Show Notes
In this episode of How AI is Built, Nicolay Gerold interviews Doug Turnbull, a search engineer at Reddit and author on “Relevant Search”. They discuss how methods and technologies, including large language models (LLMs) and semantic search, contribute to relevant search results. Key Highlights: Defining relevance is challenging and depends heavily on user intent and context Combining multiple search techniques (keyword, semantic, etc.) in tiers can improve results LLMs are emerging as a powerful tool for augmenting traditional search approaches Operational concerns often drive architectural decisions in large-scale search systems Underappreciated techniques like LambdaMART may see a resurgence Key Quotes: "There's not like a perfect measure or definition of what a relevant search result is for a given application. There are a lot of really good proxies, and a lot of really good like things, but you can't just like blindly follow the one objective, if you want to build a good search product." - Doug Turnbull "I think 10 years ago, what people would do is they would just put everything in Solr, Elasticsearch or whatever, and they would make the query to Elasticsearch pretty complicated to rank what they wanted... What I see people doing more and more these days is that they'll use each retrieval source as like an independent piece of infrastructure." - Doug Turnbull on the evolution of search architecture "Honestly, I feel like that's a very practical and underappreciated thing. People talk about RAG and I talk, I call this GAR - generative AI augmented retrieval, so you're making search smarter with generative AI." - Doug Turnbull on using LLMs to enhance search "LambdaMART and gradient boosted decision trees are really powerful, especially for when you're expressing your re-ranking as some kind of structured learning problem... I feel like we'll see that and like you're seeing papers now where people are like finding new ways of making BM25 better." - Doug Turnbull on underappreciated techniques Doug Turnbull LinkedIn X (Twitter) Web Nicolay Gerold: LinkedIn X (Twitter) Chapters 00:00 Introduction and Guest Introduction 00:52 Understanding Relevant Search Results 01:18 Search Behavior on Social Media 02:14 Challenges in Defining Relevance 05:12 Query Understanding and Ranking Signals 10:57 Evolution of Search Technologies 15:15 Combining Search Techniques 21:49 Leveraging LLMs and Embeddings 25:49 Operational Considerations in Search Systems 39:09 Concluding Thoughts and Future Directions
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Nicolay here,most AI conversations focus on training bigger models with more compute. This one explores the counterintuitive world where averaging weights from different models creates better performance than expensive post-training. Today I have the chance to talk to Maxime Labonne, who's a researcher at Liquid AI and the architect of some of the most popular open source models on Hugging Face. He went from researching neural networks for cybersecurity to building "Frankenstein models" through techniques that shouldn't work but consistently do. Key Insight: Model Merging as a Free LunchThe core breakthrough is deceptively simple: take two fine-tuned models, average their weights layer by layer, and often get better performance than either individual model. Maxime initially started writing an article to explain why this couldn't work, but his own experiments convinced him otherwise. The magic lies in knowledge compression and regularization. When you train a model multiple times on similar data, each run creates slightly different weight configurations due to training noise. Averaging these weights creates a smoother optimization path that avoids local minima. You can literally run model merging on a CPU - no GPUs required. In the podcast, we also touch on: Obliteration: removing safety refusal mechanisms without retraining Why synthetic data now comprises 90%+ of fine-tuning datasets The evaluation crisis and automated benchmarks missing real-world performance Chain of thought compression techniques for reasoning models 💡 Core Concepts Model Merging: Averaging weights across layers from multiple fine-tuned models to create improved performance without additional training Obliteration: Training-free method to remove refusal directions from models by computing activation differences Linear Merging: The least opinionated merging technique that simply averages weights with optional scaling factors Refusal Direction: The activation pattern that indicates when a model will output a safety refusal 📶 Connect with Maxime: X / Twitter: https://x.com/maximelabonne LinkedIn: https://www.linkedin.com/in/maxime-labonne/ Company: https://www.liquid.ai/ 📶 Connect with Nicolay: LinkedIn: https://www.linkedin.com/in/nicolay-gerold/ X / Twitter: https://x.com/nicolaygerold Website: https://www.nicolaygerold.com/ ⏱ Important Moments Model Merging Discovery Process: [00:00:30] Maxime explains how he started writing an article to debunk model merging Two Main Merging Use Cases: [11:04] Clear distinction between merging checkpoints versus combining different task-specific capabilities Linear Merging as Best Practice: [21:00] Why simple weight averaging consistently outperforms more complex techniques Layer Importance Hierarchy: [21:18] First and last layers have the most influence on model behavior Obliteration Technique Explained: [36:07] How to compute and subtract refusal directions from model activations Synthetic Data Dominance: [50:00] Modern fine-tuning uses 90%+ synthetic data 🛠 Tools & Tech Mentioned MergeKit: https://github.com/cg123/mergekit Transformer Lens: https://github.com/TransformerLensOrg/TransformerLens Hugging Face Transformers: https://github.com/huggingface/transformers PyTorch: https://pytorch.org/ 📚 Recommended Resources Maxime's Model Merging Articles: https://huggingface.co/blog/merge Model Soups Paper: https://arxiv.org/abs/2203.05482 Will Brown's Rubric Engineering: https://x.com/willccbb/status/1883611121577517092
Nicolay here, Most AI coding tools obsess over automating everything. This conversation focuses on the right balance between human skill and AI assistance - where manual context beats web search every time. Today I have the chance to talk to Ben Holmes, a software engineer at Warp, where they're building the AI-first terminal. Manual context engineering trumps automated web search for getting accurate results from coding assistants. Key Insight Expansion The breakthrough insight is brutally practical: manual context construction consistently outperforms automated web search when working with AI coding assistants. Instead of letting your AI tool search for documentation, find the right pages yourself and feed them directly into the model's context window. Ben demonstrated this with OpenAI's Realtime API documentation - after an hour of back-and-forth with web search, he manually found the correct API signatures and saved them as a reference file. When building new features, he attached this curated documentation directly, resulting in immediate success rather than repeated failures from outdated or incorrect search results. This approach works because you can verify documentation accuracy before feeding it to the AI, while web search often returns the first result regardless of quality or recency. In the podcast, we also touch on: Why React Native might become irrelevant as AI translation between native languages improves Model-specific strengths: Gemini excels at debugging while Claude dominates f unction calling The skill of working without AI assistance - "raw dogging" code for deep learning Warp's architecture using different models for planning (O1/O3) vs. coding (Claude/Gemini) 💡 Core Concepts Manual Context Engineering: Curating documentation, diagrams, and reference materials directly rather than relying on automated web search. Model-Specific Workflows: Matching AI models to their strengths - O1 for planning, Claude for f unction calling, Gemini for debugging. Raw Dog Programming: Coding without AI assistance to build f undamental skills in codebase navigation and problem-solving. Agent Mode Architecture: Multi-model system where Claude orchestrates task distribution to specialized agents through f unction calls. 📶 Connect with Ben: Twitter/X, YouTube, Discord (Warp Community), Website 📶 Connect with Nicolay: LinkedIn, X/Twitter, Bluesky, Website, [email protected] ⏱ Important Moments React Native's Potential O bsolescence: [08:42] AI translation between native languages could eliminate cross-platform frameworks Manual vs Automated Context: [51:42] Why manually curating documentation beats AI web search Raw Dog Programming Benefits: [12:00] Value of coding without AI assistance during Ben's first week at Warp Model-Specific Strengths: [26:00] Gemini's superior debugging vs Claude's speculative code fixes OpenAI Desktop App Advantage: [13:44] O utperforms Cursor for reading long files Warp's Multi-Model Architecture: [31:00] How Warp uses O1/O3 for planning, Claude for orchestration Function Calling Accuracy: [28:30] Claude outperforms other models at chaining f unction calls AI as Improv Partner: [56:06] Current AI says "yes and" to everything rather than pushing back 🛠 Tools & Tech Mentioned Warp Terminal, OpenAI Desktop App, Cursor, Cline, Go by Example, OpenAI Realtime API, MCP 📚 Recommended Resources Warp Discord Community, Ben's YouTube Channel, Go Programming Documentation 🔮 What's Next Next week, we continue exploring production AI implementations with more insights into getting generative AI systems deployed effectively. 💬 Join The Conversation Follow How AI Is Built on YouTube, Bluesky, or Spotify. Discord coming soon! ♻ Building the platform for engineers to share production experience. Pay it forward by sharing with one engineer facing similar challenges. ♻