Updates and recent posts about AIStor..

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@mikeschinkel started using tool GNU/Linux , 3 weeks ago.

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@mikeschinkel started using tool Docker , 3 weeks ago.

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@varbear shared a link, 3 weeks, 2 days ago

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Cracking the Python Monorepo

Outlines a Python monorepo setup that pairsuvworkspaces withDaggerandBuildKitcaching. Builds container stages programmatically. Keeps things cache-friendly and predictable. Parsespyproject.tomland extracts the workspace graph. Copies required local packages into intermediate stages. Installs them in.. read more

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@varbear shared a link, 3 weeks, 2 days ago

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Using Rust and Postgres for everything: patterns learned over the years

Rust and PostgreSQL are considered the best tools in the software world due to their performance and reliability. Rewriting a backend service from Go to Rust led to significant improvements in processing speed and memory usage. Using sqlx for database operations and leveraging PostgreSQL features li.. read more

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@varbear shared a link, 3 weeks, 2 days ago

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A new chapter for the Nix language, courtesy of WebAssembly

Determinate Nix introduces experimental WebAssembly host calls. It lets Nix invoke Wasm modules, pass and return complex Nix values, and support Rust, C++, and Zig toolchains. It runs on Wasmtime/Cranelift and slashes runtime and memory: Fibonacci test 0.33s vs 79.33s, 30MB vs 4.5GB. Per-call instan.. read more

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@varbear shared a link, 3 weeks, 2 days ago

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I built a programming language using Claude Code

Cutlet usesClaude Code. The LLM emits every line. Source, build steps, and examples live on GitHub. It runs on macOS and Linux and ships aREPL. It supports arrays, strings, double numbers, a vectorizingmeta-operator, zip/filter indexing, prototypal inheritance, and a mark-and-sweepGC. Development ra.. read more

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@varbear shared a link, 3 weeks, 2 days ago

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Why value streams and capability maps are your new governance control plane

The piece flips enterprise AI fromgenerativetoagentic. Agents getstructured autonomyto perceive, plan, and execute across systems. It turnsvalue streammaps into a control plane withautonomy zones,halt-on-exceptiongates, cryptographicflight recorders, andpolicy-as-code. Result: less hallucination and.. read more

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@kaptain shared a link, 3 weeks, 2 days ago

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Running Agents on Kubernetes with Agent Sandbox

Agent Sandbox unveils the Sandbox CRD to map long-lived, singleton AI agents onto Kubernetes. It adds stable identity and lifecycle primitives. It supports runtimes like gVisor and Kata Containers. It enables zero-scale resume. It includes SandboxWarmPool with SandboxClaim and SandboxTemplate to kil.. read more

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@kaptain shared a link, 3 weeks, 2 days ago

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Securing Production Debugging in Kubernetes

The post prescribes an on-demand SSH gateway pod. It usesshort-lived, identity-bound credentialsandKubernetes RBACto grant scoped, auditable debug sessions. It recommends anaccess brokerthat binds Roles to groups, issues ephemeral certs and OpenSSH user certificates, rotates CAs, enforces command-le.. read more

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@kaptain shared a link, 3 weeks, 2 days ago

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The Invisible Rewrite: Modernizing the Image Promoter

SIG Release rewrote theimage promotercore. It cut 20% of the code. It added apipeline engine,cosignsigning, andSLSAattestations. Signing now sits separate fromsignature replication. Registry reads run in parallel - plan time dropped ~20m → ~2m. Per-request timeouts, retries, and HTTP connection reus.. read more

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.