ScaleOut Software’s Post

New Post: Efficient Memory‑Aware Scheduling for Containerized Microservices in Edge Video Analytics - **Abstract** Edge analytics for real‑time video streams demands strict latency guarantees while operating under limited compute and memory resources. Conventional container orchestration schemes, such as Kubernetes with default cgroup limits, often lead to sub‑optimal memory utilization and frequent out‑of‑memory \(OOM\) events when workloads exhibit bursty memory footprints. This study introduces a dynamic, memory‑aware scheduler that \[…\] \[Source & Legal Disclaimer\] This is an AI-generated simulation research dataset provided by Freederia.com, released under the Apache 2.0 License. Users may freely modify and commercially use this data \(including patenting novel improvements\); however, obtaining exclusive patent rights on the original raw data itself is prohibited. As this is AI-simulated data, users are strictly responsible for independently verifying existing copyrights and patents before use. The provider assumes no legal liability. For future Enterprise API access and bulk dataset purchase inquiries, please contact Freederia.com.

Your AI agent is deep in the middle of a task: packages installed, files written, tools active. Then Kubernetes evicts the pod. Session lost. User hits an error. That’s a guaranteed support ticket by morning. 🚨 𝐇𝐞𝐫𝐞’𝐬 𝐭𝐡𝐞 𝐜𝐨𝐫𝐞 𝐢𝐬𝐬𝐮𝐞 𝐰𝐢𝐭𝐡 𝐀𝐈 𝐚𝐠𝐞𝐧𝐭𝐬 𝐨𝐧 𝐊𝐮𝐛𝐞𝐫𝐧𝐞𝐭𝐞𝐬: The platform is built for stateless workloads, but agents accumulate real in-process state. Each message depends on the last. You can’t just dump state to a database and call it production-ready. Last week, we shipped the 𝐑𝐞𝐠𝐢𝐧𝐚 𝐂𝐨𝐨𝐫𝐝𝐢𝐧𝐚𝐭𝐨𝐫 to handle exactly this. The Regina Coordinator sits in front of your pods and manages the entire session lifecycle: ✅ Routing ✅ Failure detection ✅ Backup and recovery Pod crashes, rolling deploys, and idle suspensions are all handled automatically. Users stay online. No interruptions. 𝐏𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧-𝐠𝐫𝐚𝐝𝐞 𝐟𝐞𝐚𝐭𝐮𝐫𝐞𝐬: ✅ Three backup backends: S3, Redis, shared filesystem ✅ Three allocation strategies: round-robin, least-loaded, random ✅ Crash recovery: 30-second detection, automatic session migration, zero manual intervention 𝐒𝐡𝐢𝐩𝐩𝐞𝐝 𝐚𝐥𝐨𝐧𝐠𝐬𝐢𝐝𝐞 eBPF Sandbox. Kernel-level security for every line of code your agents generate at runtime. Stateful sessions. Secure code execution. The two questions your platform team will ask before anything gets near production. 𝐁𝐨𝐭𝐡 𝐫𝐮𝐧 𝐢𝐧𝐬𝐢𝐝𝐞 𝐖𝐚𝐭𝐭. 𝐘𝐨𝐮𝐫 𝐢𝐧𝐟𝐫𝐚𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞. 𝐘𝐨𝐮𝐫 𝐜𝐨𝐧𝐭𝐫𝐨𝐥. 🔗 𝐑𝐞𝐚𝐝 𝐭𝐡𝐞 𝐟𝐮𝐥𝐥 𝐛𝐫𝐞𝐚𝐤𝐝𝐨𝐰𝐧 → https://lnkd.in/gxwB_QWT

Your LLM API keys sit in Lambda env vars. One bad npm install can steal all of them.   March 2026: Axios, LiteLLM, Trivy, KICS and Telnyx all got compromised. The target was never the packages. It was your long lived credentials.   1. Stop storing keys as plain environment variables. Pull them from SSM Parameter Store at cold start. Encrypt with KMS. Cache in memory with a 15 minute TTL. Never bake a key into a Lambda deployment artifact.   2. Treat every LLM provider key like an AWS root key. One key per service, per environment. No sharing across stages. Spend caps on every provider dashboard. Alert at 80 percent. Rotate monthly, even when nothing looks wrong.   3. Assume your dependency tree is already compromised. Pin every package. Use npm minimumReleaseAge of 48 hours. Run gitleaks and trufflehog in CI before every build. Disable postinstall scripts in locked environments.   The rule: plain env var keys are already leaked. ___ ♻️ Repost if this helped a fellow engineer. 💬 Where do you actually store your LLM API keys in production?   Follow Mian Zubair for daily AI + system design breakdowns.   #SystemDesign #SoftwareEngineering #AIEngineering #DistributedSystems #BuildInPublic  

New Post: Adaptive Function Layering for Reducing Cold‑Start Latency in Serverless Edge Micro‑Services \(Exploratory Study\) - — ### Abstract Cold‑start latency—the time required to materialize a serverless function instance—remains a dominant performance barrier on resource‑constrained edge nodes. This exploratory work proposes **Adaptive Function Layering \(AFL\)**, a modular framework that decomposes a serverless function into a set of lightweight, stateless *layers* and dynamically schedules those layers across heterogeneous edge resources \(CPU‑only nodes \[…\] \[Source & Legal Disclaimer\] This is an AI-generated simulation research dataset provided by Freederia.com, released under the Apache 2.0 License. Users may freely modify and commercially use this data \(including patenting novel improvements\); however, obtaining exclusive patent rights on the original raw data itself is prohibited. As this is AI-simulated data, users are strictly responsible for independently verifying existing copyrights and patents before use. The provider assumes no legal liability. For future Enterprise API access and bulk dataset purchase inquiries, please contact Freederia.com.

Every vendor is now selling the unified LLM API gateway: one key, all models, zero provider lock-in. Sounds clean. In practice it's a latency and cost tradeoff that nobody talks about. When you route every request through a middleware layer, you're adding an extra network hop. For synchronous applications — chatbots, real-time assistants, anything where users are waiting — that hop compounds. Median latency becomes p99 latency becomes frustrated users. The cost abstraction is the other problem. Vendors advertise 'unified pricing' but the math only works if your usage is evenly distributed across providers. Production traffic isn't even. It's bursty, model-specific, and context-length sensitive. A single API key across GPT-4o, Claude, and Gemini doesn't give you cost optimization — it gives you a smoothed average that obscures where your actual spend is going. Production-ready LLM applications don't need a single key. They need a deliberate architecture that handles provider failures, routes intelligently based on task type, and keeps cost visible. What's your fallback when your gateway vendor has an outage?

 Observability — Prometheus | OpenTelemetry | Jaeger 🔍 The Observability Stack (Production-Ready) 📊 Prometheus — Metrics (What is happening?) Time-series database for metrics Powerful query language (PromQL) Alerting & SLO monitoring Best for: CPU, memory, latency, request rates 👉 Think: “Is my system healthy right now?” 🔗 OpenTelemetry — Instrumentation (Collect everything) Vendor-neutral standard Collects metrics, logs, and traces Auto + manual instrumentation Works with any backend 👉 Think: “Let me capture the full picture” 🧭 Jaeger — Distributed Tracing (Why is it happening?) End-to-end request tracing Visualize service dependencies Identify latency bottlenecks Root cause analysis in microservices 👉 Think: “Where exactly is the problem?” ⚙️ How They Work Together ➡️ Applications → instrumented via OpenTelemetry ➡️ Metrics → stored & queried in Prometheus ➡️ Traces → visualized in Jaeger 🎯 Result: Full system visibility across microservices 🧠 Real-World Example User reports: “App is slow” ✔ Prometheus → shows increased latency ✔ Jaeger → identifies slow service dependency ✔ OpenTelemetry → correlates logs + traces #Observability #Prometheus #OpenTelemetry #Jaeger #Microservices #DistributedSystems #SystemDesign #DevOps #SRE #CloudArchitecture #Kubernetes #Monitoring #Tracing #Logging #APM #ScalableSystems #HighAvailability #PerformanceEngineering #TechLeadership #SoftwareArchitecture #EngineeringExcellence

New Post: Resilient Gremlin Fault Injection for Stateless Microservice Architectures in Serverless Environments - ## GREMLIN‑Stateless Resilience Optimizer \(G‑SRO\) — ## Abstract Chaos engineering is increasingly used to improve the reliability of cloud‑native applications. The Gremlin platform offers a flexible fault‑injection DSL, but its native capabilities are not specifically tuned to the probabilistic failure modes of stateless, serverless microservice stacks that rely heavily on transient compute resources. This exploratory \[…\] \[Source & Legal Disclaimer\] This is an AI-generated simulation research dataset provided by Freederia.com, released under the Apache 2.0 License. Users may freely modify and commercially use this data \(including patenting novel improvements\); however, obtaining exclusive patent rights on the original raw data itself is prohibited. As this is AI-simulated data, users are strictly responsible for independently verifying existing copyrights and patents before use. The provider assumes no legal liability. For future Enterprise API access and bulk dataset purchase inquiries, please contact Freederia.com.

Firecrawl Alternative: Same API, 5.5x Faster, Self-Hostable. The migration takes 5 minutes. Here's why. CRW implements a Firecrawl-compatible API. Same endpoints, same response format. You change one line — the base URL — and everything works. Before: const client = new FirecrawlApp({ apiUrl: "https://api.firecrawl.dev" }) After: const client = new FirecrawlApp({ apiUrl: "http://localhost:3002" }) That's it. No SDK changes. No schema migration. No rewrite. But the numbers change dramatically: Latency: - Firecrawl: 4,600ms avg - CRW: 833ms avg (5.5x faster) RAM: - Firecrawl: ~500MB - CRW: 6.6MB (75x less) Content coverage: - Firecrawl: 77.2% - CRW: 92% Docker image: - Firecrawl: 2GB+ - CRW: 8MB Cold start: - CRW: 85ms Plus: - Fully open source (AGPL-3.0) - Self-hostable on any infrastructure - Built-in MCP server for AI agent integration - No API keys, no rate limits, no vendor lock-in If you're hitting Firecrawl's rate limits, paying for usage you could self-host, or need lower latency — the switch is one line of code. github.com/us/crw | fastcrw.com #FirecrawlAlternative #WebScraping #OpenSource #SelfHosted #DevTools

The 3 Pillars of Observability: ▶️ Metrics: → Scrapes data points like CPU usage and response times to measure performance. → Provides a high-level overview of system health and behavior over time. → Uses Prometheus for efficient collection and visualization via dashboards. ▶️ Logs: → Captures detailed records of system activities, events, and error messages. → Offers a historical view to help diagnose and troubleshoot specific incidents. → Uses Loki to aggregate and index log data from multiple sources seamlessly. ▶️ Traces: → Tracks and visualizes the end-to-end flow of requests across microservices. → Captures latency data to identify exact bottlenecks in the request path. → Uses Jaeger to diagnose performance issues in complex, distributed systems. Which pillar are you currently focusing on improving in your stack?

Our TEE implementation at Chutes.AI was the foundation to provide secure compute, but there’s still work to be done. We’re adding new capabilities and features to continue moving towards true trustless compute in a decentralized network. Here are two major improvements we’re actively working on right now: 1. 𝐒𝐭𝐚𝐧𝐝𝐚𝐥𝐨𝐧𝐞 𝐓𝐄𝐄 𝐕𝐌𝐬 𝐟𝐨𝐫 𝐦𝐢𝐧𝐞𝐫𝐬 (𝐍𝐨 𝐜𝐞𝐧𝐭𝐫𝐚𝐥 𝐜𝐨𝐧𝐭𝐫𝐨𝐥 𝐧𝐨𝐝𝐞) We’re removing the dependency on a central miner control node. Soon, anyone with TDX-capable hardware — or any datacenter with idle capacity — will be able to simply boot our VM image and start earning rewards with minimal configuration. Reducing friction and lowering the effort to add secure compute to the network is one of the key levers we can pull to improve the platform. 2. 𝐖𝐨𝐫𝐤𝐥𝐨𝐚𝐝 𝐕𝐞𝐫𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 (𝐁𝐞𝐲𝐨𝐧𝐝 𝐢𝐧𝐟𝐫𝐚𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞 𝐚𝐭𝐭𝐞𝐬𝐭𝐚𝐭𝐢𝐨𝐧) We already enforce cosign signatures, build pipeline, and admission controls for workloads. We’re now exploring ways to bind the actual running workload into the TDX attestation itself. The idea is to have a trusted component inside the measured TEE environment compute a deterministic workload identity (based on image manifest digest, config, command/args, and relevant artifact hashes). This identity would then be bound into the attestation quote alongside the user nonce and E2EE public key. Users could then verify the TDX quote and compare the extracted workload identity against what they expect — without having to trust the workload itself to self-report. This area is still early, but the goal is to give users stronger cryptographic confidence that the running workload matches their expectations. These changes continue to make secure, decentralized serverless GPU compute more open, verifiable, and accessible. Live TEE models: https://chutes.ai Repo: https://lnkd.in/ejV8rX2E #ConfidentialComputing #TrustedExecutionEnvironments #DecentralizedAI #OpenSource #Bittensor #AIInfrastructure