Conflicting reports of a 2 million token context window versus 160 K highlight the importance of validating LLM context length with official API specs before relying on extended-context features.
Downloading and running a 350GB AI model like Kimi requires serious on-premises hardware, so deployment planning must account for large resource needs.
Assess your data volume and latency requirements before choosing a dedicated vector store versus using integrated solutions like Supabase PG vector to balance performance benefits and operational overhead.
Structured ETL pipelines mitigate hidden errors and maintain data quality more effectively than simply ingesting raw unstructured input into large language models.
Cameron argues that simpler short prompts and lightweight on-device models can be more effective and accessible than heavy prompt engineering with frontier models.
Process voice inputs as streaming chat interactions rather than waiting for the full transcript to enable immediate form updates and a better user experience.
LLMs employ embedding-based semantic similarity to efficiently search and retrieve conceptually related content rather than relying on basic keyword matching
While enterprise clients impose strict constraints on using proprietary code and data for training, their interaction patterns and generated eval sets are an underleveraged resource.
User interaction data in AI products now serves as an accelerant effect across a customer base, transforming it from a product management metric into valuable training and evaluation material.
Ambient AI agents that autonomously fetch and return code suggestions can offer a hands-off developer experience compared to fully integrated IDE plugins.
The three major cloud providers (AWS, Azure, Google Cloud) will likely corner the majority of AI-driven SaaS value, leaving less opportunity for independent app-layer vendors.
Aaron Levy observes that unlike the first wave of cloud adoption, no enterprise CTO or software buyer believes AI won’t change their business—instead they’re racing to embed it for competitive advantage.
