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@laura_garcia shared a post, 5 months ago
Software Developer, RELIANOID

🎉 In case you missed it!

We’re still grateful for the amazing article published earlier this year on Lockerz.com: 🛡️ “Strengthening Your Defense: A Guide to Web Application Security and the Role of RELIANOID Load Balancer” It’s a great read on why web application security matters—and how RELIANOID Load Balancer helps organi..

Lockerz Article about RELIANOID
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@angie shared a post, 5 months ago
Product Marketing Analyst, manageengine

Conversational Observability: Your applications are ready to talk back (in real-time!)

From passive telemetry to interactive intelligence

The gist: Most observability systems are silent partners. They stream data, light up dashboards, and trigger alerts—but they don't interact. We're proposing something radically new: Conversational Observability. Imagine applications that don't just emit signals, but actively respond to your questions, offer real-time feedback, and expose their internal state on demand through self-description APIs.

Think about it: what if you could ask your app, "Why did you crash?", and get a structured, intelligent, and introspective answer?

In this deep dive, we'll cover:

Why this concept is crucial in the age of AI, ephemeral infrastructure, and auto-remediation.

What it looks like to implement conversational observability today.

The evolving role of modern APM platforms like ManageEngine Applications Manager in making this paradigm a reality.

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@laura_garcia shared a post, 5 months ago
Software Developer, RELIANOID

🌍 Cybersecurity & Sustainability: Building a Responsible Industrial Future

At RELIANOID, we believe a greener industry must also be a safer industry. 🔐 A new report from the Industrial Cybersecurity Center (CCI) highlights a powerful trend: aligning cybersecurity with ESG and sustainability goals is becoming essential for modern industrial resilience. From secure OT enviro..

Blog Cybersecurity and Sustainability A Joint Path Toward Responsible Industry
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@anjali shared a link, 5 months ago
Customer Marketing Manager, Last9

How Prometheus 3.0 Fixes Resource Attributes for OTel Metrics

Prometheus 3.0 supports resource attribute promotion for OpenTelemetry metrics, enabling direct labeling without `target_info` joins.

Prometheus_resource_attributes
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@angie shared a post, 5 months ago
Product Marketing Analyst, manageengine

The future of observability is developer-native: A deep dive into the next wave of diagnostics

"The systems we build are becoming too complex to merely monitor. We need systems that explain themselves."

Once upon a time, just monitoring your systems was enough. You had a few servers, maybe Nagios or Zabbix, some uptime checks, and that was it.

Then everything changed. The cloud arrived, followed by containers, microservices, and serverless. Suddenly, your "app" wasn't just a server; it was dozens of services, scattered across data centers, ephemeral environments, third-party APIs, and edge locations.

Monitoring just doesn't cut it anymore.

Today, we're firmly in the era of observability.

The future of observability is developer-native: A deep dive into the next wave of diagnostics
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@eon01 shared a post, 5 months ago
Founder, FAUN.dev

Halfway Through 2025: These Are the Open Source Tools Everyone’s Talking About

2025 isn’t slowing down. From AI agents and lightweight frameworks to infrastructure that actually scales, open source is where the real innovation’s happening.

We’ve combed through FAUN.dev newsletters and community picks to bring you the standout projects developers are loving this year.

Every click you've made is a vote for what's hot and what's not in the open source world. We've analyzed thousands of interactions to identify the tools that have truly captured the attention and imagination of the FAUN.dev community.

If you're curious what's making waves in real dev workflows, this list is your shortcut.

The Hottest Open Source Projects
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@faun shared a link, 5 months ago
FAUN.dev()

Policy Zones: How Meta enforces purpose limitation at scale in batch processing systems

Meta’s Privacy Aware Infrastructure (PAI) slamsGovernable Data Annotations (GDAs)at runtime. It parses SQL across itsexabyte-scalewarehouse and blocks any flow that floutspurpose-usepolicies. It welds Unified Programming Model (UPM), Policy Evaluation Service (PES), Warehouse Permission Service (WP.. read more  

Policy Zones: How Meta enforces purpose limitation at scale in batch processing systems
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@faun shared a link, 5 months ago
FAUN.dev()

To be a better programmer, write little proofs in your head

Developer sketchesproofsmid-code. This drives first-run correctness by leaning onmonotonicity,immutability,invariants, andpre/postconditions. They carve code into atomic steps. They erectfirewallsto contain impact zones. They wield induction for recursive logic—proof-affinity blooms. They drill form.. read more  

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@faun shared a link, 5 months ago
FAUN.dev()

TODOs aren’t for doing

Teams balk at trackingTODOcomments. Some funnel them into bug trackers. Others prune stale tags. The post saysTODOs stash edge-case insights, not tickets... read more  

BigQuery is a cloud-native, serverless analytics platform designed to store, query, and analyze massive volumes of structured and semi-structured data using standard SQL. It separates storage from compute, automatically scales resources, and eliminates the need for infrastructure management, indexing, or capacity planning.

BigQuery is optimized for analytical workloads such as business intelligence, log analysis, data science, and machine learning. It supports real-time data ingestion via streaming, batch loading from cloud storage, and federated queries across external data sources like Cloud Storage, Bigtable, and Google Drive.

Query execution is distributed and highly parallel, enabling interactive performance even on petabyte-scale datasets. The platform integrates deeply with the Google Cloud ecosystem, including Looker for BI, Vertex AI for ML workflows, Dataflow for streaming pipelines, and BigQuery ML, which allows users to train and run machine learning models directly using SQL.

Built-in security features include fine-grained IAM controls, column- and row-level security, encryption by default, and audit logging. BigQuery follows a consumption-based pricing model, charging for storage and queries (on-demand or reserved capacity).