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← There is very little hard data on which factors are critical to making a data science team successful.
17 connected korrents · 16 moments from 23 Jul 2017 to 21 Oct 2026.
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startups Same subject Same subject Same subject Same subject Same subject Same subject Same subject Same subject Same subject Same subject Same subject Same subject Same subject Same subject Same subject Same subject Same subject Read this korrent: There is very little hard data on which factors are critical to making a data science team successful. There is very little hard data on which factors are critical to making a data science team successful. Last stated 9 years ago
23 Jul 2017
RC
Rui Carmo — holds since 2017-07-23 — tap for who they are
Same subject: A few larger teams under experienced engineering managers outperform many tiny teams each led by a working tech lead. — tap to centre the map on it A few larger teams under experienced engineering managers outperform many tiny teams each led by a working tech lead. Last stated 3 years ago
5 Jan 2024
CM
Charity Majors — holds since 2024-01-05 — tap for who they are
Same subject: Not every team has to have the same composition of people and skill sets. — tap to centre the map on it Not every team has to have the same composition of people and skill sets. Last stated 6 months ago
28 Apr 2026
PD
Polly D'Arcy — holds since 2026-04-28 — tap for who they are
Same subject: Team output is still what matters, however hard the industry pushes to measure individual output. — tap to centre the map on it Team output is still what matters, however hard the industry pushes to measure individual output. Last stated 3 months ago
12 Aug 2026
CM
Charity Majors — holds since 2026-08-12 — tap for who they are
Same subject: A team of all the same people who are good at the same thing is not going to help deliver the best product experience. — tap to centre the map on it A team of all the same people who are good at the same thing is not going to help deliver the best product experience. Last stated 6 months ago
28 Apr 2026
PD
Polly D'Arcy — holds since 2026-04-28 — tap for who they are
Same subject: The fundamentals matter way more than how quickly someone works, how many tools they use, or how they optimise for the models. — tap to centre the map on it The fundamentals matter way more than how quickly someone works, how many tools they use, or how they optimise for the models. Last stated 2 months ago
25 Aug 2026
JB
John Bai — holds since 2026-08-25 — tap for who they are
Same subject: The fastest-growing companies are not using data well, because they reached enormous scale before they had time to build any of the instrumentation. — tap to centre the map on it The fastest-growing companies are not using data well, because they reached enormous scale before they had time to build any of the instrumentation. Last stated a year ago
21 Sept 2025
JZ
Julie Zhuo — holds since 2025-09-21 — tap for who they are
Same subject: It is rare to find one person who is great at every dimension of team leadership. — tap to centre the map on it It is rare to find one person who is great at every dimension of team leadership. Last stated 2 years ago
21 Jul 2024
BK
Ben Kuhn — holds since 2024-07-21 — tap for who they are
Same subject: If you cannot say crisply what success looks like, you will not get great work — out of a team or out of an AI. — tap to centre the map on it If you cannot say crisply what success looks like, you will not get great work — out of a team or out of an AI. Last stated a year ago
21 Sept 2025
JZ
Julie Zhuo — holds since 2025-09-21 — tap for who they are
Same subject: A badly written AI outbound email is evidence of a bad vendor, not of a limit of AI. — tap to centre the map on it A badly written AI outbound email is evidence of a bad vendor, not of a limit of AI. Last stated 10 months ago
1 Jan 2026
JL
Jason Lemkin — holds since 2026-01-01 — tap for who they are
Same subject: A bigger context window does not give you a smarter model; the intelligence of the model is what decides how much of that window it can actually attend to. — tap to centre the map on it A bigger context window does not give you a smarter model; the intelligence of the model is what decides how much of that window it can actually attend to. Last stated 4 months ago
15 Jul 2026
DH
Dex Horthy — holds since 2026-07-15 — tap for who they are
Same subject: A body of publications is easy for AI models to consume via pretraining corpora but cumbersome for most humans due to CAPTCHAs, accounts, and paywalls. — tap to centre the map on it A body of publications is easy for AI models to consume via pretraining corpora but cumbersome for most humans due to CAPTCHAs, accounts, and paywalls. Last stated a month ago
17 Sept 2026
PC
Patrick Collison — holds since 2026-09-17 — tap for who they are
Same subject: A PHP vulnerability found with GPT-5.6 Sol will cause havoc this week. — tap to centre the map on it A PHP vulnerability found with GPT-5.6 Sol will cause havoc this week. Last stated 2 weeks ago
21 Oct 2026
PS
Peter Steinberger — holds since 2026-10-21 — tap for who they are
Same subject: A startup should not begin life as a nonprofit and bolt a for-profit arm on later, whatever OpenAI's own history suggests. — tap to centre the map on it A startup should not begin life as a nonprofit and bolt a for-profit arm on later, whatever OpenAI's own history suggests. Last stated 3 years ago
18 Mar 2024
SA
Sam Altman — holds since 2024-03-18 — tap for who they are
Same subject: A tiny language model inventing a plausible-sounding name is the same phenomenon as a large one confidently stating a false fact. — tap to centre the map on it A tiny language model inventing a plausible-sounding name is the same phenomenon as a large one confidently stating a false fact. Last stated 9 months ago
12 Feb 2026
AK
Andrej Karpathy — holds since 2026-02-12 — tap for who they are
Same subject: "Scaling" was powerful because it was one word: naming a research direction is what tells a whole field what to do next. — tap to centre the map on it "Scaling" was powerful because it was one word: naming a research direction is what tells a whole field what to do next. Last stated 11 months ago
25 Nov 2025
IS
Ilya Sutskever — holds since 2025-11-25 — tap for who they are
Same subject: A benchmark result should be reported under a stated budget, or as a curve against test-time compute — never as a single number. — tap to centre the map on it A benchmark result should be reported under a stated budget, or as a curve against test-time compute — never as a single number. Last stated 4 months ago
26 Jun 2026
NB
Noam Brown — holds since 2026-06-26 — tap for who they are
Same subject: A lab should spend most of its compute on research rather than on building the next model, because research is where the tenfold yearly efficiency gains come from. — tap to centre the map on it A lab should spend most of its compute on research rather than on building the next model, because research is where the tenfold yearly efficiency gains come from. Last stated 8 months ago
13 Mar 2026
DP
Dylan Patel — holds since 2026-03-13 — tap for who they are
At the centre
There is very little hard data on which factors are critical to making a data science team successful.
Holds Read this korrent →
A few larger teams under experienced engineering managers outperform many tiny teams each led by a working tech lead.
Holds
Not every team has to have the same composition of people and skill sets.
Holds
Team output is still what matters, however hard the industry pushes to measure individual output.
Holds
A team of all the same people who are good at the same thing is not going to help deliver the best product experience.
Holds
The fundamentals matter way more than how quickly someone works, how many tools they use, or how they optimise for the models.
Holds
The fastest-growing companies are not using data well, because they reached enormous scale before they had time to build any of the instrumentation.
Holds
It is rare to find one person who is great at every dimension of team leadership.
Holds
If you cannot say crisply what success looks like, you will not get great work — out of a team or out of an AI.
Holds
A badly written AI outbound email is evidence of a bad vendor, not of a limit of AI.
Holds
A bigger context window does not give you a smarter model; the intelligence of the model is what decides how much of that window it can actually attend to.
Holds
A body of publications is easy for AI models to consume via pretraining corpora but cumbersome for most humans due to CAPTCHAs, accounts, and paywalls.
Holds
A PHP vulnerability found with GPT-5.6 Sol will cause havoc this week.
Holds
A startup should not begin life as a nonprofit and bolt a for-profit arm on later, whatever OpenAI's own history suggests.
Holds
A tiny language model inventing a plausible-sounding name is the same phenomenon as a large one confidently stating a false fact.
Holds
"Scaling" was powerful because it was one word: naming a research direction is what tells a whole field what to do next.
Holds
A benchmark result should be reported under a stated budget, or as a curve against test-time compute — never as a single number.
Holds
A lab should spend most of its compute on research rather than on building the next model, because research is where the tenfold yearly efficiency gains come from.
Holds