HighSNR
Cut length and noise from your LLM context
Details
- External ID
- 47397702
- Source
- HN
- Company
- —
- Product
- HighSNR
- Website domain
- high-snr.com
- Launched
- March 16, 2026
- Cohort
- —
- Upvotes
- 6
- Upvotes percentile
- 0.2853628536285363
- 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
- Horizontal
- Function
- Observability & eval
- Audience
- Developer
- AI stance
- AI feature
- Project type
- Commercial product
- Normalized one-liner
- reduce noise and length in llm context
- Manually corrected
- False
Could you build this?
Partial The API gateway wrapper is simple, but achieving sub-second, deterministic context compression that outperforms full-document baselines on QA benchmarks requires specialized information retrieval algorithms.
What it would actually take: The service needs a high-performance backend (Rust or Python/C++) implementing deterministic text-ranking algorithms such as LexRank, semantic density scoring, or graph centrality heuristics. The hard part is tuning passage-importance algorithms to maintain multi-hop question answering accuracy across dense documents without invoking an LLM. It requires domain expertise in information retrieval, NLP evaluation frameworks, and algorithmic optimization.
Discussion
5 comments analyzed.
Concerns raised: Tokenizer accuracy affected benchmark results initially, Quality degradation at lower budgets (Qasper ~98% of full-context)
Feature requests: Standard practices documentation for hint parameter usage, Guidance on when to omit hint for summarization tasks
Competitors
Other products that read as similar to this one — 311 launches clear the similarity bar, closest 8 shown.
Attention rank: #230 of 312 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).
Launched 138 days after the earliest competitor.
- A tool to properly observe your LLM's context window · hn · 2025-10-30 · 8 upvotes · similarity 0.57
- Ctrlb-decompose: Strip the noise from logs before sending to LLMs · hn · 2026-09-09 · 14 upvotes · similarity 0.56
- LLM fine-tuning without infra or ML expertise · hn · 2026-01-21 · 5 upvotes · similarity 0.53
- Reducing LLM input tokens by 70% · hn · 2026-05-12 · 56 upvotes · similarity 0.53
- LLM Attention Visualization · hn · 2026-09-08 · 173 upvotes · similarity 0.53
- The Token Company: Intelligent compression for LLM context bloat · yc · 2026-03-03 · 25 upvotes · similarity 0.53
- LLMExperiments · github · 2026-09-24 · 7 upvotes · similarity 0.52
- KillSwitch · hn · 2026-09-19 · 10 upvotes · similarity 0.51
Other launches for this product
- No other launches for this product.
Same idea, different domain
Nobody's really built a observability & eval tool for Media & entertainment yet.