Nicheloom

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Continual Learning with .md

Details

External ID
47757552
Source
HN
Company
—
Product
Continual Learning with .md
Website domain
github.com
Launched
April 13, 2026
Cohort
—
Upvotes
34
Upvotes percentile
0.8046272493573264
Tags
—
Fetched at
Sept. 7, 2026, 9:26 p.m.
Updated at
Sept. 7, 2026, 9:26 p.m.

Description

I have a proposal that addresses long-term memory problems for LLMs when new data arrives continuously (cheaply!). The program involves no code, but two Markdown files.For retrieval, there is a semantic filesystem that makes it easy for LLMs to search using shell commands.It is currently a scrappy v1, but it works better than anything I have tried.Curious for any feedback!

Enrichment

Theme
ML inference and model optimization
Vertical
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Function
—
Audience
—
AI stance
—
Project type
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Normalized one-liner
—
Manually corrected
False

Could you build this?

Yes The project is literally described as involving 'no code, but two Markdown files' along with basic shell commands for semantic file search. Anyone can create or adapt this setup in an afternoon with an AI assistant.

Discussion

20 comments analyzed.

Competitors mentioned: LLM Wiki (Karpathy's approach), Embedding-based knowledge bases, MCPTube

Concerns raised: Embedding-based methods struggle matching user queries to stored information, Risk of memory pollution and false information persisting (Memento Effect), No continuous running or LLM ablation studies yet (MVP stage), Lack of benchmark measuring how well memories contribute to future interactions, Query reformulation with embedding-based methods is tricky with chat history conditioning

Feature requests: Implement removal/pruning mechanism to refactor filesystem daily, Create benchmark to measure memory contribution to future interactions, Add continuous running capability and LLM comparison studies, Support for native video file reading on Gemini without embeddings

Competitors

Other products that read as similar to this one — 130 launches clear the similarity bar, closest 8 shown.

Attention rank: #32 of 131 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).

Launched 165 days after the earliest competitor.

Other launches for this product