the research lab

TEV2 revolutionizes AI art generation by drawing from diverse web data sources to build rich, multi-layered visual narratives. By fusing agentic creativity and systems thinking, it produces artworks that feel both new and old at the same time.

New because it synthesizes patterns and perspectives no single artist or dataset could produce alone, continuously updating from live cultural and digital signals.

Old because it echoes familiar visual grammars, archetypes and aesthetics, remixing them into images that look familiar but are not!

Stop losing context between conversations. Learn how to implement long-term memory for AI agents using a two-tier architecture: Redis for fast short-term access and vector databases for persistent storage. This article discusses our approach which includes automatic archiving, metadata preservation, and minimal-latency background processing.

Transform your chatbot from forgetful to context-aware. Discover the architecture patterns, implementation steps, and critical considerations for production-ready persistent memory systems.

Ever wondered how punctuation and word placement shape AI-generated responses? In this article, we explore the hidden mechanics behind prompt engineering, revealing how subtle tweaks—like commas and phrase order—can dramatically alter AI outputs.

Our latest research dives into practical demonstrations using AI art, showing how strategic prompt structuring enhances creativity and precision. Discover how to refine your prompts for more creative outputs.

Inference-only compute is revolutionizing AI economics by letting users pay only for the compute used during inference, eliminating costs for idle resources.

Platforms like Modal dynamically allocate GPUs for precise, real-time usage, enabling startups to experiment freely, scale efficiently, and optimize budgets.

This groundbreaking approach empowers businesses to innovate faster without the financial burden of traditional per-hour billing models, unleashing the true potential of AI-driven applications.

Integrating timestamped embeddings into our AI systems reflects our commitment to capturing the temporal nuances of data, significantly enhancing relevance and precision. By embedding timestamps, we ensure our AI retrieves contextually accurate information, crucial for dynamic sectors like finance and technology.

This approach allows us to finely tune data interaction and tailor responses, reflecting a deeper understanding of evolving trends and user needs. Our methods encompass seamless Python implementation to maintain cutting-edge relevance in data retrieval.

A truly intelligent AI will be able to perform multiple tasks with ease and accuracy. No need for separate writing assistants, customer support etc.

We propose an agentic and dynamic system prompt approach that uses a separate LLM to analyse the users queries and transform the insights into a useful system prompt to align the AI with the user more closely.

In today's digital age, AI-generated content is everywhere, from personalized news feeds to customized music playlists. But there's a catch: the filter bubble paradox. While these algorithms tailor content to our preferences, they can also limit exposure to diverse viewpoints. This paradox extends beyond art into digital journalism and education, where personalized feeds can lead to selective exposure and a fragmented understanding of current events. Balancing personalization with diversity is crucial.

The TEV1 algorithm is a smart tool that turns your ideas into amazing visual images. It connects related concepts, making sure the visuals are consistent and true to your theme. TEV1 keeps getting better by learning from feedback and adjusting what it creates. It’s designed to be both precise and creative, using a lot of training data to perfect its performance.

Learn more about the beauty of systems in practise in this article.

Mastering prompt engineering can elevate your creative projects to new heights. At Asycd, we offer ten crucial tips to refine your AI prompts, enhancing outputs for poetry, marketing copy, and more. From leveraging multiple AI systems to defining clear instructions, these tips will help you become a more productive generative AI user.

The RAG approach enhances an LLM's responses by providing additional context. This significantly improves relevancy and accuracy. However, one of the methods we've been delving into, the LLM-Assisted Multi Query RAG, does have its limitations. Primarily, it's quite token-intensive. Generating multiple queries through an LLM can consume a significant number of tokens, which could be a concern if you're working with a limited API usage budget.

To tackle this, we've been experimenting with different strategies. For instance, dynamic query generation, where the number of queries varies based on the initial input’s complexity, has shown some promise. We're also looking into optimizing embedding retrieval as a way to better manage token usage without sacrificing response quality.

Definition: ReAct, short for “Reasoning and Acting,” revolutionizes AI decision-making by separating reasoning from action.

How it Works: It updates the LLM’s context with new observations, prompting a reassessment and refined response.

An Example: Using Langchain agents and zero-shot prompting, ReAct dynamically searches and evaluates information, crafting precise answers to complex queries like “What is Asycd?”

Discover the future of art with TEV1: the AI-powered Thematic Explorer that transforms your creative vision into stunning visuals. Unleash the power of DALL-E 3 and GPT-4o, and revolutionize your artistic process. Dive into a world where your ideas come to life effortlessly. Explore TEV1—where art meets innovation.