Coder Legion - Recent posts tagged multicloud

Scale Computing Unifies Edge Infrastructure with Single-Platform Architecture

Mon, 02 Feb 2026 15:56:16 +0000

At the 66th IT Press Tour, Scale Computing presented its consolidated edge computing platform following its merger with Acumera. The company now manages over 100,000 sites and 250,000 edge computing workloads through a unified software stack.

Technical Architecture

Scale Computing's platform consists of four integrated components that handle everything from virtualization to network security:

SC//HyperCore combines compute, storage, and virtualization into a single software layer. The system runs on any x86 server and includes self-healing automation that detects and recovers from infrastructure issues without manual intervention.

SC//Fleet Manager provides centralized orchestration through a cloud-based management interface. Zero-touch provisioning automates deployments across the entire fleet. You can push application updates to thousands of locations without site-by-site scripting.

SC//Reliant Platform delivers edge computing as a service with an API-powered management system. The platform supports both VMs and containers on dedicated server environments.

SC//AcuVigil handles managed network services with SDWAN and next-generation firewall capabilities. The service includes hardened network devices deployable in high-availability configurations.

Running AI at the Edge

Scale Computing positions its platform as infrastructure for decentralized AI systems. The edge deployment model addresses four specific requirements:

Reduced latency for real-time processing
Data privacy through local processing
Deployment speed with zero-touch provisioning
Scalability across distributed locations

The Yum! Brands' case study demonstrates this approach. Taco Bell deployed proprietary Voice AI technology on the Reliant Platform across hundreds of drive-thru locations. The system processes natural language orders locally and integrates with POS systems and digital menu boards.

Development and Deployment

For developers working with edge applications, the platform supports standard containerized workloads alongside traditional VMs. The Advance Auto Parts deployment shows this flexibility—they replaced single-server POS systems with SC//Platform to support their container strategy while maintaining existing VM workloads.

Breedon Group's implementation illustrates the infrastructure approach. They run Kubernetes nodes on NVMe-powered clusters dedicated to data analytics, connecting remote IoT devices through Azure IoT Hub and serving Power BI users across the organization.

The platform handles replication between sites automatically. Snapshots and failover mechanisms operate without custom scripting.

Integration Points

SC//Platform integrates with common enterprise tools:

Cloud providers for hybrid deployments
Container orchestration platforms
Analytics and BI tools
PCI compliance tools and security scanning
Payment processing systems
Network monitoring and traffic analysis

The architecture separates the management plane from the edge platform, enabling centralized configuration and policy management while maintaining local processing capabilities.

Deployment Scale

Scale Computing operates in 30+ countries with 4,000 customers. The platform scales beyond 10,000 locations from a single management interface.

Performance metrics from customer deployments show uptime above 99%, a 70% reduction in overall costs, and a 50% improvement in operational efficiency.

The company holds 30+ patents related to edge computing and network security technologies.

What This Means for Edge Development

Edge computing infrastructure is consolidating. Instead of managing separate systems for virtualization, networking, security, and application deployment, you can work with a unified platform.

The shift to zero-touch provisioning changes how you approach edge deployments. Applications need to support automated deployment and configuration without onsite technical staff.

Container support at the edge opens standard DevOps workflows for distributed environments. You can use the same tools and processes you use in the data center.

Network security integration at the platform level reduces the complexity of securing edge applications. The infrastructure handles SDWAN, firewall rules, and compliance requirements.

Scale Computing's approach reflects where edge computing is headed—toward platforms that handle the full stack from hardware management to application deployment.

Helikai's Micro AI Architecture: Building Enterprise-Grade AI Agents That Actually Work

Wed, 28 Jan 2026 19:15:58 +0000

At the 66th IT Press Tour in January 2026, Helikai presented a compelling alternative to the "boil the ocean" approach that's dominated enterprise AI conversations. Instead of trying to build omniscient AI systems, they're focused on micro AI agents—purpose-built components that do one thing exceptionally well, then chain together for complex workflows.

The Micro AI Philosophy

The core insight driving Helikai's approach emerged from real-world failure. Co-founder Jamie Lerner recounted how, as CEO of a public company, he issued the typical AI mandate: "Everyone needs to use AI because it's the future." A year later, despite smart engineers and meaningful projects, they had little to show for it.

The breakthrough came from radical simplification: "Just give me something. Make something simple work. Use AI to validate an address, calculate shipping, something simple." That constraint—moving from lofty ambitions to discrete, achievable tasks—became the foundation of their micro AI methodology.

SPRAG: Private RAG for the Enterprise

Helikai's platform centers on SPRAG (Secure Private Retrieval Augmented Generation), which addresses the primary concern keeping enterprises from adopting AI: data sovereignty.

Key Technical Features:

Model Agnostic: Supports 40-60 different models simultaneously (OpenAI, Gemini, Anthropic, Grok, open-source models)
On-Premise Deployment: Full 120B parameter models run on customer hardware with zero internet connectivity
Lightweight Infrastructure: A $22,000 server handles most enterprise workloads; high-end GPU optional but not required for many use cases
Multi-Database Architecture: Combines vector databases for semantic understanding with relational databases for deterministic operations

The platform's approach to model selection is particularly clever. Rather than forcing customers into vendor lock-in, SPRAG can run multiple models concurrently and let developers compare results: "What would OpenAI say? What would Gemini say? What would Grok say?"

The Helibot Catalog: Pre-Built Agents at Scale

With 200+ pre-built agents (Helibots), the company has created something between traditional software libraries and custom development:

Enterprise IT & Business Automation:

Inbound document processing (invoices, POs, forms with layout-aware parsing)
Outbound document generation (proposals, CPQ, quotations)
Semantic search across knowledge silos
Conversational agents for support and sales

Technical Implementation Patterns:

Agents operate on "one agent, one outcome" principle
Complex workflows chain multiple agents through orchestration
Each agent has predictable cost, scope, and delivery timeline
Human-in-the-loop intervention points configurable at any workflow stage

Media & Entertainment Catalog:

Multilingual subtitle generation using Whisper + pyannote-audio for speaker diarization
Voice cloning and TTS with XTTSv2/Coqui
Film restoration (scratch/dust removal, audio cleanup, conform/reconform)
Color grading and visual effects generation

The technical stack for their subtitle generation agent demonstrates the composition approach:

ffmpeg → WhisperX → pyannote-audio → MarianMT → Aeneas → Output
(audio) → (transcribe) → (diarize) → (translate) → (align) → (subtitles)

KaiFlow: Human-in-the-Loop Orchestration

KaiFlow provides the supervisory layer that makes micro agents enterprise-ready:

Chain-of-Thought Logging: Complete audit trail of every decision
Citation-Backed Outputs: Document lineage for compliance
Intervention Points: Stop workflows at any step for human review
Personality & Voice: Agents can have conversational interfaces that build relationships with users

Example: An invoice processing agent handling 100K invoices/week can flag anomalies ("This invoice looks fraudulent," "This vendor isn't approved") and route to appropriate humans rather than blindly processing everything.

Architecture Deep Dive

The SPRAG platform architecture follows a clear separation of concerns:

Data Ingestion Layer:

File/directory crawlers for local storage
Website scrapers for public documentation
Object handlers for S3-compliant storage
Parsers, chunkers, embedders in pipeline

Storage & Retrieval:

Vector database for semantic search
RDBMS/columnar for structured data
Cache service for performance
Search with RBAC enforcement

Orchestration & Execution:

RAG orchestrator for N-stage retrieval
Platform orchestrator for Helibot execution
MCP (Model Context Protocol) support for external integrations
API, OAuth, CLI interfaces

Model Layer:

Open-source LLMs (ChatGPT OSS, Gemma, Mistral, LLaMA)
Commercial LLMs (ChatGPT, Gemini, Claude, Grok)
Swap models without re-architecting workflows

Deployment Models & Hardware

Helikai offers three deployment approaches:

1. Helikai Enterprise (On-Premise)
Hardware requirements scale based on workload:

Small: 8-core Xeon, 1x RTX 6000, 512GB RAM, 20TB SSD ($22K range)
Medium: 16-core Xeon, 2x RTX 6000, 1TB RAM, 100TB SSD
Large: 32-core Xeon, 1x H100, 2TB RAM, 200TB SSD
X-Large: 56-core Xeon, 2x H100, 4TB RAM, 500TB SSD

2. Helikai SaaS Cloud

Virtualized in customer VPC
Data isolation (no multi-tenancy)
12-month minimum commitment

3. Pay-as-you-Go

Per-project, per-document, per-image pricing
No infrastructure investment

The on-premise story challenges common assumptions about AI infrastructure needs. As Jamie noted: "People think AI is gigantic. It isn't. The only thing that's gigantic is the training models." For inference on enterprise data, hardware requirements are surprisingly modest.

Deterministic + Non-Deterministic Workflows

One of Helikai's key insights: not every step in a workflow needs AI.

"If I have to log into an Oracle database and say, what is the tax rate in Texas? I don't want AI to do that. I don't need AI to be creative. That's just classic old-fashioned query."

Their workflow engine weaves between AI-powered steps (semantic understanding, content generation) and deterministic operations (database queries, rule enforcement). This hybrid approach achieves enterprise-grade accuracy where pure AI solutions struggle.

Example: An ERP integration might use AI to extract data from an unstructured purchase order, but use deterministic logic to validate addresses, check credit limits, and enforce approval workflows.

Development Approach: Subject Matter Experts + Engineers

Helikai's team composition is telling: 50% subject matter experts, 50% engineers. The SMEs (pathologists, legal experts, IT specialists) explain how they do their jobs, and engineers figure out how to automate the routine, error-prone tasks.

For pathology work: "We do a lot of work in pathology. We work with pathologists who explain how they look in their microscope at cells and analyze those cells. That informs our engineers to say, how do I automate what that person does?"

This approach builds agents that actually match real-world workflows rather than imposing AI-first thinking on established processes.

Roadmap: First Half 2026

SPRAG Platform:

AI agent development templates and UI
Workflow/pipeline templates
Integration with Power Automate, AI Builder, N8N
Large-scale HA and clustering
Furiosa chip qualification (alternative to Nvidia)

New Helibots:

DICOM image PII redaction
Snowflake and Salesforce semantic interfaces
Pediatric radiology analysis
Invoice fraud detection
End credits QA for film
Catalog photography generation

The Enterprise AI Reality Check

Helikai's approach acknowledges a truth many AI vendors ignore: most enterprises aren't ready for ambitious AI projects. Using the MITRE AI Maturity Model, they assess where customers actually are versus where they think they are.

"I know you want to do this really adventurous thing, but you're here, right? You don't have a platform, you don't really have any resources, your organization's kind of new to this, your data isn't really organized for AI. So why don't we start here and walk you up this staircase?"

The workshop-first methodology focuses on quick wins with pre-built agents before graduating to custom development. Success breeds success; big AI failures kill momentum.

Why This Matters for Developers

The micro AI agent approach offers several advantages for development teams:

Predictability: Fixed scope, known costs, reliable timelines
Composability: Chain simple agents into complex workflows
Debuggability: Clear audit trails, intervention points
Model Flexibility: Swap underlying models without rewriting code
Enterprise Grade: 99%+ accuracy through hybrid AI + deterministic approaches

For developers building AI-powered applications, Helikai's platform provides the middle ground between "build everything from scratch" and "hope OpenAI's API does what you need."

The 200+ agent catalog means many common patterns (document processing, semantic search, conversational interfaces) are already solved. The SPRAG platform handles the infrastructure complexity (model management, RAG orchestration, access control). Development teams can focus on business logic and workflow design.

Graid's SupremeRAID uses GPU acceleration to eliminate storage bottlenecks, delivering 28M IOPS for developers.

Mon, 07 Jul 2025 10:33:49 +0000

Graid Technology Revolutionizes Storage Performance with GPU-Accelerated RAID for Developers

The storage landscape for high-performance computing and AI development is undergoing a fundamental transformation. Traditional CPU-based RAID solutions have long created bottlenecks that limit application performance, forcing developers to architect around storage constraints rather than focusing on optimal code design. Graid Technology's SupremeRAID™ represents a paradigm shift that developers have been waiting for: a GPU-accelerated RAID that eliminates these performance limitations.

Breaking Free from CPU-Based Storage Constraints

SupremeRAID™ addresses this limitation by offloading RAID operations entirely to dedicated GPU hardware. This "out-of-path" architecture means storage operations no longer compete with application logic for CPU resources, fundamentally changing how developers can approach performance-critical applications.

The results speak for themselves. Graid's flagship solution achieves up to 28 million IOPS and 260GB/s throughput with a single card supporting up to 32 native NVMe drives. Perhaps more importantly for developers, the company reports achieving over 95% of raw NVMe performance in production environments—performance that was previously impossible with traditional RAID solutions.

Game-Changing Performance for AI Development

AI and machine learning developers face unique storage challenges. Training large models requires massive datasets that must be continuously fed to GPUs at tremendous speeds. Any storage bottleneck directly translates to expensive GPU idle time and extended training cycles.

SupremeRAID AE (AI Edition) specifically addresses these challenges with features such as GPUDirect Storage (GDS) support and an Intelligent Data Offload Engine. GDS enables direct NVMe-to-GPU memory transfers, eliminating CPU bottlenecks. The Intelligent Data Offload Engine can put the RAID kernel to sleep during intensive GPU computation phases, freeing up additional GPU resources for AI workloads.

For developers building AI training pipelines, this means they can finally design systems where storage performance matches computational capability. No more architectural compromises to work around storage limitations; just pure performance that scales with your data requirements.

Simplifying Development with Software-Defined Architecture

Traditional RAID controllers require developers to understand complex hardware configurations and work within rigid performance constraints. SupremeRAID™'s software-defined approach provides flexibility that hardware solutions cannot match.

The platform supports seamless scaling across multiple GPU servers through features like NVMe-over-Fabrics (NVMe-oF). Developers can design distributed applications that treat storage as a unified resource pool rather than managing individual server limitations. A single SupremeRAID™ installation can provide RAID protection across multiple servers, dramatically simplifying application architecture.

Integration with existing developer workflows is straightforward. The solution includes RESTful APIs for modern IT automation and supports popular clustering storage systems, such as BeeGFS, Lustre, and Ceph, without requiring data migration.

Real-World Developer Impact

The practical implications for developers are substantial. Consider real-time analytics applications that previously required careful buffering strategies to accommodate storage latency. With SupremeRAID™, developers can design more responsive systems that process data streams without artificial delays.

Game development studios leveraging real-time asset streaming can eliminate loading bottlenecks that previously required complex content management strategies. Scientific computing applications can process larger datasets in memory without the traditional tradeoffs between storage speed and data protection.

Financial trading systems benefit enormously from the ultra-low latency capabilities. TelSwitch, a legal data analytics company, has deployed SupremeRAID™ across multiple systems specifically for processing massive datasets with small block sizes, precisely the kind of workload that traditional RAID struggles with.

Enterprise-Grade Reliability for Mission-Critical Code

Despite its performance focus, SupremeRAID™ maintains enterprise-grade data protection features that developers can rely on. The solution supports RAID 6 configurations, protecting against multiple drive failures while maintaining RAID 10-level performance characteristics.

Advanced features, such as journaling, bad block detection, and extended retry mechanisms, enable developers to architect systems with confidence in data integrity. The US Department of Defense has deployed SupremeRAID for edge computing applications that require "military-grade" reliability standards.

The Future of High-Performance Development

NVIDIA CEO Jensen Huang's recent statement, "for the very first time, your storage system will be GPU-accelerated," validates what Graid Technology has been building. As AI workloads become increasingly central to application development, storage architectures that match GPU performance capabilities become essential.

For developers building the next generation of data-intensive applications—whether AI training systems, real-time analytics platforms, or high-performance scientific computing solutions—SupremeRAID™ represents more than just faster storage. It's the foundation for applications that were previously impossible to build efficiently.

The era of designing around storage limitations is coming to an end. With GPU-accelerated RAID, developers can finally focus on building better software rather than working around infrastructure constraints.

Graid's SupremeRAID uses GPU acceleration to eliminate storage bottlenecks, delivering 28M IOPS for developers.

Sun, 08 Jun 2025 00:01:50 +0000

Graid Technology Revolutionizes Storage Performance with GPU-Accelerated RAID for Developers

The storage landscape for high-performance computing and AI development is undergoing a fundamental transformation. Traditional CPU-based RAID solutions have long created bottlenecks that limit application performance, forcing developers to architect around storage constraints rather than focusing on optimal code design. Graid Technology's SupremeRAID™ represents a paradigm shift that developers have been waiting for—GPU-accelerated RAID that eliminates these performance limitations.

Breaking Free from CPU-Based Storage Constraints

For decades, developers working on data-intensive applications have faced a fundamental challenge: storage performance that couldn't keep pace with computational demands. Traditional RAID controllers consume valuable CPU cycles and create throughput bottlenecks that limit the effectiveness of multi-threaded applications, real-time analytics systems, and AI training workloads.
SupremeRAID™ addresses this limitation by offloading RAID operations entirely to dedicated GPU hardware. This "out-of-path" architecture means storage operations no longer compete with application logic for CPU resources, fundamentally changing how developers can approach performance-critical applications.

Game-Changing Performance for AI Development

SupremeRAID AE (AI Edition) specifically addresses these challenges with features such as GPUDirect Storage (GDS) support and an Intelligent Data Offload Engine. GDS enables direct NVMe-to-GPU memory transfers, eliminating CPU bottlenecks entirely. The Intelligent Data Offload Engine can actually put the RAID kernel to sleep during intensive GPU computation phases, freeing up additional GPU resources for AI workloads.

For developers building AI training pipelines, this means they can finally design systems where storage performance matches computational capability. No more architectural compromises to work around storage limitations—just pure performance that scales with your data requirements.

Simplifying Development with Software-Defined Architecture

Integration with existing developer workflows is straightforward. The solution includes RESTful APIs for modern IT automation and supports popular clustering storage systems like BeeGFS, Lustre, and Ceph without requiring data migration.

Real-World Developer Impact

Financial trading systems greatly benefit from ultra-low latency capabilities. TelSwitch, a legal data analytics company, has deployed SupremeRAID™ across multiple systems specifically for processing massive datasets with small block sizes, precisely the kind of workload that traditional RAID struggles with.

Enterprise-Grade Reliability for Mission-Critical Code

The Future of High-Performance Development

Lucidity AutoScaler automates cloud storage management, cutting costs 70% and eliminating manual provisioning tasks.

Tue, 03 Jun 2025 22:24:00 +0000

Lucidity's AutoScaler Eliminates Storage Headaches for Developers With Zero-Downtime Automation

For developers working in cloud environments, few things are more frustrating than storage management bottlenecks that interrupt development workflows. Manual disk provisioning, capacity planning guesswork, and downtime-prone scaling operations have long plagued engineering teams trying to focus on building applications rather than managing infrastructure. Lucidity's breakthrough AutoScaler technology promises to eliminate these pain points entirely.

The Developer Pain Points Lucidity Solves

According to Vatsal Rastogi, Lucidity's Co-Founder, organizations typically waste 50-70% of their provisioned disk space due to over-provisioning—essentially paying three times what they actually need. For developers, this translates to several critical problems that impact daily productivity:

Manual Provisioning Hell: Traditional storage management requires developers to estimate future capacity needs, often leading to either insufficient storage that causes application failures or massive over-provisioning that wastes budget. As Rastogi demonstrated, "Capacity provisioning is a guesstimate" that forces teams into reactive firefighting mode.

Downtime Dependencies: Perhaps most frustrating for developers is that shrinking storage volumes requires downtime—something that's simply unacceptable in modern development workflows. This limitation forces teams to live with over-provisioned storage indefinitely, as the operational cost of downsizing isn't worth the disruption.

Performance Impacts: Manual expansion processes don't just require downtime—they often impact application performance during scaling operations, creating unpredictable slowdowns that can affect user experience and development testing.

The AutoScaler Developer Experience

Lucidity's solution fundamentally changes how developers interact with storage infrastructure. The AutoScaler operates as a lightweight agent that runs on virtual machines, continuously monitoring storage metrics and making real-time adjustments without any developer intervention.

True Zero-Ops Experience: Once deployed, developers literally forget about storage management. The system maintains 75-80% disk utilization automatically, compared to the industry average of 30%. This optimization happens transparently, allowing developers to focus entirely on application logic rather than infrastructure concerns.

Sub-Minute Response Times: When applications need more storage, AutoScaler expands capacity within one minute with zero downtime. This responsiveness means developers never hit storage walls that interrupt development or deployment workflows.

Revolutionary Shrinking Capability: Most impressively, Lucidity has solved the industry's biggest technical challenge, live shrinking of storage volumes without downtime. This breakthrough means storage automatically rightsizes as application needs change, preventing the cost accumulation that traditionally plagues cloud projects.

Real-World Developer Impact

The numbers from Lucidity's customer case studies paint a compelling picture for development teams:

Major US Airline: Automated 7,500 provisioning tasks that previously required manual intervention, reclaiming hundreds of developer hours. The 77% cost reduction freed up budget for actual feature development rather than infrastructure overhead.

Big 4 Accounting Firm: Eliminated 51,000 manual provisioning tasks during peak tax season, allowing their development team to focus on application optimization rather than storage firefighting.

Leading IT Management Company: Achieved $1.5M in savings while managing over 1 petabyte of data, with 435,000 automated operations that would have otherwise consumed significant developer time.

Technical Integration for Development Teams

For developers concerned about integration complexity, Lucidity's approach is refreshingly simple. The system works by:

Agent-Based Architecture: A lightweight agent deploys on each VM, communicating storage metrics to Lucidity's AutoScaler service. This architecture requires no changes to application code or deployment pipelines.

Multi-Cloud Compatibility: Whether your applications run on AWS EBS volumes, Azure Managed Disks, or Google Cloud Persistent Disks, AutoScaler works transparently across all major cloud providers.

Zero Application Changes: Unlike other storage optimization solutions that require application modifications or specific file system configurations, Lucidity operates at the infrastructure layer without touching application logic.

The Business Impact Developers Care About

Beyond the technical benefits, AutoScaler delivers outcomes that matter to development organizations:

Budget Predictability: With automatic optimization reducing storage costs by 50% on average, development teams can better predict infrastructure spending and allocate more budget to innovation rather than waste.

Reduced Operational Overhead: Teams report reclaiming hundreds of hours previously spent on manual storage management, time that can be redirected to feature development and technical debt reduction.

Improved Application Performance: The 2x IOPS performance gains that result from optimized storage allocation directly benefit application responsiveness and user experience.

Developer-Friendly Implementation

What sets Lucidity apart for development teams is the implementation simplicity. As Raj Dutt, VP of Marketing, emphasized during the presentation, "Once you do a one-time onboarding activity, you're through. All you care about is the efficiency number."

The onboarding process involves:

Simple account integration through cloud provider APIs
One-click agent installation across VM instances
Automatic disk discovery and optimization recommendations
Transparent ongoing management with detailed monitoring dashboards

For developers building modern applications that demand elastic storage capabilities, Lucidity's AutoScaler represents a fundamental shift from reactive storage management to proactive automation. By eliminating the traditional tradeoffs between cost, performance, and operational overhead, it allows development teams to focus on what they do best—building exceptional applications.

The technology's ability to deliver both immediate cost savings and long-term operational efficiency makes it particularly compelling for development organizations looking to modernize their cloud infrastructure without adding complexity to their development workflows.