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Who does your assistant serve?

OpenAI’s release of GPT-5 backfired: instead of excitement, users felt betrayed by a forced upgrade that stripped away the warmth and reliability they had come to rely on in GPT-4o. Many treated the model as more than a tool — a companion, therapist, or emotional support — so when its personality sh.. read more  

Who does your assistant serve?
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AWS deleted my 10-year account and all data without warning

AWS permanently nuked a 10-year customer account—data, backups, everything—after a payment verification failed. That alone broke their own 90-day retention policy. It gets messier. Looks like an internal script meant to run as a “dry run” went full send in production. Blame a Java CLI parsing edge .. read more  

AWS deleted my 10-year account and all data without warning
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AWS Lambda now supports GitHub Actions to simplify function deployment

AWS Lambda just got a smoother ride to prod. There’s now a nativeGitHub Actions integration—no more DIY scripts to ship your serverless. On commit, the new action packages your code, wires up IAM viaOIDC, and deploys using either.zip bundles or containers. All from a tidy, declarative GitHub workfl.. read more  

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We built an MCP server so Claude can access your incidents

Incident.io dropped an open sourceMCP server in Gothat plugs Claude into their API using theModel Context Protocol. That means Claude can now ask questions, spin up incidents, and dig into timelines—just by talking. The server translates Claude’s prompts into REST calls, turning AI babble into real.. read more  

We built an MCP server so Claude can access your incidents
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How to use Terraform to generate secrets

Terraform just leveled up secret handling inAzure Key Vault. It now supports automated secret generation withrandom_password, plus full lifecycle control—rotation, expiration, and storage—baked right into your IaC. Secrets stay marked as sensitive. They're managed in one place. And thanks to Terraf.. read more  

How to use Terraform to generate secrets
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@faun shared a link, 9 months, 3 weeks ago
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Does platform engineering make sense for startups?

Platform engineering isn't just for the big dogs anymore. Startups are picking it up as astrategic edge, building tight, high-leverage tooling from day one. Think:templated CI/CD pipelines, plug-and-play infra modules, zero-handoff onboarding. Done right, these early bets smooth the path and keep d.. read more  

Does platform engineering make sense for startups?
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A practical guide on how to use the GitHub MCP server

GitHub offers a managed MCP endpoint to simplify infrastructure management and streamline AI workflows, enhancing collaboration and code review processes... read more  

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Proton launches free standalone cross-platform Authenticator app

Proton just droppedProton Authenticator, a free 2FA app that actually respects your privacy. It’s cross-platform, offline-friendly, and skips the usual junk—no ads, no trackers, no bait-and-lock-in. It’s gotend-to-end encryption, a biometric lock, and lets youexport TOTP seedslike it’s your data (b.. read more  

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Pinterest Uncovers Rare Search Failure During Migration to Kubernetes

Pinterest hit a weird one-in-a-million query mismatch during its search infra move to Kubernetes. The culprit? A slippery timing bug. To catch it, engineers pulled out every trick—live traffic replays, their own diff tools, hybrid rollouts layered on both the legacy and K8s stacks. Painful, but it .. read more  

Pinterest Uncovers Rare Search Failure During Migration to Kubernetes
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Who does the unsexy but essential work for open source?

Oracle led the line-count race in the Linux 6.1 kernel release—beating out flashier open source names. Most of its work isn’t headline material. It’s deep-core stuff: memory management tweaks, block device updates, the quiet machinery real systems run on... read more  

Who does the unsexy but essential work for open source?
AIStor is an enterprise-grade, high-performance object storage platform built for modern data workloads such as AI, machine learning, analytics, and large-scale data lakes. It is designed to handle massive datasets with predictable performance, operational simplicity, and hyperscale efficiency, while remaining fully compatible with the Amazon S3 API. AIStor is offered under a commercial license as a subscription-based product.

At its core, AIStor is a software-defined, distributed object store that runs on commodity hardware or in containerized environments like Kubernetes. Rather than being limited to traditional file or block interfaces, it exposes object storage semantics that scale from petabytes to exabytes within a single namespace, enabling consistent, flat addressing of vast datasets. It is engineered to sustain very high throughput and concurrency, with examples of multi-TiB/s read performance on optimized clusters.

AIStor is optimized specifically for AI and data-intensive workloads, where throughput, low latency, and horizontal scalability are critical. It integrates broadly with modern AI and analytics tools, including frameworks such as TensorFlow, PyTorch, Spark, and Iceberg-style table engines, making it suitable as the foundational storage layer for pipelines that demand both performance and consistency.

Security and enterprise readiness are central to AIStor’s design. It includes capabilities like encryption, replication, erasure coding, identity and access controls, immutability, lifecycle management, and operational observability, which are important for mission-critical deployments that must meet compliance and data protection requirements.

AIStor is positioned as a platform that unifies diverse data workloads — from unstructured storage for application data to structured table storage for analytics, as well as AI training and inference datasets — within a consistent object-native architecture. It supports multi-tenant environments and can be deployed across on-premises, cloud, and hybrid infrastructure.