Implementing identity recognition modules within AI systems enables personalized interactions and secure access management.

Analyzing distribution vectors in AI models provides insights into data representation and model behavior under different sampling regimes.

Implement a generic API call pattern that retrieves Google Drive documents by passing a document ID and optional fields to flexibly fetch only the nee...Show more

Use Anthropic’s code execution mode (inspired by Cloudflare’s code mode) to build AI systems that run and deploy code securely and at scale.

Leverage the Agent Builder’s ability to convert high-level natural language specifications into a fully designed AI agent, streamlining early prototyp...Show more

Integrate a hybrid "model router" into the operating system to dynamically switch between local and cloud models based on resource needs and latency r...Show more

Sparse Mixture-of-Experts models scale to over a trillion parameters by activating only a subset (e.g., 32 billion activated), but are impractical for...Show more

Setting up smaller open-source models as systems or multi-agent workflows can yield surprisingly strong results without state-of-the-art scale.

Updating model parameters with new data often leads to catastrophic forgetting that requires architectural tweaks and better optimization rules to ret...Show more

Nested learning treats continual learning as a set of smaller nested optimization problems that self-modify to mitigate forgetting in local models.

Interleaved thinking breaks down the REACT loop into modular thinking blocks for better introspection and control when building AI agent workflows.

Building dedicated 'prompts' and 'resources' modules in your MCP server architecture creates more modular, maintainable pipelines for AI workflows.

Adopting a formal 'experiments' framework alongside evaluation ('eval') pipelines ensures systematic benchmarking of model changes before production.

Their entire AI stack has pivoted to agent-based workflows, pushing teams to focus next on fine-tuning token models rather than just calling APIs.

The free LangChain Academy course covers setting up tracing, testing and evaluation, and prompt engineering as core modules for building and optimizin...Show more

AI engineering or agent engineering is framed as a continuous optimization process—refining offline, online, and real-time tests to gather feedback an...Show more

Use a contrarian market analysis framework by comparing standard valuation metrics like P/E ratios against actual growth drivers to identify mispriced...Show more

Use conversational AI to model and visualize options trading scenarios with dynamic parameters like stock price moves and volatility to estimate costs...Show more

By orchestrating smaller open-source GPT models as systems or multi-agent pipelines, you can achieve strong results without needing state-of-the-art c...Show more

Use a language graph agent with embedded decision trees and sub-agents to abstract traditional if-else and for-loop automation for options trading log...Show more