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fintech company
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Macquarie has raised a record of more than €8bn for its new European infrastructure fund in the latest sign of investor appetite for critical assets.
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• †（十字）和‡（双十字）- 十字表示第一个脚注，双十字表示第二个脚注。
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HoloTile
Disney
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Custom LLM and AI Agents (RAG) On Structured + Unstructured Data - AI Brain For Your Organization
Imagine a ChatGPT-like interface over all your structured (database) and unstructured data. Ideally, you want to ask a question to an AI bot, and it should be able to run multiple parallel queries on your database, look up relevant information from your docs, collate all the relevant information, and create a coherent response.
As an example - it should be able to respond intelligently to the request - Pull the top 10 customers by usage, closed by the sales rep John Doe, and draft a "thank you" email to each of them.
Your AI agent/ Custom LLM must do multiple things to satisfy this request.
- pull all the customers closed by sales rep John Doe from Salesforce
- run a query on Snowflake to get the usage data per customer
- find the thank you email template from your Google Docs folder
- draft emails to all of these customers
Systems that are capable of tasks that are moderately complex, like this one, really improve employee productivity significantly. Organizations that embrace AI in this way will easily outperform their competition.
So here is how you would build this:
Data pipelines and connectors - You need connectors to systems like Salesforce, snowflake, and folders. Chat query orchestrator - you need to be able to parse incoming chat queries, fire multiple calls to an LLM, generate SQL queries, or craft API requests to your vector databases (doc retrievers) Doc retrievers/vector stores - You must embed and create a vector store for all your shared docs. Final response creator - A final LLM that will generate the final response based on all the data.
In the example above, it would generate the final emails from the email template and the data generated by the SQL queries.
Building these systems from scratch is doable, and there are several open-source libraries you can use to do so. However, the challenge will be iterating on the solution to make sure that the accuracy is high enough to be usable in production.
Several systems have to be tuned, and multiple iterations need to evaluate the system before putting this in production.
Our platform, Abacus AI, can help build, test, and launch a system like this in a matter of days. You can set up the various components quickly and then create multiple iterations and evaluate them across multiple LLMs and configurations.
Finally, you can set up all the pipelines and push the system to production in a matter of days. We offer several open-source fine tunes, which means you won't be paying for an expensive LLM if you don't have to!
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Nightshade
a tool that turns any image into a data sample that is unsuitable for model training.
More precisely, Nightshade transforms images into "poison" samples, so that models training on them without consent will see their models learn unpredictable behaviors that deviate from expected norms, e.g. a prompt that asks for an image of a cow flying in space might instead get an image of a handbag floating in space.
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orchard
cirruslabs • Updated Jan 7, 2025
Orchestrator for running Tart Virtual Machines on a cluster of Apple Silicon devices
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Cohere
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