Project Kalos – Zero-copy C/CUDA sidecar for 0.46ms LLM memory recall
7.25x Faster Total Response Completion (72B Models): Cuts total user-sent to output-completion latency on 72B parameter models from 23.71 seconds down to 3.27 seconds. 99.8% VRAM Footprint Reduction: Compresses 16.38 GB KV-cache bloat down to 2.50 MB of sparse associative neural templates. Sub-Millisecond Reflex Sentry (15.16 μs): 4.19-Million CUDA spiking neurons for instant event detection.
- ▪7.25x Faster Total Response Completion (72B Models): Cuts total user-sent to output-completion latency on 72B parameter models from 23.71 seconds down to 3.27 seconds.
- ▪99.8% VRAM Footprint Reduction: Compresses 16.38 GB KV-cache bloat down to 2.50 MB of sparse associative neural templates.
- ▪Sub-Millisecond Reflex Sentry (15.16 μs): 4.19-Million CUDA spiking neurons for instant event detection.
Hacker News (AI / LLM) files mainly under ai. We currently carry 4,006 of its stories.
Story provenance
Source · retrieval · rights · ranking — open for full record
inspect →
Story provenance
Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | GitHub |
| Canonical URL | https://github.com/MongooseReborn/kalos-engine |
| Publication time | Fri, 07 Aug 2026 16:35:13 +0000 |
| Retrieval time | 2026-08-07T16:40:42.051Z |
| Last seen | 2026-08-07T16:40:42.051Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | q_mIShwzAaiM · 1 stories |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
license mit language en tags cuda neuromorphic spiking-neural-network associative-memory llm-acceleration vram-optimization c-cpp pipeline_tag text-generation library_name c-cuda extra_gated_heading Project Kalos C/CUDA Neuromorphic Suite 🌿 Project Kalos: High-Performance C/CUDA Neuromorphic Engine Suite Project Kalos is a high-performance, biological-resonant C/CUDA neuromorphic engine suite designed to decouple long-range dialogue memory, spiking neural reflexes, and physical sensory haptics from large language model (LLM) text tokenization. 💥 Key Features & Performance Highlights O(1) Microsecond Memory Recall (465.12 μs): Replaces linear text re-tokenization (20,441.90 ms) with constant-time CUDA vector lookup.
…
Excerpt limited to ~120 words for fair-use compliance. The full article is at GitHub.