Weaviate Podcast

Latest Episodes:

SkyDiscover with Alexander Krentsel, Shu Liu, Shubham Agarwal, and Mert Cemri - Weaviate Podcast #147! Oct 05, 2026

Show notes

Shu Liu, Alexander Krentsel, Mert Cemri, and Shubham Agarwal, PhD students at U.C. Berkeley's Sky Computing Lab, join the Weaviate Podcast to discuss SkyDiscover and what it means for AI to build entire systems, not just code! AI coding went from Copilot-style line completion to function- and file-level generation with Claude Code. The next step is to optimize or synthesize systems that used to take PhD-level expertise, such as databases, inference engines, and key-value stores. With coding agents, "just-in-time systems" become possible: software tailored to one workload, free of the "generality tax" of general-purpose systems.The conversation centers on synthesizing a key-value store, where workloads vary in read/write ratio, hot keys, consistency, reliability, and latency versus throughput goals. Running on curated and production traces from Tencent and Twitter, the agents designed new caching and hierarchical memory schemes that reached up to 4x better performance than specialized systems from Microsoft. The hard part is trust. On YCSB traces, early builds showed 30–40x throughput because Claude was reward hacking: it worked out values from the keys. SkySynth acts as a "vaccine" against this. It asks the user questions to build the spec, then takes either a test-driven route or a formal verification route. In the test-driven route, a test only counts if it can tell a correct reference implementation from an incorrect one.From there, the discussion covers how the spec decides the programming language (Rust for memory safety, C for raw performance, Lean, Rocq, CompCert, and Verus for proofs). A surprising result: building on top of Microsoft FASTER gave lower throughput than synthesizing from scratch. The panel's advice is to "skate to where the puck is going" and let capable models design without constraints. The panel also imagines hosted systems resynthesized daily on recent traffic, and PRDs as the spec for consumer products.The episode closes on optimization. Unlike text optimization with GEPA, 80–90% of system optimization time goes to evaluation, and vLLM alone has more than 25,000 tests. That makes finding the right problem, building proxy evaluators, and generating adversarial workloads a meta-problem in its own right.

Chapters

0:00 Welcome Shu, Shubham, Alexander, and Mert!

2:22 An Overview of SkyDiscover

7:55 Specialized Database Systems

12:33 DSPy for Systems

23:23 Formal Verification of AI-Generated Systems

26:34 Programming Languages and Vibe Coding

32:48 Building Systems from Scratch

40:26 Software for Everyone!

45:26 GEPA for Vibe Coding

In-Context Retrieval with Siddharth Gollapudi - Weaviate Podcast #146! Oct 01, 2026

Show notes

Siddharth Gollapudi, a researcher at UC Berkeley, joins the Weaviate Podcast to discuss in-context retrieval and his paper "Can Language Models Actually Retrieve In-Context? Drowning in Documents at Million Token Scale." The idea is simple and radical: instead of encoding documents into embeddings and building a nearest neighbor index, put the entire corpus into an LLM's context window and use attention itself as the retriever. The conversation opens by separating this from generative retrieval, which memorizes documents into model parameters and needs gradient updates every time the corpus changes, and from listwise re-ranking, which Siddharth frames as an easier subset of first-stage retrieval. Lost in the middle, he argues, turned out to be much less of a problem with today's long-context models.The discussion then dives into BlockSearch, a 0.6B parameter in-context retriever trained on MS MARCO (cleaned with RLHN) plus a mix of BEIR datasets. Reaching 500 documents was relatively easy; scaling to 2,500, 5,000, and 10,000 documents, or roughly a million tokens, is where things break. Siddharth explains why random document codes beat positional codes (they force the model to actually read the documents), and how an on-policy loss that corrects the model's own rollouts keeps it disciplined when hard negatives pull it off course midway through generating a code.From there, the conversation moves to attention dilution: as the corpus grows, the softmax denominator swamps the relevant document's score. Two fixes help, length-dependent temperature scaling and sparse attention that drops irrelevant documents before attention runs, bringing BlockSearch roughly level with dense retrieval at a million tokens. This opens a big question for vector databases: is there a sublinear, ANN-style version of attention that can be trained end to end, in the spirit of ReFrag and ColBERT's late interaction?The episode closes on timelines for LLM-based re-rankers, the latency trade-offs of high-latency search, "No More Free Lunch" and whether long-context LLMs can eat the database, recursive language models, and Siddharth's excitement for unsupervised notions of relevance that could help models make genuinely new discoveries, such as proving open theorems.

Chapters0:00 Welcome Siddharth!1:21 In-Context Retrieval8:16 Drowning in Documents and Reranker Scaling13:05 BlockSearch, 0.6B In-Context Retriever28:21 Vector Databases for LLM Inference40:46 Timelines for In-Context Retrievers46:54 Will LLMs eat Databases?51:38 Exciting Directions for AI

humans& with Alexis Ross, Manya Bansal, and Niloofar Mireshghallah - Weaviate Podcast #145! Sep 21, 2026

Show notes

Alexis, Manya, and Niloofar from humans& join the Weaviate Podcast to introduce Persimmon, a user model built to simulate how humans actually behave in multi-turn, multi-party conversations. Persimmon is explicitly not an assistant, a companion, or a Character AI-style stand-in, it is a research preview aimed at faithfully capturing the distribution of human behavior. This includes the natural friction of frustration, excitement, and group dynamics that assistant chatbots trained to be helpful never exhibit. Alexis, Manya, and Niloofar bring a striking mix of backgrounds to the problem: AI tutoring and student modeling, programming languages for high-performance computing, and privacy and information-flow research at Carnegie Mellon. The conversation opens with whether the Turing test is solved. Humans& runs a distributionally grounded, multi-turn version where the judge sees many examples of human and AI behavior. Frontier models fool it less than 5% of the time, while Persimmon reaches roughly 20% against a 50% ceiling. From there, the discussion dives into training for non-verifiable tasks: why rubrics-as-rewards approaches invite reward hacking, why the team refuses to impose its own theory of human behavior, and how distribution matching, with the multi-turn Turing test as a North Star metric operating in an implicit feature space, rather than Earth mover's distance over hand-picked features anchors both training and evaluation. They walk through evaluating on real human interaction data like the TIDES meeting transcripts and the TutorMoments tutoring dataset, and why role-played or scripted dialogue doesn't count.The discussion then moves into theory of mind and world models as twin goals, with Persimmon enabling multi-agent environments where assistants get realistic human feedback at training time. The podcast further covers the choice of NVIDIA's Nemotron 3 Ultra and why starting from a base model matters: post-training causes mode collapse, you can't prompt-optimize your way out of it, and injected randomness drifts away over long rollouts. The conversation lands on what excites each guest next: personalized tutors, models that balance overlapping human goals, and training paradigms with long-term social pressures.

Chapters

0:00 Welcome Niloofar, Manya, and Alexis!2:22 An Overview of Persimmon6:08 Solving the Turing Test13:36 User Models and AGI16:00 RL with Non-Verifiable Rewards20:35 Distribution Matching28:00 Collecting Human Data32:32 Theory of Mind in AI37:40 NVIDIA Nemotron 3 Ultra43:49 Prompt Optimization46:00 Exciting Directions for AI

Delegance Brokerage with Alex Ledbetter - Weaviate Podcast #144! Sep 17, 2026

Show notes

Alex Ledbetter is the founder of Delegance Brokerage, an AI-native commercial insurance brokerage he built entirely on his own. The conversation opens with his origin story: an internship on a political risk, credit, and bond underwriting team in New York, where he watched a three-trillion-dollar portfolio run off the back of Excel and realized commercial insurance brokers earning 15–35% annual commissions could be disintermediated the way Robinhood disintermediated retail stock brokers. After raising $200K and securing 190 state licenses in 45 days, he spent seven months teaching himself to build with AI coding tools. Alex compares this process to hammering sheet metal, walking through his own product flow 700 times before showing it to a client.From there, the discussion dives into the architecture. Clients drop in insurance binders approaching a thousand pages, and a document processing pipeline classifies up to 50 commercial insurance document types, vectorizes everything into Weaviate, builds a table of contents per document for multi-hop traversal search, and runs trailing async extractors that pull structured fields into Postgres, processing 80 pages per minute per concurrent upload by converting PDFs into images for vision APIs rather than relying on flat OCR. Three million carrier appetite rules then route each client to the right insurer. Since only 7 of his 27 carrier partners have APIs, browser agents log into carrier portals, handle one-time passwords, and fill out underwriting questionnaires by querying Weaviate and Supabase in real time and monthly compliance agents renew licenses across 50 states.The conversation moves into agent harnesses: multi-model developer pipelines using Codex, Claude, and BugBot that have opened over 900 PRs a month, an email operating system with a chief of staff over iMessage, and how Weaviate unifies memory across web, iOS, Slack, email, and phone. It lands on what's next: a fresh fundraise, SOC 2, and running lean with embedded partnerships instead of brokers.

Chapters:0:00 Welcome Alex!0:38 Founding Story of Delegance Brokerage

6:38 Insurance Brokers and AI

12:15 A Knowledge Base for Insurance

24:20 Vibe Coding

28:50 Browser Agents

34:14 Email Agents

37:30 What is an Agent Harness?

49:00 Weaviate and Postgres Database Design

55:15 The Future of Delegance Brokerage

AutoIndex with Sam O'Nuallain - Weaviate Podcast #143! Sep 07, 2026

Show notes

Sam O'Nuallain joins the Weaviate Podcast to discuss AutoIndex, research from UMass Amherst and Databricks on learning representation programs for retrieval. Instead of tuning the retriever or re-ranker, AutoIndex asks how the data itself should be represented: a two-agent system, an analysis agent and a code agent, writes and refines Python programs that chunk, enrich, and reorganize a corpus to optimize downstream metrics like Recall and nDCG.The conversation opens with why indexing is such a natural target for code optimization. Frontier LLMs are exceptional at writing code, a representation program applies cheaply across an entire corpus without passing every document through an LLM, and code is verifiable. Every hypothesis the code agent proposes is gated against a validation set before it is accepted. From there, the discussion dives into the optimization signal. The analysis agent uses tools to read documents, query the retriever, and inspect where gold documents rank, turning a bare score like "recall went up" into rich natural language feedback about why a representation is failing, echoing ideas like GEPA's reflective metrics and the value of small-margin positives for training re-rankers.That leads into why BM25 pairs so well with agents: its lexical transparency makes failures easy to diagnose, illustrated by case studies from the CRUMB benchmark. This includes LaTeX formatting errors sinking Stack Overflow retrieval and Tip-of-the-Tongue movie search, where AutoIndex learned to repeat plots to up-weight terms and expand documents with synonym dictionaries. The conversation moves through connections to document enrichment methods like Anthropic's contextual retrieval, doc2query, and EnrichIndex, generalizing representation programs to text-to-SQL schemas and data lakehouses. The podcast concludes by discussing Sam's lessons transitioning from research to production AI engineering: loop engineering, QA, and evals. It lands on the directions that excite Sam most: harness design, continual learning, and memory as a retrieval problem, squeezing more out of the models we already have without touching the weights.

Chapters

0:24 An Overview of AutoIndex

4:04 Retrieval Indexing as Code Optimization

8:47 Optimizing Chunking and Database Schemas

15:57 Feedback for Search Optimization

23:57 Future Directions for AutoIndex

27:34 Document Enrichment for RAG

36:32 Web Search vs. Databases

40:40 AI Engineering

49:30 Exciting Directions for AI

Recursive Language Models with Alex Zhang - Weaviate Podcast #142! Aug 26, 2026

Show notes

Alex Zhang, a PhD student at MIT, joins the Weaviate Podcast to discuss Recursive Language Models (RLMs), a new abstraction for designing agent harnesses. Instead of the standard ReAct-style loop that stuffs every tool observation into an ever-growing prompt, an RLM treats the prompt as a variable in a program. The model writes code that manipulates its own context and spawns recursive LLM calls over pieces of it. The published headline was long-context performance, but the deeper intention, inspired by how DSPy programmers decompose tasks, is letting the model do that decomposition itself, relieving context pressure so each call only sees a small, local, in-distribution problem. Stacking tool outputs into one giant sequence is terribly out of distribution for how models were trained. Frontier labs spend enormous resources making those long trajectories in-distribution, but RLMs aim to get more from the model without that.From there the conversation turns to PrimeAgent, Prime Intellect's production harness centered on an RLM, where the only tool is a persistent IPython REPL: search, compaction, sub-agents, and skills are all callable inside code, yielding strong results on ARC-AGI-3 and long-horizon benchmarks with big token savings. Alex shares why they moved from prompt tuning to RL on the weights of Qwen3, and why Claude Code's dynamic workflows are RLM-like but constrained by legacy tool-calling defaults.Alex then previews an unreleased library for speculative programmatic tool calling. Borrowing from speculative decoding and CPU speculative execution, a shadow executor queues up sub-agent calls while the main model is still generating code, delivering roughly 2x speedups and better GPU utilization for locally served agents. The discussion moves into running RLMs in the cloud with sandbox providers like Modal and Daytona, security concerns around code execution and prompt injection, and why serving engines should be redesigned around agents. It closes with RLMs and search, combining ColBERT-style retrievers as callable tools for problems like agent log analysis, and a theory that everything "long" is naturally decomposable. The podcast concludes with exciting ideas about where AI goes next from principled harness design to whether the transformer itself gets replaced with a newer neural architecture design.

Drowning in Documents with Mathew Jacob - Weaviate Podcast #141! Aug 17, 2026

Show notes

Mathew Jacob, lead author of "Drowning in Documents: Consequences of Scaling Reranker Inference" and now a PhD student in ML systems at the University of Washington, joins the Weaviate Podcast to unpack one of the most surprising results in modern search: cross-encoder rerankers get worse as you give them more documents. The paper began during his Databricks internship, where scaling reranking past roughly 100 documents sent recall@10 plummeting, a result so counterintuitive he assumed it was a bug.The conversation digs into why this happens, reframing rerankers through the lens of boosting, rather than being strictly stronger than first-stage retrievers. Cross-encoders are very good at correcting retriever errors within the distribution they were trained on. Full-scoring experiments over 10,000 randomly sampled documents drive the point home, with BM25 beating state-of-the-art cross-encoders. From there, the discussion moves into phantom hits, cases where wildly irrelevant documents scored highly. For example, a dishwasher document surfacing for a query about disease in Gabonese children. We also discuss whether ensembling rerankers can patch these false positives.The second half explores what comes next for reranking: prompt-based listwise reranking with sliding windows, which proved far more robust than pointwise scoring; RankZephyr-style fine-tuning versus encoding learning signal in prompts with GEPA and DSPy, reasoning rerankers like Rank1 and their latency trade-offs, hard negative mining behind ZeroEntropy's zELO, and pairwise and setwise designs that sit between cross-encoders and full listwise ranking. Adaptive retrieval comes into focus through Natural Language Query to Configuration for Retrieval Agents, predicting per query whether to run simple retrieval, multi-hop, or full agentic search to push the cost-quality frontier.The conversation lands on TraceLab, from Mathew's lab at UW: 40,000 real traces harvested from Claude Code and Codex usage, revealing how coding agents actually behave, prefix cache patterns, long-tailed tool calls, and how understanding these workloads unlocks the next generation of serving optimizations.

Founding Weaviate with Bob van Luijt and Etienne Dilocker - Weaviate Podcast #140! Jul 27, 2026

Show notes

Weaviate co-founders Bob van Luijt and Etienne Dilocker return to the Weaviate Podcast to celebrate seven years of building the company, answering questions submitted by the community. The conversation opens with what excites them most in AI right now: Etienne on agentic coding and the "Moore's law" of how long models can sustain autonomous loops, and Bob on world models, new architectures that could slash training energy costs, open source frontier models, and inference on new chips.From there, the discussion dives into taste and the "AI slopification" problem, why AI-generated emails, websites, and decks all look the same, how three job candidates submitted nearly identical AI-built presentations in one week, and why Weaviate runs a dedicated "slop pass" skill over every pull request to strip out phrases like "the smoking gun" and "load-bearing invariant." The human touch, they argue, is now the easiest way to stand out.The Co-Founders then retell their origin story: meeting at a European enterprise company, rewriting a NodeJS prototype in Go, betting on NLP before anyone called it AI, adopting HNSW when it was still a niche paper, and raising a $1.2M seed round from Zeta during COVID. When ChatGPT and the RAG paper hit, Weaviate had a fully working product ready for the wave.Looking forward, Bob breaks down the commoditization playbook that hits every new database category, the same skepticism MongoDB faced, and shares that the number one reason new customers cite for choosing Weaviate is that an LLM recommended it. Etienne makes the case that vector databases are evolving into context engines: context rot is real, stuffing everything into a long context window is inefficient, and retrieval, hybrid search, and structured data all serve one goal, the best possible context. The conversation lands on memory for AI agents, where the hard problem isn't what's worth remembering, but what's worth recalling.

Knowledge Engineering with Bradley Allen - Weaviate Podcast #139! Jun 01, 2026

Show notes

Dr. Bradley Allen brings five decades of AI history into a deep conversation on knowledge engineering, neurosymbolic AI, and the future of enterprise intelligence. The discussion begins with the boom-and-bust cycle of rule-based expert systems, AI winters, and why today’s large language model wave may be different. The conversation then turns to how knowledge is organized in practice, from personal piles of papers searched on demand to formal knowledge graphs built with classes, relations, ontologies, A boxes, T boxes, description logic, and subsumption-based reasoning. Allen explains why semantic web and biomedical ontology successes still leave unresolved questions about cost, maintenance, and whether LLMs can dynamically structure information in ways that preserve meaning. That leads into natural language concept definitions, LLM-based classifiers, rationales, probabilistic reasoning, and the challenge of updating classes as new edge cases emerge.From there, the focus widens to vector databases, semantic search, RAG, topic modeling, distributional semantics, and the ongoing revision required for systems that can never be “once and done.” Allen connects modern LLM behavior to the long history of formal languages, from Frege, Russell, Wittgenstein, Turing, and Gödel to theorem proving, soundness, completeness, paraconsistency, paracompleteness, and the pragmatic tradition of meaning through use. The closing stretch explores world models, reinforcement learning, tool-using agents, enterprise knowledge workflows, role-based access control, governance, normativity, and alignment, ending on the need to build accountable AI systems that channel powerful technology toward responsible outcomes.

Booking.com and Weaviate with Başak Eskili - Weaviate Podcast #138! May 18, 2026

Show notes

Başak Eskili joins the Weaviate Podcast to explore how one of the world’s largest travel platforms adopted vector search, retrieval-augmented generation, and agentic AI at production scale. The conversation begins with Booking.com’s shift from keyword matching to semantic retrieval as internal teams needed embeddings, similarity search, and eventually GenAI RAG workflows. Başak explains why OpenSearch was a practical first step on AWS, how adoption grew across teams, and why hundreds of millions of embeddings, strict latency requirements, complex filtering, and rising concurrency pushed the platform toward Weaviate.The discussion then moves into Booking.com’s partner-to-guest messaging agent, a production GenAI system that helps accommodation partners answer guest questions about check-in, parking, special requests, and reservation details. Başak breaks down the tool-calling architecture, where Weaviate retrieves relevant response templates while GraphQL APIs fetch property and booking context. The agent can suggest templates, craft grounded replies, or decline to answer and leave the conversation to a human, highlighting the practical role of human-in-the-loop design. Evaluation spans offline datasets, LLM-as-a-judge scoring, A/B testing, and live partner feedback.From there, Başak describes the platform engineering behind AI at Booking.com: a central MCP server for internal APIs and external tools, a GenAI gateway for model access, PII reduction, guardrails, prompt injection detection, logging, traceability, and cost tracking across large-scale LLM usage. She also details Booking.com’s evaluation process of Weaviate, including 100 million embeddings, filtered vector search, multi-threaded concurrency testing, reads during writes, and cost-efficient infrastructure provisioning.The episode closes with Başak’s path from computer science and NLP to MLOps and AI platforms, then looks ahead to practical AI, personalized travel agents, and memory systems that capture user preferences, session context, semantic memory, and long-term personalization for future agentic travel experiences.

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