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Fortnite Season 4 Adds A New Map, Dirt Bikes & More
Plus, a fresh battle pass lets you play as the Witcher, Geralt of Rivia or Doom Slayer from Doom and Doom Eternal.
After an explosive finale to the third chapter of Fortnite, a fresh season has arrived, with a completely new island for players to wage battle over.
The new map features a bunch of landmarks that draw inspiration from fantasy themes, including a castle dubbed “The Citadel” that’s home to “The Ageless Champion”, as well as “Anvil Square”, a medieval town surrounded by autumn foliage. On another part of the island, players can explore an abandoned farm with the ominous title of “The Frenzy Fields”.
As well as a scenic new map, chapter four of Fortnite introduces updated gameplay elements. Dirt bikes called Trail Thrashers allow players to perform stunts and tricks while firing their weapons from the saddle. When on foot, it’s now possible to vault over obstacles by sprinting into them, and you can also use a pickaxe to create a snowball which can be rolled over enemies.
One of the game’s most intriguing new elements is being dubbed Reality Augments, which lets you choose between two random buffs you can keep until your character dies, or you win a match.
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As for the behind-the-scenes technical updates, Epic has updated Fortnite to the newest version of Unreal Engine, and players on the latest platforms will now enjoy ray-traced reflections and other graphical improvements.
Finally, this season’s battle pass includes the ability to play as Doom Slayer, the protagonist of Doom and Doom Eternal, as well as Geralt of Rivia, the lead character from the Witcher. If you’re excited to get started on chapter four, season one of Fortnite, then you’ll be pleased to know the game is available to play right now.
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UAE-Built Falcon-H1 Arabic Leads LLM Benchmarks
The lean Emirati-built language model beats larger global systems and puts Arabic at the center of training.
Abu Dhabi’s Technology Innovation Institute has released an Arabic-first large language model that tops global test boards, an uncommon edge for a region long served by English-centric systems.
Falcon-H1 Arabic comes in 3B, 7B and 34B versions. The flagship posts 75.36% accuracy on comprehensive Arabic tasks and ranks first on the Open Arabic LLM Leaderboard. It also outperforms Meta’s Llama-70B and Alibaba’s Qwen-72B while using less than half their parameters. The smallest model beats Microsoft’s Phi-4 Mini by ten percentage points on equivalent benchmarks.
Arabic remains hard territory for AI. Flexible word order, dense morphology and constant switching between regional dialects and Modern Standard Arabic leave many global models missing context or tone. Academic research has pointed to a shortage of annotated datasets for dialect and informal speech. The impact shows up in classrooms, call centers and government portals where Arabic chatbots lag their English counterparts.
TII trained Falcon-H1 Arabic on formal writing, dialects and culturally grounded content. Beyond scores, it handles practical use: long conversations, reasoning rather than literal translation, and inputs of up to 192,000 words — enough for medical records or legal filings.
“The aim is innovation that is accessible, relevant, and impactful,” said Faisal Al Bannai, Adviser to the UAE President and Secretary-General of the Advanced Technology Research Council.
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Arabic is spoken by more than 450 million people across over 20 countries, yet has often been treated as a secondary language for foundation models. The UAE move signals a push to flip that logic and build Arabic-native stacks rather than wait for global systems to improve.
Falcon models have led their categories since 2023. With H1 Arabic, TII is offering free access via chat.falconllm.tii.ae for developers, media, healthcare and public-sector users looking to automate in natural Arabic.
As the region continues to invest in sovereign computing and data localization, the addition of Falcon-H1 Arabic adds a powerful tool built for the native language, instead of an afterthought attached to an English-trained system.
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