@MrStickman

spcx is turning into a clean case study for tokenized stocks. the basic setup is simple: spacex is expected to trade on nasdaq under spcx, and tokenized versions of the shares are also being discussed for solana through platforms like backpack securities and sunrise defi. a few things i’m tracking: spcx - the main equity ticker. big name, huge attention, and probably a useful benchmark for how much demand exists outside normal brokerage rails. sol - the chain getting the attention here. if tokenized public equities actually get real usage, settlement speed, liquidity, custody and compliance all matter more than the usual crypto narrative. backpack securities - worth watching because the claim is that the token is backed 1:1 by actual shares with redemption mechanics. that part matters more than just “stock on-chain.” sunrise defi - another piece of the infrastructure side. not as easy to evaluate from the headline, but tokenized equities need more than just a ticker and a smart contract. traditional brokers - still relevant here. if the normal share and the tokenized version both exist, the spread, fees, access rules and redemption process become the whole story. tldr: tokenized equities are moving from theory into more visible tests. spcx is just a much louder example because the underlying company has mainstream attention already. what would you care about most before trusting a tokenized stock: custody, redemption, liquidity, regulation, or fees? submitted by /u/GurneyStewart [link] [Kommentare]

At Foxglove, we collaborated with Aditya Kamath, resulting in another blog post in his ROS 2 LeKiwi series, this time covering the integration of SLAM and Nav2. This blog post should be relevant to anyone wanting to integrate Nav2, even if they don't have a holonomic platform. If you find this kind of content useful, let us know, and we will keep it coming! submitted by /u/arewegoing [link] [Kommentare]

Nature: Outplaying elite table tennis players with an autonomous robot (Published: 22 April 2026): https://www.nature.com/articles/s41586-026-10338-5 YouTube Sony AI: Ace vs. Kihara | Pro Match Highlights | Sony AI Table Tennis Robot: https://www.youtube.com/watch?v=TwkDm2H6ft8 From 链上小财女 on 𝕏: https://x.com/Zoozo2025/status/2064998917394374930 submitted by /u/Nunki08 [link] [Kommentare]

Hey everyone, I am planning out a new open-source infrastructure project and want to get some brutal feedback on the architecture and use-case validity from people running high volume LLM workloads in production. The Problem: Python-based proxies/gateways introduce too much latency overhead for real-time streaming agent steps or fast UI completions. Additionally, centralized semantic caching still suffers from cross-region network latency (e.g., London to us-east-1), and enterprise API costs remain a massive bottleneck for repetitive/predictable user queries (like customer support or structured data extraction). The Proposed Architecture: Instead of a heavy centralized gateway, the goal is to build a lightweight, zero-dependency semantic cache running directly at the CDN Edge using WebAssembly (WASM) compiled from Rust. The flow looks like this: Inbound Prompt: Hits the edge node closest to the user (e.g., Cloudflare Workers / Fastly Compute). Edge Embedding: The Rust/WASM module intercepts the raw text prompt and instantly generates a vector using an edge-native lightweight model (e.g., bge-small-en-v1.5). Similarity Index Check: It performs a fast cosine similarity check against an edge vector database (like Cloudflare Vectorize) to find the nearest semantic neighbor. Cache Hit: If similarity >= threshold (e.g., 0.88), it pulls the full generated response text from an edge KV store and returns it in ~5ms. The main LLM provider is never billed or touched. Cache Miss: It proxies the streaming request to OpenAI/Anthropic/vLLM, streams it back to the client, and asynchronously updates the edge vector index and KV store. Why Rust/WASM? To achieve sub-millisecond execution overhead on the proxy itself, avoid garbage collection pauses, and maintain a tiny memory footprint suitable for edge runtime constraints where traditional databases or Python scripts cannot run. My Questions for the Community: For those running LLMs in production (especially customer support, internal RAG, or autonomous agents), what is your realistic semantic cache hit rate? Is the power law of repetitive queries high enough in your domains to justify this? What are the biggest footguns with semantic caching at the edge? (e.g., Cache invalidation strategies, handling system prompt updates, or drift in embedding models). Would you actually use a drop-in open-source template/CLI that lets you spin this up on your own edge account, or do you prefer centralized API gateways? submitted by /u/Real-Huckleberry-934 [link] [Kommentare]

One of our great triumphs and economic advantages is fading away

MCP standardizes how AI applications expose and call tools, but it does not solve tool relevance. As connected servers grow, tool definitions become a context tax that can hurt cost, latency, and accuracy.

Since Colossus 1 launched in August 2024, the record for the largest AI data center has doubled every seven months. Epoch AI's breakdown of single-site compute capacity trends through 2028.