1.7x more problems than human code.

Those are, admittedly, self-serving stats from those trying to sell AI code reviewing tools.

Yet independent researchers have also found such issues. Researchers from the respected Singapore Management University published a report in April warning that “AI-generated code can introduce long-term maintenance costs into real software projects.”

Given that programmers love their AI assistants, what’s the solution?

Well, those who want to sell you AI coding agents say devs can just use AI coding agents to do the bone wearing tasks of fixing code as fast as AI spits it out. That’s what Cognition founder CEO Scott Wu suggests, maker of AI coding agent Devin.

But even he admits that, while Devin can work independently, he’d currently rate its skill between a junior and mid-level programmer, depending on the task. This is not a hand-it-off and forget it solution.

The SMU researchers suggest a more human approach. Programmers should know what tasks AI does and doesn’t do well as deeply as they know their favorite coding languages. They need strong quality assurance systems designed for AI and they are stuck with carefully reviewing the AI’s work as if it was a junior dev.

Meanwhile, the researchers say (and Wu agrees), humans should still be doing the big-picture work like software architecture and security design.

When you purchase through links in our articles, we may earn a small commission. This doesn’t affect our editorial independence.

#Coders #refusing #work #AIand #bite #TechCrunch">Coders are refusing to work without AI — and that could come back to bite them | TechCrunch