Hackathon summary video
Co:here
Co:here provides through its API unprecedented access to affordable, easy-to-deploy large language models capable of powering the next generation of game-changing AI native applications.
Powerful NLP Model
- Co:here provides through its API unprecedented access to affordable, easy-to-deploy large language models capable of powering the next generation of game-changing AI native applications.
Flexible API
- The Co:here API work with many different libraries that fit every stack. Co:here's Python, Node, and Go SDKs that make AI easy to integrate into your app.
The Future of Software
- Build innovative and extremely powerful applications that were previously not possible. The future of software development is AI native.
Co:here challenge
The challenge for this hackathon is to create an innovative solution using Cohere Embed endpoint
Prizes and Awards
🥇 $5000 worth of Cohere credits for the winning team + Cohere Swag
🥈 $2000 worth of Cohere credits for the winning team + Cohere Swag
- 🥉 Cohere Swag for the third place
Additionally, all winning teams will have the chance to meet Cohere's Product Manager for virtual coffee!

Co:here NLP Hackathon details
Join lablab and Co:here during 48h to innovate and build the new generation of NLP powered applications. Find all the relevant details below.
🗓️ Where and when
The hackathon starts on September 2nd and ends on September 4th. Over the weekend, you'll have the opportunity to learn from Co:here and lablab experts during workshops, keynotes, and mentoring sessions. The hackathon will take place on the lablab.ai platform.
🦸🏼♂️ Who should participate?
Previous experience in AI is not required but welcomed. While many participants are industry experts, we also welcome people with other types of domain knowledge that want to understand & explore how AI can be used in their fields.
🔐 Access to Co:here API
To get started with Co:here NLP API please signup using the following link: https://cohere.ai/signup. Your trial API key are free and has and can handle up to 100 calls per minute free of charge. You can find more information about the API here.
😅 How about teams?
If you don’t have a team you will be able to match and team up with other participants around the world. Finding & creating teams can be done from the dashboard you can access after you enroll. We also recommend checking our Discord server to find teammates and discuss ideas. You can join it here
🛠️ How to participate in the hackathon
The hackathon will take place online on lablab.ai platform and lablab.ai Discord Server. Please register for both in order to participate. To participate click the "Enroll" button at the bottom of the page and read our Hackathon Guidelines.
🧠 Get prepared
To get prepared for the hackathon, we recommend you to start at our Cohere technology page where you can find all the relevant information about the API and how to use it plus cohere tutorials and cohere boilerplates.
Hackathon FAQ
Who can join the Hackathon?
We welcome domain experts from all industries, not just AI/tech. Successful AI solutions require a combination of technical expertise and domain knowledge. Coding experience is recommended.
Do I need a team?
You are welcome to join as a team or solo, if solo. We encourage you to look for a team before the event. We recommend you to join the Deep Learning Labs Discord channel: https://discord.gg/gCuBwBB35k and posting in the #looking-for-team channel to get to know your potential future team members.
Do I need a Github account?
It is recommended, that at least one team member has a Github account. You can create one for free if you don't already have one.
Do I have to use Cohere's Embed?
Embed is the main theme for this Hackathon. You can use other APIs but with Embed you will be able to get some additional points.
I have other questions.
Feel free to reach us on social media, or through our Discord channel.
Event Schedule
- To be announced
Winner Submissions 🏆

Tip of my Tongue by PowerPuff
Web application that allows user to find and browse books based on short prompts, keywords or short story plots.
PowerPuff

Learn visually
From a topic, generate a list of subtopics and explore them with a visual image for each. The subtopics are presented in a manageable way by showing only the most related subtopics at any given time. This naturally leads to exploring associatively like our brains are structured, from broad to deep. Each topic has a handy explanation in text from cohere generate, too!
ThePathForward

intelliChat
IntelliChat is an app built specifically for Chat based and Conversational AI projects. *Who is this for?* - Businesses and Enterprises who are looking to optimize and enhance their Conversational AI Workflow.
💬IntelliChat
Submitted concepts, prototypes and pitches
Submissions from the teams participating in the Cohere AI Hackathon #2 event and making it to the end 👊

Tip of my Tongue by PowerPuff
Web application that allows user to find and browse books based on short prompts, keywords or short story plots.
PowerPuff

Learn visually
From a topic, generate a list of subtopics and explore them with a visual image for each. The subtopics are presented in a manageable way by showing only the most related subtopics at any given time. This naturally leads to exploring associatively like our brains are structured, from broad to deep. Each topic has a handy explanation in text from cohere generate, too!
ThePathForward

intelliChat
IntelliChat is an app built specifically for Chat based and Conversational AI projects. *Who is this for?* - Businesses and Enterprises who are looking to optimize and enhance their Conversational AI Workflow.
💬IntelliChat

All in one AI app
This app is the combination of mutiples work that can be done using LLM. We tried to make the llm to do things by changing the prompt . There is a chatbot. We have text summarizer, that can make good summarization. We also implemented machine translation, we have 6 languages that you can choose from. There other things we have are code explanation and story generation. It is created using streamlit library. A free python library to run ml apps as web service.
DreamDream

IT Services and Solutions
Constantly changing and evolving solutions, services and products available in the telecom, mobility, and IT management industry create sourcing, evaluation, and selection complexities that SMBs (small and mid-sized businesses) are not always equipped to address due to limited time or resources. How do SMBs more easily and efficiently sort out the thousands of options available to them? Our solution is an IT product Service Recommender System which will help SMBs find verified solutions to simplify the sourcing, evaluation, and selection of telecom, mobility, and IT services and solutions. Input: SLA agreements Output: Classify the SLA agreements by Subscription and One-off
Lord Christ

Wayfinder
A travel recommendation for users of any kind. Users are asked a series of questions related to their personality. Then, based on those responses, the site returns a location most relevant to their personal interests.
Googol
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TarinyoomBot
Tarinyoom is a Discord chat bot that allows users to search for messages using a fuzzy search to better retrieve past messages. The bot uses the co:here API "Embed" tool to map discord messages onto their corresponding embedding vectors. These vectors are then stored in a back-end pinecone.io instance, so that their cosine similarities can be evaluated. The bot has two main commands: "/tsleuth" and "/tsearch <string>". "/tsleuth" instructs the bot to crawl the server that the command was issued in, storing the vector embeddings of any messages found as entries in the pinecone.io instance. "/tsearch <string>" instructs the bot to find the message whose vector embedding most closely resembles the vector embedding of the passed parameter, as evaluated by pinecone.io. Using these two commands, users can search for a past conversation with only a high-level recollection of the topics discussed, instead of having to exactly match to text in that conversation, as required by the current Discord search feature.
Tarinyoom
Cohere Analyze
We all understand what Cohere Generate and Classify do. But embed ...?! Introducing ... Cohere Analyze. You can upload any csv file. You then have 3 options. EDA gives you an overview of the data and you can get some general exploratory data analysis. Cluster, allows you to do some group analysis, with keywords generated from the body of the text and using the titles. And finally, Search, allows you to query the data and retrieve the closest match. This has significant business value because now you can gain business insights using cluster that are non-trivial. You can search a knowledge-base or other internal data sources for information. Cohere Analyze is what we have all been waiting for, to better utilize Embed.
Innovate
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SimpliFY
The app lets you classify your text into Elementary, Intermediate and Advanced according to the level of complexity. Simplify functionality lets you generate an alternate simplified version of it.
AKR
Teams: Cohere AI Hackathon #2
Check out the roster and find teams to join