Weaviate Podcast

About Weaviate Podcast

Join Connor Shorten as he interviews machine learning experts and explores Weaviate use cases from users and customers.

Publisher

Weaviate

Category

technology

Language

en

Explicit

No

First episode

5 Dec 2021

Latest episode

5 Oct 2026

Latest episodes

148 episodes in the feed.

1. 

   5 Oct 2026

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

   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

2. 

   1 Oct 2026

   In-Context Retrieval with Siddharth Gollapudi - Weaviate Podcast #146!

   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

3. 

   21 Sept 2026

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

   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

Who has been a guest on Weaviate Podcast

Names identified in recent episode analyses. Showing up to five guests.

• Hailey Joren
• Nandan Thakur
• Rajesh Jayaram
• Roberto Esposito

Reach and audience

Public platform figures. Ratings count people who left a rating, not total listeners.

YouTube views

492,584

Score snapshot 15 Sept 2025

Apple Podcasts (US)

4.2 / 5

5 ratings

Spotify

4.6 / 5

10 ratings

Podcast Authority Score: 79 / 100

A composite of feed quality, social presence, YouTube performance and engagement. Read the methodology.

Quality

91

Social presence

0

YouTube

86

Engagement

84

Contact Weaviate Podcast

Guest appearances

Books guests

Based on episode analysis; this does not confirm that the show is currently accepting guests.

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