Hunter Leath, CEO of Archil, explains how they’re building a “universal storage engine” that sits between your apps and S3—making an S3 bucket behave like a fast, POSIX-compatible disk for containers, servers, and even Lambda. Along the way, we dig into how their SSD-backed clusters and custom protocol avoid the usual small-file pain and where this approach shines (and where it doesn’t).

Follow Hunter:
Twitter/X: https://twitter.com/jhleath
Archil Twitter/X: https://twitter.com/archildata
Archil: https://archil.com/

Follow Aaron:
Twitter/X: https://twitter.com/aarondfrancis
Database School: https://databaseschool.com
Database School YouTube Channel: https://www.youtube.com/@UCT3XN4RtcFhmrWl8tf_o49g (Subscribe today)
LinkedIn: https://www.linkedin.com/in/aarondfrancis
Website: https://aaronfrancis.com - find articles, podcasts, courses, and more.

Chapters:
00:00 - Intro: Archil Data and “S3 as a disk”
01:05 - Hunter’s background and the core pitch
02:32 - The real problem: state management (S3 vs block storage)
05:02 - SQLite on S3: what the stack looks like
07:13 - The missing layer: durable SSD-backed clusters
10:14 - Who uses this: unstructured data, CI/CD, Git, agents
12:15 - Small files + Git performance and avoiding S3 request explosion
16:22 - Why they built a new protocol (NFS vs Luster)
20:00 - What gets written to S3: real files in your bucket
22:29 - S3 limits, throttling, and the “keep it on SSD” escape hatch
25:32 - Multi-cloud + R2, and why regions/latency matter
32:10 - Pricing model: “pay only when data is active”
34:41 - Tradeoffs: random reads and ultra-low-latency metal
37:19 - Storage/compute separation and AI/agent-native workflows
43:21 - YC timeline + the marketing challenge of a “universal layer”
47:34 - Single-tenant clusters for enterprises and why it’s hard
50:27 - Where the company is now, hiring, and how to try it (disk.new)

Hammad Bashir, CTO of Chroma, joins the show to break down how modern vector search systems are actually built from local, embedded databases to massively distributed, object-storage-backed architectures. We dig into Chroma’s shared local-to-cloud API, log-structured storage on object stores, hybrid search, and why retrieval-augmented generation (RAG) isn’t going anywhere.

Follow Hammad:
Twitter/X: https://twitter.com/HammadTime
LinkedIn: https://www.linkedin.com/in/hbashir
Chroma: https://trychroma.com

Follow Aaron:
Twitter/X: https://twitter.com/aarondfrancis
Database School: https://databaseschool.com
Database School YouTube Channel: https://www.youtube.com/@UCT3XN4RtcFhmrWl8tf_o49g (Subscribe today)
LinkedIn: https://www.linkedin.com/in/aarondfrancis
Website: https://aaronfrancis.com - find articles, podcasts, courses, and more.

Chapters:
00:00 – Introduction From high-school ASICs to CTO of Chroma
01:04 – Hammad’s background and why vector search stuck
03:01 – Why Chroma has one API for local and distributed systems
05:37 – Local experimentation vs production AI workflows
08:03 – What “unprincipled data” means in machine learning
10:31 – From computer vision to retrieval for LLMs
13:00 – Exploratory data analysis and why looking at data still matters
16:38 – Promoting data from local to Chroma Cloud
19:26 – Why Chroma is built on object storage
20:27 – Write-ahead logs, batching, and durability
26:56 – Compaction, inverted indexes, and storage layout
29:26 – Strong consistency and reading from the log
34:12 – How queries are routed and executed
37:00 – Hybrid search: vectors, full-text, and metadata
41:03 – Chunking, embeddings, and retrieval boundaries
43:22 – Agentic search and letting models drive retrieval
45:01 – Is RAG dead? A grounded explanation
48:24 – Why context windows don’t replace search
56:20 – Context rot and why retrieval reduces confusion
01:00:19 – Faster models and the future of search stacks
01:02:25 – Who Chroma is for and when it’s a great fit
01:04:25 – Hiring, team culture, and where to follow Chroma

In this episode of Database School, Aaron Francis sits down with Jordan Tigani, co-founder and CEO of MotherDuck, to break down what DuckDB is, how MotherDuck hosts it in the cloud, and why analytics workloads are shifting toward embedded databases. They dig into Duck Lake, pricing models, scaling strategies, and what it really takes to build a modern cloud data warehouse.

Follow Jordan:
Twitter/X: https://twitter.com/jrdntgn
LinkedIn: https://www.linkedin.com/in/jordantigani
MotherDuck: https://motherduck.com

Follow Aaron:
Twitter/X: https://twitter.com/aarondfrancis
Database School: https://databaseschool.com
Database School YouTube Channel: https://www.youtube.com/@UCT3XN4RtcFhmrWl8tf_o49g (Subscribe today)
LinkedIn: https://www.linkedin.com/in/aarondfrancis
Website: https://aaronfrancis.com - find articles, podcasts, courses, and more.

Chapters:
00:00 - Introduction
01:44 - What DuckDB is and why embedded analytics matter
04:03 - How MotherDuck hosts DuckDB in the cloud
05:18 - Is MotherDuck like the “Turso for DuckDB”?
07:38 - Isolated analytics per user and scaling to zero
08:51 - The academic origins of DuckDB
10:00 - From SingleStore to founding MotherDuck
12:28 - Getting fired… and funded 12 days later
16:39 - Jordan’s background: Kernel dev, BigQuery, and Product
18:36 - Partnering with DuckDB Labs and avoiding a fork
20:52 - Why MotherDuck targets startups and the long tail
24:22 - Pricing lessons: why $25 was too cheap
28:11 - Ducklings, instance sizing, and compute scaling
34:16 - How MotherDuck separates compute and storage
37:09 - Inside the AWS architecture and differential storage
43:12 - Hybrid execution: joining local and cloud data
45:14 - Analytics vs warehouses vs operational databases
47:41 - Data lakes, Iceberg, and what Duck Lake actually is
53:22 - When Duck Lake makes more sense than DuckDB alone
56:09 - Who switches to MotherDuck and why
58:02 - PG DuckDB and offloading analytics from Postgres
1:00:49 - Who should use MotherDuck and why
1:03:39 - Hiring plans and where to follow Jordan
1:05:01 - Wrap-up