Implement a dynamic model router that abstracts cloud/local inference switching based on workload and resource needs, enabling seamless hybrid AI exec...Show more

Building a modular, open-source execution framework can break vendor lock-in and enable true AI hardware portability for training and inference.

Designing AI hardware and software together from the ground up can yield order-of-magnitude performance gains with minimal hardware changes.

Map out potential disruptions—like chip efficiency breakthroughs and stack‐architecture challengers—to forecast when high margins might collapse.

Use two simple questions—whether a bubble exists and how exposed counterparties are—to systematically assess short positions in overvalued assets.

Before acting on a chart or system diagram, ensure every symbol and data point is meaningful rather than random placeholders that mislead analysis.

Adopt nested optimization workflows where a model’s continual learning is broken into smaller, self-referential subtasks to mitigate catastrophic forg...Show more

Expose the REACT framework (plan, act, reflect, loop) as discrete thinking blocks to improve model interpretability and debugging.

Refactor a local Python MCP server by deploying it remotely with a subset of tools for isolated testing, then integrate prompts and resources in a sec...Show more

Both the AI and their entire stack have pivoted to agents, suggesting developers should structure workflows around autonomous agent patterns.

Incorporate encryption and data-filtering pipelines, as outlined in Anthropic’s privacy article, to secure and de-identify personal information in AI ...Show more

Implement AI agents with built-in scheduling capabilities to automate and sequence tasks without manual intervention.

Building a simple user interface on top of ChatGPT's code mode can automate data ingestion and filtering, making complex MCP-based workflows accessibl...Show more

Using MCP to offload data input and context management enables constructing autonomous agents that handle user data flows instead of manual interventi...Show more

Encourage AI tools to ask clarifying questions before generating code to refine functions and planning based on high-level input.

Use Playwright to programmatically navigate to pages and capture screenshots for demos or analysis.

Use a planning step in LLM prompts where the model outlines each stage (e.g., fetch episodes, extract products, find competitors) before generating th...Show more

Introduce an application-layer abstraction via an MCP server to extend LLM context window capabilities by exposing custom querying and data-manipulati...Show more

Use iterative sessions feeding generated code back into ChatGPT/GPT Pro to maintain consistency and version control across multi-session development p...Show more

Defining all available LLM tools as a TypeScript interface (with typed parameters like browser, handle, dialog, output) guides the model to generate v...Show more