Ninja19931

Cloud-Compute-Rechnungen sollten keine Black Boxes sein. Die Gesamte Netzwerk-Einnahmen (TNE) von @ionet bringt Echtzeit-Transparenz zu den GPU-Infrastrukturkosten, indem jede Transaktion auf @solana's Blockchain gesetzt wird.
Wie TNE auf Solana funktioniert
Jede io.net-Transaktion fließt durch Solana und erstellt einen unveränderlichen Datensatz der Netzwerkaktivität. Diese Blockchain-Grundlage ermöglicht:
Smart Contract Escrow-Schutz: Automatisierte Zahlungssysteme, die Mittel halten, bis GPU-Dienste erfolgreich geliefert werden. Kein Vorauszahlen mehr für Infrastruktur, die möglicherweise nicht gut funktioniert.

Der Aufbau von KI-Agenten sollte nicht erfordern, dass man fragmentierte APIs zusammenfügt oder ein Budget für überteuerte Inferenz verbrennt. Launch I/O, ein 35-tägiger Hackathon von io.net, bietet Entwicklern praktische Erfahrungen beim Bau autonomer Agenten mit io.intelligence.io.intelligence bietet Bauherren aller Erfahrungsstufen ein einheitliches Toolkit für den Agentenbau. Durch eine einzige API ermöglicht es io.intelligence, auf 30+ Open-Source-Modelle, integrierte RAG mit Dokumentenverankerung und Werkzeuge zur Orchestrierung von Agenten zuzugreifen. I/O Launch ist Ihre Chance, der Welt zu zeigen, was Sie können. Die Wettbewerbsstrecke fordert Teams heraus, "Autonome Agenten in der realen Welt" zu bauen, oder Werkzeuge, die reale Aufgaben wie Krypto-Analyse, Forschungsautomatisierung oder Inhaltserstellung durchführen. Die Anfängerstrecke führt den Agent Workflow Editor durch geführte Erkundung und Videoeinreichungen ein. Teilnehmer erhalten vorgefertigte Vorlagen, API-Schlüssel, umfassende Dokumentation und Unterstützung über Discord.

io.net Powers Privacy-First AI Training with Flashback Labs' Stargazer
io.net's decentralized GPU network is unlocking a new class of privacy-preserving AI applications that centralized cloud providers can't support. Flashback Labs demonstrates this capability through Stargazer, their flagship generative photo model that trains on personal data without ever exposing it.
Stargazer is designed to recreate emotionally significant but uncaptured moments (like a family photo that was never taken). It is the first model available for decentralized training and private inference on io.net's infrastructure today.
Why Decentralized Infrastructure Matters for AI Privacy
Big Tech cloud platforms like AWS create critical privacy limitations for AI training. Moving sensitive data to central servers introduces compliance risks and prevents many legitimate training use cases from scaling.
io.net's distributed architecture solves this through instant node deployment across 138+ countries. When Flashback Labs needs federated learning infrastructure, io.net training nodes deploy automatically, accessing data from decentralized storage without central bottlenecks.
io.net's infrastructure unlocks five key capabilities for Stargazer:
Federated Training: Personal data stays on devices or secure TEEs while io.net coordinates distributed model updates across the network.
TEE-Protected Inference: io.net's Trusted Execution Environments protect both prompts and model weights during generation.
Geographic Distribution: io.net's global node network enables training on location-specific data while respecting regional privacy regulations.
Context-Rich Processing: io.net's infrastructure handles tagged emotions, locations, and cultural metadata to create emotionally accurate outputs.
Consent-Driven Scaling: io.net's token-based reward system enables contributors to improve models while maintaining data ownership.
io.net's Architecture for Privacy-First AI
io.net's decentralized approach addresses technical limitations that prevent federated learning from scaling on AWS or similar platforms. The network's token-based payment system and instant provisioning eliminate traditional cloud friction for privacy-sensitive workloads.
Training occurs within io.net's Trusted Execution Environments, ensuring data privacy throughout the process. Once complete, encrypted model weights return to researchers while training nodes terminate, leaving no data traces on io.net's infrastructure.
This multi-layer privacy architecture preserves data through federated learning (data stays local), decentralized storage (no central failure points), and encrypted weight distribution (protecting intellectual property).
Addressing AI Bias Through Decentralization
io.net's geographic distribution enables AI companies to train models on diverse, location-specific datasets that reflect regional nuances traditional centralized datasets miss. This addresses the Western bias problem plaguing current AI models.
Flashback Labs selected io.net in February 2025 specifically for this distributed training capability and novel approach to on-demand node deployment. Currently, io.net handles inference workloads for Flashback Labs, with plans to expand to fully decentralized training as user density increases.
Stargazer is live on the upcoming Flashback Mobile App BETA, proving that io.net can support end-to-end privacy-preserving AI at scale. It represents the first model:
Trained via federated learning on io.net
Running inference in io.net's secure TEEs
Audited and consent-verified via on-chain logs
Governed by contributors rather than centralized entities
Stargazer demonstrates that io.net's decentralized infrastructure enables powerful AI without data exploitation. It just requires the right architecture for permission, privacy, and distributed processing.
Ready to build privacy-first AI applications? Try IO Intelligence now for unified model access and secure inference capabilities.

io.net is proud to announce the launch of Retrieval Engine, a brand new premium Retrieval Augmented Generation (RAG) feature that transforms how organizations interact with their knowledge bases. Retrieval Engine is the start of io.net's mission to deliver grounded AI.Retrieval Engine grounds AI responses in your trusted documents, reducing hallucinations while delivering intuitive, conversational access to your most critical information without the expensive model training.
The Challenge: AI's Promise vs. Reality
AI promises huge benefits for enterprises, but falls short when accuracy is underwhelming. AI hallucinations, fabricated or inaccurate outputs that appear authoritative, are one of the most significant barriers to enterprise AI adoption. For organizations, especially those in medical, legal, academic, and other precision-critical sectors, these hallucinations aren't merely inconvenient; they're potentially catastrophic.
Traditional solutions either require expensive model retraining on proprietary data (costing tens of thousands of dollars) or rely on brittle keyword search that misses conceptual connections. The result? Organizations face an impossible choice between powerful but unreliable AI or accurate but limited search capabilities.
With today's launch of Retrieval Engine, io.net mitigates this dilemma by connecting your grounding data to the ideal AI model for your use case.
How Retrieval Engine Works
io.net
's Retrieval Engine elegantly addresses this dilemma through our advanced implementation of Retrieval-Augmented Generation:
Document Processing Upload: your documents through our intuitive drag-and-drop web interface. The system automatically chunks content and stores it in a vector database optimized for semantic retrieval.Semantic Search: When you ask a question, Retrieval Engine identifies the most relevant document sections based on meaning and concepts, not just keywords.Grounded Generation: The system generates responses using the retrieved information, reducing mistakes and improving accuracy.Citation and Verification: Every response includes precise citations to source documents with visual confirmation of the exact paragraphs referenced, creating a verifiable audit trail.
Unlike solutions requiring expensive fine-tuning runs or specialized machine learning expertise, Retrieval Engine works immediately with your existing documents, making advanced AI capabilities accessible to organizations of all technical capacities.
Turn Your Documents Into Insights
Until now, analyzing differences between your service level agreements and a competitor's would require hours of manual document review, using Ctrl+F to jump between sections and mentally tracking the differences.
With Retrieval Engine, simply upload both documents and ask: "What are the key differences between our uptime requirements and Amazon's?" Instantly receive a structured comparison with direct citations to the exact paragraphs where this information appears, allowing you to take immediate action based on verified facts.
Retrieval Engine doesn't just make your documents searchable. It makes them actionable in new ways. If you’re in an enterprise business drowning in documents, this matters for a few big reasons:
Truth Grounded: Every answer is grounded in your verified documents with rigorous citationsCost-Efficient: Eliminate expensive model training on proprietary data, saving tens of thousands of dollarsSerious Reliability: Built for organizations where accuracy is non-negotiable. Every response includes precise references with visual verification of source materialIndustry-Leading Pricing: Token-based pricing that dramatically undercuts competitor offerings
Verticals Where Grounded AI Is A Must
Medical Sector
Medical professionals can query complex treatment protocols and patient histories with confidence that responses are grounded in verified medical documentation, reducing AI fabrications. The medical sector was one of the primary drivers behind this technology due to its extremely low tolerance for error when human lives are at stake.
Legal Applications
Law firms can search across vast case libraries and legal documents, receiving precise, citation-backed answers that reference specific precedents and statutes. Instead of hours of manual document review, attorneys can instantly locate and verify relevant information.
Customer Service Excellence
io.net
's internal teams are using Retrieval Engine to query historical support tickets and knowledge bases, dramatically reducing response times while improving answer accuracy. Support agents now receive contextually relevant suggestions grounded in previous resolutions, complete with citations to specific ticket numbers.
Unlock Elite AI Insights
Until now, sophisticated RAG implementations have been the exclusive domain of major AI companies with extensive resources. These are the internal tools that power the most advanced AI systems, but are rarely exposed directly to users due to their computational demands.
Retrieval Engine democratizes these capabilities, making them accessible to enterprises of all sizes through a:
Simple Web UI: Get started in minutes with an intuitive drag-and-drop interfaceAPI Availability: Seamlessly integrate with existing workflowsToken-Based Pricing: Transparent, predictable costs that are magnitudes lower than competitor offerings
For the first time, organizations outside the AI giants can enjoy the same powerful document intelligence capabilities that until now required massive investments in infrastructure and expertise.
Implement in 1-2-3
Getting started with Retrieval Engine requires minimal technical lift:
Log in to io.net at: https://ai.io.net/ai/reUpload your documents through the drag-and-drop interfaceStart querying your knowledge base through natural language
There are no special technical requirements or setup processes. If you can drag-and-drop a file, you can use Retrieval Engine immediately. For organizations requiring deeper integration, our API documentation provides straightforward implementation guidance with sample code in multiple languages.
Why io.net’s Retrieval Engine Stands Apart
While numerous RAG solutions have emerged, io.net's implementation offers unique advantages:
Rigorous Citation: Every response includes precise source references with visual verificationZero Technical Barriers: No prompt engineering or technical expertise requiredUnmatched Cost Efficiency: Token-based pricing that dramatically undercuts the "dollar per query" model of competitors
Through conversations with our earliest adopters, we've consistently heard that Retrieval Engine delivers tangible productivity gains measurable in multiples rather than percentages. Users report completing research and analysis tasks 3-10x faster than with traditional methods while maintaining higher confidence in the results.
Deep Research Is Coming
Retrieval Engine is another step towards io.net's vision for grounded AI, with Deep Research being the ultimate goal.
Future releases will extend beyond search to action, allowing your AI agents to not only find information but take appropriate steps based on it, similar to how our customer support team is implementing Retrieval Engine to both understand support tickets and suggest appropriate responses.
Try Retrieval Engine Now
Retrieval Engine is available starting today as a premium io.net offering. Unlike competitors charging up to a dollar per query, io.net continues its commitment to democratizing AI with industry-leading token-based pricing that makes advanced capabilities affordable for organizations of all sizes.
Experience the power of truly grounded AI today. Try the drag-and-drop document interface and see how Retrieval Engine can transform your enterprise knowledge management.
For enterprise inquiries, detailed pricing information, or to request a personalized demo, contact our team at business@io.net.