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Mike Freedman

Co-founder & CTO, Timescale · 1 appearance on the record.

computed by AI from the episodes · how this works → · full disclaimer →

founderexecutiveacademicscientist@michaelfreedman ↗LinkedIn ↗cs.princeton.edu/~mfreed ↗Wikipedia ↗

Michael J. Freedman is a distributed systems and networking researcher who co-founded Timescale, a database company building high-performance time-series, analytics, and vector capabilities on PostgreSQL. At Princeton, his research helped pioneer software-defined networking and decentralized content delivery, earning him recognition as an ACM Fellow and the ACM Grace Murray Hopper Award.

9statements → 6claims → 4claims resolved → 100%fully supported → 4.56/5average certainty → 2.22/5average debate potential →

4 supported 0 partly supported 0 contradicted 2 not checkable as stated how the 6 claims stand · each chip opens the sources

6 assertions · 1 insight · 2 disclosures · every statement was checked. The predictions and assertions are the 6 claims: statements the public record can support or contradict. 4 are resolved, and 2 name no date, number or outcome precise enough to check. Everything else (opinions, insights, what ifs, disclosures) can never be settled by the record, so it carries no assessment.

The record, in short

What the tape says about how Mike argues and how the claims held up. Everything they said, and everything said about them, is in the tabs below.

Their most notable supported claim

Assertion Supported
Freedman: pgvectorscale is 28 times faster than Pinecone for high recall
“Compared to one of the leading vector-only databases, Pinecone, a PG vector scale is 28 times faster for a high recall scenario.”
Mike Freedman Sep 25, 2024 ▶ 9:56 Turbocharging Postgres for Time Series & Vectors — Timescale CTO Mike Freedman | Data Driven NYC

Expressed certainty vs assessment result

none yet certainty 1
none yet certainty 2
none yet certainty 3
100% certainty 4
100% certainty 5

weighted support: a fully supported claim counts one, a partly supported claim counts half. Each filled bar is clickable and opens exactly those claims; "none yet" means nothing said at that certainty level has resolved yet

How they sound: speaking style how? →

257 words/min while actually speaking · 29.5 um and uh per 1k words

No argument clarity score for Mike Freedman: only 1 usable question→answer exchange on raw tape (a fair score needs 8+). We do not score a sample that small. Roundtable and news formats yield far fewer direct exchanges than interviews.

Measured by listening to the audio itself: 3,357 words across 1 episode of raw-level tape, transcribed verbatim with every um and uh kept, each one attributed only where the alignment onto our timed stream is unambiguous. These are measurements of speaking style. We do not rank them: across this corpus, fluency and argument quality are nearly uncorrelated (ρ≈0.2), and smooth talking does not signal clear thinking. How it's measured →

Everything Mike Freedman said on the MAD Podcast that made the record, most notable first. Filter by type, assessment or year in the ledger →

Assertion Not checkable as stated
Freedman: Timescale outperforms vanilla Postgres, InfluxDB, and AWS Timestream
“In fact, not only is Timescale a better database for time series than vanilla Postgres, including compared to both Amazon's RDS and Aurora, but it is also better than custom-built proprietary time series databases like InfluxDB and AWS Timestream, even with th…”
Mike Freedman Sep 25, 2024 ▶ 6:08 Turbocharging Postgres for Time Series & Vectors — Timescale CTO Mike Freedman | Data Driven NYC
Assertion Supported
Freedman: pgvectorscale is 28 times faster than Pinecone for high recall
“Compared to one of the leading vector-only databases, Pinecone, a PG vector scale is 28 times faster for a high recall scenario.”
Mike Freedman Sep 25, 2024 ▶ 9:56 Turbocharging Postgres for Time Series & Vectors — Timescale CTO Mike Freedman | Data Driven NYC
Assertion Supported
Freedman: pgvectorscale on AWS is 75% cheaper than Pinecone
“The monthly costs of running such a PG vector scale deployment on AWS is 75% less expensive than PyIncome.”
Mike Freedman Sep 25, 2024 ▶ 10:22 Turbocharging Postgres for Time Series & Vectors — Timescale CTO Mike Freedman | Data Driven NYC
Assertion Not checkable as stated
Freedman: Nobody runs RAG applications at hundreds of thousands of RPS
“Nobody is taking rag apps to that scale today.”
Mike Freedman Sep 25, 2024 ▶ 20:00 Turbocharging Postgres for Time Series & Vectors — Timescale CTO Mike Freedman | Data Driven NYC
Insight
Freedman: New data workloads do not require new database architectures
“A new data workload does not require new database architecture.”
Mike Freedman Sep 25, 2024 ▶ 15:24 Turbocharging Postgres for Time Series & Vectors — Timescale CTO Mike Freedman | Data Driven NYC
Assertion Supported
Freedman: Amazon Redshift is a fork of PostgreSQL 8
“Amazon Redshift is actually a fork of Postgres eight.”
Mike Freedman Sep 25, 2024 ▶ 16:31 Turbocharging Postgres for Time Series & Vectors — Timescale CTO Mike Freedman | Data Driven NYC
Disclosure
Freedman: Timescale's insights service ingests 800 billion records daily
“That insights product is actually backed by a timescale service, much the same as any of our customer could do, that has more than a petabyte of data and currently is ingesting, I think last week I saw, eight hundred billion records a day.”
Mike Freedman Sep 25, 2024 ▶ 25:22 Turbocharging Postgres for Time Series & Vectors — Timescale CTO Mike Freedman | Data Driven NYC
Assertion Supported
Freedman: NYC grew from 11 to 144 unicorns between 2015 and 2022
“From 2015 to 2022, New York grew from 11 to a 144 unicorns.”
Mike Freedman Sep 25, 2024 ▶ 1:02 Turbocharging Postgres for Time Series & Vectors — Timescale CTO Mike Freedman | Data Driven NYC
Disclosure
Timescale Cloud reaches over 1,000 customers alongside open-source adoption
“We offer both open source software with hundreds of thousands of teams using TimescaleDB, and primarily build our commercial business on our managed Timescale Cloud with more than a thousand customers.”
Mike Freedman Sep 25, 2024 ▶ 2:23 Turbocharging Postgres for Time Series & Vectors — Timescale CTO Mike Freedman | Data Driven NYC

Appearances (1)

EpisodeDateSpeaking time
Turbocharging Postgres for Time Series & Vectors — Timescale CTO Mike Freedman | Data Driv Sep 25, 2024 16m
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