A Hierarchical Multi-task Approach for Learning Embeddings from Semantic Tasks
Paper
•
1811.06031
•
Published
•
1
Dimpho
Dpp1
AI & ML interests
None yet
Recent Activity
upvoted
a
paper
5 days ago
A Hierarchical Multi-task Approach for Learning Embeddings from Semantic
Tasks
updated
a collection
8 days ago
Datasets-1T+
liked
a dataset
8 days ago
randall-lab/cifar100
Organizations
None yet
upvoted
a
paper
5 days ago
updated
a
collection
8 days ago
liked
a
dataset
8 days ago
updated
a
model
12 days ago
published
a
model
12 days ago
updated
a
collection
25 days ago
liked
a
dataset
about 1 month ago
liked
a
Space
about 1 month ago
liked
a
model
about 1 month ago
updated
a
collection
about 1 month ago
reacted to
abidlabs's
post with đź‘Ť
about 1 month ago
Post
8346
Why I think local, open-source models will eventually win.
The most useful AI applications are moving toward multi-turn agentic behavior: systems that take hundreds or even thousands of iterative steps to complete a task, e.g. Claude Code, computer-control agents that click, type, and test repeatedly.
In these cases, the power of the model is not how smart it is per token, but in how quickly it can interact with its environment and tools across many steps. In that regime, model quality becomes secondary to latency.
An open-source model that can call tools quickly, check that the right thing was clicked, or verify that a code change actually passes tests can easily outperform a slightly “smarter” closed model that has to make remote API calls for every move.
Eventually, the balance tips: it becomes impractical for an agent to rely on remote inference for every micro-action. Just as no one would tolerate a keyboard that required a network request per keystroke, users won’t accept agent workflows bottlenecked by latency. All devices will ship with local, open-source models that are “good enough” and the expectation will shift toward everything running locally. It’ll happen sooner than most people think.
The most useful AI applications are moving toward multi-turn agentic behavior: systems that take hundreds or even thousands of iterative steps to complete a task, e.g. Claude Code, computer-control agents that click, type, and test repeatedly.
In these cases, the power of the model is not how smart it is per token, but in how quickly it can interact with its environment and tools across many steps. In that regime, model quality becomes secondary to latency.
An open-source model that can call tools quickly, check that the right thing was clicked, or verify that a code change actually passes tests can easily outperform a slightly “smarter” closed model that has to make remote API calls for every move.
Eventually, the balance tips: it becomes impractical for an agent to rely on remote inference for every micro-action. Just as no one would tolerate a keyboard that required a network request per keystroke, users won’t accept agent workflows bottlenecked by latency. All devices will ship with local, open-source models that are “good enough” and the expectation will shift toward everything running locally. It’ll happen sooner than most people think.
- 8 replies
updated
a
collection
about 1 month ago
upvoted
a
paper
about 1 month ago
updated
a
collection
about 1 month ago
upvoted
a
paper
about 1 month ago
updated
a
collection
about 1 month ago
upvoted
3
papers
about 1 month ago
liked
a
model
about 1 month ago
