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Link Xygeni Team
@mashka shared a link, 2 months, 1 week ago
Paid Acquisition and Growth Marketing, xygeni

Software Supply Chains Under Pressure: What Malware and AI Changed in 2025 and what to Expect in 2026

2025 exposed a shift in software supply chain attacks. AI-assisted malware, self-propagating techniques, and widespread trust abuse altered how compromises spread across dependencies, registries, and CI/CD pipelines.

This upcoming LinkedIn Live SafeDev Talk examines what truly changed, why long-held security assumptions are breaking down, and what development teams need to rethink as they head into 2026.

📅 January 20th | ⏰ Time: 𝟏𝟔:𝟑𝟎 (𝐂𝐄𝐒𝐓)/𝟏𝟎:𝟑𝟎 (𝐄𝐃𝐓)

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SafeDev Talk 1 2026 - Yearly Recap (4)
Link FAUN.dev() Team
@eon01 shared a link, 2 months, 1 week ago
Founder, FAUN.dev

2025 Internet Trends

Cloudflare just released its 2025 Radar Year in Review, a systems report on how the Internet actually behaved last year. A few things stood out in my opinion: 👉 Most AI systems take far more than they give back. AI bots now account for a meaningful slice of web traffic. Googlebot alone generates mor.. read more  

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@varbear shared a link, 2 months, 1 week ago
FAUN.dev()

Stop Forwarding Errors, Start Designing Them

A fresh take on Rust error handling just dropped - and it's calling out the usual suspects. Forget blindly forwarding errors withanyhowor smearing context around withProvider. This approach pushes forstructured, intent-driven error types- errors that say what to do next (like "retry this") instead o.. read more  

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@varbear shared a link, 2 months, 1 week ago
FAUN.dev()

The Code Review That Cost $2 Million, CodeGood

New data shows only15% of code review comments catch real bugs. The rest? Nitpicks on style, naming, or formatting - stuff linters and AI were made to handle. Human reviews burn through$3.6M a yearin larger orgs and still miss the tough stuff: threading issues, system integration bugs, rare edge cas.. read more  

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@varbear shared a link, 2 months, 1 week ago
FAUN.dev()

Distinguishing yourself early in your career as a developer

A seasoned dev maps the job market into three tiers:local/public companies,VC-backed/startups, andBig Tech/finance. Each step up brings more money, more competition, and a steeper climb. Category 3(Big Tech/finance): Highest salaries. Broadest interview access. Brutal prep required. Category 2(start.. read more  

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@kaptain shared a link, 2 months, 1 week ago
FAUN.dev()

BadPods Series: Everything Allowed on AWS EKS

A security researcher ran a full-blown container escape on EKS usingBadPods- a tool that spins up dangerously overprivileged pods. The pod broke out of its container, poked around the host node, moved laterally, and swiped AWS IAM creds. All of it slipped past EKS’s defaultPod Security Admission (PS.. read more  

BadPods Series: Everything Allowed on AWS EKS
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@kaptain shared a link, 2 months, 1 week ago
FAUN.dev()

Streamline your containerized CI/CD with GitLab Runners and Amazon EKS Auto Mode

GitLab Runners now work withAmazon EKS Auto Mode. That means hands-off infra, smarter scaling, and built-in AWS security. Runners spin up onEC2 Spot Instances, so teams can cut CI/CD compute costs by as much as90%- without hacking together flaky pipelines... read more  

Streamline your containerized CI/CD with GitLab Runners and Amazon EKS Auto Mode
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@kaptain shared a link, 2 months, 1 week ago
FAUN.dev()

Kubernetes GPU Management Just Got a Major Upgrade

Kubernetes 1.34 droppedDynamic Resource Allocation (DRA)- think persistent volumes, but for GPUs and custom hardware. Vendors can now plug in drivers and schedulers for their devices, and workloads can pick exactly what they need. Coming in 1.35: a newworkload abstractionthat speaks the language of .. read more  

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@kaptain shared a link, 2 months, 1 week ago
FAUN.dev()

From Deterministic to Agentic: Creating Durable AI Workflows with Dapr

Dapr droppedDurable Agents- a mashup of classic workflows and LLM-driven agents that can actually get things done and survive rough edges. They track reasoning steps, tool calls, and chat states like a champ. If things crash, no problem: Dapr Workflows and Diagrid Catalyst bring it all back... read more  

From Deterministic to Agentic: Creating Durable AI Workflows with Dapr
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@kaptain shared a link, 2 months, 1 week ago
FAUN.dev()

Implementing assurance pipeline for Amazon EKS Platform

AWS released a full-stack CI/CD validation pipeline forAmazon EKS. It pulls in six layers of testing,Terraform,Helm,Locustload testing, and evenAWS Fault Injectionfor pushing resilience to the edge. The goal: bake policy checks, functional tests, and brutal load tests right into pre-deployment. Fewe.. read more  

NanoClaw is an open-source personal AI agent designed to run locally on your machine while remaining small enough to fully understand and audit. Built as a lightweight alternative to larger agent frameworks, the system runs as a single Node.js process with roughly 3,900 lines of code spread across about 15 source files.

The agent integrates with messaging platforms such as WhatsApp and Telegram, allowing users to interact with their AI assistant directly through familiar chat applications. Each conversation group operates independently and maintains its own memory and execution environment.

A core design principle of NanoClaw is security through isolation. Every agent session runs inside its own container using Docker or Apple Container, ensuring that the agent can only access files and resources that are explicitly mounted. This approach relies on operating system–level sandboxing rather than application-level permission checks.

The architecture is intentionally simple: a single orchestrator process manages message queues, schedules tasks, launches containerized agents, and stores state in SQLite. Additional functionality can be added through a modular skills system, allowing users to extend capabilities without increasing the complexity of the core codebase.

By combining a minimal architecture with container-based isolation and messaging integration, NanoClaw aims to provide a transparent, customizable personal AI agent that users can run and control entirely on their own infrastructure.