Alephic Research

In this episode of Alephic Research, we dive into DeepSeek AI’s meteoric rise as hedge-fund veteran Liang Wen Feng and a team of fresh PhD graduates turned U.S. GPU export curbs into a software-centric breakthrough—R1, an open-source reasoning model that cost just $5.6 M to train yet wiped nearly $600 billion off Nvidia’s market cap in a single day. We unpack the clever mixture-of-experts architecture, reinforcement-learning training, and viral open-source launch that slashed usage costs by up to 96%. Along the way, we draw big-picture lessons on how constraints, lean teams, and shared innovation can upend tech giants and democratize AI.

In this episode of Alephic Research, we trace the drum machine’s journey from Henry Cowell’s avant-garde 1931 Rhythmicon to Roland’s legendary TR-808, exploring how technical hiccups and happy accidents spurred musical revolutions. Tune in to discover how these early electronic beat-makers reshaped pop, hip-hop and electronic music—and laid the groundwork for today’s AI-driven rhythm frontier.

In this episode of Alephic Research, we travel back to the sweltering summer of 1956 at Dartmouth College to relive the workshop that officially launched artificial intelligence. Through memoirs and correspondence, we sit alongside John McCarthy, Marvin Minsky, Allen Newell, Herbert Simon and others as they clash over symbolic logic vs. neural nets, unveil early programs like the Logic Theorist, and sketch out AI’s core research agenda. Tune in to hear how a hot Hanover classroom—and a bit of chalk dust—sparked collaborations and debates whose legacy still drives AI today.

Explore how composite AI—integrating multiple specialized models, live data sources and automated tools—outperforms a single monolithic LLM in accuracy, flexibility and maintainability. Hosts of Alephic Research unpack key patterns like retrieval-augmented generation, agentic tool integrations, post-processing layers and multi-model ensembles using real-world examples from Vercel V0 to GitHub Copilot, and share the trade-offs, frameworks and skills you need to build smarter AI applications.