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Remory

Remory: Learning Residual Memory for Context Compaction

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This is 1 of 269 launches in ai agent memory infrastructure — see how it stacks up on momentum and crowding →

1885 other launches read as similar to this one →

Details

External ID
1408334244
Source
GITHUB
Company
—
Product
Remory
Website domain
github.com
Launched
Oct. 7, 2026
Cohort
—
Upvotes
43
Upvotes percentile
0.68
Tags
context-compaction, inference, llm-agents, residual-memory

Enrichment

Niche
ai agent memory infrastructure
Vertical
Horizontal
Function
Model & infra
Audience
Developer
AI stance
AI-native
Project type
Hobby / open-source project
Normalized one-liner
context compaction mechanism for llm memory
Manually corrected
False

Could you build this?

No Remory is an academic machine learning research project focused on learning residual memory for context compaction in LLMs, which requires novel ML architecture design and heavy GPU training.

What it would actually take: Building this requires deep ML research expertise in transformer architectures, attention mechanisms, and context compression. The stack relies on PyTorch, Triton/CUDA kernels for custom attention, and clusters of high-end GPUs (e.g., H100s) to run pre-training and fine-tuning experiments across large language model benchmarks.

Competitors

Other products that read as similar to this one — 1885 launches clear the similarity bar, closest 8 shown.

Attention rank: #572 of 1886 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).

Launched 342 days after the earliest competitor.

Other launches for this product