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Reducing LLM input tokens by 70%

Details

External ID
48109600
Source
HN
Company
—
Product
Reducing LLM input tokens by 70%
Website domain
adola.app
Launched
May 12, 2026
Cohort
—
Upvotes
56
Upvotes percentile
0.8465266558966075
Tags
—
Fetched at
Sept. 7, 2026, 9:26 p.m.
Updated at
Sept. 7, 2026, 9:26 p.m.

Enrichment

Theme
ML inference and model optimization
Vertical
—
Function
Model & infra
Audience
Developer
AI stance
AI feature
Project type
Hobby / open-source project
Normalized one-liner
llm input token compression technique
Manually corrected
False

Could you build this?

Partial Building an OpenAI-compatible proxy gateway with billing and streaming is straightforward, but achieving a 70% input token reduction without catastrophic semantic loss requires non-trivial prompt compression algorithms or custom context-caching infrastructure.

What it would actually take: The architecture uses a high-performance API proxy (Go or Rust) exposing an OpenAI-compatible interface in front of open-source model providers. The hard part is the token compression engine, which requires semantic pruning algorithms, selective syntactic tree stripping, or attention-based token elimination (such as LLMLingua) running in low-latency memory before forwarding the payload to upstream inference.

Discussion

20 comments analyzed.

Concerns raised: Borrowed hero treatment gave bad first impression for technical audience, Comments appear to be botted

Competitors

Other products that read as similar to this one — 341 launches clear the similarity bar, closest 8 shown.

Attention rank: #49 of 342 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).

Launched 195 days after the earliest competitor.

Other launches for this product