artificialinteligence

As artificial intelligence moves from experimental side projects to the core of the enterprise tech stack, the attack surface for modern organizations is expanding rapidly. AI workloads introduce unique risks—from "agentic" systems that can autonomously ship code to non-deterministic models vulnerable to prompt injection.

To help security teams keep pace, Datadog has outlined a comprehensive framework for AI security. Here are the essential best practices for securing AI from development to production.

1. Implement Runtime Visibility
Traditional security scanners often fall short in AI environments because they cannot account for the "live" behavior of autonomous agents. Effective security requires continuous runtime visibility. This allows teams to detect when an AI service begins making unauthorized API calls or minting secrets without human intervention. By monitoring the actual execution of AI workloads, organizations can catch cascading breaches before they move across the entire stack.

2. Hardening Against Prompt Injection and Toxicity
Unlike traditional software, AI models are susceptible to "behavioral" attacks.

Prompt Injection: Malicious inputs designed to bypass safety filters or extract sensitive data.

Toxicity Checks: Continuous monitoring of both prompts and responses to ensure the AI does not generate harmful, biased, or non-compliant content.
Using tools like Datadog LLM Observability, teams can perform real-time integrity checks to ensure models remain within their intended operational bounds.

3. Prevent Data Leakage with Advanced Scanning
AI models are only as good as the data they are trained on, but that data often contains sensitive information. Personally Identifiable Information (PII) or proprietary secrets can inadvertently leak into LLM training sets or inference logs.

Best Practice: Use a Sensitive Data Scanner (SDS) to automatically detect and redact sensitive information in transit. This is especially critical for data stored in cloud buckets (like AWS S3) or relational databases used for RAG (Retrieval-Augmented Generation) workflows.

4. Adopt AI-Driven Vulnerability Management
The sheer volume of code generated or managed by AI can overwhelm traditional security teams. To avoid "alert fatigue," organizations should shift toward AI-driven remediation:

Automated Validation: Use AI to filter out false positives from static analysis tools, allowing developers to focus on high-risk, reachable vulnerabilities.

Batched Remediation: Leverage AI agents to generate proposed code patches. This allows developers to review and apply fixes in bulk, significantly reducing the mean time to repair (MTTR).

5. Align with Global Standards
Securing AI shouldn't mean reinventing the wheel. Frameworks like the NIST AI Risk Management Framework provide a structured way to evaluate AI security. Modern security platforms now offer out-of-the-box mapping to these standards, helping organizations ensure their AI infrastructure meets compliance requirements for misconfigurations, unpatched vulnerabilities, and unauthorized access.

Conclusion
The shift toward "Agentic AI" means that a single mistake in a microservice can have far-reaching consequences. By combining traditional observability with specialized AI security controls, organizations can innovate with confidence, ensuring their AI transformations are as secure as they are powerful.

#ai #ArtificialInteligence #AIAgents

#robo $ROBO
Robotics is no longer a distant vision of the future — it’s happening now. The convergence of AI, advanced hardware, edge computing, and decentralized infrastructure is unlocking a new industrial revolution. Over the next two years, robotics is projected to surpass $150B in market value, reshaping logistics, retail, manufacturing, healthcare, and beyond.

At the center of this shift are organizations bold enough to build foundational infrastructure instead of incremental tools. That’s why the work being done by @Fabric Foundation is so important.

@Fabric Foundation Fabric Foundation is helping power the next generation of AI-native robotics — not just with capital or hype, but with deep ecosystem collaboration and technical infrastructure. As robotics moves from research labs into real-world environments, we need scalable compute, secure coordination, interoperable systems, and resilient decentralized frameworks. Fabric Foundation is positioned right at that intersection.
One of the most exciting aspects of this movement is the collaboration between OpenMind and industry leaders like @NVIDIA, @UnitreeRobotics and @Binance Square Official

NVIDIA continues to provide the computational backbone for embodied AI. From GPU acceleration to robotics simulation environments, NVIDIA’s stack enables robots to learn, adapt, and operate in dynamic physical spaces. Their AI frameworks are no longer just training models in the cloud — they are driving intelligence directly into autonomous machines operating in warehouses, retail stores, hospitals, and public environments.
Unitree Robotics represents the physical embodiment of that intelligence. Their agile quadruped and humanoid platforms demonstrate how quickly hardware innovation is accelerating. These robots are no longer experimental prototypes; they are becoming viable commercial tools capable of inspection, logistics support, surveillance, and interactive service roles.
Circle, as a financial infrastructure leader, plays a critical but often overlooked role. As robotics fleets scale globally, machine-to-machine payments, automated settlement systems, and programmable financial rails become essential. The integration of programmable digital dollars into autonomous systems unlocks entirely new economic models: robots paying for charging, maintenance, API calls, data access, and even labor coordination in real time.
OpenMind’s contribution alongside these major players is focused on building intelligent coordination layers — the software that allows machines to operate collectively rather than in isolation. The future is not a single robot performing a task; it is swarms of AI-powered agents interacting with humans, digital systems, and each other seamlessly.
Imagine walking into a grocery store where inventory robots monitor stock levels continuously, autonomous carts assist elderly customers, predictive systems reorder goods automatically, and back-end logistics are coordinated through decentralized AI networks. That’s not science fiction — it’s the next deployment wave.
Retail is just one example.
In logistics, fleets of semi-autonomous robots will collaborate to load, unload, inspect, and transport goods with minimal human intervention. In agriculture, AI-powered robotics will manage crop health, precision irrigation, and harvesting. In healthcare, robotic assistants will handle repetitive transport tasks, freeing human staff to focus on patient care.
The economic implications are massive.
Robotics powered by AI doesn’t just reduce costs — it increases system intelligence. It transforms static infrastructure into adaptive environments. It enables 24/7 operational continuity. It generates real-time data loops that improve performance over time.
But scaling robotics requires more than hardware and AI models. It requires trust frameworks, secure data exchange, financial rails, and distributed coordination systems. This is where the deeper infrastructure layer becomes essential.

@Fabric Foundation understands that robotics is not a standalone vertical — it is a convergence point. AI, blockchain, edge computing, and programmable finance are merging into a unified stack that supports machine autonomy at scale.
The next frontier of AI isn’t just chat interfaces and digital copilots. It’s embodied intelligence operating in the physical world.
We are entering an era where:
• Robots will transact autonomously
• Machines will coordinate tasks across decentralized networks
• AI systems will manage physical infrastructure in real time
• Hardware will continuously learn from global data streams
• Financial systems will integrate directly into machine workflows
This transformation will redefine productivity, labor models, urban planning, and global trade.
Organizations that build foundational infrastructure today will define the next decade.
That’s why the collaboration between @Fabric Foundation, OpenMind, @NVIDIA, @Unitree Robotics, and @Circle is worth paying attention to. It represents the fusion of compute, embodiment, and programmable economics.
The robotics wave is not coming — it’s already here.
The question is not whether AI will move into the physical world. It’s how fast we can build the systems that support it responsibly, securely, and at scale.
The next frontier isn’t virtual.
It’s embodied.
#BlockAILayoffs #JaneStreet10AMDump
#ArtificialInteligence

Inteligența artificială face acum parte din viața de zi cu zi. Ajută la redactarea de e-mailuri, răspunsuri la întrebări, rezumarea cercetărilor și chiar la ghidarea deciziilor financiare. Dar există o problemă majoră.
AI poate face greșeli și, adesea, sună foarte sigur de sine chiar și atunci când greșește.
Uneori, un AI oferă un răspuns care pare corect, dar nu este complet adevărat. De fapt, studiile au arătat că modelele lingvistice mari pot genera informații incorecte sau înșelătoare în până la 15% din cazuri, în funcție de complexitatea sarcinii.

As artificial intelligence continues to scale, one of the biggest challenges is verification. How do we know that AI-generated outputs are accurate, tamper-proof, and trustworthy in decentralized environments? This is where @mira_network introduces a powerful solution. Instead of relying on blind trust, Mira focuses on building a validation-first AI infrastructure layer designed specifically for Web3 ecosystems.
At its core, $MIRA plays a critical role in aligning incentives between compute providers, validators, and network participants. By creating an economic structure where honest validation is rewarded, the network strengthens reliability while maintaining decentralization. This approach reduces single points of failure and supports transparent coordination across distributed systems.
What makes @mira_network particularly interesting is its focus on scalable verification. As AI adoption grows across DeFi, governance, and on-chain automation, the need for provable and secure outputs becomes essential. $MIRA is positioned as the backbone token powering these verification mechanisms and long-term ecosystem growth.
The convergence of blockchain and AI demands infrastructure that prioritizes integrity, scalability, and incentive alignment. Mira is building toward that future step by step. #Mira #ArtificialInteligence

Interacționăm cu inteligența artificială în fiecare zi. Aceasta scrie e-mailurile noastre, rezumă știrile și chiar ne ajută să tranzacționăm. Dar există un secret murdar despre care industria AI nu vorbește întotdeauna: nu poți avea încredere deplină în rezultatul generat.
Modelele de Limbaj Mari (LLMs) sunt probabilistice, nu deterministe. Ele prezic următorul cuvânt, ceea ce înseamnă că pot—și adesea o fac—să genereze informații false cu o încredere deplină. Acest fenomen, cunoscut sub numele de "halucinație," este o barieră critică pentru potențialul AI. Dacă dorim ca AI să gestioneze finanțele, să diagnosticheze problemele de sănătate sau să funcționeze autonom, avem nevoie de o modalitate de a verifica ce ne spune. Aceasta este problema exactă pe care @mira_network este construit să o rezolve.

The Future of Trustless Intelligence: Why Mira Network is the Missing Piece in AI
As we move deeper into 2026, the initial hype surrounding Artificial Intelligence has been replaced by a more sober reality: the reliability gap. While Large Language Models (LLMs) are more capable than ever, they still struggle with "hallucinations" and inherent bias. This is exactly where @Mira - Trust Layer of AI steps in, positioning itself as the decentralized "trust layer" for the next generation of AI.
Closing the AI Reliability Gap
Most current AI systems operate as "black boxes"—you provide a prompt and hope the output is accurate. Mira Network fundamentally changes this by utilizing a decentralized verification protocol. Instead of relying on a single model, Mira breaks down AI outputs into verifiable claims. These claims are then cross-checked by a distributed network of independent AI models to reach a consensus.
95%+ Accuracy: By using multi-model verification, Mira has demonstrated the ability to boost AI output accuracy from a baseline of ~70% to over 95%.
Autonomous Operation: This level of trust allows AI to move into high-stakes industries like healthcare, finance, and legal services, where error margins are virtually zero.
The Utility of $MIRA
At the heart of this ecosystem is the $MIRA token. Far from being just a speculative asset, $MIRA serves as the economic engine for the entire network:
Staking & Security: Node operators stake $MIRA to participate in the verification process, ensuring they have "skin in the game."
Payment for Verification: Developers and enterprises use $MIRA to access the "Verified Generate API," ensuring their applications deliver error-free content.
Governance: Token holders help shape the future of the protocol, from model routing strategies to ecosystem incentives.
Why #Mira Matters Now
With the recent launch of its SDK and the expansion of community initiatives, @Mira - Trust Layer of AI is making it easier for developers to build truly autonomous, on-chain AI. By bridging the gap between blockchain’s transparency and AI’s intelligence, we are finally seeing a future where machines can be verified, not just trusted.
Whether you are a developer looking for reliable infrastructure or an enthusiast following the intersection of AI and Web3, keeping an eye on this project is essential.
#Mira #Aİ #Web3 #blockchain #ArtificialInteligence #MIRA
@mira_network

Introducere
Traderii cu amănuntul adesea urmăresc semnalele fără a înțelege mecanismele mai profunde ale modului în care instituțiile mișcă piața. Dar iată adevărul: băncile, fondurile speculative și banii inteligenți nu tranzacționează ca noi. Ele manipulează lichiditatea, construiesc inversări și creează capcane de continuare. Foaia de trucuri pentru acțiunea de preț instituțional este harta ta pentru decodarea acestor mișcări — și odată ce o stăpânești, nu te vei mai uita niciodată la grafice în același mod.
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Concepturi cheie pe care trebuie să le cunoști....👇
- CHoCH (Schimbarea caracterului): Prima indicatie ca momentum-ul se schimba. Instituțiile folosesc acest lucru pentru a prinde traderii înainte de a inversa.