AI Startups vs. Big Chatbots — With Olivia Moore
In this episode, originally aired on Big Technology Podcast, Olivia Moore discusses whether AI startups can compete with the big chatbots, why American sentiment toward AI is so negative, and what she learned from giving LLMs personality tests. She also breaks down where ChatGPT, Claude, and Gemini are diverging, why Open Claw signals a new wave of agentic products, and what makes memory the most underrated feature in consumer AI. Resources: Follow Olivia Moore on X: https://x.com/omooretweets Follow Alex Kantrowitz on X: https://x.com/Kantrowitz List to Big Technology Podcast: https://www.youtube.com/playlist?list=PLADd6sStSis77HKfbf4KCY6SvthfxeUgn Stay Updated: Find a16z on YouTube: YouTube Find a16z on X Find a16z on LinkedIn Listen to the a16z Show on Spotify Listen to the a16z Show on
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Production RAG: From Anti-Patterns to Platform Engineering
RAG is a distributed system . It becomes clear when moving beyond demos into production. It consists of independent services such as ingestion, retrieval, inference, orchestration, and observability. Each component introduces its own latency, scaling characteristics, and failure modes, making coordination, observability, and fault tolerance essential. RAG flowchart In regulated environments such as banking, these systems must also satisfy strict governance, auditability, and change-control requirements aligned with standards like SOX and PCI DSS. This article builds on existing frameworks like 12 Factor Agents (Dex Horthy)¹ and Google’s 16 Factor App² by exploring key anti-patterns and introducing the pillars required to take a typical RAG pipeline to production. I’ve included code snippet
Word2Vec Explained: The Moment Words Became Relations
How models first learned meaning from context — and why that changed everything In the first post, we built the base layer: Text → Tokens → Numbers → (lots of math) → Tokens → Text In the second post, we stayed with the deeper question: Once words become numbers, how does meaning not disappear? We saw that the answer is not “because numbers are magical.” The answer is this: the numbers are learned in a space that preserves relationships. That was the real story of embeddings. Now we are ready for the next step. Because once you accept that words can become numbers without losing meaning, the next question becomes unavoidable: How are those numbers actually learned? This is where Word2Vec enters the story. And Word2Vec matters for more than historical reasons. It was not just a clever neura
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Production RAG: From Anti-Patterns to Platform Engineering
RAG is a distributed system . It becomes clear when moving beyond demos into production. It consists of independent services such as ingestion, retrieval, inference, orchestration, and observability. Each component introduces its own latency, scaling characteristics, and failure modes, making coordination, observability, and fault tolerance essential. RAG flowchart In regulated environments such as banking, these systems must also satisfy strict governance, auditability, and change-control requirements aligned with standards like SOX and PCI DSS. This article builds on existing frameworks like 12 Factor Agents (Dex Horthy)¹ and Google’s 16 Factor App² by exploring key anti-patterns and introducing the pillars required to take a typical RAG pipeline to production. I’ve included code snippet
YouTube blokkeert Nvidia s DLSS 5-video na auteursclaim Italiaanse tv-zender
De Italiaanse tv-zender La7 claimt auteursrechten op beeldmateriaal met Nvidia s DLSS 5-technologie en laat die blokkeren. Googles videoplatform YouTube blokkeert nu videomateriaal met DLSS 5-beeld, wat ook de officiële aankondigingsvideo van Nvidia raakt.
BIRA: A Spherical Bistatic Radar Reflectivity Measurement System
arXiv:2407.13749v5 Announce Type: replace Abstract: The upcoming 6G mobile communication standard will offer a revolutionary new feature: Integrated sensing and communication (ISAC) reuses mobile communication signals to realize multi-static radar for various applications including localization. Consequently, applied ISAC propagation research necessitates to evolve from classical monostatic radar cross section (RCS) measurement of static targets on to bistatic radar reflectivity characterization of dynamic objects. Here, we introduce our Bistatic Radar (BIRA) measurement facility for independent spherical positioning of two probes with sub-millimeter accuracy on a diameter of up to 7 m and with almost continuous frequency coverage from 0.7 up to 260 GHz. Currently, BIRA is the only bistati
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