# data[].attributes.attachment_link.title
Claude Design | Turn Ideas into Design | Claude by Anthropic

# data[].attributes.attachment_link.description
Describe a prototype, deck, or one-pager, and Claude builds a draft. Refine it yourself, or hand it off to your tools or Claude Code. You’re the designer, from start to finish.

# data[].attributes.attachment_link.title
How Chatbots and Large Language Models Work

# data[].attributes.attachment_link.description
Learn strategies and tactics for better results using large language models in the OpenAI API.

# data[].attributes.body
Benchmarks of models accessed through their APIs.

# data[].attributes.body
Document summaries, quizzes, notes, plus audio and video generation.

# data[].attributes.attachment_link.title
Gemini Notebook | AI Research Tool & Thinking Partner

# data[].attributes.attachment_link.description
Meet Gemini Notebook, the AI research tool and thinking partner that can analyze your sources, turn complexity into clarity and transform your content.

# data[].attributes.body
Ollama, Open WebUI and Hugging Face can also call online services: check the model and its tag. Legally sensitive data: genuinely local only, via DCSR. AI_GO decides what you may use, not this board.

# data[].attributes.body
Run open-weight models locally (Mac, Windows, Linux).

# data[].attributes.attachment_link.title
Download LM Studio - Mac, Linux, Windows

# data[].attributes.body
Exploratory AI search over Google Scholar. Google sign-in required.

# data[].attributes.attachment_link.title
We’re introducing Google Scholar Labs to answer your research questions.

# data[].attributes.attachment_link.description
Today, we are introducing Google Scholar Labs, a new feature that explores how generative AI can transform the process of answering detailed scholarly research questions…

# data[].attributes.attachment_link.description
Consensus is an AI academic search engine for peer-reviewed literature—your research OS for finding, organizing, and analyzing science 10x faster.

# data[].attributes.body
High-throughput inference and serving engine for open-weight LLMs.

# data[].attributes.attachment_link.title
GitHub - vllm-project/vllm: A high-throughput and memory-efficient inference and serving engine for LLMs

# data[].attributes.attachment_link.description
A high-throughput and memory-efficient inference and serving engine for LLMs - vllm-project/vllm

# data[].attributes.body
Official UNIL questionnaire. Complete it before using any AI tool. French and English versions.

# data[].attributes.attachment_link.title
AI_GO : Quel outil IA pour vos données ? — UNIL

# data[].attributes.attachment_link.description
Avertissement : Outil d’aide à la décision, il ne garantit pas une sécurité et une conformité légale à 100 %, l’utilisateur·rice demeure responsable de l’évaluation finale et des mesures mises en œuvre.

# data[].attributes.attachment_link.description
Your code’s new collaborator. Embed Claude directly in your terminal or IDE to manage million-line codebases and implement changes.

# data[].attributes.body
Visuals generated from text.

# data[].attributes.attachment_link.title
Napkin AI - The AI assistant for visual communication

# data[].attributes.attachment_link.description
Just type, copy-paste or generate your text and Napkin will instantly transform it into insightful visuals. Make your communication more effective with Napkin.

# data[].attributes.body
AI-generated slides and websites.

# data[].attributes.attachment_link.description
Réalisé avec Gamma. Un nouveau moyen de présenter des idées, alimenté par l’IA.

# data[].attributes.attachment_link.description
Explore developer resources, tutorials, API docs, and dynamic examples to get the most out of OpenAI’s platform.

# data[].attributes.attachment_link.description
Use AI to search, summarize, extract data from, and chat with over 125 million papers. Used by over 2 million researchers in academia and industry.

# data[].attributes.headline
Microsoft 365 Copilot Chat: UNIL's only contracted LLM

# data[].attributes.subject
Microsoft 365 Copilot Chat: UNIL's only contracted LLM

# data[].attributes.body
Sign in with @unil.ch and check the Protected badge: the same link opens consumer Copilot otherwise. Personal data or official secrecy: use the UNIL 📘 [Sécurisé] agent, which disables web search. Legally sensitive data: forbidden here, use local LLMs.

# data[].attributes.attachment_link.description
ChatGPT helps you get answers, find inspiration, and be more productive.

# data[].attributes.attachment_link.title
Microsoft Designer - Stunning designs in a flash

# data[].attributes.attachment_link.description
A graphic design app that helps you create professional quality social media posts, invitations, digital postcards, graphics, and more. Start with your idea and create something unique for you.

# data[].attributes.headline
DCSR-LLM: open models on UNIL infrastructure

# data[].attributes.subject
DCSR-LLM: open models on UNIL infrastructure

# data[].attributes.body
Data stays on UNIL infrastructure. For personal, unpublished or internal data. Contact helpdesk@unil.ch, subject DCSR-LLM.

# data[].attributes.attachment_link.title
DCSR-LLM: using artificial intelligence to support research at UNIL - Intelligence artificielle — Regards sur l’IA à l’UNIL

# data[].attributes.attachment_link.description
Compare models, extract structured data, reproduce workflows: DCSR-LLM helps UNIL researchers use large language models in a more controlled and reproducible way.

# data[].attributes.body
Permitted uses, accounts and licences.

# data[].attributes.attachment_link.description
UNIL offers an approach based on the excellence of its research, the interdisciplinarity of its teaching and the commitment of its community.

# data[].attributes.attachment_link.title
Gemini AI image generator - text to image in seconds

# data[].attributes.attachment_link.description
With the Gemini AI image generator powered by Imagen 3, you can create high-quality images in seconds for whatever inspires you.

# data[].attributes.attachment_link.description
Codex is OpenAI’s series of AI coding tools that help developers move faster by delegating tasks to powerful cloud and local coding agents.

# data[].attributes.headline
Corv: audio and video transcription (DCSR)

# data[].attributes.subject
Corv: audio and video transcription (DCSR)

# data[].attributes.body
Whisper-based. UNIL VPN required.

# data[].attributes.attachment_link.title
Transcription automatique de fichiers audios et vidéos

# data[].attributes.attachment_link.description
Ce service permet d’accéder aux infrastructures de calcul haute performance (clusters) de l’UNIL pour le traitement de données de recherche non sensibles.

# data[].attributes.attachment_link.readable_text
Use our API to create new user experiences, products, and ways to work with the most advanced AI models on the market.

Build on your own

Launch your own generative AI solution with:

Access to all Claude models

Usage-based tiers

Automatically increasing rate limits

Simple pay-as-you-go pricing

Self-serve deployment on workbench

Prompting guides and developer docs

Get extra support

Need custom rate limits or hands-on help? Contact our sales team for:

Anthropic-supported onboarding

Custom rate limits

Billing via monthly invoices

Prompting support

Deployment support

Right-sized for any task, our models offer the best combination of speed and performance.

Fable 5

Next generation intelligence for long-running agents

Opus 4.8

Ideal for complex agentic coding and enterprise work

Sonnet 5

High-performance model for coding and agents

*Introductory pricing of $2/$10 per million input/output tokens through August 31, 2026; $3/$15 standard pricing thereafter.

For workloads that need to run in the US, US-only inference is available at 1.1x pricing for input and output tokens. Learn more.

Get up to 2.5x faster speeds with fast mode for Opus 4.8 at 2x standard pricing. Learn more.

Prompt caching pricing reflects 5-minute TTL. Learn about extended prompt caching.

Integrate Claude's powerful AI capabilities into your apps and deliver production-grade solutions faster.

You are an AI assistant specialized in classifying customer support tickets. Your task is to analyze the content of a given ticket and assign it to the most appropriate category from a predefined list. You will also provide reasoning for your classification decision.

First, let's review the available categories:

Now, here is the content of the support ticket you need to classify:

Please follow these steps to complete the task:
- Carefully read and analyze the ticket content.
- Consider how the content relates to each of the available categories.
- Choose the most appropriate category for the ticket.
- Provide a detailed explanation of your reasoning process.

Use the following structure for your response:
<classification_analysis>
In this section, break down your thought process:
- Quote the most relevant parts of the ticket content.
- List each category and note how it relates to the ticket content.
- For each category, provide arguments for and against classifying the ticket into that category.
- Rank the top 3 most likely categories.
</classification_analysis>

<classification>
<category>Your chosen category goes here</category>
<reasoning>A concise summary of your reasoning for choosing this category</reasoning>
</classification>

Remember to be thorough in your analysis and clear in your explanation. Your goal is to provide an accurate classification with well-supported reasoning.

Build, test, and iterate on your deployment:

Automatically generate or improve existing prompts

Evaluate model responses against real-world scenarios

Build faster with pre-built cookbooks and guides

Our models are constantly improving on coding, math, and reasoning. Claude can complete complex engineering tasks to solve problems that would typically take a full day of engineering work.

Claude offers superior instruction following, tool selection, error correction, and advanced reasoning for customer-facing agents and complex AI workflows.

Claude can extract relevant information from business emails and documents, categorize and summarize survey responses, and wrangle reams of text with high speed and accuracy.

Claude can handle ticket triage, on-demand complex inquiries using rich context awareness, and multi-step support workflows-all with a casual tone and conversational responses.

# data[].attributes.attachment_link.description
Use our API to create new user experiences, products, and ways to work with the most advanced AI models on the market.

# data[].attributes.attachment_link.description
Talk with Claude, an AI assistant from Anthropic

# data[].attributes.body
Compare popular LLMs on several performance metrics.

# data[].attributes.attachment_link.title
Canva AI 2.0 – AI design, writing, and creative tools | Canva

# data[].attributes.attachment_link.description
AI tools for design, writing, brand, coding, and more – available now. Canva AI 2.0 is a new way to design through conversation – coming soon.

# data[].attributes.attachment_link.description
ChatGPT Images 2.0 présente un modèle de génération d'images de pointe, avec un rendu de texte amélioré, une prise en charge multilingue et des capacités avancées de raisonnement visuel.

# data[].attributes.body
Agent-first dev platform: desktop app, IDE and terminal CLI. Successor to Gemini CLI.

# data[].attributes.headline
eScriptorium: OCR and HTR transcription (DCSR)

# data[].attributes.subject
eScriptorium: OCR and HTR transcription (DCSR)

# data[].attributes.body
UNIL VPN required.

# data[].attributes.attachment_link.title
Reconnaissance automatique de texte (OCR/HTR) - Ci UNIL

# data[].attributes.attachment_link.description
Ce service permet d’accéder aux infrastructures de calcul haute performance (clusters) de l’UNIL pour le traitement de données de recherche non sensibles.

# data[].attributes.attachment_link.description
Gemini is your personal, proactive, and powerful AI assistant from Google. Try it for free to help with work, school, and at home for whatever inspires you.

# data[].attributes.headline
AI and the law: Philippe Gilliéron (in French)

# data[].attributes.subject
AI and the law: Philippe Gilliéron (in French)

# data[].attributes.body
Video interview with a UNIL law professor and lawyer (10 min).

# data[].attributes.attachment_link.title
IA Générative et enseignement supérieur, entretien avec le professeur Philippe Gilliéron

# data[].attributes.body
Open-weight models via an OpenAI-compatible API.

# data[].attributes.attachment_link.title
AI Service - IA Open Source à la demande via API, hébergé en Europe

# data[].attributes.attachment_link.description
Créez des applications avec les meilleurs modèles d’IA open source via API. Hébergées en Suisse, elles garantissent souveraineté, sécurité, conformité RGPD et faible empreinte carbone.

# data[].attributes.body
One OpenAI-compatible API for hundreds of models, with price and performance comparison.

# data[].attributes.body
Search-oriented assistant for online research.

# data[].attributes.attachment_link.description
Perplexity is a free AI-powered answer engine that provides accurate, trusted, and real-time answers to any question.

# data[].attributes.body
Nature, March 2025: projected electricity demand of AI and the lack of corporate transparency.

# data[].attributes.attachment_link.readable_text
The aroma of hay and manure hangs over Culpeper County, Virginia, where there's a cow for every three humans. "We've got big farms, most still family-owned, and a lot of forests," says Sarah Parmelee, one of the county's 55,000 residents. "It's very charming small-town USA," she adds.

But this pastoral idyll is in the middle of a twenty-first-century shift. Over the past few years, the county has approved the construction of seven large data-centre projects, which will support technology firms in their expansive plans for generative artificial intelligence (AI). Inside these giant structures, rows of computer servers will help to train the AI models behind chatbots such as ChatGPT, and deliver their answers to what might be billions of daily queries from around the world.

In Virginia, the construction will have profound effects. Each facility is likely to consume the same amount of electrical power as tens of thousands of residential homes, potentially driving up costs for residents and straining the area's power infrastructure beyond its capacity. Parmelee and others in the community are wary of the data centres' appetite for electricity - particularly because Virginia is already known as the data-centre capital of the world. A state-commissioned review, published in December 2024, noted that although data centres bring economic benefits, their growth could double electricity demand in Virginia within ten years.

"Where is power going to come from?" asks Parmelee, who is mapping the rise of data centres in the state and works for the Piedmont Environmental Council, a non-profit organization headquartered in Warrenton, Virginia. "They're all saying, 'We'll buy power from the next district over.' But that district is planning to buy power from you."

Similar conflicts about AI and energy are brewing in many places around the globe where data centres are sprouting up at a record pace. Big tech firms are betting hard on generative AI, which requires much more energy to operate compared with older AI models that extract patterns from data but don't generate fresh text and images. That is driving companies to collectively spend hundreds of billions of dollars on new data centres and servers to expand their capacity.

From a global perspective, AI's impact on future electricity demand is actually projected to be relatively small. But data centres are concentrated in dense clusters, where they can have profound local impacts. They are much more spatially concentrated than are other energy-intensive facilities, such as steel mills and coal mines. Companies tend to build data-centre buildings close together so that they can share power grids and cooling systems and transfer information efficiently, both among themselves and to users. Virginia, in particular, has attracted data-centre firms by providing tax breaks, leading to even more clustering.

"If you have one, you're likely to have more," says Parmelee. Virginia already has 340 such facilities, and Parmelee has mapped 159 proposed data centres or expansions of existing ones in Virginia, where they account for more than one-quarter of the state's electricity use, according to a report by EPRI, a research institute in Palo Alto, California. In Ireland, data centres account for more than 20% of the country's electricity consumption - with most of them situated on the edge of Dublin. And the facilities' electricity consumption has surpassed 10% in at least 5 US states.

Complicating matters further is a lack of transparency from firms about their AI systems' electricity demands. "The real problem is that we're operating with very little detailed data and knowledge of what's happening," says Jonathan Koomey, an independent researcher who has studied the energy use of computing for more than 30 years and who runs an analytics firm in Burlingame, California.

"I think every researcher on this topic is going crazy because we're not getting the stuff we need," says Alex de Vries, a researcher at the Free University of Amsterdam and the founder of Digiconomist, a Dutch company that explores the unintended consequences of digital trends. "We're just doing our best, trying all kinds of tricks to come up with some kind of numbers."

Working out AI's energy demands

Lacking detailed figures from firms, researchers have explored AI's energy demand in two ways. In 2023, de Vries used a supply-chain (or market-based) method. He examined the power draw of one of the NVIDIA servers that dominates the generative AI market and extrapolated that to the energy required over a year. He then multiplied that figure by estimates of the total number of such servers that are being shipped, or that might be required for a particular task.

De Vries used this method to estimate the energy needed if Google searches used generative AI. Two energy-analyst firms had estimated that implementing ChatGPT-like AI into every Google search would require between 400,000 and 500,000 NVIDIA A100 servers, which, based on the power demand of those servers, would amount to 23-29 terawatt hours (TWh) annually. Then, estimating that Google was processing up to 9 billion searches daily (a ballpark figure from various analysts), de Vries calculated that each request through an AI server requires 7-9 watt hours (Wh) of energy. That is 23-30 times the energy of a normal search, going by figures Google reported in a 2009 blogpost (see go.nature.com/3d8sd4t). When asked to comment on de Vries' estimate, Google did not respond.

This energy calculation felt like "grasping at straws", de Vries says, because he had to rely on third-party estimates that he could not replicate. And his numbers quickly became obsolete. The number of servers required for an AI-integrated Google search is likely to be lower now, because today's AI models can match the accuracy of 2023 models at a fraction of the computational cost, as US energy-analyst firm SemiAnalysis (whose estimates de Vries had relied on) wrote in an e-mail to Nature.

Even so, the firm says that the best way of assessing generative AI's energy footprint is still to monitor server shipments and their power requirements, which is broadly the method used by many analysts. However, it is difficult for analysts to isolate the energy used solely by generative AI, because data centres generally perform non-AI tasks as well.

Bottom-up estimates

The other way to examine AI's energy demand is 'bottom-up': researchers measure the energy demand of one AI-related request in a specific data centre. However, independent researchers can perform the measurements using only open-source AI models that are expected to resemble proprietary ones.

The concept behind these tests is that a user submits a prompt - such as a request to generate an image, or a text-based chat - and a Python software package called CodeCarbon then allows the user's computer to access technical specifications of the chips that execute the model in the data centre. "At the end of the run, it'll give you an estimate of how much energy was consumed by the hardware that you were using," says Sasha Luccioni, an AI researcher who helped to develop CodeCarbon, and who works at Hugging Face, a firm based in New York City that hosts an open-source platform for AI models and data sets.

Luccioni and others found that different tasks require varying amounts of energy. On average, according to their latest results, generating an image from a text prompt consumes about 0.5 Wh of energy, while generating text uses a little less. For comparison, a modern smartphone might need 22 Wh for a full charge. But there is wide variation: larger models require more energy (see 'How much energy does AI use?'). De Vries says that the numbers are lower than those in his paper, but that might be because the models used by Luccioni and others are at least an order of magnitude smaller than the model underlying ChatGPT - and because AI is becoming more efficient.

The numbers are a lower bound, according to Emma Strubell, a computer scientist and collaborator of Luccioni's at Carnegie Mellon University in Pittsburgh, Pennsylvania. Otherwise, "companies would be coming out and correcting us", she says. "They're not doing that."

What's more, firms generally withhold the information the software needs to estimate the energy used for data-centre cooling. CodeCarbon also cannot access the energy consumption of some types of chip. This includes Google's proprietary TPU chips, says Benoît Courty, a data scientist in France who maintains CodeCarbon.

Luccioni has also studied how much energy it takes to train generative AI models - when a model extracts statistical patterns from massive amounts of data. But if models are receiving billions of queries daily, as de Vries assumed for his Google estimates, then the energy used to answer those queries - amounting to terawatt hours of electricity - will dominate AI's annual energy demands. Training something the size of GPT-3, the model behind the first version of ChatGPT, requires energy in the order of a gigawatt hour.

Last month, Luccioni and other researchers launched the AI Energy Score project, a public initiative to compare the energy efficiency of AI models on different tasks, which gives each model a star rating. Developers of proprietary, closed models are also able to upload test results, although only the US software firm Salesforce has participated so far, Luccioni says.

Companies are becoming increasingly close-mouthed about the energy requirements of their up-to-date industry models, Strubell says. As competition has heated up, "there has been a closing down of information shared outside of the company", she says. But firms such as Google and Microsoft have reported that their carbon emissions are increasing, which they have ascribed to data-centre construction in support of AI. (Companies including Google, Microsoft and Amazon didn't address criticisms about a lack of transparency when asked by Nature; instead they emphasized that they were working with local authorities to ensure that new data centres don't affect local utility supplies.)

Some governments now require more transparency from companies. In 2023, the European Union adopted an Energy Efficiency Directive, which requires operators of data centres that are rated for at least 500 kilowatts of power to report their energy consumption each year.

Global projections

On the basis of supply-chain estimation methods, analysts say that data centres currently use just a small proportion of the world's electricity demand. The International Energy Agency (IEA) estimates that the electricity used by such facilities in 2022 was 240-340 TWh, or 1-1.3% of world demand (if cryptocurrency mining and data-transmission infrastructure are included, this raises the proportion to 2%).

The AI boom will increase this, but with world electricity consumption expected to grow by more than 80% by 2050 owing to the electrification of many industries, the rise of electric cars and greater demand for air conditioning, data centres "account for a relatively small share of overall electricity demand growth", the IEA reports (see 'World electricity growth').

Even with approximations of AI's current energy demands, it's difficult to project future trends, warns Koomey. "Nobody has any idea what data centres, either AI or conventional, will use even a few years from now," he says.

The main problem is disagreements about the number of servers and data centres that will be needed, and it's an area where utilities and tech companies have financial incentives to inflate numbers, he says. Many of their predictions are based on "simple-minded assumptions" he adds. "They extrapolate recent trends out 10 or 15 years into the future."

Late last year, Koomey co-authored a report funded by the US Department of Energy, which estimated that US data centres currently use 176 TWh (4.4%) of the country's electricity, and that this might double or triple by 2028 to between 7% and 12%.

# data[].attributes.attachment_link.title
How much energy will AI really consume? The good, the bad and the unknown

# data[].attributes.attachment_link.description
Researchers want firms to be more transparent about the electricity demands of artificial intelligence. Researchers want firms to be more transparent about the electricity demands of artificial intelligence.

# data[].attributes.attachment_link.title
Semantic Scholar | AI-Powered Research Tool

# data[].attributes.attachment_link.description
Semantic Scholar uses groundbreaking AI and engineering to understand the semantics of scientific literature to help Scholars discover relevant research.

# data[].attributes.attachment_link.title
Adobe Firefly: Your all-in-one AI creative studio

# data[].attributes.attachment_link.description
Firefly gives you the speed, control, and creative freedom to go from idea to high-quality content. Generate images, video, audio and more with 30+ AI models, all in one place.

# data[].attributes.attachment_link.title
Connected Papers | Find and explore academic papers

# data[].attributes.attachment_link.description
Connected Papers is a visual tool to help researchers and applied scientists find academic papers relevant to their field of work.

# data[].attributes.body
Energy measurements across a range of AI tasks.

# data[].attributes.attachment_link.title
AI Energy Score Leaderboard - a Hugging Face Space by AIEnergyScore

# data[].attributes.attachment_link.description
This application helps users explore and compare energy-efficient AI models across various tasks. Users can view detailed tables and charts showing the energy consumption and efficiency scores for ...

# data[].attributes.attachment_link.authors[].name
Faith LierheimerSenior Technical Instructor at dbt Labs

# data[].attributes.attachment_link.description
One tool that does it all. Search, generate, analyze, and chat—right inside Notion.

# data[].attributes.attachment_link.readable_text
Une IA générative ouverte et portable pour les développeurs et les entreprises.

Nos 3 modèles open source, Mistral 7B, Mixtral 8x7B et Mixtral 8x22B, sont construits sur une base open source sous la licence Apache 2.0. Ils peuvent être utilisés et personnalisés pour une grande variété de cas d'utilisation, et peuvent être téléchargés ou utilisés à la demande via notre plateforme.

Téléchargez-les pour les déployer dans votre propre environnement

Utilisez-les sur la Plateforme pour bénéficier d'une disponibilité, d'une vitesse et d'un contrôle de qualité de pointe sur le marché

Nos modèles optimisés, Mistral Small et Mistral Large, offrent une valeur ajoutée à votre entreprise : des performances supérieures, une efficacité accrue et des capacités supplémentaires.

Accédez-y sur La Plateforme et via nos partenaires cloud (Azure, AWS, Snowflake)

Contactez notre équipe pour un déploiement sur votre propre infrastructure (y compris un accès transparent aux poids des modèles)

Nos modèles sont utilisés par de nombreuses entreprises, tant par des startups d'IA que par des grandes entreprises.

Rejoignez une petite équipe créative et exigeante. Nous concevons des modèles d'IA utiles, performants, et efficace en calcul. La science et l'entreprenariat sont au coeur de notre culture.
Nous rejoindre

© 2024 Mistral AI, All rights reserved - Legal notice

# data[].attributes.attachment_link.readable_text
"The best thing is that you don't have to go through all the struggle to figure out what words are required for your research and you can just sit back put a link of paper that you already have and start reading."

- A Happy Rabbit

"Having papers suggested that are both earlier and later than those already on my list, as well as the similar papers function, have helped me to find work I would have needed to pore over reference lists for ages to find otherwise. It's a huge time-saver!"

- An Efficient Rabbit

"It's intuitive and tremendously useful. You save a lot of time identifying the most influential papers in a topic/field. Every scientist should know this!"

- A Delighted Rabbit

"You have done an amazing job with this app-Revolutionary, in my humble opinion."

- A Jubilant Rabbit

"I'm delighted with this excellent app. How easy it is to get related papers, how fast it is to get a similar article. But mostly, the way to cluster papers by their relationship and by timeline is a fantastic feature. You have done something incredible."

- An Analytical Rabbit

# data[].attributes.attachment_link.description
The most powerful discovery app ever built for researchers 🔥

# data[].attributes.body
Free alternative models.

# data[].attributes.attachment_link.description
DeepSeek, unravel the mystery of AGI with curiosity. Answer the essential question with long-termism.

# data[].attributes.attachment_link.description
LangChain is an open source framework with a prebuilt agent architecture and integrations for any model or tool—so you can build agents that adapt as fast as the ecosystem evolves

# data[].attributes.body
Anonymised chat with several major models. Conversations are not used for training.

# data[].attributes.body
Python and TypeScript framework for RAG and LLM agents.

# data[].attributes.attachment_link.description
LlamaIndex is the leading framework for building LLM-powered agents over your data with LLMs and workflows.

# data[].attributes.attachment_link.title
SciSpace by Typeset | Discover, Create, Publish, and Promote your research paper

# data[].attributes.attachment_link.description
Your platform to explore and explain papers. Search for 270M+ papers, understand them in simple language, and find connected papers, authors, topics.

# data[].attributes.headline
Stable Diffusion / Stable Image (Stability AI)

# data[].attributes.subject
Stable Diffusion / Stable Image (Stability AI)

# data[].attributes.body
Downloadable weights under the Community License, not OSI-approved. Main offering is a paid hosted API.

# data[].attributes.attachment_link.readable_text
Stable Diffusion 3, our most advanced image model yet, features the latest in text-to-image technology with greatly improved performance in multi-subject prompts, image quality, and spelling abilities.

The model is available via API today and we are continuously working to improve the model in advance of its open release. To learn more about deployment options please contact us.

# data[].attributes.attachment_link.title
Stability AI Image Models - Stability AI

# data[].attributes.attachment_link.description
Experience unparalleled image generation capabilities with SDXL Turbo and Stable Diffusion XL. Our models use shorter prompts and generate descriptive images with enhanced composition and realistic aesthetics.

# data[].attributes.body
AI video translation and dubbing, with voice cloning and lip-sync.

# data[].attributes.attachment_link.title
AI Video Translator : Translate Videos into 175+ Languages

# data[].attributes.attachment_link.description
Translate videos into 175+ languages and dialects instantly with AI. No voice actors or dubbing needed. Fast, accurate, and easy to use.

# data[].attributes.attachment_link.title
GitHub - openai/whisper: Robust Speech Recognition via Large-Scale Weak Supervision

# data[].attributes.attachment_link.description
Robust Speech Recognition via Large-Scale Weak Supervision - GitHub - openai/whisper: Robust Speech Recognition via Large-Scale Weak Supervision

# data[].attributes.headline
Pyannote for detecting multiple speakers

# data[].attributes.subject
Pyannote for detecting multiple speakers

# data[].attributes.attachment_link.title
GitHub - pyannote/pyannote-audio: Neural building blocks for speaker diarization: speech activity detection, speaker change detection, overlapped speech detection, speaker embedding

# data[].attributes.attachment_link.description
Neural building blocks for speaker diarization: speech activity detection, speaker change detection, overlapped speech detection, speaker embedding - GitHub - pyannote/pyannote-audio: Neural building blocks for speaker diarization: speech activity detection, speaker change detection, overlapped speech detection, speaker embedding

# data[].attributes.attachment_link.readable_text
Custom integrations

Would you like to complement your services with our AI-based discovery tools? Using our Custom Services, organisations are able to embed Open Knowledge Maps components in their own discovery systems.

Explore live case studies

# data[].attributes.attachment_link.title
Open Knowledge Maps - Your guide to scientific knowledge

# data[].attributes.attachment_link.description
Start your literature search here: get a visual overview of a research topic, find relevant papers, and identify important concepts.

# data[].attributes.body
:cloud models run on Ollama's servers, not your machine, even via localhost. Use tags without :cloud.

# data[].attributes.attachment_link.description
Get up and running with large language models.

# data[].attributes.body
Self-hosted interface for local or remote models (Ollama, OpenAI-compatible APIs). Source-available, not an OSI licence.

# data[].attributes.attachment_link.description
Open WebUI is an extensible, self-hosted interface for AI that adapts to your workflow, all while operating entirely offline; Supported LLM runners include Ollama and OpenAI-compatible APIs.

# data[].attributes.body
Reference hub for open-source models.

# data[].attributes.attachment_link.title
Hugging Face - The AI community building the future.

# data[].attributes.attachment_link.description
We're on a journey to advance and democratize artificial intelligence through open source and open science.