Aybarszafer

Tentang
Sistem AI dengan cepat memperoleh kemampuan untuk bernalar, bertindak, dan beroperasi di dunia fisik. Ketika mesin cerdas mulai melakukan pekerjaan penting di bidang manufaktur, kesehatan, pendidikan, dan kehidupan sehari-hari, kita harus memastikan mereka tetap selaras dengan nilai-nilai manusia dan dapat diakses oleh semua.

Yayasan Fabric adalah organisasi independen, nirlaba yang didedikasikan untuk membangun infrastruktur pemerintahan, ekonomi, dan koordinasi yang memungkinkan manusia dan mesin cerdas bekerja sama dengan aman dan produktif.

10,000 pemenang membagi $51,000 dalam $MIRA
Termasuk satu hadiah utama $1,000
Mira Network Mengumumkan Tokenomik $MIRA
Mira Network telah mengungkapkan tokenomik lengkap untuk token $MIRA mereka di jaringan Base.
Detail Token
Token: $MIRA
Total Pasokan: 1,000,000,000 MIRA
Jaringan: Base (ERC-20)
Total Pasokan Beredar Awal: 19.12% pada TGE
Distribusi Airdrop Awal
6% dari total pasokan telah dialokasikan untuk airdrop awal kepada peserta ekosistem awal, termasuk:Pengguna aplikasi KlokPengguna aplikasi AstroDelegator nodeKaito yappers

Mira Network had recently applied for and been accepted into the OVHcloud Web3 accelerator program and met DevOps consultancy Dysnix through the scheme. Dysnix engineered, planned, and conducted a transition to OVHcloud, leveraging high-performance cloud and managed Kubernetes services. The strategy focused on "right-sizing," "automation-first," and visibility principles.

The Dysnix team designed a migration and implementation roadmap that aimed to tackle the transition within 17-25 days, leaving ample time for troubleshooting. The Dysnix team re-architected the infrastructure from the ground up, making sure that there was monitoring, alerting, and logging software present from the very first day, as well as scalability by design, planned for the future implementation.

The previous hyperscale solution handled provisioning via manual systems, which the Dysnix team switched over to Terraform and Terragrunt, running infrastructure as code. Orchestration changed from either being unmanaged or manual Kubernetes to OVHcloud’s managed Kubernetes service, and the oversized RDS database moved to an optimized, managed PostgreSQL database. The new solution used predictive / horizontal pod autoscaling to ensure scalability, and VictoriaMetrics, Grafana, and Loki monitoring; none of these systems were in place previously.

The core backend and data layers were moved into a streamlined, two-zone high-availability setup in the OVHcloud Germany (Limburg) region, and instances were chosen based on carefully profiled traffic needs. The Dysnix team also put load balancers in place to ensure traffic optimization.
“We took several steps to make sure the system worked well,” said Daniel Yavorovych, CTO at Dysnix. “At the backend, we configured health checks to detect and restart unhealthy pods automatically, as well as rolling updates to ensure that traffic always kept moving during deployments – which also allowed us to trigger automatic rollbacks if error rates spiked after a new deployment. There was a lot of work that went into designing every aspect of the system, because we knew that Mira Network was growing fast – and that meant keeping things moving.”

The frontend assets were migrated from the hyperscale provider to OVHcloud's object storage and CDN, working closely with OVHcloud’s customer success team, ultimately reducing latency for end users globally. CI/CD pipelines were configured for the front end, ensuring new versions could be seamlessly deployed to S3. The previous setup relied on manual deployments and ad-hoc scripts. Dysnix deployed a GitHub-based CI/CD pipeline using industry-standard tools that automated every step from code commit to production release. This unified deployment model eliminated the operational friction of managing separate frontend and backend release cycles.

The Dysnix team also put in place a multi-layered monitoring stack to make the system self-aware and self-healing. This included using Prometheus to scrape metrics from every Kubernetes node, pod, and application endpoint every 15 seconds. This captures CPU, memory, disk I/O, network throughput, and custom application metrics, such as query latency and blockchain transaction confirmation times.

The Grafana stack (Loki, Alloy, Grafana) dashboards provided real-time visualization of system health, and a set of automated alert rules was also put in place.

#Miranetwork #MİRA
$MIRA

MIRA Token adalah mesin di balik aksi jaringan. Dari membayar biaya hingga mengamankan rantai, ia memiliki banyak peran untuk menjaga segala sesuatunya tetap berfungsi.

Gas dan Biaya Transaksi
Setiap gerakan di MIRA-20 membutuhkan gas, dibayar dengan MIRA Token, untuk menghargai validator dan menjaga keamanan. Biaya transaksi opsional memungkinkan pengembang mendapatkan tambahan, mendorong pertumbuhan aplikasi dan energi ekosistem.

Kekuatan Kontrak Pintar
MIRA Token menggerakkan kontrak pintar, memungkinkan aplikasi DeFi seperti pertukaran token atau permainan. Ini menutupi gas dan biaya tambahan, memastikan kesepakatan otomatis berjalan lancar di seluruh jaringan.

OM1 allows AI agents to be configured and deployed in both the digital and physical worlds. You can create one AI persona and run it in the cloud but also on physical robot hardware such as Quadrupeds, TurtleBot 4, and Humanoids.

With OM1, you can interact with OpenAI's gpt-4o (or Gemini, Claude, DeepSeek, or Ollama for local inference) and shake hands with it, mediated by physical robot hardware controlled by one or more LLMs. Agents/robots built on OM1 can ingest data from multiple sources (the web, X/Twitter, cameras, and LIDAR) and can then tweet, explore your house, and help your kids with their math homework.

Since it's open source, you have control and can optimize the system for your home or workplace.

This guide offers an overview of the OM1 agent runtime system, helping developers understand its core components and workflows. Inside, you'll find explanations of OM1’s CLI commands, recommended project structure, step-by-step instructions for adding new inputs and actions, and guidance on configuring your agents and robots for different environments. Additionally, the guide includes practical development tips to streamline your workflow.

Whether you're just getting started with OM1 or looking to optimize an existing project, this guide will equip you with the tools and best practices to develop, deploy, and maintain high-performance agents and robots.

OM1 Capabilities
Title

Description

Simple, modular architecture

Human-intelligible architecture with natural language data buses.

All Python

Independent modules that are easy to maintain, debug, and extend.

Easy to add new data inputs

Seamlessly integrate new data without major changes to the existing architecture.

Easy to support new hardware

Via plugins for API endpoints and specific robot hardware.

Supports Standard Middleware

ROS2, Zenoh, and CycloneDDS

Includes a simple web-based debug display

Watch the system work (WebSim at http://localhost:8000).

Preconfigured endpoints

Text-to-Speech, OpenAI's gpt-4o, DeepSeek, Gemini, Openrouter, Near AI, Ollama (local), and multiple VLMs.

Simulators

Gazebo, Isaac Sim (Coming Soon)
To get started with OM1, head over to Installation Guide.
#robo #OM1 $ROBO
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$ROBO is the core utility and governance asset of the Fabric Foundation and is instrumental in the non-profit's mission: Own the Robot Economy. As Fabric scales its network for building and governing safe, general-purpose robots, and as robots become more capable, human society needs infrastructure for open and verifiable human↔machine alignment. $ROBO aligns incentives and enables participation across Fabric. To elaborate on our whitepaper, $ROBO functions as:

Network Fees for Payments, Identity, and Verification
The future of autonomous robots will be onchain. Unlike humans, robots cannot open bank accounts or own passports. They will need web3 wallets funded with crypto as well as onchain identities to track payments. All transaction fees will be paid in $ROBO. The Fabric network will initially be deployed on Base, but as adoption grows, Fabric will migrate and become its own L1 chain, capturing economic value from robot activity.

Crowdsourced Robot Coordination
The protocol enables a decentralized mechanism for coordinating the genesis and activation of robot hardware through $ROBO-denominated participation units. Participants contribute tokens solely to access protocol functionality and coordinate network initialization, receiving priority access weighting for task allocation during a robot's initial operational phase. A portion of protocol revenue is used to acquire $ROBO on the open market, creating persistent buy pressure. Participation does not represent ownership of robot hardware, fractionalized interests, revenue rights, or economic claims. To participate in network coordination, users are required to stake $ROBO.

Participant Ecosystem Entry
As the Fabric ecosystem and robot adoption grows, developers and businesses will want to build applications on the network to access the robot team. Fabric will require these builders to buy and stake a fixed amount of $ROBO, aligning their interests with the success of the network. These incentives will bootstrap early deployment and coordination, structured as network participation primitives such as priority access. Rewards are subsequently paid for verified work, from skill development to task completion, data contributions, compute, and validation.

Governance
The autonomous future should benefit all of humanity Therefore, $ROBO will play a key role in formulating and guiding the network, such as setting fees and operational policies. Fabric Foundation's goal is to build an open network for general-purpose robots, in which anybody can participate and contribute. To support that mission, the initial token allocation is designed to 1) fund long-term ecosystem growth, 2) keep the Foundation sufficiently resourced to manage the network responsibly, and 3) align core contributors and early backers through structured vesting, as summarized below.

Allocation % Vesting
Investors 24.3% 12-month cliff, 36-month linear
Team and Advisors 20.0% 12-month cliff, 36-month linear
Foundation Reserve 18.0% 30% at TGE, 40-month linear for remainder
Ecosystem and Community 29.7% 30% at TGE, 40-month linear; Proof of Robotic Work
Community Airdrops 5.0% 100% at TGE
Liquidity Provisioning and Launch 2.5% 100% at TGE
Public Sale 0.5% 100% at TGE

Mira Network adalah platform infrastruktur terdesentralisasi yang fokus pada membuat kecerdasan buatan lebih mudah diakses dan dapat diandalkan. Platform ini menggunakan teknologi blockchain untuk memungkinkan verifikasi tanpa kepercayaan terhadap output AI, memastikan akurasi dan keandalan hasil yang dihasilkan oleh AI. Melalui kemitraannya dengan io.net, hyperbolic, aethir, spheron, dan exabits, jaringan infrastruktur fisik terdesentralisasi (DePIN), Mira Network memanfaatkan kekuatan komputasi GPU terdistribusi untuk mendukung validasi output AI yang lebih cepat.

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24 Feb 2026

Fabric: Miliki Ekonomi Robot
Industri robotika berada pada titik belok, berkat pertemuan tiga kekuatan: 1) sistem AI yang mulai mampu memahami, memprediksi, dan menavigasi lingkungan fisik yang sangat dinamis, 2) perangkat keras yang cukup murah dan andal untuk diterapkan secara besar-besaran, dan 3) kekurangan tenaga kerja manusia yang kronis dalam profesi seperti keperawatan, pendidikan, manufaktur, dan pembersihan lingkungan. Titik belok besar berikutnya adalah membangun sistem global untuk menavigasi masa depan ini, di mana mesin yang berpikir, mengingat, dan belajar, bekerja di samping kita untuk mengatasi tantangan terbesar kita.