NerfStatus: HF Inference Monitor
📊
1
Stefan Wiest
skew202
AI & ML interests
AI Research & Engineering · Multi-Agent System Coordination
Recent Activity
liked
a Space
1 day ago
skew202/nerfstatus-hf-monitor
updated
a Space
1 day ago
skew202/nerfstatus-hf-monitor
liked
a Space
1 day ago
HuggingFaceTB/smol-training-playbook
Organizations
liked
a
Space
1 day ago
updated
a
Space
1 day ago
liked
2
Spaces
1 day ago
upvoted
a
collection
1 day ago
upvoted
2
articles
1 day ago
Article
We Got Claude to Fine-Tune an Open Source LLM
•
556
Article
huggingface_hub v1.0: Five Years of Building the Foundation of Open Machine Learning
- +2
•
74
liked
a
Space
1 day ago
liked
2
models
1 day ago
liked
2
Spaces
1 day ago
upvoted
a
paper
1 day ago
upvoted
a
collection
1 day ago
reacted to
Kseniase's
post with 👍
1 day ago
Post
2066
What we learned about memory in 2025: 8 comprehensive resources
If models forget everything, how can they be reliable? AI systems need to remember past interactions, update knowledge, stay consistent over time, and work beyond a single prompt. That's why many start to talk more about memory in AI.
Here’s a useful set of studies and videos on where AI memory stands today:
1. Memory in the Age of AI Agents (2512.13564)
A great survey that organizes agent memory research. It gives concrete taxonomies across memory form, function, and dynamics, summarizes benchmarks, frameworks, and emerging directions for building systematic agent memory systems
2.When Will We Give AI True Memory? A conversation with Edo Liberty, CEO and founder @ Pinecone -> https://youtu.be/ITbwVFZYepc?si=_lAbRHciC740dNz0
Edo Liberty discusses what real memory in LLMs requires beyond RAG - from scalable vector storage to reliable knowledge systems - and why storage, not compute, is becoming the key bottleneck for building dependable AI agents.
3. Why AI Intelligence is Nothing Without Visual Memory | Shawn Shen on the Future of Embodied AI -> https://youtu.be/3ccDi4ZczFg?si=SbJg487kwrkVXgUu
Shawn Shen argues AI needs a separate, hippocampus-like memory to move beyond chatbots, enabling long-term visual memory, object permanence, and on-device intelligence for robots, wearables, and the physical world
4. From Human Memory to AI Memory: A Survey on Memory Mechanisms in the Era of LLMs (2504.15965)
Links human memory types to LLM memory, introduces a taxonomy across object, form, and time, and identifies concrete limitations and future research directions
5. Rethinking Memory in AI: Taxonomy, Operations, Topics, and Future Directions -> https://arxiv.org/abs/2505.00675v2
Proposes a concrete taxonomy, core operations, and research directions to systematically organize and advance agent memory systems.
Read further below ⬇️
If you like it, also subscribe to the Turing Post: https://www.turingpost.com/subscribe
If models forget everything, how can they be reliable? AI systems need to remember past interactions, update knowledge, stay consistent over time, and work beyond a single prompt. That's why many start to talk more about memory in AI.
Here’s a useful set of studies and videos on where AI memory stands today:
1. Memory in the Age of AI Agents (2512.13564)
A great survey that organizes agent memory research. It gives concrete taxonomies across memory form, function, and dynamics, summarizes benchmarks, frameworks, and emerging directions for building systematic agent memory systems
2.When Will We Give AI True Memory? A conversation with Edo Liberty, CEO and founder @ Pinecone -> https://youtu.be/ITbwVFZYepc?si=_lAbRHciC740dNz0
Edo Liberty discusses what real memory in LLMs requires beyond RAG - from scalable vector storage to reliable knowledge systems - and why storage, not compute, is becoming the key bottleneck for building dependable AI agents.
3. Why AI Intelligence is Nothing Without Visual Memory | Shawn Shen on the Future of Embodied AI -> https://youtu.be/3ccDi4ZczFg?si=SbJg487kwrkVXgUu
Shawn Shen argues AI needs a separate, hippocampus-like memory to move beyond chatbots, enabling long-term visual memory, object permanence, and on-device intelligence for robots, wearables, and the physical world
4. From Human Memory to AI Memory: A Survey on Memory Mechanisms in the Era of LLMs (2504.15965)
Links human memory types to LLM memory, introduces a taxonomy across object, form, and time, and identifies concrete limitations and future research directions
5. Rethinking Memory in AI: Taxonomy, Operations, Topics, and Future Directions -> https://arxiv.org/abs/2505.00675v2
Proposes a concrete taxonomy, core operations, and research directions to systematically organize and advance agent memory systems.
Read further below ⬇️
If you like it, also subscribe to the Turing Post: https://www.turingpost.com/subscribe
- 3 replies
upvoted
4
collections
1 day ago
upvoted
an
article
1 day ago
Article
Nemotron 3 Nano \- A new Standard for Efficient, Open, and Intelligent Agentic Models
•
104