This post contains content not supported on old Reddit. Click here to view the full post submitted by /u/discordditapp [link] [Kommentare]

I sold my 2025 BMW 330e with only 5,000 miles for $40,000 and put every dollar into Bitcoin. Now I have no car and no savings, but I’m not worried. Cars keep losing value, while I believe Bitcoin will keep growing long term. Don’t repeat my actions, I am just a stupid gambler submitted by /u/MobApps1 [link] [Kommentare]

I recently visited a robotics space that’s focused specifically on humanoid robots. Not industrial arms, warehouse AGVs, or general automation, but bipedal / human-form platforms. It’s part of an incubator-style setup for early-stage teams working in this niche. What surprised me most wasn’t actually the full robots. The complete humanoid demos were interesting, of course, but the component side stood out more: actuators, dexterous hands, sensing systems, and all the less visible hardware that makes these machines possible. It made me think that the real progress may be happening below the “cool demo video” layer. Another thing I noticed was the visitor mix. Over just a couple of weeks, there seemed to be people coming through from different parts of the world: corporate visitors, researchers, MBA / exec ed groups, and others trying to understand where the field really is. The common question seemed to be: are humanoids actually close to being useful in real-world environments, or is this still mostly future-facing R&D? The incubator model itself also felt notable. Instead of every startup trying to build everything alone, the space seems designed to put founders, suppliers, researchers, and component companies near each other. That kind of clustering has worked in other deep-tech sectors, so I’m curious whether humanoids need the same thing to move faster. A few questions I’m still thinking about: Are humanoid robots finally approaching real product-market fit, or are we still in the “ten years away” phase? Which use cases are most likely to come first: logistics, manufacturing, elder care, inspection, retail, or something else? Is the recent momentum mostly driven by hype and funding, or are there specific technical bottlenecks that have genuinely improved? Are components like actuators and robotic hands the real near-term market before full humanoids become practical? I’m interested in how people here read the current moment. For those working in robotics, automation, or related hardware: does this feel meaningfully different from previous humanoid waves? submitted by /u/Great_Arachnid5776 [link] [Kommentare]

Talk to a lot of ML teams who ship models but skip any adversarial testing before deployment. Feels like security review for models is way behind where it is for regular software. Anyone here actually doing this at their job? submitted by /u/Xorphian [link] [Kommentare]

Hey everyone! 👋 If you’re building RAG or autonomous AI agents, you’ve probably hit the "Vector DB Wall": flat Euclidean vectors suck at modeling complex hierarchical reasoning, and loading millions of 1536D vectors + JSON metadata into memory causes massive RAM bloat and OOM crashes. We spent the last few months solving this from the ground up. Today, we are releasing HyperspaceDB v3.1.0, transitioning from a standard vector index to a full Spatial AI Engine. Here is what’s under the hood: 1. The RAM Diet (Schema-Driven MRL) Instead of loading full dense vectors into memory, we built native support for Matryoshka Representation Learning (MRL). The engine keeps a lightweight navigation core (e.g., 129 dimensions) in ultra-fast RAM, while the heavy semantic tail (672 dimensions) streams dynamically from NVMe SSDs for final top-K re-ranking. The benchmark: In our stress tests with 100,000 vectors, HyperspaceDB consumed just ~72.0 MB of RAM compared to >3,000 MB for Chroma and ~1,700 MB for Milvus. 2. 801D Hybrid Vectors (Lorentz + Euclidean) Flat vectors fail at taxonomy (e.g., Legal Codes, Medical Trees). We introduced an 801D Hybrid Vector. The first 33 dimensions live in a negatively curved Lorentz hyperboloid (allowing for native graph/tree embeddings), while the remaining 768 dimensions handle Euclidean semantic density. Agents can now verify facts geometrically using geodesic path tracing. 3. Killing the "Two-Database Problem" Gluing Pinecone to MongoDB for document storage is painful. We built Sidecar Document Storage. You store massive raw texts directly in the index, which automatically compresses (Zstd) and pushes them to fractal .hyp chunks on disk. Meanwhile, Typed Metadata (int, bool, enum) is compiled directly into the HNSW graph nodes in RAM, providing zero-latency pre-filtering with no JSON-parsing overhead. 4. Lock-Free Rust Performance Under a 1,000-concurrent-client stress test, our lock-free HNSW and L0/L2 DashMap cache held flat at 9,476 QPS with a p99 latency of 11.83 ms. Competitors hit severe lock contention at this scale, with latencies spiking over 2,000 ms. We’ve also added a WASM runtime, Raspberry Pi ARM64 support, and native LangChain/LlamaIndex/MCP integrations. Would love to hear your thoughts, answer any questions about the architecture, or get feedback from anyone pushing the limits of Agentic RAG! Ask me anything! 🚀 submitted by /u/Sam_YARINK [link] [Kommentare]

According to Reuters, it is very likely that Binance's MiCa application will be rejected by the Greek regulators: https://www.reuters.com/business/finance/binance-set-lose-eu-licence-bid-permission-offer-services-bloc-sources-say-2026-06-16/ If such scenario becomes true, and you are based in the EU, where are you going to park your funds, beside self-hosted wallets? submitted by /u/Joonto [link] [Kommentare]