Despite big players failing to deliver on business promises, there will always be room for competitors in the AI app ecosystem.

Beware that platform owners may absorb popular third-party apps and rebuild them natively, potentially displacing independent developers.

End users shouldn’t see MCP or connector complexities; instead deliver AI features as turnkey apps that abstract away backend orchestration.

Without integrated tools like MCP, AI outputs are considered nearly worthless, underscoring reliance on built-in tool integration in AI development.

Many AI stacks like Anthropic, LangChain, and Langsmith use simple text or markdown files as pointers to larger resources, creating a powerful yet str...Show more

Users may prefer transparency into background code for data transformations to verify correctness, rather than full abstraction, especially for critic...Show more

Relying on the LLM to write fresh code each turn means consistency in behavior but not verbatim reproducibility, so teams must trust the model rather ...Show more

Use this demo for demonstration but avoid treating it as a primary agent in production, emphasizing the importance of robust, purpose-built deployment...Show more

Most AI chatbots only simulate running code and review their output internally, which can mislead developers who assume the code has been executed liv...Show more

Without MCP-enabled context loading, multi-step AI tasks become impractical and the model is effectively worthless for complex workflows.

Loading full tool definitions into the context can consume tens of thousands of tokens and cause diminishing returns in multicall protocols.

ChatGPT 5.1 could blow fine-tuned experimental models out of the water, calling into question the value of incremental model tweaks.

Nvidia’s Omniverse world models aren’t simple or easy enough for experimentation to drive widespread adoption, making them less appealing.

There’s high appeal in dynamically moving AI workloads between local clusters and cloud for flexible, low-complexity operations.

Most problems attributed to fine-tuning may be solved simply by formatting and pointing a powerful pre-trained model at the right data, rather than co...Show more

Fine-tuned models become static products that go out of date, whereas renting evolving base models lets you benefit from continuous upstream improveme...Show more

Fine-tuning large models is often unnecessary when prompt engineering, retrieval augmented generation, and memory architectures can achieve the same u...Show more

Tom Spencer argues that generic large language models often meet application needs, making the effort and cost of custom fine-tuning unjustified unles...Show more

AI benchmarks will perpetually advance because new use-case frontiers emerge as soon as existing models meet user expectations.

Open-source local AI models will inevitably trail frontier models in raw capabilities but can satisfy most application needs due to rapid OSS improvem...Show more