Develop specialized on-device AI offerings leveraging compact OSS models to meet privacy and offline use-case demands in enterprise and consumer marke...Show more

As of May 2025, DeepSeq architectures match or exceed prior state-of-the-art sequence models, signaling rapid iterative improvements in model design

Open-source local models will always trail frontier counterparts but are closing the gap quickly, making performance differences less stark over time

Balance pushing AI capabilities with pragmatic release schedules by assessing feature completeness against strategic product timelines

Systematically evaluate open-source models like GPT-5.0 OSS 20B on standard intelligence benchmarks before public release to inform performance gaps a...Show more

Combine frontier (cloud-based) models with local on-device solutions to create flexible AI pipelines that optimize for latency, cost, and privacy

Meta’s AI benchmark serves as a meta-evaluation tool to assess proprietary and open-weight models across standardized metrics.

Kimi K2 and Minimax are advanced AI models positioned as new workhorses for scalable inference and research in cloud environments.

Tom Spencer shares his own experience of starting a startup later in his career, highlighting that passion and persistence trump chronological age.

Offer a consulting or SaaS service that uses meta-benchmark results to recommend the optimal proprietary or open-weight AI model for specific enterpri...Show more

Proprietary AI models differ fundamentally from open-weight models, making meta-benchmarks like Meta’s AI benchmark essential for fair evaluation and ...Show more

Frontier AI models have inherent limits, so focusing on incremental enhancements often yields more practical gains than chasing bleeding-edge architec...Show more

Starting a startup later in life can succeed when driven by genuine passion and persistence rather than early-career timing.

Establish systematic benchmarks to compare model families (e.g., GPT-3.5 vs GPT-4) and choose the right model tier based on performance requirements.

Use Kimi K2 and Minimax models deployed in the cloud as scalable AI workhorses for high-throughput inference and research experiments.

Focus on practical task efficiency and real-world outcomes when leveraging AI models, instead of prioritizing benchmark scores.

Choose between Claude and GPT models based on context and use-case requirements rather than pure benchmark performance.

Deploy and test AI models in production by using Ollama Cloud as the hosting and inference platform for primary usage.

Evaluate adopting the Kimmy model as the primary AI workhorse for diverse tasks, leveraging its frontier-level capabilities.

Build a social media agency tool powered by a specialized social agent to automate content creation, scheduling, and analytics for clients.