vLLM prefill paired with TileRT decode through vLLM V1's connector interface: a specialized, latency-optimized decode engine that coexists with native vLLM deco
AI has helped resolve an important question in statistics. In the area of multiple hypothesis testing, the goal of controlling the false discovery rate (FDR) has been introduced in a seminal paper by Benjamini and Hochberg (1995). They also introduced a method (the
Callum Dare, 26, from Talbot Green, has been sentenced for encouraging and assisting swatting, both internationally and within the UK, and the possession of sophisticated phishing software.
I built a testbed comparing two ways of governing a hospital delivery robot: a conventional task pipeline and one governed by a constitutional layer I call NA. The constitution works through directional asymmetry: claims about the world can only be demoted, never promoted, without evidence, and memory is append-only so the robot can not quietly rewrite what it believed earlier. Across 13 scenarios (blocked corridors, conflicting instructions, a patient in the path), the conventional pipeline completed safely 15.4% of the time. The NA-governed pipeline completed safely 100% of the time, and the failures it avoided were the interesting kind, where the robot’s confidence outran its evidence. The whole thing runs deterministically in Python so anyone can replay every decision, and I’m now putting it on a physical robot built on a Raspberry Pi 4. I have never done this before, am I reinventing the wheel here? Happy to share the code and answer questions about the architecture. submitted by /u/willybbrown [link] [Kommentare]
TL;DR: Civic focuses primarily on KYC and identity verification for compliance. ONTO Wallet takes identity a step further by integrating data monetization, allowing users to not just prove who they are, but also earn from their verified profile. Decentralized Identity (DID) is a foundational pillar of Web3. For a long time, projects like Civic have dominated this space, focusing heavily on KYC (Know Your Customer) and Sybil resistance. They provide the necessary infrastructure for dApps to ensure their users are real people and compliant with regulations. ONTO Wallet, powered by the Ontology network, offers a more expansive vision of what Web3 identity can be. While ONTO provides robust verification (ONT ID), it doesn't stop at compliance. It treats your verified identity as an asset that can be monetized. | Feature | Civic | ONTO Wallet | | :--- | :--- | :--- | | Primary Focus | KYC & Compliance | Identity Management & Data Monetization | | Target Audience | dApps needing verification | Users wanting to control and earn from data | | Passive Income | No | Yes (via AI data markets) | | Tech Stack | Civic Pass | ONT ID & zkTLS | If a protocol simply needs to block bots, Civic is a great tool. But for users, ONTO Wallet offers a much more compelling value proposition: the ability to turn your verified digital footprint into a source of passive income in the booming AI data economy. Q: Do I have to do KYC to use ONTO Wallet? A: ONTO supports various levels of verification. You can build a robust digital profile and monetize metadata without necessarily providing a government ID, depending on the specific data market requirements. Q: How does ONTO protect my identity while monetizing it? A: Through zero-knowledge proofs (zkTLS), ONTO can prove the validity of your data to buyers without exposing your actual identity. Q: Is Civic obsolete? A: Not at all. Civic remains a strong B2B compliance tool. ONTO is simply pioneering the ToC data monetization aspect of identity. References [1] "The Next Phase of Decentralized Identity," Blockworks, 2026. submitted by /u/Rc7xn [link] [Kommentare]
Hey everyone! Kaushik here, built https://www.zero2robot.com/ Neural nets have Zero-to-Hero. Deep RL has Spinning Up. Robot learning never got the same thing: a path where you build the whole stack yourself, from nothing, and understand every piece. So I spent some time building it. Zero2Robot is a free, open-source interactive textbook for robot learning. You start with a blank simulation loop and build behavior cloning, diffusion, PPO/SAC, a tiny VLA, a browser demo path, and even parts of a physics engine—one runnable file at a time. Runs on a laptop or free Colab. No robot required. Do give it a try, and let me know what you think. submitted by /u/Few-Sleep2303 [link] [Kommentare]
Does anyone know of any evidence/forum/paper analyzing benchmark result differences between cloud inference platforms (togetherai) and running models locally with vLLM under greedy decoding? submitted by /u/No_Cardiologist7609 [link] [Kommentare]
Run AI coding agents in isolated environments
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