YAR

Some milestones aren’t measured in revenue. They’re measured in trust. This week, one of the world’s five largest space agencies started evaluating HyperspaceDB. The goal is simple, but technically demanding: provide an energy-efficient semantic memory with near-instant access to technical documentation onboard a spacecraft. For obvious reasons, I can’t disclose the organization yet. I can only say that it is not NASA and not SpaceX. This isn’t a commercial contract. Not yet. It’s an engineering evaluation, and potentially the beginning of a much larger partnership. For me, that’s even more exciting. When you’re building infrastructure, validation doesn’t always come from funding rounds or headlines. Sometimes it comes from organizations that literally build spacecraft. If everything goes well, HyperspaceDB could reach space next year. Not bad for an open source vector database that started as an ambitious idea. Back to work. 🚀 yar.ink/hyperspace #HyperspaceDB #SpaceTech #AI #AIInfrastructure #VectorDatabase #OpenSource #RAG #Engineering #EnterpriseAI #DeepTech

Today we shipped HyperspaceDB v3.1.1. yar.ink/hyperspace Most users won’t notice the difference. That’s exactly the point. The biggest engineering work often happens where nobody can see it: stability, architecture, developer experience, and the foundation for what’s coming next. We also updated our website, and we’re getting very close to opening HyperspaceDB Cloud for public access. But the part I’m most excited about isn’t the release itself. Next week, we’ll update the website once again. This time, we’ll start showcasing the companies that are integrating HyperspaceDB into their AI infrastructure and internal workflows. The list is still modest, but it’s incredibly motivating to see names such as Wells Fargo, Costco, Nokia, BNP Paribas, Cloudflare, Alibaba, ByteDance, LOT, and several others experimenting with our technology. For an open source project, real adoption matters far more than GitHub stars. Of course, I hope that over time these companies will support the project not only by building with it, but also by contributing back to the ecosystem. That’s the beauty of open source: everyone benefits when innovation flows in both directions. We’re just getting started. #HyperspaceDB #OpenSource #VectorDatabase #AIInfrastructure #RAG #EnterpriseAI #Startup #DeveloperTools #Cloud #MaaS

The roadmap for the next few weeks looks roughly like this: 1. Targeted outreach on Reddit. 2. Launch the HyperspaceDB SaaS. 3. Finalize pricing. 4. Finish the developer documentation for our DePIN architecture. 5. Help 5 to 10 companies already integrating HyperspaceDB reach production. 6. Build skills_mcp and wiki_mcp. The last two are still under wraps. 🤫 One thing I’ve learned over the last year: Building the technology is only half the job. The other half is making sure people actually discover it. Back to work yar.ink

HyperspaceDB v3.1.0: The Spatial AI Engine (Zero-OOM, Lorentz Geometry, & MRL Cascade) github.com/YARlabs/hyperspac…

Traditional vector databases were built for flat PDF chatbots. But AI is moving to autonomous agents and robotics. 🤖 Today, we are thrilled to announce HyperspaceDB v3.0 LTS: The world’s first Spatial AI Engine. github.com/YARlabs/hyperspac…

HyperspaceDB v2.0 is live. Serverless core. Lock-free engine. 12,000 QPS search. 1,000 concurrent clients. 230MB Docker image. We didn’t add features. We removed bottlenecks. 1M vectors • 1024d • 1000 concurrent clients Search QPS: - Hyperspace v2.0 → 11,964 - Milvus → 4,848 - Qdrant → 4,133 Ingest QPS: - Hyperspace v2.0 → 59,208 - Milvus → 28,173 - Qdrant → 2,102 ~2.5–3x faster on search. Up to 28x faster on ingestion. And still the lightest serious vector DB: - 230MB Docker image - Serverless idle eviction - Sub-ms cold start - Native multi-tenancy Lock-free reads (ArcSwap). Work-stealing scheduler (Rayon). SIMD acceleration. No cache-line ping-pong. No contention storms. Infinite memory. Zero latency. v2.0 is a different class. github.com/YARlabs/hyperspac… #VectorDatabase #AIInfrastructure #HyperspaceDB #Rust #Serverless #AI

We are building something fundamentally different. An ecosystem built on hyperbolic geometry Lobachevsky. Lorentz. Curved space instead of flat assumptions. And here’s the uncomfortable truth: Every time we try to measure progress, we hit a wall. Vector DBs have benchmarks. Run it. 2 minutes. You know where you stand. Hyperbolic vectors? No benchmark. No standard way to compare: • Euclidean vs Hyperbolic • Hierarchical vs non-hierarchical data • 1024d vs 64d curved space So we built one. Then we discovered something worse. Benchmarks lie. Not because math lies. Because people tune. Run it 10 times. Adjust parameters. Cherry-pick the best curve. Publish the number that looks heroic. But try a single cold run in production? Those numbers evaporate. If your metric only exists after 15 optimization passes, is it performance or is it theater? We need: • reproducible runs • cold-start metrics • geometry-aware evaluation • fair Euclidean vs Hyperbolic comparison To colleagues in the field: Why are we optimizing for leaderboard screenshots instead of reality? #AI #Benchmarks #VectorSearch #Hyperbolic #MLInfra #AGI

Neural Git: a use case that is impossible (or insanely expensive) on Milvus / Pinecone / Postgres. Imagine 50 developers working on a giant monolith. Each has an AI agent in their IDE. Today: • Agents only see local files • Cloud RAG kills latency • No one syncs “understanding” of the code • Git syncs text, not meaning Now imagine this: 3-Level Distributed Semantic Memory Level 1 — The Coder (Laptop) • HyperspaceDB in Hyperbolic 64d • 150k QPS ingestion • Async mode • Rebuilds in seconds if IDE crashes • Tiny footprint No Docker monster. No 4GB RAM sacrifice. Level 2 — The Team Lead (Local Team Server) • Merkle Tree Sync • Delta-only vector exchange • No re-uploading gigabytes • Agents warn each other in near real-time “Hey. You’re calling an old Auth API. It was just rewritten.” Before a git pull. Level 3 — The Auditor (Corporate Archive) • Strict durability • Full AI decision traceability • Compliance-safe ⸻ Why is this impossible elsewhere? • Milvus / Weaviate → too heavy for laptops • Pinecone → cloud-bound, privacy nightmare • SQLite / Chroma → no efficient delta sync • 1024d embeddings → gigabytes of memory With HyperspaceDB (Hyperbolic 64d): • 1M vectors → ~687 MB • 6.4s ingestion • 1ms latency You don’t just sync code. You sync understanding. That’s Neural Git. That’s Digital Thalamus yar.ink/hyperspace #HyperspaceDB #AIInfrastructure #VectorDatabase #DevTools #AGI #SemanticSSL

We didn’t just optimize ingestion. We rewired geometry. HyperspaceDB v1.5.0 (Native Hyperbolic Mode): • 156,587 QPS insert • 1M vectors in 6.4s • 1.07ms P50 search latency • 2.47ms P99 • 687 MB for 1M vectors Poincaré. 64 dimensions. Real hardware. This is what happens when storage matches structure. Digital Thalamus is not theory. It’s running. #VectorDatabase #HyperbolicGeometry #AIInfrastructure #Poincare #DigitalThalamus #SemanticSSL

HyperspaceDB v1.5.0 is out. 1M vectors in 56 seconds. • 82× ingestion speedup • 17,721 QPS sustained • 50% less disk than Milvus • Zero performance degradation Native hyperbolic mode: • 1M vectors in 6.4s • 156k QPS • 687 MB storage This is not a roadmap. This is shipping. 🔗 yar.ink #VectorDB #AIInfra #RAG #Databases #OpenSource #Benchmarks

Big context windows don’t fix bad RAG. They hide it. Temporarily. Typical setup: • Context: 32k–128k tokens • Retrieval precision: 75–85% • Irrelevant tokens in context: 40–60% • Cost grows ~linear with window size Hyperbolic retrieval: • Context: 4k–8k tokens • Precision: 92–96% • Noise: <10% • Same or better answer quality More context ≠ better grounding. Better geometry does. #RAG #LLM #AIInfra #ContextWindow #HyperbolicAI

RAG failures are geometric, not model-related. Typical Euclidean setup: • 768–1536 dims • Recall@10: ~82–88% • Hallucination rate: 12–18% • Ambiguous concepts collapse into the same neighborhood Hyperbolic space: • 48–96 dims • Recall@10: 92–96% • Hallucination rate: <4% • Contexts diverge naturally as semantic trees Same data. Different geometry. Completely different outcome. #RAG #VectorDB #AIInfra #Embeddings #HyperbolicAI

Euclidean embeddings are a temporary compromise. Hyperbolic embeddings are the destination. Today we project flat vectors into hyperbolic space. Tomorrow, models will generate hyperbolic vectors natively. What changes? • 1000d → 16–32d without losing structure • 10–50x less memory • 10–50x faster search • Hierarchy encoded directly in vector norms • Dramatically fewer RAG hallucinations Hyperbolic space stores meaning as trees, not lists. HyperspaceDB is already built for that future. When hyperbolic embeddings arrive, we won’t adapt. We’ll just turn the key. #AI #VectorDB #RAG #Embeddings #Hyperbolic #AGIInfra

We shipped. Not a slide. Not a promise. HyperspaceDB is live. The fastest & lightest vector database we’ve built. • runs on Raspberry Pi • works in the browser • ultra-low latency • minimal memory footprint A real Digital Thalamus can’t exist without lightweight, fast semantic memory. We’re not just talking about AGI protocols. We’re building them. 🔗 github.com/YARlabs/hyperspac… #AI #VectorDB #OpenSource #DigitalThalamus #SemanticAI #Infra

AI Firewall should be free. Not as a feature. As a rule. Security can’t be optional. It can’t be “Enterprise-only”. And it definitely can’t be a growth lever. If your AI product becomes dangerous the moment users scale — you built a trap, not a platform. We believe AI safety must be: • default • invisible • free Everything else is negligence. #AI #Security #AIFirewall #LLM #Infra #DigitalThalamus

We didn’t pivot because the old idea was wrong. We pivoted because we found the real bottleneck. AI doesn’t fail because models are weak. It fails because: • context is fragmented • security is optional • cost is unmanaged • orchestration is naive Agents, tools, workflows are effects, not causes. The cause lives deeper. That’s where we’re building now. #DigitalThalamus #AIInfrastructure #Pivot #LLM #DeepTech

Pivot announcement. Everything we shared before was real. Everything we’re building next is bigger. We didn’t pivot features. We pivoted the layer we’re operating on. We’re not building another LLM. We’re making every LLM significantly better than it is today. Security. Context. Compression. Latency. Cost. This is the line between before and after. We call it Digital Thalamus 🧠 More details soon. Developer access + investment round opening shortly. #pivot #AI #Infrastructure #LLM #DeepTech #BuildInPublic #Startup yar.ink

Just published a short piece on LinkedIn comparing Recursive Language Models (MIT CSAIL) with the system we’re building. TL;DR: #AI #RLM is an algorithm for how #LLM think. Our work is about what they’re allowed to think about: attention, memory, latency, and security. They’re not competitors. They’re different layers of the same stack. LinkedIn article ↓ linkedin.com/posts/glukhota_…

This year, two technologies will finally arrive — the ones the market has been missing for a long time: 1️⃣ An AI Firewall — accessible to every user, not just enterprises 2️⃣ A truly unlimited context window — for any LLM And yes — we’re the ones bringing both of them to life. 🚀 Stay tuned. This is just the beginning.