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← Open-source text embedding models run well enough on consumer hardware to be practical for real tasks.
17 connected korrents · 15 moments from 10 Dec 2015 to 17 Oct 2026. Nearly all of them are about LLMs .
Everything filed under open source
open source
Everything filed under neural networks
neural networks
Everything filed under China
China
Everything filed under transformers
transformers 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: Open-source text embedding models run well enough on consumer hardware to be practical for real tasks. Open-source text embedding models run well enough on consumer hardware to be practical for real tasks. Last stated 9 months ago
23 Jan 2026
AW
Adam Wiggins — holds since 2026-01-23 — tap for who they are
Same subject: It is fine that “open source” and “open weights” will be used interchangeably for AI models. — tap to centre the map on it It is fine that “open source” and “open weights” will be used interchangeably for AI models. Last stated 3 years ago
20 Jul 2023
AF
Alessio Fanelli — holds since 2023-07-20 — tap for who they are
Same subject: The local AI niche will end up dominated by whichever open-weight model runs practically fast on high-end consumer hardware, like a Mac or a 'GPU in a box' rig. — tap to centre the map on it The local AI niche will end up dominated by whichever open-weight model runs practically fast on high-end consumer hardware, like a Mac or a 'GPU in a box' rig. Last stated 6 months ago
14 May 2026
SS
Salvatore Sanfilippo — holds since 2026-05-14 — tap for who they are
Same subject: Text embeddings produced by different large language models converge on a shared, universal geometric structure regardless of architecture or training data. — tap to centre the map on it Text embeddings produced by different large language models converge on a shared, universal geometric structure regardless of architecture or training data. Last stated a year ago
5 Jun 2025
SH
Samuel Hammond — holds since 2025-06-05 — tap for who they are
Same subject: The performance gap between closed-source and open-weight language models is closing. — tap to centre the map on it The performance gap between closed-source and open-weight language models is closing. Last stated 3 years ago
15 Apr 2024
SR
Sebastian Ruder — holds since 2024-04-15 — tap for who they are
Same subject: Larger language models already have enough capacity that extra per-layer embedding tricks add little benefit to them. — tap to centre the map on it Larger language models already have enough capacity that extra per-layer embedding tricks add little benefit to them. Last stated 6 months ago
16 May 2026
SR
Sebastian Raschka — holds since 2026-05-16 — tap for who they are
Same subject: There will always be a fairly capable open-source model in the world, which puts a permanent floor under what anyone can charge for one. — tap to centre the map on it There will always be a fairly capable open-source model in the world, which puts a permanent floor under what anyone can charge for one. Last stated 12 months ago
12 Nov 2025
SN
Satya Nadella — holds since 2025-11-12 — tap for who they are
Same subject: Large language models are broad and impressive but not precise, and are not fundamentally about deep computation. — tap to centre the map on it Large language models are broad and impressive but not precise, and are not fundamentally about deep computation. Last stated 8 months ago
23 Feb 2026
SW
Stephen Wolfram — holds since 2026-02-23 — tap for who they are
Same subject: Western open-weight models receive significantly more adoption than Chinese models with similar performance-to-size footprints. — tap to centre the map on it Western open-weight models receive significantly more adoption than Chinese models with similar performance-to-size footprints. Last stated 3 weeks ago
17 Oct 2026
NL
Nathan Lambert — holds since 2026-10-17 — tap for who they are
Same subject: A layer should learn a residual with reference to its own input rather than an unreferenced function, which is what makes great depth trainable. — tap to centre the map on it A layer should learn a residual with reference to its own input rather than an unreferenced function, which is what makes great depth trainable. Last stated 11 years ago
10 Dec 2015
JS
Jian Sun — holds since 2015-12-10 — tap for who they are
KH
Kaiming He — holds since 2015-12-10 — tap for who they are
Same subject: A project-level agent memory file works like a neural net, steering the agent much as a fine-tuned LoRA steers a model. — tap to centre the map on it A project-level agent memory file works like a neural net, steering the agent much as a fine-tuned LoRA steers a model. Last stated 2 months ago
23 Aug 2026
KC
Kun Chen — holds since 2026-08-23 — tap for who they are
Same subject: A small enough winning ticket learns faster than the network it was cut out of, and ends up more accurate than it. — tap to centre the map on it A small enough winning ticket learns faster than the network it was cut out of, and ends up more accurate than it. Last stated 9 years ago
9 Mar 2018
MC
Michael Carbin — holds since 2018-03-09 — tap for who they are
JF
Jonathan Frankle — holds since 2018-03-09 — tap for who they are
Same subject: A winning ticket wins on its initial weights: the connections it keeps started at values that happen to make training work. — tap to centre the map on it A winning ticket wins on its initial weights: the connections it keeps started at values that happen to make training work. Last stated 9 years ago
9 Mar 2018
MC
Michael Carbin — holds since 2018-03-09 — tap for who they are
JF
Jonathan Frankle — holds since 2018-03-09 — tap for who they are
Same subject: Intelligence is the execution of small, simplicity-weighted programs found by search over smooth loss landscapes in overparameterized networks, approaching Bayes-optimal reasoning as scale increases. — tap to centre the map on it Intelligence is the execution of small, simplicity-weighted programs found by search over smooth loss landscapes in overparameterized networks, approaching Bayes-optimal reasoning as scale increases. Last stated 5 years ago
11 Jun 2021
GW
Gwern — holds since 2021-06-11 — tap for who they are
Same subject: Pruning cuts the parameters of a trained network by over 90% without costing accuracy, so inference gets smaller and faster for free. — tap to centre the map on it Pruning cuts the parameters of a trained network by over 90% without costing accuracy, so inference gets smaller and faster for free. Last stated 9 years ago
9 Mar 2018
MC
Michael Carbin — holds since 2018-03-09 — tap for who they are
JF
Jonathan Frankle — holds since 2018-03-09 — tap for who they are
Same subject: A transduction model can rely entirely on self-attention for input and output representations without sequence-aligned RNNs or convolution. — tap to centre the map on it A transduction model can rely entirely on self-attention for input and output representations without sequence-aligned RNNs or convolution. Last stated 9 years ago
12 Jun 2017
IP
Illia Polosukhin — holds since 2017-06-12 — tap for who they are
JU
Jakob Uszkoreit — holds since 2017-06-12 — tap for who they are
NP
Niki Parmar — holds since 2017-06-12 — tap for who they are
AV
Ashish Vaswani — holds since 2017-06-12 — tap for who they are
NS
Noam Shazeer — holds since 2017-06-12 — tap for who they are
AG
Aidan N. Gomez — holds since 2017-06-12 — tap for who they are
+2
2 more
Same subject: Self-attention can yield more interpretable models. — tap to centre the map on it Self-attention can yield more interpretable models. Last stated 9 years ago
12 Jun 2017
IP
Illia Polosukhin — holds since 2017-06-12 — tap for who they are
JU
Jakob Uszkoreit — holds since 2017-06-12 — tap for who they are
NP
Niki Parmar — holds since 2017-06-12 — tap for who they are
AV
Ashish Vaswani — holds since 2017-06-12 — tap for who they are
NS
Noam Shazeer — holds since 2017-06-12 — tap for who they are
AG
Aidan N. Gomez — holds since 2017-06-12 — tap for who they are
+2
2 more
Same subject: A single AI paper rarely changes everything; the last one that did was 'Attention Is All You Need' in 2017. — tap to centre the map on it A single AI paper rarely changes everything; the last one that did was 'Attention Is All You Need' in 2017. Last stated 2 years ago
6 Jan 2025
AR
Aishwarya Naresh Reganti — holds since 2025-01-06 — tap for who they are
At the centre
Open-source text embedding models run well enough on consumer hardware to be practical for real tasks.
Holds Read this korrent →
It is fine that “open source” and “open weights” will be used interchangeably for AI models.
Holds
The local AI niche will end up dominated by whichever open-weight model runs practically fast on high-end consumer hardware, like a Mac or a 'GPU in a box' rig.
Holds
Text embeddings produced by different large language models converge on a shared, universal geometric structure regardless of architecture or training data.
Holds
The performance gap between closed-source and open-weight language models is closing.
Holds
Larger language models already have enough capacity that extra per-layer embedding tricks add little benefit to them.
Holds
There will always be a fairly capable open-source model in the world, which puts a permanent floor under what anyone can charge for one.
Holds
Large language models are broad and impressive but not precise, and are not fundamentally about deep computation.
Holds
Western open-weight models receive significantly more adoption than Chinese models with similar performance-to-size footprints.
Holds
A layer should learn a residual with reference to its own input rather than an unreferenced function, which is what makes great depth trainable.
Holds
A project-level agent memory file works like a neural net, steering the agent much as a fine-tuned LoRA steers a model.
Holds
A small enough winning ticket learns faster than the network it was cut out of, and ends up more accurate than it.
Holds
A winning ticket wins on its initial weights: the connections it keeps started at values that happen to make training work.
Holds
Intelligence is the execution of small, simplicity-weighted programs found by search over smooth loss landscapes in overparameterized networks, approaching Bayes-optimal reasoning as scale increases.
Holds
Pruning cuts the parameters of a trained network by over 90% without costing accuracy, so inference gets smaller and faster for free.
Holds
A transduction model can rely entirely on self-attention for input and output representations without sequence-aligned RNNs or convolution.
Holds
Self-attention can yield more interpretable models.
Holds
A single AI paper rarely changes everything; the last one that did was 'Attention Is All You Need' in 2017.
Holds