📖 Article 2 - Our Solution to the Scalability... Problem in Web3 🌐 ▶️ Highlayer is built to directly address the challenges of scaling networks to meet global demand. 💿 Our self-sustainable data availability system (SSDAS) is an immutable, tamper-proof storage system that ensures data is immediately and permanently available to everybody. 🏋️‍♂️ The hybrid execution model allows heavy on-chain computations where they matter most. This provides us with the most scalable layer 2 smart contract solution 😏 Read all about it 👇show more

Laika AI x Cdari Excited to announce our partnership... with CDARI Cdari is a Web3 E-Commerce platform built on Matchain, an AI-powered Layer 2 solution on the BNB Chain. Our on-chain analytics now securing all Cdari transactions AI phishing detection protecting Cdari shoppers Smart contract verification for Cdari merchants Cross-chain support across BNB ecosystem.show more

Big news: Quant is partnering with The Clearing House... to bring on-chain, #programmablemoney to the US. We will provide the interoperability layer that lets #tokeniseddeposits move freely within the regulated banking system. This builds on our work in the UK, Europe and beyond. Read the press release here: or the blog here: #OnChainMoneyInitiativeshow more

There is no relationship in the world quite like... the one between Canada and the United Kingdom. Built on shared history, common values, and enduring people-to-people ties, our partnership continues to evolve to meet today’s global challenges. It was a pleasure to meet with UK Foreign Secretary Ed Miliband for the first time in our roles as counterparts. We discussed our steadfast support for Ukraine, the urgent humanitarian situation in Gaza, and the importance of advancing stability across the Middle East. Canada and the United Kingdom will continue coordinating closely as we respond to today’s most pressing global challenges.show more

Say hello to Boojum 👋: zkSync Era’s new high-performance... proof system for radical decentralization. Boojum is an upgrade that will transition zkSync Era to a STARK-powered proof system, providing world-class performance on consumer-grade hardware. 💡 Learn more: TL;DR 👇 Boojum is the name of our Rust-based cryptographic library, which we use to implement the upgraded version of the ZK circuits for zkSync Era and the ZK Stack. The name Boojum was inspired by Lewis Carroll's poem "The Hunting of the Snark," where the Boojum represents the most fearsome kind of Snark. We intentionally designed zkSync Era in a way that cryptographic upgrades can be made without a regenesis, meaning that the Boojum upgrade won’t cause any user disruptions. Why Boojum❓ From day one, zkSync’s mission is to advance personal freedom for all — making digital self-ownership universally accessible by building a blockchain network that is trustless, secure, permissionless, affordable, easy to use, resilient and limitlessly scalable. Boojum plays an important role in advancing this mission by delivering: 1. World-class performance zkSync Era’s current SNARK-based proof system is effective today, but it won’t scale to the volume that we envision for hyperchains. zkSync Era’s sequencer can already process over 100 TPS; Boojum orders of magnitude improvements to performance complements this well. 2. Reduced hardware requirements for decentralization Our long-term goal is to enable user-powered, decentralized proof generation. Boojum represents a breakthrough in this direction — with the prover running on consumer-grade GPUs requiring only 16 GB GPU RAM. Boojum’s Journey to Mainnet 🚴🏽‍♀️ Boojum is now live on Mainnet, generating and verifying ‘shadow proofs’ today with real production data so that we can carefully test the system ahead of fully migrating. Today, we’re also open-sourcing the repo; if you’d like to take a look, you can find it here 👇 This is the first of a series of posts on Boojum. We will provide updates on our progress, including more details on implementation, security, and performance. Watch here for more, anon ∎show more

this AI engineering breakdown from IBM Technology is pure... f*cking treasure it explains the role as a connected system 01 Python and APIs give you the surface to build on: services, data, integrations, and the software around the model 02 RAG and embeddings solve the context layer: finding the right information before the model has to reason about it 03 agents turn that context into work: they choose tools, run steps, and move toward an outcome 04 observability closes the feedback loop: traces, metrics, failures, latency, and the evidence needed to improve the system 05 deployment is where the system becomes real: reliability, constraints, and the conditions that let people actually depend on it the model sits inside this architecture the AI engineer connects every layer into one operating loop: build → retrieve → act → observe → ship save this, then build your solo business with an AI employee ⭣show more

Don’t get lost in the cables, get powered by... the Pyth Network 🔮 🔮 Today, Pyth is proud to welcome Cables Finance as a new data publisher to the network Learn more about this partnership below: ℹ️ About Cables Finance Cables is building a unified DeFi and Cefi liquidity layer, aiming to bring FX on-chain. Cables has built a suite of products on the Avalanche and Stellar blockchains, supporting both DeFi and CeFi users through a multi-chain DEX and stablecoin bridge. Cables is revolutionizing the aging, monopolized foreign exchange (FX) industry and strengthening the young cryptocurrency industry by bringing blockchain rails to the FX markets, as well as bringing FX liquidity and trading products to the stablecoin market. By bridging established centralized technology with the transparency and efficiency of decentralized technologies, Cables is creating a blended, balanced, and trusted ecosystem for finance to be executed on-chain. 🗣️ Quotes “We are thrilled to continue our mission of bringing reliable FX on-chain by providing Pyth with accurate TradFi data,” said Benjamin Goldberg, Product Lead at Cables Finance. “Through this partnership, real-time FX data can be available directly on the blockchain for a global development network.”show more

Starting a new project today, building an end to... end system to forecast traffic of flights across cities (starting with Mumbai) The idea is to implement > ingestion service with kafka > data etl with polars > feast for feature store // mlflow for model registry > batch inferencing // dashboard > s3 // postgres for data storage All this orchestrated across multiple DAGs built with Airflow. This year has just been Agents and LLMs all along. Not a bad idea to keep revisiting the traditional format :) Will be posting more of this in the coming days, stay tunedshow more

Exciting news from HYPE3.cool! We’re teaming up with Chainbase... (💜,💛), the world’s largest omnichain data network to supercharge AI agents with robust on-chain data. Very soon, users will be able to integrate real-time blockchain insights when configuring their agents—making them smarter and more capable than ever! What this means: - Real-time on-chain data for enhanced decision-making - Greater agent intelligence powered by advanced data analytics - Seamless blockchain integration backed by Chainbase (💜,💛)’s decentralized infrastructure About Chainbase: Chainbase is the world’s largest omnichain data network built to power the AI economy with high-quality on-chain data. Through its innovative four-layer, dual-consensus architecture, Chainbase’s network, anchored by Chainbase AVS, provides secure, interoperable data for AI and Web3. Your AI agents can now tap into rich, reliable on-chain insights—paving the way for more dynamic and intelligent interactions. This is just the start of making AI agents truly Web3-native. Stay tuned as we build the future of intelligent property in partnership with Chainbase (💜,💛)! #Web3 #AI #Blockchainshow more

Introducing COTI 2.0 Private Programmable Money COTI shifts into... its next phase, with a new website to match. > Same foundation. Bolder vision. 1. The Vision The world's money will soon move the way information does, on open networks. But those networks are public, and nobody wants to run their finances in public. COTI is now critically tasked with changing that. Its mission is to make private programmable money the global standard, so institutions, businesses, developers and individuals can hold, move and settle value on public blockchains, confidentially. COTI exists to champion this cause. 2. The Foundation Since 2017, COTI has built the privacy technology this next phase runs on. It was the first network to run garbled circuits in production, computing directly on encrypted data so inputs stay encrypted from submission to settlement. Its institutional-grade technology has been developed across leading institutions: 1. European Central Bank 2. Bank of Israel 3. Input Output Global (IOG) 3. Privacy-on-Demand Privacy-on-Demand serves as COTI's privacy layer for other blockchains. It allows applications to keep their users and assets on their existing chain, while COTI handles the private computation. Avalanche was the first network to bring it to production. Since September 17, applications on Avalanche have been computing on confidential data, and users have been shielding AVAX and USDC through the Privacy Portal. Expansion to further networks is a core part of the roadmap. 4. New Products At the centre of COTI 2.0 are two new products, designed to keep transactions private when moving money between blockchains, and when spending it in day to day life. 1. Interphase - The flagship. It serves as the global privacy layer for moving assets between blockchains, and executing confidential, unlinked transfers across 30+ major networks. 2. COTI Global - A new central application, which allows COTI to be spent as a privacy payment rail for everything in day to day life. 5. What's Next The new website and public roadmap are the first of several major upgrades coming this month.show more

How do you monitor wildlife in places where infrastructure... is limited? Some of the world's most valuable biodiversity data comes from remote, hard-to-reach locations, where researchers can't rely on traditional connectivity or computing. This is what the Open Science Conservation Fund is working to solve with Trapper, an open source platform that helps researchers analyze camera trap data closer to where it's collected. This demo, running on Ubuntu and Canonical Kubernetes, uses NextComputing’s fly-away kit - a portable edge computing system that can be carried into remote locations and deployed wherever it's needed, making it easier to turn observations into knowledge that the global scientific community can build on. #OpenSource #Ubuntu #Kubernetesshow more

🎇 Aleph Zero EVM is live! Developed in collaboration... with Gelato using Arbitrum Orbit, Aleph Zero EVM is a specialized layer 2 that will bring EVM-compatible ZK-privacy and rely on the Aleph Zero WASM L1 as a Data Availability layer. Here's what to expect: → Seamless integration with Ethereum: a rapid, secure, and scalable execution environment thanks to Arbitrum Orbit; → Speed: block times of up to 250ms; → DeFi growth: liquidity transfers and stablecoin support with MOST; → Advanced apps with account abstraction enabled via thirdweb; → And with Aleph Zero's zkOS, it means a new standard in blockchain privacy, making it usable and fast. Stay tuned for more, as the week is just starting, and we're nowhere near being done with the news! Learn more:show more

System ID for legged robots is hard: (1) Discontinuous... dynamics and (2) many parameters to identify and hard to "excite" them. SPI-Active is a general tool for legged robot system ID. Key ideas: (1) massively parallel sampling-based optimization, (2) structured parameter space, and (3) active exploration based on Fisher Information to collect the most informative data in real. SPI-Active provides an accurate robot model and effectively reduces the sim2real gap. In sim2real policy learning setting, it outperforms baselines by 42-63% in various quadruped & humanoid tasks. Led by Nikhil Sobanbabu Guanqi Heshow more

In 2025, the AgentFlayer exploit highlighted a new category... of risk in AI systems. It was not a traditional breach involving stolen credentials or broken encryption. Instead, it demonstrated how an autonomous AI agent could be manipulated into executing unintended actions by processing malicious instructions embedded inside content it automatically processes. The incident did not expose a flaw in one specific integration. It revealed a structural weakness in how many modern AI agents are built. Today’s agents are no longer passive language models. They read documents automatically, scan emails, connect to SaaS tools, access cloud storage, and execute actions across multiple systems. To be useful, they are granted meaningful permissions. That capability creates value, but it also expands the attack surface. Most agent environments operate in a trusted, plaintext execution model. Data is encrypted at rest and in transit, but it is typically decrypted during inference so the model can process it. That runtime visibility is where potential risk lies. In a zero-click scenario like AgentFlayer, an attacker can embed hidden instructions inside a document that the AI processes automatically. Because the agent may have access to connected systems such as Google Drive, Slack, or GitHub, it can potentially be influenced to retrieve sensitive information or perform unintended actions. The user does not need to click a malicious link or approve a suspicious request. Therefore, the core issue is that during execution, the system may have access to sensitive data and broad privileges, meaning whoever controls the execution environment ultimately controls access to that data. Now consider a different architectural approach. If a system is designed so that data remains protected during execution, the risk profile changes. On Nesa, privacy is enforced at the execution layer through Equivariant Encryption. Computation can occur on encrypted data, reducing the visibility surface during runtime. Sensitive inputs and models do not need to be exposed in plain text to infrastructure operators for inference to occur. This does not eliminate prompt injection, logic manipulation, or tool misuse. Encryption alone cannot prevent an agent from being instructed to take an unintended action if it has been granted that permission. What it does do is materially reduce confidentiality risk. By limiting access to readable sensitive data during execution and reducing unilateral visibility at the infrastructure layer, the potential blast radius of a successful manipulation attempt is constrained. As AI agents become more autonomous and embedded into enterprise workflows, security must move deeper into architecture. The goal is not to claim invulnerability. It is to reduce trust concentration and contain systemic exposure when failures occur. AgentFlayer was not simply a one-off exploit. It was a reminder that in autonomous systems, execution-layer design determines how risk propagates.show more