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@kala shared an update, 2ย months, 2ย weeks ago
FAUN.dev()

A Meta AI Agent Posted Without Permission. Then Things Got Worse.

OpenClaw

A Meta AI agent posted to an internal forum without authorization, triggering a Sev 1 incident that exposed proprietary code and user data for two hours. The advice it gave was wrong. The engineer followed it anyway. This wasn't a one-off - autonomous agents now account for more than 1 in 8 enterprise AI breaches, and most organizations have no mechanism to stop them from acting beyond their intended scope.

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@laura_garcia shared a post, 2ย months, 2ย weeks ago
Software Developer, RELIANOID

Discover why Cloud Security Posture Management (CSPM) is a game-changer for companies in today's digital landscape!

Discover why Cloud Security Posture Management (CSPM) is a game-changer for companies in today's digital landscape! In our latest article, we take you on a narrative journey exploring the vital role CSPM plays in maintaining robust cloud securityโ€”from continuously monitoring digital infrastructures ..

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@viktoriiagolovtseva shared a post, 2ย months, 2ย weeks ago

Data Center Migration to Cloud: Step-by-Step Guide

Teams are migrating away fromJira Data Centerdue to its impending end-of-life, and staying put increases risks over time.Atlassian recently announced the end of life for Data Centerand is focusing its investments onJira Cloud, where new features, automation, and improvements to roadmaps and dashboar..

Zrzut ekranu 2026-03-23 180645
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@laura_garcia shared a post, 2ย months, 2ย weeks ago
Software Developer, RELIANOID

๐Ÿšจ ๐—ก๐—œ๐—ฆ๐Ÿฎ ๐—ถ๐˜€ ๐—ฐ๐—ต๐—ฎ๐—ป๐—ด๐—ถ๐—ป๐—ด ๐˜๐—ต๐—ฒ ๐—ฐ๐˜†๐—ฏ๐—ฒ๐—ฟ๐˜€๐—ฒ๐—ฐ๐˜‚๐—ฟ๐—ถ๐˜๐˜† ๐—ด๐—ฎ๐—บ๐—ฒ ๐—ถ๐—ป ๐—˜๐˜‚๐—ฟ๐—ผ๐—ฝ๐—ฒ

Itโ€™s no longer just about protection โ€” itโ€™s about ๐—ฎ๐—ฐ๐—ฐ๐—ผ๐˜‚๐—ป๐˜๐—ฎ๐—ฏ๐—ถ๐—น๐—ถ๐˜๐˜†, ๐—ฐ๐—ผ๐—บ๐—ฝ๐—น๐—ถ๐—ฎ๐—ป๐—ฐ๐—ฒ, ๐—ฎ๐—ป๐—ฑ ๐—ฏ๐˜‚๐˜€๐—ถ๐—ป๐—ฒ๐˜€๐˜€ ๐—ฟ๐—ฒ๐˜€๐—ถ๐—น๐—ถ๐—ฒ๐—ป๐—ฐ๐—ฒ. โš ๏ธ Fines up to โ‚ฌ10M ๐Ÿ” Supplier & partner scrutiny ๐Ÿ›ก๏ธ Mandatory risk management The question is: ๐—ฎ๐—ฟ๐—ฒ ๐˜†๐—ผ๐˜‚ ๐—ฟ๐—ฒ๐—ฎ๐—ฑ๐˜†? ๐Ÿ“– Read our latest blog to understand the impact and how to prepare: NIS2 Directive and..

Link Xygeni Team
@mashka shared a link, 2ย months, 2ย weeks ago
Paid Acquisition and Growth Marketing, xygeni

You donโ€™t have a vulnerability problem. You have a prioritization problem.

Most teams today donโ€™t struggle to find vulnerabilities; they struggle to decide what to fix first. With SAST, SCA, secrets, and CI/CD checks all generating signals, the real challenge is prioritization: whatโ€™s actually exploitable, whatโ€™s reachable, and what can be fixed without breaking things. Instead of relying only on severity, modern teams are shifting toward risk-based remediation, combining exploitability, context, and stability, while reducing noise across tools and automating safe fixes through PRs. If youโ€™re dealing with alert fatigue or slow remediation cycles, this checklist is a practical starting point โ†’ https://go.xygeni.io/ai-driven-remediation-risk-prioritization-checklist

Ai-Driven Checklist
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@varbear shared a link, 2ย months, 2ย weeks ago
FAUN.dev()

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 ย 

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@varbear shared a link, 2ย months, 2ย weeks ago
FAUN.dev()

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 ย 

Why value streams and capability maps are your new governance control plane
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@varbear shared a link, 2ย months, 2ย weeks ago
FAUN.dev()

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 ย 

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@varbear shared a link, 2ย months, 2ย weeks ago
FAUN.dev()

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 ย 

A new chapter for the Nix language, courtesy of WebAssembly
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 AIStorโ€™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 โ€” from unstructured storage for application data to structured table storage for analytics, as well as AI training and inference datasets โ€” within a consistent object-native architecture. It supports multi-tenant environments and can be deployed across on-premises, cloud, and hybrid infrastructure.