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Fundamentals

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Linux Tech Enthusiast
Linux Tech Enthusiast
Oct 14, 2026 · Fundamentals

Linux Process Sleep and Wakeup: Preventing Lost Wakeups with Correct State Ordering

This article explains Linux process states (TASK_RUNNING, TASK_INTERRUPTIBLE, TASK_UNINTERRUPTIBLE), the schedule() and wake_up_process() mechanisms, the lost wakeup race condition that occurs when a process checks a condition before setting its sleep state, and the correct pattern used in the Linux kernel—setting the state to interruptible before condition checking and looping with schedule()—to avoid indefinite sleep.

Linux kernelTASK_INTERRUPTIBLElost wakeup
0 likes · 14 min read
Linux Process Sleep and Wakeup: Preventing Lost Wakeups with Correct State Ordering
AI Engineer Programming
AI Engineer Programming
Oct 12, 2026 · Fundamentals

Reliability in Software Engineering: Definitions, Key Concerns, and Distributed Systems Reality

This article defines software reliability per ISO/IEC 25010, distinguishes it from availability and stability, and details five operational concerns — correct user-visible results, change management, blast radius, detection/recovery speed, and failure semantics — with concrete distributed-systems patterns like idempotency, fault domains, burn-rate alerting, and fault injection.

CAP theoremError BudgetFault Injection
0 likes · 34 min read
Reliability in Software Engineering: Definitions, Key Concerns, and Distributed Systems Reality
IT Services Circle
IT Services Circle
Oct 11, 2026 · Fundamentals

Why Linus Torvalds Insists on ECC Memory: Reliability Over Raw Speed

This article explains Error-Correcting Code (ECC) memory, how it detects and corrects single-bit errors while detecting double-bit errors, why Linus Torvalds experienced silent memory corruption in 2022, and why ECC remains rare in consumer PCs despite its critical role in data integrity for servers, databases, and long-running Linux workloads.

DDR5ECC memoryEDAC
0 likes · 13 min read
Why Linus Torvalds Insists on ECC Memory: Reliability Over Raw Speed
IT Services Circle
IT Services Circle
Oct 10, 2026 · Fundamentals

Why .catch() Fails to Catch Sync Errors: Promise.try() & async Solutions

The article explains why .catch() doesn't catch synchronous exceptions thrown before a Promise is returned, illustrates common scenarios like validation and JSON.parse, shows debugging clues (Uncaught Error vs unhandledRejection), and presents solutions: Promise.try(), async functions, and try/catch with await, including browser/Node support and a polyfill for older runtimes.

DebuggingError HandlingJavaScript
0 likes · 15 min read
Why .catch() Fails to Catch Sync Errors: Promise.try() & async Solutions
IT Services Circle
IT Services Circle
Oct 10, 2026 · Fundamentals

What Regex Can't Do: SNOBOL Did It in 1962 with 32KB

This article explores SNOBOL, a 1962 string-processing language that pioneered first-class patterns, automatic garbage collection, a portable virtual machine, and associative arrays—features decades ahead of their time—yet lost to simpler regex-based tools like AWK and Perl.

Bell LabsSNOBOLassociative arrays
0 likes · 13 min read
What Regex Can't Do: SNOBOL Did It in 1962 with 32KB
Linyb Geek Road
Linyb Geek Road
Oct 10, 2026 · Fundamentals

8 Palantir Ontology Anti-Patterns That Break Your Data Model

This article details eight common Palantir Ontology anti-patterns grouped into four root causes—system silos, department silos, overloaded objects, wrong execution layers, and poor naming—and provides a prioritized repair checklist to keep ontology models aligned with business reality.

Action SprawlAnti-PatternsData Modeling
0 likes · 25 min read
8 Palantir Ontology Anti-Patterns That Break Your Data Model
Linyb Geek Road
Linyb Geek Road
Oct 9, 2026 · Fundamentals

Palantir Ontology Modeling: 4 Core Principles for Scalable Domain Design

This article breaks down Palantir's four prioritized ontology modeling principles—domain-driven design, DRY with Rule of Three, open/closed extension, and composition over inheritance—illustrating each with concrete anti-patterns and a practical 12-question review checklist to prevent model fragmentation as teams scale.

Composition over InheritanceDRYData Modeling
0 likes · 20 min read
Palantir Ontology Modeling: 4 Core Principles for Scalable Domain Design
Network Intelligence Research Center (NIRC)
Network Intelligence Research Center (NIRC)
Oct 9, 2026 · Fundamentals

Why Averages Lie: Simpson's Paradox Explained with a Concrete Example

This article explains Simpson's Paradox through a numerical example where Plan A outperforms Plan B in both simple and difficult tasks individually, but Plan B appears better when aggregated due to differing task distributions, illustrating how hidden variables can reverse aggregate conclusions and how to avoid such pitfalls.

AveragesData AnalysisData Interpretation
0 likes · 6 min read
Why Averages Lie: Simpson's Paradox Explained with a Concrete Example
Linux Tech Enthusiast
Linux Tech Enthusiast
Oct 8, 2026 · Fundamentals

Network Hardware Evolution: From Cables to Data Center Architectures

This article traces the historical development of network hardware, explaining how each device—from cables and repeaters to switches, routers, firewalls, and wireless APs—solves specific connectivity, distance, speed, security, and congestion challenges, culminating in modern home, enterprise, campus, and data center network architectures.

bridgesfirewallshubs
0 likes · 12 min read
Network Hardware Evolution: From Cables to Data Center Architectures
Linyb Geek Road
Linyb Geek Road
Oct 8, 2026 · Fundamentals

Palantir Ontology Explained: Core Concepts, Agent Integration, and Building a Minimum Viable Ontology

This article breaks down Palantir Ontology's core components—Object Types, Properties, Link Types, and Action Types—using a high-risk order example, contrasts it with knowledge graphs, details how Agents leverage it for decision-making, and recommends starting with a minimum viable ontology for practical adoption.

Action TypeAgentFoundry
0 likes · 18 min read
Palantir Ontology Explained: Core Concepts, Agent Integration, and Building a Minimum Viable Ontology
Model Perspective
Model Perspective
Oct 5, 2026 · Fundamentals

How Detective Intuition Becomes Mathematics: Modeling Forensic Trace Evidence

The article uses the film 'Detective Traces' and its real-life prototype Cui Daozhi to explain how forensic trace examination — bite marks, fingernail fragments, fingerprints — is formalized into a mathematical model of feature measurement, random-match probability, error trade-offs, Bayesian updating, and experimental validation.

Bayesian inferenceCui Daozhierror rates
0 likes · 11 min read
How Detective Intuition Becomes Mathematics: Modeling Forensic Trace Evidence