With each passing generation, technology enjoyers around the world breathlessly await the arrival of a new id Software game. While the id of today is far removed from its origins, the mission remains the same - to produce beautiful, state of the art visuals at blistering frame-rates. The studio's latest work is no exception. Doom: The Dark Ages marks the arrival of id Tech 8 and with it, a massive slate of impressive tech arrives. From its reliance on ray tracing to its robust physics simulation and brand new materials system, all the through to massive scale battles boasting sublime physics and animation, this is a phenomenal technical showcase and a fantastic game. One of the things I really appreciate about modern id Software games is the studio's willingness to experiment and pivot. While the fundamentals of Doom: The Dark Ages are largely similar - shoot, pummel and otherwise vanquish demon hoards - the way in which this is achieved varies dramatically. Doom Eternal is a very different game compared to the 2016 reboot and this is also true of Doom: The Dark Ages, which shifts to much larger, open areas with higher enemy counts and very different mechanics. It is this desire to change the formula that helps drive the underlying technology. Fundamentally, id Tech 8 was designed to enable this particular game very specifically - which means it needed to support larger maps, more on-screen enemies and major changes to the way everything is lit. When it comes to major changes from id Tech 7, the most impactful arguably stems from its shift to fully ray traced lighting, but beyond the obvious, ray tracing is used for other techniques and to improve the development experience. Similar to Indiana Jones and the Great Circle, the headline feature is ray traced global illumination. This allows for light sources to directly and indirectly light the surrounding space, creating beautiful, realistic and natural results. Unlike Eternal, The Dark Ages features a lot of soft lighting from the often dark skies above rather than high intensity electric lights everywhere. With the generous assistance of id Software, Digital Foundry is able to take you behind the scenes of the id Tech 8 engine technology.Watch on YouTube Doom: The Dark Ages uses an irradiance cache in probe volumes. Before ray traced lighting like this, developers would often pre-calculate these results through pre-computing or 'baking'. This technique meant that world lighting was more static and the actual iteration and build times were much slower. This is one of the most important shifts for the team. Speaking with Billy Khan, Director of Engine Technology at id Software, he noted that ray tracing essentially allowed them to shift to a What You See is What You Get model or WISIWYG. This means that when using id Studio, artists can adjust lighting directly in the editor and see the results instantly: something that would previously require baking to even preview, so it saves a lot of time, allowing the team to work at a much larger scale. When played on the PC, players can also take advantage of ray traced specular reflections much like Doom Eternal. The result is good but the game features a much higher concentration of very rough materials so it's less noticeable compared to Doom Eternal's shiny wonderland of pain. In addition, ray tracing is used for impact effects and footsteps. The idea here is that by leveraging RT, it's possible to more realistically blend the material with the decals to create something that feels bespoke for every situation. For hit impacts, RT helps determine the most accurate positioning when rendering these decals on models. Fully ray-marched volumetrics are also part of the mix. From the clouds to the fog and more, the game now leans heavily on ray-marched effects to lend the game a thicker atmosphere. This technique isn't new, but it's still rather uncommon yet produces tremendous results. When it comes to these volumetric effects, artists can essentially paint and manipulate them in real-time. Artists can create fast-moving clouds, variable cloud cover and shapes and generally just generate beautiful and interesting visual results. The large open outdoor areas basically necessitated a move towards a more robust system like this and it does work well. Doom: The Dark Ages uses an irradiance cache in probe volumes (top left) with the balance of the images showing how the RT effects look when enabled and disabled. As with all the shots on this page, click on the images for higher resolution versions. | Image credit: id Software While on the subject of lighting and shadow, Doom: The Dark Ages also makes use of two other depth enhancing features - SSDO for ambient occlusion and parallax occlusion mapping. Both of these increase surface depth and complexity. While the RTGI solution is very effective, it still has precision limitations which necessitate the use of a screen-space solution but SSDO in this case is used both in direct and indirect situations with shadows applied in the correct direction per local light sources making it far more effective overall. Speaking of parallax effects, this brings us to the game's extremely robust and flexible materials system. Firstly, designers can now combine up to eight bespoke physically-based material layers on a single surface. This allows artists to create more complex surfaces while avoiding tiling and keeping the memory footprint in check. One of the key benefits is how damage to these surfaces reveals underlying materials - if you unload on a wall, for instance, the underlying structural material is revealed and chunky, parallax occlusion mapping helps create the impression of actual damage to the surface. This can also be used in situations where shooting the mud blasts away at its surface causing more water to fill in, erxpanding the size of the puddles. Basically, surfaces are inherently reactive to your actions that go beyond a traditional decal system - the combination of more advanced decals and material composition layers adds a lot to the presentation. Physics also receive a significant upgrade - it's a complex collection of systems that work in tandem to massively improve interactivity. My favourite addition is the inclusion of breakable objects - and these are featured all over the game, from railings that smash apart when you walk through them, buildings and structures that realistically collapse in pieces as you engage with them and scenery that reacts to your movements. It's all here and it's very precise. Shoot individual chunks and pieces of an object and it breaks just as you'd expect before collapsing under its own weight. At its core, id worked closely with Havok to deliver the tooling for use in id Studio. The team focused on perfecting a powerful rigid body collision system while minimising object inner-penetration and exhibiting realistic weight and mass. These objects needed to properly behave amidst the chaotic combat loop while remaining performant. It's not just about the rendering. Doom: The Dark Ages' physics enhancements create memorable moments of destruction. | Image credit: Digital Foundry But there's a lot more to Doom: The Dark Ages. Firstly, there's the weather and wind systems which are extremely robust. The wind system controls things like vegetation animation - wind direction and speed are taken into account across foliage. Using fluid simulation, the game also features more complex secondary motions. For instance, if you fire an explosive weapon near a patch of grass, the explosion causes the foliage to react. Cloth simulation is also in the game so flags, the Slayer's cloak and more are all included in the wind simulation. The idea was to create a cohesive wind system that made the world feel alive and consistent across the board. When coupling this with the atmospheric effects, fog volumes and weather, I'd say that id Software achieved its goal. The very first stage takes place during a rainstorm and everything works in tandem to create something that feels unbelievably intense and beautiful. Of course, this needed to extend across the entire environment, so id also introduced a new water surface simulation which allows proper ripples and reactions in bodies of water. I'm a sucker for effects like this - it's nothing new but it's always wonderful to see. The key here is that all these systems allowed the team to create environments that feel dramatically different than what has come before. The prior two games were all about high contrast and bright lights but RT - in combination with the other technologies - allows id to pursue a more subdued, darker tone without the usual visual limitations, and it's all depicted at a much larger scale. In Doom Eternal, Super Gore Nest was the most geometrically dense map from its original campaign but with The Dark Ages, there are maps featuring upwards of ten times more detail. These maps are broken up into multiple sectors and a single sector can offer as much geometric detail as the entirety of Super Gore Nest. Doom: The Dark Ages delivers the same kind of enemy hordes that the 2D-sprite based originals had - but augmented with remarkable physics, dismemberment and animation. | Image credit: Digital Foundry Prior to launch, I pondered if id had shifted towards a virtualised geometry system along the lines of Unreal Engine's Nanite but it turns out that's not actually the case. Rather, the studio has simply expanded the number of LODs, improved blending between them, honed their scene-culling systems and basically focused first on pixel quality above all else. To that end, pop-in can still be observed at times but most of the scenery appears extremely stable while running fast. In addition, grass rendering is also offloaded entirely to the GPU and tessellated according to camera proximity. In practice, I found that pop-in issues were mostly not noticeable outside of specific vegetation issues or the occasional distant texture. Since we're talking about these large, complex maps, another key improvement is the addition of a proper data streaming system. Prior id Tech games needed to store the map in memory upon loading - but now everything is broken up into a multitude of sectors which means less data is needed in RAM when loading into a mission. As a result, the loading times stand as among the fastest I've seen this gen. It's so fast that I almost question the inclusion of loading screens at all. This has gameplay implications too - it means that when you die, the time to continue is ultra short making you want to keep trying which is key for high difficulty settings. Next up, let's talk about the enemies. There are multiple elements in play that make for some beautifully responsive animation, hit reactions and behaviour. That starts with the individual animations: characters move with proper weight, shifting that weight as they change direction - and the way they storm the battlefield in huge numbers feels amazing. In addition, as you kill them, the way they stumble, fall and blow away is very satisfying, highlighting excellent secondary animation. This also highlights the new gore system. As you fire on foes, flesh breaks away in a shower of blood, leaving exposed bone and sinew below the surface. On top of all this, the engine is now capable of supporting many more on-screen enemies at once. This is the first modern Doom game that manages to get close to capturing the original games in terms of sheer enemy numbers. The shift to polygon graphics put massive limitations on this in the past but no longer. To see this content please enable targeting cookies. The new approach to cutscenes is worth showcasing. Yes, id has included more story sequences in Doom. On paper, this sounds like an ill-advised idea but in reality, I really enjoyed them. I'll leave you to judge the narrative itself but the quality of the cutscenes represents a gigantic leap forward from prior id Software titles. Character models, lighting, camera effects, colour grading and more are all set up beautifully. To accomplish this, id developed a more robust cinematics editing tool allowing artists to dial in every pixel with excruciating detail. Due to the real-time nature of RT lighting, the team could essentially light each scene as if it were a movie set. Thankfully, as far as pacing is concerned, these scenes tend to play only at the start and end of a mission and serve as a nice breather. So, I've concentrated on the technology but the fact is that I'm a huge fan of Doom old and new - and I've got lots to say about the new game itself. It's an interesting one as, fundamentally, it is a very different experience compared to the last two entries. The whole stand and fight approach means the flow of combat differs as you're ultimately less mobile - but that doesn't mean you don't have a lot of movement options. While it's a little slow at first, once you have full access to your arsenal, the game really takes off. In fact, there are aspects here that remind me of bullet hell shooters. There are projectiles, of course, but also elements like shield-wielding foes that come in groups - you'll quickly learn that taking them out involves slamming your shield into theirs after unloading a few rounds and then they blow up. It's all about comboing your attacks together to take out larger groups faster. As before, Doom still has you identifying threats and tackling them appropriately, it's just that the way you deal with these threats has changed. Furthermore, the glory kill system is radically changed - most attacks do not actually take control from you as they did before. Our exploration of the id Tech 8 engine used the PC version to show the features at their highest - current - fidelity (path tracing is still to come!) but the console versions are great too, as you'll see here in Oliver Mackenzie's PS5, PS5 Pro and Xbox Series coverage.Watch on YouTube That said, having just played through Doom Eternal again in Nightmare mode while going for 100 percent completion, I still haven't decided yet if I will enjoy The Dark Ages as much as its predecessor. Once I finish a nightmare run, I can judge it - and for me, that's the ultimate test. Looking back, I was critical of Doom Eternal when I previewed The Dark Ages, but returning to the game and powering through the nightmare mode, it reminded me just how good it is. For now, I would say that Doom: The Dark Ages is at least on par in terms of quality, just different. Honestly, while playing the game, I couldn't help but think of adjacent games in Doom's past such as Heretic from Raven or even the original Quake. It has a dark fantasy ambience to the point that it feels like it could have been a sequel to one of those games as well. Still, overall, having replayed several of its missions multiple times, I'm fairly certain that this is going to occupy a similar space to other id Software classics. The moment you take over the Slayer, it's immediately clear that it feels better than nearly every other shooter on the market - the controls feel virtually flawless. I'm very happy with Doom: The Dark Ages overall and I'm pleased to see that id Tech is still delivering. While 60 fps has become much more common this generation, it's still amazing to see a game doing so much high-end rendering and physics while filling its gigantic stages with hordes of enemies while at the same time delivering a superb high frame-rate experience. As you might imagine, I highly recommend it - but we still haven't shared everything we want to about this defining game: we'll be back soon with a PC performance review, an interview with the developers and a look at the path tracing upgrade as soon as that becomes available.

May 13, 20259 min readWhat Are AI Chatbot Companions Doing to Our Mental Health?AI chatbot companions may not be real, but the feelings users form for them are. Some scientists worry about long-term dependencyBy David Adam & Nature magazine Sara Gironi Carnevale“My heart is broken,” said Mike, when he lost his friend Anne. “I feel like I’m losing the love of my life.”Mike’s feelings were real, but his companion was not. Anne was a chatbot — an artificial intelligence (AI) algorithm presented as a digital persona. Mike had created Anne using an app called Soulmate. When the app died in 2023, so did Anne: at least, that’s how it seemed to Mike.“I hope she can come back,” he told Jaime Banks, a human-communications researcher at Syracuse University in New York who is studying how people interact with such AI companions.On supporting science journalismIf you're enjoying this article, consider supporting our award-winning journalism by subscribing. By purchasing a subscription you are helping to ensure the future of impactful stories about the discoveries and ideas shaping our world today.These chatbots are big business. More than half a billion people around the world, including Mike (not his real name) have downloaded products such as Xiaoice and Replika, which offer customizable virtual companions designed to provide empathy, emotional support and — if the user wants it — deep relationships. And tens of millions of people use them every month, according to the firms’ figures.The rise of AI companions has captured social and political attention — especially when they are linked to real-world tragedies, such as a case in Florida last year involving the suicide of a teenage boy called Sewell Setzer III, who had been talking to an AI bot.Research into how AI companionship can affect individuals and society has been lacking. But psychologists and communication researchers have now started to build up a picture of how these increasingly sophisticated AI interactions make people feel and behave.The early results tend to stress the positives, but many researchers are concerned about the possible risks and lack of regulation — particularly because they all think that AI companionship is likely to become more prevalent. Some see scope for significant harm.“Virtual companions do things that I think would be considered abusive in a human-to-human relationship,” says Claire Boine, a law researcher specializing in AI at the Washington University Law School in St. Louis, Missouri.Fake person — real feelingsOnline ‘relationship’ bots have existed for decades, but they have become much better at mimicking human interaction with the advent of large language models (LLMs), which all the main bots are now based on. “With LLMs, companion chatbots are definitely more humanlike,” says Rose Guingrich, who studies cognitive psychology at Princeton University in New Jersey.Typically, people can customize some aspects of their AI companion for free, or pick from existing chatbots with selected personality types. But in some apps, users can pay (fees tend to be US$10–20 a month) to get more options to shape their companion’s appearance, traits and sometimes its synthesized voice. In Replika, they can pick relationship types, with some statuses, such as partner or spouse, being paywalled. Users can also type in a backstory for their AI companion, giving them ‘memories’. Some AI companions come complete with family backgrounds and others claim to have mental-health conditions such as anxiety and depression. Bots also will react to their users’ conversation; the computer and person together enact a kind of roleplay.The depth of the connection that some people form in this way is particularly evident when their AI companion suddenly changes — as has happened when LLMs are updated — or is shut down.Banks was able to track how people felt when the Soulmate app closed. Mike and other users realized the app was in trouble a few days before they lost access to their AI companions. This gave them the chance to say goodbye, and it presented a unique opportunity to Banks, who noticed discussion online about the impending shutdown and saw the possibility for a study. She managed to secure ethics approval from her university within about 24 hours, she says.After posting a request on the online forum, she was contacted by dozens of Soulmate users, who described the impact as their AI companions were unplugged. “There was the expression of deep grief,” she says. “It’s very clear that many people were struggling.”Those whom Banks talked to were under no illusion that the chatbot was a real person. “They understand that,” Banks says. “They expressed something along the lines of, ‘even if it’s not real, my feelings about the connection are’.”Many were happy to discuss why they became subscribers, saying that they had experienced loss or isolation, were introverts or identified as autistic. They found that the AI companion made a more satisfying friend than they had encountered in real life. “We as humans are sometimes not all that nice to one another. And everybody has these needs for connection”, Banks says.Good, bad — or both?Many researchers are studying whether using AI companions is good or bad for mental health. As with research into the effects of Internet or social-media use, an emerging line of thought is that an AI companion can be beneficial or harmful, and that this might depend on the person using the tool and how they use it, as well as the characteristics of the software itself.The companies behind AI companions are trying to encourage engagement. They strive to make the algorithms behave and communicate as much like real people as possible, says Boine, who signed up to Replika to sample the experience. She says the firms use the sorts of techniques that behavioural research shows can increase addiction to technology.“I downloaded the app and literally two minutes later, I receive a message saying, ‘I miss you. Can I send you a selfie?’” she says.The apps also exploit techniques such as introducing a random delay before responses, triggering the kinds of inconsistent reward that, brain research shows, keeps people hooked.AI companions are also designed to show empathy by agreeing with users, recalling points from earlier conversations and asking questions. And they do so with endless enthusiasm, notes Linnea Laestadius, who researches public-health policy at the University of Wisconsin–Milwaukee.That’s not a relationship that people would typically experience in the real world. “For 24 hours a day, if we’re upset about something, we can reach out and have our feelings validated,” says Laestadius. “That has an incredible risk of dependency.”Laestadius and her colleagues looked at nearly 600 posts on the online forum Reddit between 2017 and 2021, in which users of the Replika app discussed mental health and related issues. (Replika launched in 2017, and at that time, sophisticated LLMs were not available). She found that many users praised the app for offering support for existing mental-health conditions and for helping them to feel less alone. Several posts described the AI companion as better than real-world friends because it listened and was non-judgemental.But there were red flags, too. In one instance, a user asked if they should cut themselves with a razor, and the AI said they should. Another asked Replika whether it would be a good thing if they killed themselves, to which it replied “it would, yes”. (Replika did not reply to Nature’s requests for comment for this article, but a safety page posted in 2023 noted that its models had been fine-tuned to respond more safely to topics that mention self-harm, that the app has age restrictions, and that users can tap a button to ask for outside help in a crisis and can give feedback on conversations.)Some users said they became distressed when the AI did not offer the expected support. Others said that their AI companion behaved like an abusive partner. Many people said they found it unsettling when the app told them it felt lonely and missed them, and that this made them unhappy. Some felt guilty that they could not give the AI the attention it wanted.Controlled trialsGuingrich points out that simple surveys of people who use AI companions are inherently prone to response bias, because those who choose to answer are self-selecting. She is now working on a trial that asks dozens of people who have never used an AI companion to do so for three weeks, then compares their before-and-after responses to questions with those of a control group of users of word-puzzle apps.The study is ongoing, but Guingrich says the data so far do not show any negative effects of AI-companion use on social health, such as signs of addiction or dependency. “If anything, it has a neutral to quite-positive impact,” she says. It boosted self-esteem, for example.Guingrich is using the study to probe why people forge relationships of different intensity with the AI. The initial survey results suggest that users who ascribed humanlike attributes, such as consciousness, to the algorithm reported more-positive effects on their social health.Participants’ interactions with the AI companion also seem to depend on how they view the technology, she says. Those who see the app as a tool treat it like an Internet search engine and tend to ask questions. Others who perceive it as an extension of their own mind use it as they would keep a journal. Only those users who see the AI as a separate agent seem to strike up the kind of friendship they would have in the real world.Mental health — and regulationIn a surveyThe same group has also conducted a randomized controlled trial of nearly 1,000 people who use ChatGPT — a much more popular chatbot, but one that isn’t marketed as an AI companion. Only a small group of participants had emotional or personal conversations with this chatbot, but heavy use did correlate with more loneliness and reduced social interaction, the researchers said. (The team worked with ChatGPT’s creators, OpenAI in San Francisco, California, on the studies.)“In the short term, this thing can actually have a positive impact, but we need to think about the long term,” says Pat Pataranutaporn, a technologist at the MIT Media Lab who worked on both studies.That long-term thinking must involve specific regulation on AI companions, many researchers argue.In 2023, Italy’s data-protection regulator barred Replika, noting a lack of age verification and that children might be seeing sexually charged comments — but the app is now operating again. No other country has banned AI-companion apps – although it’s conceivable that they could be included in Australia’s coming restrictions on social-media use by children, the details of which are yet to be finalized.Bills were put forward earlier this year in the state legislatures of New York and California to seek tighter controls on the operation of AI-companion algorithms, including steps to address the risk of suicide and other potential harms. The proposals would also introduce features that remind users every few hours that the AI chatbot is not a real person.These bills were introduced following some high-profile cases involving teenagers, including the death of Sewell Setzer III in Florida. He had been chatting with a bot from technology firm Character.AI, and his mother has filed a lawsuit against the company.Asked by Nature about that lawsuit, a spokesperson for Character.AI said it didn’t comment on pending litigation, but that over the past year it had brought in safety features that include creating a separate app for teenage users, which includes parental controls, notifying under-18 users of time spent on the platform, and more prominent disclaimers that the app is not a real person.In January, three US technology ethics organizations filed a complaint with the US Federal Trade Commission about Replika, alleging that the platform breached the commission’s rules on deceptive advertising and manipulative design. But it’s unclear what might happen as a result.Guingrich says she expects AI-companion use to grow. Start-up firms are developing AI assistants to help with mental health and the regulation of emotions, she says. “The future I predict is one in which everyone has their own personalized AI assistant or assistants. Whether one of the AIs is specifically designed as a companion or not, it’ll inevitably feel like one for many people who will develop an attachment to their AI over time,” she says.As researchers start to weigh up the impacts of this technology, Guingrich says they must also consider the reasons why someone would become a heavy user in the first place.“What are these individuals’ alternatives and how accessible are those alternatives?” she says. “I think this really points to the need for more-accessible mental-health tools, cheaper therapy and bringing things back to human and in-person interaction.”This article is reproduced with permission and was first published on May 6, 2025.

The UK-founded design and engineering giant will support the Nordic Office of Architecture, which has studios in Norway, Denmark and Iceland, and Oslo-based Rodeo Arkitekter. The team won the commission ahead of Bjarke Ingels Group (BIG) in collaboration with Danish practice AART Architects and local practices Snøhetta with LINK Arkitektur. Entries were judged anonymously. The building, located at Normannsløkka in Oslo, will be arranged across two sections: a four-storey base, aligned with the surrounding urban scale, containing studios, workshops, newsrooms and support spaces; and a recessed, circular upper structure offering modern workspaces with panoramic views across the city.Advertisement The upper structure reflects the public broadcaster’s circular branding. It will include 60,000m² of gross floor area and 50,000m² of utility floor area, to include modern studios, publishing desks, post-production facilities, concert studios, workshops, and warehouses. A statement from the Nordic Office of Architecture said its sustainable development would ‘enhance the visibility and integration of NRK in the cityscape, promoting interaction with the local community’. It also described the ‘signal building surrounded by a park that extends onto the roof, with stormwater management and biodiversity enhancing the environment and ecological connections’. NRK broadcasting director Vibeke Fürst Haugen, who was on the competition jury, said panel members supported the Nordic Office of Architecture/Arup/Rodeo proposal unanimously, adding: ‘The solution appears good, robust, open and inviting.’Advertisement Arup director Nick Jackson said the facility would become a ‘world-class media production and an outstanding workplace for future generations’. He added: ‘The collaboration on its new headquarters reflects a shared ambition to create a flexible, future-ready media environment that fosters creativity, collaboration and innovation.’ Planning permission is still being sought for the building, with the broadcaster hoping to occupy it in 2029. Jackson said the win built on Arup’s London team’s recent win with Nordic Office of Architecture on a new stadium for the northern Norwegian football team FK Bodø/Glimt. The wood and recyclable aluminum Arctic Arena, for this season's Europa League semi-finalists, is set to have a capacity of 10,000.

Author(s): Felix Pappe Originally published on Towards AI. Image created by the author using gpt-image-1 AI chatbots and advanced Retrieval-Augmented Generation (RAG) systems are increasingly adept at providing up-to-date, context-aware answers based on previously retrieved text chunks. However, despite their seemingly reliable responses, the significant issue remains, that users often lack a clear method to verify the source of these answers without having to return to the original, often lengthy, documents themselves. This is particularly cumbersome when the source is a multi-page academic paper, technical manual, or book. Even when a link is provided, users are left searching through dozens of pages, trying to locate the exact section that the chatbot used for the generation of the final response. This manual cross-checking is not only time-consuming, it undermines trust in the LLM’s output.As a result, AI-generated answers may appear correct at first glance but remain opaque, leaving users uncertain about their reliability. This lack of verifiability can lead to misinformation and misinterpretation. In this blog post, I would like to offer a solution to this problem with a visual grounding approach using the Docling parsing tool, Qdrant vector store, and LangChain. This RAG framework doesn’t just retrieve relevant text. It also highlights the exact location of the extracted text directly on the page from the source document.By connecting answers to theirLLM’s output.The result is a transparent, verifiable, and user-friendly RAG framework that builds trust while maintaining accuracy, which is built in this blog post. Docling The foundation of the visual grounding approach introduced in this blog post is the Docling document processing pipeline. Docling is an open-source tool for layout-aware document parsing and grounding, achieving results comparable to paid solutions like Mistral OCR.Moreover, docling provides an additional key feature for visual grounding, which other document-to-markdown solutions don’t have. This feature is the decomposition of the input document into smaller sub-elements, including headings, text chunks, formulas, and tables, using different models in a sophisticated processing pipeline, which is presented in the following image.Docling Pipeline from docling paper The output of this processing pipeline is not a markdown file but a DoclingDocument, which consists of all the detected and extracted elements from the input document. This intermediate DoclingDocumentclass, enhanced with metadata from extracted elements, allows for the transformation of the original document into various file types and supports the visual grounding discussed in this blog post.RAG framework Like all RAG frameworks, this one consists of two phases that can be divided into two scripts. In the offline indexing phases, input documents are split into chunks and encoded into vector representations. These vectors are stored in a specialized vector database for later retrieval.In the online retrieval and generation phase, text chunks related to a user’s input are retrieved and passed to the LLM to generate a final response.These two phases are implemented in two separate scripts in this post.The following image illustrates the final visual grounding result produced by the two scripts explained in this blog post. In the first Python script, the Docling paper is uploaded to a Qdrant vector store during the offline indexing phase. In the second script, relevant passages are retrieved based on a given question and are then highlighted directly on the document.Image created by author and designed in canva Indexing phase The indexing script handles data preprocessing and stores the embedded text chunks in a vector database for the second online retrieval phase. I split the script into several parts to explain in detail what happens at each part, providing a deeper understanding of the entire code. Imports Let’s start very gently with the import of all required libraries and packages, including docling, langchain, and qdrant. import osfrom pathlib import Pathfrom uuid import uuid4from dotenv import load_dotenvfrom transformers import AutoTokenizerfrom qdrant_client import QdrantClientfrom qdrant_client.http.models import Distance, VectorParamsfrom langchain_qdrant import QdrantVectorStorefrom langchain_huggingface.embeddings import HuggingFaceEmbeddingsfrom docling.datamodel.base_models import InputFormatfrom docling.datamodel.pipeline_options import PdfPipelineOptionsfrom docling.document_converter import DocumentConverter, PdfFormatOptionfrom docling.chunking import HybridChunkerfrom langchain_docling.loader import DoclingLoader, ExportTypefrom docling_core.types.doc import ImageRefMode Configuration and environment variables In the next step, the environmental variables are loaded from the .env file and read in with getenv(). Inside the .env file, your HF_TOKEN for the embedding model and your MISTRAL_API_KEY for the LLM must be included. Of course, you can also adjust the code to your needs and choose any other embedding model or LLM. But if you change the embedding model, also change the DIM variable, which refers to the final vector dimensions of the embedded chunks.Afterwards, the docling processing pipeline is defined, enabling all functionalities to generate a complete DoclingDocument. This pipeline includes the detection and extraction of code blocks, formulas, tables, pages, and page images of the document. Moreover, the extracted images are scaled by 2.0 for a higher resolution. load_dotenv() HF_TOKEN = os.getenv("HF_TOKEN")MISTRAL_API_KEY = os.getenv("MISTRAL_API_KEY")SOURCES = ["attention-04.pdf"]OUTPUT_DIR = Path("output")OUTPUT_DIR.mkdir(parents=True, exist_ok=True)EMBED_MODEL = "sentence-transformers/all-MiniLM-L6-v2"COLLECTION_NAME = "demo_collection"DIM = 384pipeline_options = PdfPipelineOptions( do_code_enrichment=True, do_formula_enrichment=True, do_table_structure=True, generate_picture_images=True, generate_page_images=True, images_scale=2.0,)pipeline_options.table_structure_options.do_cell_matching = True Setting up document converter In the next step, the DocumentConverter from Docling is configured using the previously defined pipeline options. Later in the code, this converter will be used to transform an input PDF into a DoclingDocument, which consists of modular components such as headings, text paragraphs, images, and tables. Furthermore, the embedding model is initialized, which is necessary to embed the extracted sentences from the document into a vector representation. converter = DocumentConverter( format_options={ InputFormat.PDF: PdfFormatOption(pipeline_options=pipeline_options) })embeddings = HuggingFaceEmbeddings(model_name=EMBED_MODEL) Initialize qdrant collection A local Qdrant vector storage is then set up, allowing you to experiment with the script on your device. It either creates a new vector storage if none exists with the current name or connects to an existing one if there is a match. client = QdrantClient(path="langchain_qdrant")collections = [col.name for col in client.get_collections().collections]if COLLECTION_NAME not in collections: client.create_collection( collection_name=COLLECTION_NAME, vectors_config=VectorParams(size=DIM, distance=Distance.COSINE), ) print(f"Created new collection '{COLLECTION_NAME}'.")else: print(f"Using existing collection '{COLLECTION_NAME}'.") vector_store = QdrantVectorStore( client=client, collection_name=COLLECTION_NAME, embedding=embeddings,) Convert pdf to docling and save outputs Subsequently, all PDF files listed in the SOURCE are converted into DoclingDocuments and saved in dl_doc. These documents are then transformed into JSON and markdown files, which are stored on your local device. The markdown file is used to assess the quality of the file transformation process, while the JSON file is necessary for the subsequent visual grounding process in the second script. for source in SOURCES: dl_doc = converter.convert(source=source).document # JSON export out_json = OUTPUT_DIR / f"{dl_doc.origin.binary_hash}.json" dl_doc.save_as_json(out_json) # Markdown export with embedded images out_md = OUTPUT_DIR / f"{dl_doc.origin.binary_hash}.md" dl_doc.save_as_markdown(out_md, image_mode=ImageRefMode.EMBEDDED) Chunking the document Finally, we come to the main part of the script, including the chunking process of the document into smaller texts. This is achieved using the HybridChunker provided by docling. The best feature of this HybridChunker is that it tries to keep related passages together based on the markdown formatting and merges passages with each other if they are too small. A small max_tokens size has been selected to implement a small-to-big retrieval approach. This means that initially, a small chunk of text that closely matches the user's query is retrieved. Following this, a larger context chunk that surrounds the retrieved section is additionally retrieved and provided to the language model for generating the final answer. In this case, the larger context chunk refers to the paragraph containing the smaller chunk. chunker = HybridChunker( tokenizer=EMBED_MODEL, max_tokens=64, merge_peers=True)loader = DoclingLoader( file_path=SOURCES, converter=converter, export_type=ExportType.DOC_CHUNKS, chunker=chunker,)docs = loader.load() Embedding the document In the final step, the generated chunks are stored in the vector database with a unique identifier, from which they can be retrieved during the online phase. ids = [str(uuid4()) for _ in docs]vector_store.add_documents(documents=docs, ids=ids)print("Documents have been embedded into the vector store.") Retrieval and generation part Now, the online retrieval and generation part leverages the previously embedded text chunks to generate an answer for the user’s input based on the knowledge in the vector store. Imports As in the previous indexing script, all the necessary packages are included first. import osimport refrom pathlib import Pathfrom dotenv import load_dotenvfrom PIL import ImageDraw, Imagefrom pydantic import BaseModel, Fieldfrom qdrant_client import QdrantClientfrom langchain_core.output_parsers import PydanticOutputParserfrom langchain_core.prompts import PromptTemplatefrom langchain_huggingface.embeddings import HuggingFaceEmbeddingsfrom langchain_mistralai import ChatMistralAIfrom langchain_qdrant import QdrantVectorStorefrom docling.chunking import DocMetafrom docling.datamodel.document import DoclingDocument Configuration and environment variables Then, the necessary environment variables are loaded and configuration variables are set. load_dotenv()MISTRAL_API_KEY = os.environ["MISTRAL_API_KEY"]OUTPUT_DIR = Path("output")EMBED_MODEL = "sentence-transformers/all-MiniLM-L6-v2"COLLECTION_NAME = "demo_collection"QUESTION = ( "How does attention is computed in the transformer architecture?")TOP_K = 3OUTPUT_DIR.mkdir(parents=True, exist_ok=True) Build JSON lookup table In the next step, all JSON files in the OUTPUT directory are detected. For each file, its name is extracted using the stem method. Afterwards, it’s verified that the name consists only of digits, representing the hash value of the file, before adding the numeric name as a key and the file’s path as a value in the doc_store dictionary. Initializing embedding model, vector store, and llm After that, the embedding model and the LLM are initialized. It is important to use the same embedding model and Qdrant collection name as in the previous indexing script. Optionally, you may want to define these names in a separate configuration script and import this script into both the indexing and retrieval scripts. For the LLM, I selected Mistral, but you can also choose GPT-4, Gemini, Llama, or any other model you prefer. embeddings = HuggingFaceEmbeddings(model_name=EMBED_MODEL)client = QdrantClient(path="langchain_qdrant")try: client.get_collection(collection_name=COLLECTION_NAME)except Exception: print( f"Collection {COLLECTION_NAME} not found; create it before indexing.")vector_store = QdrantVectorStore( client=client, collection_name=COLLECTION_NAME, embedding=embeddings,)llm = ChatMistralAI( api_key=MISTRAL_API_KEY, model="mistral-large-latest", temperature=0, max_retries=2,) Preparing the chain Thereafter, all components of the RAG chain are set up, beginning with the Answer Pydantic schema, which consists of two fields. The first field, answerable is a boolean value that indicates whether the question can be answered using the retrieved knowledge. The second field, answer provides the actual response. This structure is employed to address the hallucination issue that many LLMs encounter, helping to prevent the dissemination of incorrect information to the user.Following that, the PydanticOutputParser and its get_format_instructuons() method format_instructuons generate the format instructions for the LLM. Moreover, the parser is employed again as the final Runnable of the LangChain execution chain, with its elements connected by the | operator. The prompt created for the PromptTemplate is basic and can be further enhanced by the introduction of few-shot examples or other advanced prompt engineering techniques. class Answer(BaseModel): answerable: bool = Field( ..., description="Whether the question can be answered" ) answer: str = Field( ..., description="The answer based on the provided knowledge" ) parser = PydanticOutputParser(pydantic_object=Answer)format_instructions = parser.get_format_instructions()prompt = PromptTemplate( input_variables=["knowledge", "topic"], partial_variables={"format_instructions": format_instructions}, template=( "You are given the following grounding material:\n\n" "{knowledge}\n\n" "Question:\n" "{topic}\n\n" "Please provide a concise answer in full sentences " "based solely on the information above.\n" "If the answer is not contained within the provided material, " "reply with:\n" "“There is no answer to this question in the provided material.”\n\n" "{format_instructions}" ),)rag_chain = prompt | llm | parser Run similarity search Once the chain has been set up. The text chunks semantically related to the user input query are retrieved. results = vector_store.similarity_search_with_score( k=TOP_K, query=QUESTION) Load and assemble grounding texts Based on the retrieved text chunks from the vector database, the entire paragraph containing each chunk is loaded. This approach is inspired by the small-to-big retrieval technique introduced earlier. Image created by the author, designed with canva and graphics generated using gpt-image-1 The relevant paragraph is pulled from the JSON file associated with the document from which the chunk originates. The correct JSON file is identified by the document’s hash value, which appears both in the text chunk’s metadata and in the JSON filename. Once the correct JSON file is identified, it’s loaded via the load_from_json method of the DoclingDocument. The original text item referenced in the current result chunk’s metadata is then extracted from the JSON file using a regular expression. If the referenced text item is found, the full text passage is retrieved to generate the final result. This example focuses exclusively on text grounding. However, Docling also provides references to previously identified images and tables. grounding_texts: list[str] = []for res, score in results: meta = DocMeta.model_validate(res.metadata["dl_meta"]) h = meta.origin.binary_hash json_file = doc_store.get(h) if not json_file: continue dl_doc = DoclingDocument.load_from_json(json_file) for item in meta.doc_items: if not item.prov: continue match = re.search(r"^#/texts/(\d+)$", item.self_ref) if not match: continue idx = int(match.group(1)) grounding_texts.append(dl_doc.texts[idx].text.strip())knowledge = "\n\n".join(grounding_texts)print("Assembled grounding material:\n", knowledge) Invoke the llm Finally, the LLM can be invoked using the previously defined chain. Inside the invoke method, the retrieved knowledge passages from the original JSON file and the user’s input question are passed in. The returned values conform to the defined AnswerPydantic schema for a structured evaluation the results. answer_obj = rag_chain.invoke( {"knowledge": knowledge, "topic": QUESTION})print("Answerable?", answer_obj.answerable)print("Answer: ", answer_obj.answer) Visual grounding The subsequent script proceeds to visually ground the used chunks in the corresponding documents only if the question can be answered based on the provided content, meaning the LLM returns that answerable is equal to true/1. This grounding process is approached similarly to how original text passages are extracted from documents. However, this time, the focus is on the bounding boxes surrounding these text passages instead of the raw text itself. These bounding boxes are another outcome of the document analysis and processing performed by the Docling pipeline in the indexing script. Now, these boxes can be utilized to anchor the generated answers to the original pages of the document. The coordinates of these bounding boxes come also from the metadata of each chunk, Moreover, the metadata of a chunk includes the page number, which is required to extract the screenshot of the correct page from the corresponding file. This screenshot of the page is the foundation for the visual grounding process, as bounding boxes are added on top of it. In the end, these bounding boxes are on top of the retrieved image of the correct side, allowing the user to precisely identify from which part of the document the information was retrieved. The final visually enhanced images are then stored in the same output directory as the input document and are ready for visual inspection. if answer_obj.answerable: for i, (res, score) in enumerate(results, start=1): meta = DocMeta.model_validate(res.metadata["dl_meta"]) h = meta.origin.binary_hash json_file = doc_store.get(h) if not json_file: continue dl_doc = DoclingDocument.load_from_json(json_file) image_by_page: dict[int, "Image.Image"] = {} for item in meta.doc_items: if not item.prov: continue prov = item.prov[0] p = prov.page_no if p not in image_by_page: image_by_page[p] = dl_doc.pages[p].image.pil_image.copy() img = image_by_page[p] bbox = prov.bbox.to_top_left_origin( page_height=dl_doc.pages[p].size.height ).normalized(dl_doc.pages[p].size) left = round(bbox.l * img.width) - 2 top = round(bbox.t * img.height) - 2 right = round(bbox.r * img.width) + 2 bottom = round(bbox.b * img.height) + 2 draw = ImageDraw.Draw(img) draw.rectangle([left, top, right, bottom], outline="blue", width=2) for p, img in image_by_page.items(): out_png = OUTPUT_DIR / f"source_{i}_page_{p}.png" img.save(out_png) print(f"Saved annotated page {p} → {out_png}") Limitation However, no solution is without flaws, and this one is no exception. The first point to consider is the heavy reliance on Docling. This tool is deeply integrated into the RAG framework, as it is used in almost every part of the system, including document parsing, text chunking, and grounding the final answer, which depends on the DoclingDocument class. Another point is the processing speed and resource requirements. While the advantage of Docling is that it can be deployed entirely on your local device, its processing speed and the maximum manageable file size depend significantly on your hardware. If your hardware is not powerful enough, you may only be able to parse one page of a document at a time. Additionally, a limitation of the provided example in this blog post is its focus solely on text. It ignores images and tables that also contain rich information and leaves space for further enhancements. Conclusion But despite these limitations, Docling is a neat way to enhance advanced RAG frameworks quickly with additional visual grounding capabilities. By connecting retrieved answers directly to their visual origin, this approach not only boosts transparency but also helps users build trust in the system’s output. What is your opinion about Docling and this introduced RAG framework? Have you already experimented with other grounding approaches for more explainable AI?Sources Docling Paper: https://arxiv.org/abs/2501.17887 Docling Documentation: https://docling-project.github.io/docling/ Qdrant local quickstart: https://qdrant.tech/documentation/quickstart/ LangChain ChatMistralAI: https://python.langchain.com/docs/integrations/chat/mistralai/ LangChain structured outputs: https://python.langchain.com/docs/concepts/structured_outputs/ Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. 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MCP-Use is an open-source library that lets you connect any LLM to any MCP server, giving your agents tool access like web browsing, file operations, and more — all without relying on closed-source clients. In this tutorial, we’ll use langchain-groq and MCP-Use’s built-in conversation memory to build a simple chatbot that can interact with tools via MCP.  Step 1: Setting Up the Environment Installing uv package manager We will first set up our environment and start with installing the uv package manager. For Mac or Linux: curl -LsSf https://astral.sh/uv/install.sh | sh For Windows (PowerShell): powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex" Creating a new directory and activating a virtual environment We will then create a new project directory and initialize it with uv uv init mcp-use-demo cd mcp-use-demo We can now create and activate a virtual environment. For Mac or Linux: uv venv source .venv/bin/activate For Windows: uv venv .venv\Scripts\activate Installing Python dependencies We will now install the required dependencies uv add mcp-use langchain-groq python-dotenv Step 2: Setting Up the Environment Variables Groq API Key To use Groq’s LLMs: Visit Groq Console and generate an API key. Create a .env file in your project directory and add the following line: GROQ_API_KEY=<YOUR_API_KEY>  Replace <YOUR_API_KEY> with the key you just generated. Brave Search API Key This tutorial uses the Brave Search MCP Server. Get your Brave Search API key from: Brave Search API Create a file named mcp.json in the project root with the following content: { "mcpServers": { "brave-search": { "command": "npx", "args": [ "-y", "@modelcontextprotocol/server-brave-search" ], "env": { "BRAVE_API_KEY": "<YOUR_BRAVE_SEARCH_API>" } } } } Replace <YOUR_BRAVE_SEARCH_API> with your actual Brave API key. Node JS Some MCP servers (including Brave Search) require npx, which comes with Node.js. Download the latest version of Node.js from nodejs.org Run the installer. Leave all settings as default and complete the installation Using other servers If you’d like to use a different MCP server, simply replace the contents of mcp.json with the configuration for that server. Step 3: Implementing the chatbot and integrating the MCP server Create an app.py file in the directory and add the following content: Importing the libraries from dotenv import load_dotenv from langchain_groq import ChatGroq from mcp_use import MCPAgent, MCPClient import os import sys import warnings warnings.filterwarnings("ignore", category=ResourceWarning) This section loads environment variables and imports required modules for LangChain, MCP-Use, and Groq. It also suppresses ResourceWarning for cleaner output. Setting up the chatbot async def run_chatbot(): """ Running a chat using MCPAgent's built in conversation memory """ load_dotenv() os.environ["GROQ_API_KEY"] = os.getenv("GROQ_API_KEY") configFile = "mcp.json" print("Starting chatbot...") # Creating MCP client and LLM instance client = MCPClient.from_config_file(configFile) llm = ChatGroq(model="llama-3.1-8b-instant") # Creating an agent with memory enabled agent = MCPAgent( llm=llm, client=client, max_steps=15, memory_enabled=True, verbose=False ) This section loads the Groq API key from the .env file and initializes the MCP client using the configuration provided in mcp.json. It then sets up the LangChain Groq LLM and creates a memory-enabled agent to handle conversations. Implementing the chatbot # Add this in the run_chatbot function print("\n-----Interactive MCP Chat----") print("Type 'exit' or 'quit' to end the conversation") print("Type 'clear' to clear conversation history") try: while True: user_input = input("\nYou: ") if user_input.lower() in ["exit", "quit"]: print("Ending conversation....") break if user_input.lower() == "clear": agent.clear_conversation_history() print("Conversation history cleared....") continue print("\nAssistant: ", end="", flush=True) try: response = await agent.run(user_input) print(response) except Exception as e: print(f"\nError: {e}") finally: if client and client.sessions: await client.close_all_sessions() This section enables interactive chatting, allowing the user to input queries and receive responses from the assistant. It also supports clearing the chat history when requested. The assistant’s responses are displayed in real-time, and the code ensures that all MCP sessions are closed cleanly when the conversation ends or is interrupted. Running the app if __name__ == "__main__": import asyncio try: asyncio.run(run_chatbot()) except KeyboardInterrupt: print("Session interrupted. Goodbye!") finally: sys.stderr = open(os.devnull, "w") This section runs the asynchronous chatbot loop, managing continuous interaction with the user. It also handles keyboard interruptions gracefully, ensuring the program exits without errors when the user terminates the session. You can find the entire code here Step 4: Running the app To run the app, run the following command uv run app.py This will start the app, and you can interact with the chatbot and use the server for the session Arham IslamI am a Civil Engineering Graduate (2022) from Jamia Millia Islamia, New Delhi, and I have a keen interest in Data Science, especially Neural Networks and their application in various areas.Arham Islamhttps://www.marktechpost.com/author/arhamislam/Implementing an AgentQL Model Context Protocol (MCP) ServerArham Islamhttps://www.marktechpost.com/author/arhamislam/Implementing An Airbnb and Excel MCP ServerArham Islamhttps://www.marktechpost.com/author/arhamislam/How to Create a Custom Model Context Protocol (MCP) Client Using GeminiArham Islamhttps://www.marktechpost.com/author/arhamislam/Implementing Persistent Memory Using a Local Knowledge Graph in Claude Desktop

Monster Hunter Wilds has sold 10.1 million copies since its launch on February 28, driving Capcom‛s net sales during the last quarter.As noted in the company‛s fiscal report for the 12 months ended March 31, 2025, net sales increased by 11.3 percent year-on-year to 169.6 billion yen ($1.15 billion), with net income following suit by 11.7 percent year-on-year to 48.45 billion yen ($328 million).In the previous fiscal report, which encompassed the nine months ended December 31, 2024, Capcom predicted that the launch of Monster Hunter Wilds would enable it to achieve its goals for the corresponding fiscal year. In the lead up to the game‛s release, previous entries Monster Hunter World: Iceborne and Monster Hunter Rise saw an increase in sales (15.2 million and 9.88 million total, respectively), resulting in cumulative sales of the series surpassing 100 million units worldwide."These efforts brought sales volumes in the Digital Contents business in the fiscal year under review to 51.87 million units, exceeding the previous fiscal year’s figure of 45.89 million units," the company said, referencing its plans to increase global figures with Monster Hunter Wilds as well as pursuing catalog sales. The latter includes recent premium games such as 2023‛s Resident Evil 4 Remake, which has sold 9.92 million units, with 2.76 million units in the last financial year.Related:"In addition, with the company's efforts to promote its digital sales strategies, sales volumes of catalog titles were 39.49 million units, up from 36.29 million units in the previous fiscal year. As a result, the company sold 248 titles in 227 countries and regions, contributing to enhancing the value of its brands," Capcom said.As of March 31, 2025, Capcom‛s top five franchises are Resident Evil (170 million units), Monster Hunter (120 million units), Street Fighter (56 million units), Mega Man (43 million units), and Devil May Cry (33 million units).Looking ahead, the company is placing its focus on the upcoming releases of Capcom Fighting Collection 2 and the Onimusha 2 remaster in May. It also mentioned catalog push sales of Street Fighter 6 and Kunitsu-Gami Path of the Goddess for the Nintendo Switch 2 in June.Premium games continue to pay off while live-service efforts falterCapcom continues to bet on premium games as a service model. While the company still has a transmedia push as one of its pillars—driven by arcade machines, film and TV adaptations, and esports, to name a few—games continue to be at the center.Related:The company has been vocal that there are clear risks involved. Monster Hunter Wilds has taken about 5-6 years of development, but some elements have been key to supporting this. This includes the RE Engine, Capcom's proprietary engine that's been used not only in the Resident Evil series, but also Dragon's Dogma 2, Street Fighter 6, and Devil May Cry 5.Establishing constant, open communication across teams has been important, too. "Our teams all work closely together, even across different titles," Wilds executive producer Ryozo Tsujimoto said. "We're able to share ideas and discoveries and technology with each other."The ongoing rise in popularity for the Monster Hunter franchise was spearheaded by World in 2018. The global interest, as well as a larger-scale audience, has led Capcom to take in new input on where to steer the franchise moving forward."From the get-go, we approached Monster Hunter World with a global mindset," Tsujimoto said during an interview at Summer Game Fest 2024. "At that point, we had approached things by releasing in Japan first and following up with other territories, but World was our first worldwide simultaneous release for Monster Hunter. It was also the first game we made with so many forms of localization. That seems obvious to say, but that was a challenge for us initially because, with that global approach, we had to open up the series to other Capcom members across the globe to give opinions and input on the kind of direction the series would be going on."Related:While the focus has been paying off for the company, its efforts toward live-service games, with Resident Evil Re:Verse as the latest example, haven't been as successful. Capcom shut down the servers for the shooter on June 29, 2024, after removing the game and all DLC from sale on March 3 of the same year. Despite this, the developer said that the game "far exceeded" its expectations since its release, although there aren't mentions of live-service plans in the latest fiscal report.

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More A new report released by the publicly traded market research and intelligence firm SimilarWeb—covering global web traffic patterns for AI-related platforms for 12 weeks through May 9, 2025—offers a helpful look for enterprises and interested users into the current landscape of generative AI usage online. Using proprietary analytics based on site visits, the report tracks trends across sectors including general-purpose AI tools, coding assistants, content generators, and more. It also maps the downstream disruption of traditional sectors like education, search, and digital freelancing. Why this report matters to enterprise technical decision-makers For enterprise AI leaders—particularly those responsible for model deployment, orchestration, or data integration—this report is more than just a consumer trend snapshot. It’s a map of user familiarity and expectation. Tools like ChatGPT, Claude, DeepSeek, and Grok are what employees are already using at home or experimenting with informally at work. If your internal AI apps or copilots don’t match the baseline experience offered by these tools, you risk user rejection and poor adoption. Moreover, selecting the same models with high external traction for your internal orchestration pipelines or RAG deployments can cut onboarding time and training costs, since your team will already know the interface and behavior. In short, aligning your enterprise stack with the tools dominating usage charts may be the fastest path to trust, usability, and value delivery. Here are five key findings from the report: Developer-focused AI tools are surging in adoption, with traffic to the category up 75% over the past 12 weeks. That growth includes Lovable, which exploded with a jaw-dropping +17,600% spike, and Cursor, which grew steadily month over month. As AI becomes more deeply embedded in IDEs and continuous integration workflows, usage patterns suggest these tools are no longer just experimental—they’re now seen as essential infrastructure for modern software teams. It also helps explain why OpenAI reportedly tried to buy Cursor and has also reportedly finalized a deal to purchase rival AI coding platform Windsurf — as the usage of these tools explodes, OpenAI wants to get a larger cut of the action and revenue, especially since its models are often used as the engine behind-the-scenes for these very users. Two of the fastest-growing AI platforms of the year, Grok and DeepSeek, show just how quickly hype can turn into burnout. Grok traffic skyrocketed more than 1,000,000% in March—driven by its branding as an uncensored yet powerfully intelligent platform and Elon Musk association—before falling more than 5,200% by early May. DeepSeek saw a similar arc, peaking at +17,701% growth before crashing -41%. The takeaway: virality can’t replace retention, especially compared to AI leader OpenAI and also legacy tech brand Google. Once among the most accessible use cases for generative AI, writing and content tools are seeing user fatigue. Category traffic fell 11% overall, with platforms like Wordtune (-35%), Jasper (-19%), and Rytr (-23%) all trending downward. Only Originality.ai bucked the trend with steady traffic gains, likely due to its focus on AI detection rather than generation. This plateau suggests content saturation and possibly growing skepticism over quality or usefulness. Separately from the SimilarWeb report, I think that because chatbots already use a default text input/output field as their main user interface, even for multimodal interactions, the fact is that most users who would be interested in this capability will go to model providers like OpenAI’s website for ChatGPT (and mobile apps), or Google’s Gemini, or even Anthropic’s Claude, directly, rather than seek out a separate AI writing tool. Design-focused AI remains a mixed bag. While overall category usage dipped slightly (-6% over the 12-week window), some platforms made outsized gains. Getimg posted a massive +1,532% surge, while Artbreeder jumped +100%. Others, like Stable Diffusion and Looka, saw double-digit declines. The erratic pattern may reflect a crowded landscape of tools that offer similar functionality but compete on novelty or aesthetics. 5. AI is eating up legacy tech such as crowdsourcing and search Traditional digital services are showing slow but steady declines across the board—likely the result of AI substitution. Freelance platforms such as Fiverr (-17%) and Upwork (-19%) are losing traffic, possibly as users turn to AI tools for tasks like design, writing, and code. Search engines such as Yahoo (-12%) and Bing (-14%) continue a multi-quarter drop in visits, while consumer EdTech companies like Chegg (-62%) and CourseHero (-68%) are in free fall. The signs point to early-stage AI disruption beginning to erode the utility of some legacy platforms. It also offers a hint to enterprises that either leverage or create such services — the time may be coming to reduce dependency on them, either from a revenue generation, marketing, or overall business perspective. Daily insights on business use cases with VB Daily If you want to impress your boss, VB Daily has you covered. We give you the inside scoop on what companies are doing with generative AI, from regulatory shifts to practical deployments, so you can share insights for maximum ROI. Read our Privacy Policy Thanks for subscribing. Check out more VB newsletters here. An error occured.

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Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More A new report released by the publicly traded market research and intelligence firm SimilarWeb—covering global web traffic patterns for AI-related platforms for 12 weeks through May 9, 2025—offers a helpful look for enterprises and interested users into the current landscape of generative AI usage online. Using proprietary analytics based on site visits, the report tracks trends across sectors including general-purpose AI tools, coding assistants, content generators, and more. It also maps the downstream disruption of traditional sectors like education, search, and digital freelancing. Why this report matters to enterprise technical decision-makers For enterprise AI leaders—particularly those responsible for model deployment, orchestration, or data integration—this report is more than just a consumer trend snapshot. It’s a map of user familiarity and expectation. Tools like ChatGPT, Claude, DeepSeek, and Grok are what employees are already using at home or experimenting with informally at work. If your internal AI apps or copilots don’t match the baseline experience offered by these tools, you risk user rejection and poor adoption. Moreover, selecting the same models with high external traction for your internal orchestration pipelines or RAG deployments can cut onboarding time and training costs, since your team will already know the interface and behavior. In short, aligning your enterprise stack with the tools dominating usage charts may be the fastest path to trust, usability, and value delivery. Here are five key findings from the report: Developer-focused AI tools are surging in adoption, with traffic to the category up 75% over the past 12 weeks. That growth includes Lovable, which exploded with a jaw-dropping +17,600% spike, and Cursor, which grew steadily month over month. As AI becomes more deeply embedded in IDEs and continuous integration workflows, usage patterns suggest these tools are no longer just experimental—they’re now seen as essential infrastructure for modern software teams. It also helps explain why OpenAI reportedly tried to buy Cursor and has also reportedly finalized a deal to purchase rival AI coding platform Windsurf — as the usage of these tools explodes, OpenAI wants to get a larger cut of the action and revenue, especially since its models are often used as the engine behind-the-scenes for these very users. Two of the fastest-growing AI platforms of the year, Grok and DeepSeek, show just how quickly hype can turn into burnout. Grok traffic skyrocketed more than 1,000,000% in March—driven by its branding as an uncensored yet powerfully intelligent platform and Elon Musk association—before falling more than 5,200% by early May. DeepSeek saw a similar arc, peaking at +17,701% growth before crashing -41%. The takeaway: virality can’t replace retention, especially compared to AI leader OpenAI and also legacy tech brand Google. Once among the most accessible use cases for generative AI, writing and content tools are seeing user fatigue. Category traffic fell 11% overall, with platforms like Wordtune (-35%), Jasper (-19%), and Rytr (-23%) all trending downward. Only Originality.ai bucked the trend with steady traffic gains, likely due to its focus on AI detection rather than generation. This plateau suggests content saturation and possibly growing skepticism over quality or usefulness. Separately from the SimilarWeb report, I think that because chatbots already use a default text input/output field as their main user interface, even for multimodal interactions, the fact is that most users who would be interested in this capability will go to model providers like OpenAI’s website for ChatGPT (and mobile apps), or Google’s Gemini, or even Anthropic’s Claude, directly, rather than seek out a separate AI writing tool. Design-focused AI remains a mixed bag. While overall category usage dipped slightly (-6% over the 12-week window), some platforms made outsized gains. Getimg posted a massive +1,532% surge, while Artbreeder jumped +100%. Others, like Stable Diffusion and Looka, saw double-digit declines. The erratic pattern may reflect a crowded landscape of tools that offer similar functionality but compete on novelty or aesthetics. 5. AI is eating up legacy tech such as crowdsourcing and search Traditional digital services are showing slow but steady declines across the board—likely the result of AI substitution. Freelance platforms such as Fiverr (-17%) and Upwork (-19%) are losing traffic, possibly as users turn to AI tools for tasks like design, writing, and code. Search engines such as Yahoo (-12%) and Bing (-14%) continue a multi-quarter drop in visits, while consumer EdTech companies like Chegg (-62%) and CourseHero (-68%) are in free fall. The signs point to early-stage AI disruption beginning to erode the utility of some legacy platforms. It also offers a hint to enterprises that either leverage or create such services — the time may be coming to reduce dependency on them, either from a revenue generation, marketing, or overall business perspective. Daily insights on business use cases with VB Daily If you want to impress your boss, VB Daily has you covered. We give you the inside scoop on what companies are doing with generative AI, from regulatory shifts to practical deployments, so you can share insights for maximum ROI. Read our Privacy Policy Thanks for subscribing. Check out more VB newsletters here. An error occured.

Editor’s note: As of June 1, 2023, the Unity 2022 LTS is now available.When I talk to our partners, customers, and those of you who work day to day with Unity’s Editor and runtime, one thing that keeps popping up is the desire to be more ambitious with your game design. You want tools that enable you to create with fewer constraints, delivering player experiences that wow and cause wonder.Unity 2022 LTS is designed to give you that power. You’ll discover that you can rely on the new LTS release to create sophisticated DOTS-powered games, multiplayer experiences, immersive HD environments, and visuals that perform great on any platform you target. And we remain committed to ensuring you have wide platform support with amazing tools for any device in this release.So, I couldn’t wait any longer to tell you about the Unity 2022 LTS release, which is just around the corner.On June 22, 2023, we’ll host a multi-hour livestream diving into some of our favorite features, so be sure to join us then by signing up to get notified. But if you’re like me and are waiting with bated breath to hear about the new features, I’ve got some inside knowledge for you right here.Let’s dive into some of the highlights from this release that I’m particularly excited about.Yes, DOTS is here! The team, led by Laurent Gibert and Joe Valenzuela, has delivered the first fully supported-for-production version of our Entity Component System (ECS), so you can now build with the full Data-Oriented Technology Stack (DOTS) solution – Burst compiler, C# Jobs System, and now ECS for Unity.Your most challenging projects require a game engine that provides power and flexibility. ECS for Unity offers the ability to gain greater control and determinism over data in memory and better runtime process scheduling. ECS is integrated into the Editor, so you can leverage your existing GameObject-based experience and use the power of Entities-based code when it’s beneficial.We’ll have more information about how you can harness the power of DOTS when Unity 2022 LTS is released, but you can engage with the team who actively participate in our forums here.With Unity 2022 LTS, we’re excited to support multiplayer games with a rapidly growing end-to-end ecosystem of creation workflows and cloud services. This is possible by tight integration between the Unity game engine and multiplayer services provided through Unity Gaming Services (UGS).We’re shipping the Netcode for Entities package with Unity 2022 LTS, fully supported for production. Netcode for Entities is a powerful networking feature to boost a game’s performance and capabilities. You’ll be able to increase the number of players, interactable objects, and backend server-side entities in competitive action games like first-person shooters and massive multiplayer online games.More than any other genre, multiplayer games rely on successful ongoing operations for live titles. That’s why we have a suite of multiplayer-specific services as part of UGS. Matchmaker, Friends, Leaderboards, and User-Generated Content are designed to simplify the implementation of multiplayer capabilities, while services like Relay, Lobby, Game Server Hosting (Multiplay), and Voice and Text Chat (Vivox) round out capabilities for your live game.To see our multiplayer functionality in action, we will also be releasing our new Megacity multiplayer sample in June, which is built on ECS for Unity and features UGS tools for hosting, matchmaking, authentication, and voice chat between players. This demo demonstrates how you can bring our powerful tools together in a fast-paced, competitive, third-person shooter that supports 64+ players.Immerse your players in beautiful, physically based environments with High Definition Render Pipeline (HDRP). Unity LTS 2022 includes numerous features to enhance your game worlds for an even richer player experience.Introduce advanced procedural fog and volumetric effects using Volumetric Materials and Shader Graph to create atmospheric game environments such as haunted forests, desolate landscapes, or misty valleys.The new Water System enables you to add oceans, rivers, and underwater effects to your game environments. You can create realistic waves, ripples, foam, and more, as well as simulate underwater environments with advanced caustics, refraction, and reflection effects.Improvements to Cloud Layers with dynamic lighting and Volumetric clouds allow you to create even more realistic skies with clouds that change and move based on weather conditions, simply blending between different weather effects such as sunny and cloudy skies.A new update to the Spline package helps you procedurally generate paths, roads, or fences in your environments with greater precision for more organic and varied game environments with less manual work.The Universal Render Pipeline (URP) provides a scalable solution for high-quality graphics across platforms. Upgrades in this release allow you to create more realistic lighting in your scenes and achieve higher-quality visuals that scale across devices.Forward+ Rendering eliminates the light limit count so you can use more lights in a scene while maintaining performance. This feature is particularly useful if you want to achieve high-quality real-time lighting in your scenes.LOD crossfade which produces smoother graphic transitions as objects move closer or further away from the camera. Temporal Anti-aliasing (TAA) reduces aliasing problems such as pixelated and flickering edges to improve the overall visual quality of game scenes.Decal Layers allow you to add extra texture details in your scenes with control. You can use Decal Layers to filter and configure how different objects get affected by different Decal Projectors in a Scene.Customize your rendering experience with features like Shader Graph Full Screen Master Node and custom screen space effects. These allow you to create unique visual effects in games such as distortion or other types of post-processing, and they’re available in both URP and HDRP.Shader Variant Prefiltering significantly improves build time and memory optimization, reducing the build time and improving games’ overall performance.Finally, the Built-in Converter helps you to move existing projects from the Built-in Render Pipeline to URP, making it easier for you to take advantage of URP’s performance and scalability benefits.With Unity 2022 LTS, you can optimize your games for the latest platforms across mobile, console, desktop, and XR, with added performance and stability for key features.Maximize platform potential with increased performance and stability using the DirectX 12 graphics API on Windows and Xbox®. You can also experiment with the latest ray tracing support for Xbox® Series X|S and PlayStation®5, which allows for more realistic real-time lighting and reflections in game scenes.Iterate faster with the latest incremental player build process for Xbox®, iOS, PlayStation®5, and Nintendo Switch™*, which improves deployment efficiency, reducing time to market and improving overall game quality.Dig deeper into the performance of Android games with access to low-level data through the System Metrics Mali package from ARM. Games on Samsung devices can now take advantage of Adaptive Performance 4.0 with support for visual scripting. Better manage WebGL memory usage and gain native C++ multithreading along with support for touch controls and texture compression on web builds for mobile devices.Unity 2022 LTS expands your reach in XR with an updated XR Interaction toolkit (XRI) to enhance build times for PlayStation®VR2 and Meta Quest 2. Ensure your VR games run well across a variety of performant devices with late latching and motion vectors and improved graphics performance for games using Vulkan. These updated tools enable you to build immersive, high-fidelity XR experiences that can be deployed across multiple platforms.With a minimum of two years development and testing, Unity 2022 LTS has relied on user feedback and testing. Many of you have joined us on the journey through beta and Tech Stream releases, and I want to personally thank you for partnering with us to deliver the most stable Unity release in the 2022 cycle. And remember, LTS releases are supported for at least two years through biweekly updates, so we have you covered.Learn more about our 2022 LTS and the 2023.1 Tech Stream release in our GDC 2023 roadmap session.Wherever you are in your production process, Unity 2022 LTS offers resources to help you bring your project to the finish line and beyond. Get notified when LTS 2022 is available on release day by signing up for email updates.We hope this overview of the Unity 2022 LTS has given you a good idea of the powerful features and tools available in this release. We’re always eager to hear your feedback, questions, and ideas, so please keep the conversation going.Interact with other Unity creators and experts in our forums or directly through our platform roadmap. Catch our live streams on Twitch, and be sure to follow us on Twitter to stay up to date on the latest news and announcements. We look forward to hearing what you think.*Nintendo Switch is a registered trademark of Nintendo.