Add community posting drafts (Reddit, HN) in EN/KO

docs/pr/2026-03-31-reddit-localllama.md — KV compression focus
docs/pr/2026-03-31-hackernews.md — Show HN technical depth
Both with Korean translations and posting strategy notes

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

Author: unamedkr

docs/pr/2026-03-31-hackernews-ko.md

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+# Hacker News 한글 포스팅 — 2026-03-31
+
+## 제목
+
+Show HN: 순수 C LLM 엔진, KV 캐시 3.8배 압축 (9K 줄, 의존성 없음)
+
+## URL
+
+https://github.com/quantumaikr/TurboQuant.cpp
+
+## 첫 댓글
+
+LLM 추론 시 KV 캐시를 실시간 압축하는 순수 C 엔진을 만들었습니다.
+
+문제: 32K 토큰 이상의 긴 컨텍스트에서 KV 캐시가 가중치보다 많은 메모리를 차지합니다. 4B 모델의 32K 컨텍스트 KV 캐시는 FP16으로 4.4 GB입니다.
+
+TurboQuant는 KV 캐시를 추론 중 Q4로 양자화하여 1.2 GB로 줄입니다 (3.8배 압축, FP16 출력 대비 코사인 유사도 0.999). 최신 논문 3편 기반: TurboQuant (ICLR '26), QJL (AAAI '25), PolarQuant (AISTATS '26).
+
+기술 상세:
+- C11 9,000줄, libc만 사용, 외부 의존성 없음
+- Q4 가중치 양자화 + ARM NEON 2-row 배치
+- 스레드 풀, 정수 Q4×Q8 어텐션 (vdotq_s32)
+- 멀티 아키텍처: Qwen3.5 (DeltaNet) + Gemma 3 (슬라이딩 윈도우)
+- 듀얼 토크나이저: GPT2 바이트 BPE + SentencePiece 자동 감지
+- TQM 포맷: 사전 양자화 mmap 바이너리
+
+llama.cpp와 단일 스레드 속도 동등 (51 vs 50.7 tok/s). 핵심 가치는 속도가 아닌 긴 컨텍스트에서의 메모리 효율입니다.
+
+Claude Code와 함께 2일 만에 구축. v0.1.0 릴리스.

docs/pr/2026-03-31-hackernews.md

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+# Hacker News Post — 2026-03-31
+
+## Title
+
+Show HN: Pure C LLM engine with 3.8x KV cache compression (9K lines, zero deps)
+
+## URL
+
+https://github.com/quantumaikr/TurboQuant.cpp
+
+## Comment (post immediately after submission)
+
+Hi HN, I built a LLM inference engine in pure C that compresses the KV cache during inference.
+
+The problem: at long contexts (32K+ tokens), the KV cache — not the weights — becomes the memory bottleneck. A 4B model at 32K context needs 4.4 GB just for KV in FP16.
+
+TurboQuant quantizes the KV cache to Q4 on-the-fly, reducing that to 1.2 GB (3.8x compression) with 0.999 cosine similarity to FP16 output. Based on three recent papers: TurboQuant (ICLR '26), QJL (AAAI '25), PolarQuant (AISTATS '26).
+
+Technical details:
+- 9,000 lines of C11, libc only, no external dependencies
+- Q4 weight quantization with ARM NEON 2-row batching
+- Thread pool, integer Q4×Q8 attention (vdotq_s32)
+- Multi-architecture: Qwen3.5 (DeltaNet hybrid) + Gemma 3 (sliding window)
+- Dual tokenizer: GPT2 byte-level BPE + SentencePiece
+- TQM format: pre-quantized mmap binary for instant loading
+
+Speed matches llama.cpp single-thread (51 vs 50.7 tok/s on Qwen3.5-0.8B Q4). The value is memory efficiency at long contexts, not raw speed.
+
+Built in 2 days with Claude Code. v0.1.0 just released.
+
+---
+
+## Posting Notes
+
+- **Best time**: US weekday morning (UTC 14:00-16:00)
+- **HN audience cares about**: technical depth, honesty, zero-dep C code, paper implementations
+- **Avoid**: marketing language, speed claims without context, "revolutionary" etc.
+- **Expected questions**:
+  - "How does KV quantization affect perplexity?" → 0.999 cosine, per-layer verified
+  - "Why not contribute to llama.cpp?" → Different approach (KV compression is orthogonal)
+  - "Built in 2 days?" → With AI assistance (Claude Code), honest about it

docs/pr/2026-03-31-reddit-localllama-ko.md

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+# r/LocalLLaMA 한글 포스팅 — 2026-03-31
+
+## 제목
+
+TurboQuant.cpp — KV 캐시 3.8배 압축으로 32K 컨텍스트에서 llama.cpp 대비 3.2 GB 절약하는 순수 C 추론 엔진
+
+## 본문
+
+llama.cpp가 하지 않는 것 하나에 집중한 C 추론 엔진을 만들었습니다: **KV 캐시 압축**.
+
+짧은 컨텍스트에서는 KV 메모리가 큰 문제가 아닙니다. 하지만 32K 토큰 이상에서는 모델 가중치보다 KV 캐시가 더 많은 메모리를 차지합니다.
+
+**실측 데이터 (Gemma 3 4B):**
+
+```
+컨텍스트    llama.cpp KV (FP16)    TurboQuant KV (Q4)    절약
+─────────   ──────────────────     ──────────────────    ──────
+4K 토큰           544 MB                145 MB           399 MB
+32K 토큰        4,352 MB              1,156 MB         3,196 MB
+128K 토큰      17,408 MB              4,624 MB        12,784 MB
+```
+
+3.8배 압축, PyTorch 대비 레이어별 정확도 검증 완료.
+
+**속도는 경쟁력 있지만 핵심이 아닙니다:**
+- 단일 스레드 Q4: 51.1 tok/s (llama.cpp: 50.7 tok/s) — 동등 수준
+- 더 빠르다는 주장이 아닙니다
+
+**차별점:**
+- ICLR 2026 TurboQuant 논문 기반 KV 캐시 3.8배 압축
+- 3개 모델 지원: Gemma 3 4B, Qwen3.5-0.8B, Gemma 3 270M
+- 순수 C, 외부 의존성 없음, ~1MB 바이너리
+- 멀티 아키텍처: DeltaNet (Qwen) + 슬라이딩 윈도우 (Gemma)
+- Gemma 4 대응 준비 완료
+
+```bash
+git clone https://github.com/quantumaikr/TurboQuant.cpp && cd TurboQuant.cpp
+bash scripts/quickstart.sh "What is deep learning?"
+```
+
+2일 만에 구축. C 9,000줄. 테스트 스위트 20개. 첫 릴리스 v0.1.0.
+
+KV 캐시 압축은 제한된 RAM에서 긴 컨텍스트를 사용하는 시나리오에서 가장 큰 가치를 가집니다 — 로컬 LLM 사용자에게 가장 중요한 시나리오입니다.
+
+GitHub: https://github.com/quantumaikr/TurboQuant.cpp

docs/pr/2026-03-31-reddit-localllama.md

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+# r/LocalLLaMA Post — 2026-03-31
+
+## Title
+
+TurboQuant.cpp — Pure C inference engine with 3.8x KV cache compression. Runs Gemma 3 4B at 32K context using 1.2 GB KV instead of 4.4 GB.
+
+## Body
+
+We built a C inference engine from scratch focused on one thing llama.cpp doesn't do: **compressing the KV cache**.
+
+At short contexts, KV memory doesn't matter much. But at 32K+ tokens, it becomes the dominant memory cost — often larger than the model weights themselves.
+
+**The numbers (Gemma 3 4B):**
+
+```
+Context     llama.cpp KV (FP16)    TurboQuant KV (Q4)    Saved
+─────────   ──────────────────     ──────────────────    ──────
+4K tokens          544 MB                145 MB           399 MB
+32K tokens       4,352 MB              1,156 MB         3,196 MB
+128K tokens     17,408 MB              4,624 MB        12,784 MB
+```
+
+3.8x compression with verified output quality (per-layer exact match against PyTorch).
+
+**Speed is competitive, not the selling point:**
+- Single-thread Q4: 51.1 tok/s (llama.cpp: 50.7 tok/s) on Qwen3.5-0.8B
+- Same ballpark. We're not claiming to be faster.
+
+**What's different:**
+- 3.8x KV cache compression (TurboQuant/PolarQuant/QJL algorithms from ICLR 2026)
+- 3 models: Gemma 3 4B, Qwen3.5-0.8B, Gemma 3 270M
+- Pure C, zero dependencies, ~1MB binary
+- Multi-architecture: DeltaNet hybrid (Qwen) + sliding window (Gemma)
+- Gemma 4 ready (same architecture family)
+
+**Quick start:**
+```bash
+git clone https://github.com/quantumaikr/TurboQuant.cpp && cd TurboQuant.cpp
+bash scripts/quickstart.sh "What is deep learning?"
+```
+
+Built in 2 days. 9,000 lines of C. 20 test suites. First release: v0.1.0.
+
+The KV compression matters most for long context on limited RAM — exactly the scenario local LLM users care about.
+
+GitHub: https://github.com/quantumaikr/TurboQuant.cpp
+
+---
+
+## Posting Notes
+
+- **Flair**: `New Model` or `Resource`
+- **Best time**: UTC Tue-Thu 1-3 PM (US East morning)
+- **Expected questions**:
+  - "What about quality degradation?" → 0.999 cosine similarity, per-layer PyTorch match
+  - "vs llama.cpp?" → Same speed, different value prop (KV compression)
+  - "Only 3 models?" → Multi-arch engine, more coming. Gemma 4 ready.
+  - "Q4 KV vs FP16 isn't fair" → Both are valid choices. We offer the option llama.cpp doesn't.