Technical articles on embedded systems, AI, software architecture, business strategy, philosophy, and life sciences.
Genomics revolution in evolutionary biology — comparative genomics, gene duplication & genome evolution, horizontal gene transfer, transposable elements, epigenetics & inheritance, CRISPR in evolutionary research, metagenomics, phylogenomics, and future frontiers.
Reading the fossil record — radiometric dating techniques, index fossils, transitional fossils (Tiktaalik, Archaeopteryx), taphonomy, trace fossils, fossilization processes, biostratigraphy, and integrating paleontological data with molecular evidence.
Mathematical models in evolutionary biology — fitness landscapes, adaptive dynamics, evolutionary stable strategies (ESS), Lotka-Volterra models, coalescent theory, Price equation, diffusion models, evolutionary optimization, and computational simulations.
Evolution of behavior and social structures — altruism, kin selection, reciprocal altruism, evolutionary game theory, sexual selection strategies, mate choice, social insect evolution, eusociality, and cultural transmission of behavior.
Evo-Devo — how developmental processes drive evolutionary change. Hox genes, gene regulatory networks, modularity, morphological novelty, heterochrony, phenotypic plasticity, and constraints shaping body plan evolution.
The Big Five mass extinction events, causes of extinction (climate change, asteroid impacts, volcanism), biodiversity patterns, speciation vs extinction rates, latitudinal diversity gradient, genetic diversity preservation, evolutionary rescue, and Anthropocene extinction risks.
Species interactions and co-evolutionary dynamics — mutualism, commensalism, parasitism, predator-prey arms races, host-parasite evolution, pollinator-plant relationships, endosymbiosis, microbiome evolution, and the holobiont concept.
The hominin lineage from early primates through Homo sapiens — Australopithecines, Homo erectus, fossil & genetic evidence, ancient DNA, Neanderthal introgression, Denisovans, population bottlenecks, language emergence, and gene-culture coevolution.
Cladogram branch rotation, tree thinking, and phylogenetic relationships — why rotating branches around a node does not change relationships. Cladistics, monophyletic vs paraphyletic groups, molecular phylogenetics, Bayesian methods, and modern genomic classification.
Species concepts, modes of speciation (allopatric, peripatric, parapatric, sympatric), adaptive speciation, ecological speciation, magic traits, sensory drive, reinforcement, reproductive isolation, adaptive radiation, ecological niches, island evolution, and macroevolutionary patterns.
Molecular foundations of evolution — DNA structure, mutation types, gene expression, recombination, population genetics, Hardy-Weinberg equilibrium, genetic drift, gene flow, quantitative genetics, neutral theory, and molecular clocks.
Explore the foundations of evolutionary theory — Darwin and Wallace
ARMv9.0 (2021) was the first major architectural version since ARMv8 in 2011. It mandated SVE2 for server-class cores, introduced the Realm Management Extension for confidential computing, and continued a decade-long trend of adding new feature flags instead of breaking instruction semantics. This
Clinical biochemistry — blood glucose testing, HbA1c, liver function tests (ALT, AST, GGT, ALP, bilirubin), kidney function markers (creatinine, BUN, eGFR), lipid panels, cardiac biomarkers (troponin, BNP), and point-of-care testing.
Security research on ARM64 blends intimate knowledge of the ISA with an understanding of OS memory layout, compiler mitigations, and hardware defences. This part covers the attack path from memory disclosure through ROP/JOP chain execution, examines kernel exploitation patterns specific to AArch64,
Molecular disease mechanisms — diabetes (type 1 & 2), cancer metabolism (Warburg effect), neurodegeneration (Alzheimer
Cross-compiling for ARM64 on an x86 host seems straightforward until ABI mismatches, missing sysroots, and LLVM triple confusion bite you. This part covers correct toolchain selection for both bare-metal and Linux targets, CMake toolchain files, LLVM/Clang cross setup, and automated firmware
Theory is validated in production. This part walks through five real deployment environments where ARM assembly knowledge is directly exercised: Android native code via the NDK, two embedded RTOSes (FreeRTOS and Zephyr), the U-Boot bootloader that brings up hundreds of millions of Linux boards, and
Integrative metabolism — fed vs fasting state, organ fuel selection (brain, muscle, liver, adipose), starvation adaptation, Cori & alanine cycles, metabolic hormonal control, and exercise physiology.
Every ARM binary you run passed through a linker that stitched object files together, assigned addresses, and emitted relocations that the dynamic loader resolves at runtime. This part walks from raw ELF sections through RELA relocation entries, PLT/GOT lazy binding, position-independent code on
Observability is the ability to understand the internal state of a system by examining its external outputs. Unlike traditional monitoring — which tells you what is broken — observability answers why it broke and how to fix it. In complex distributed systems, you cannot predict every failure mode
Logs are the most universal form of telemetry — every application produces them. Yet the format you choose determines how effectively you can search, filter, alert on, and correlate log data in systems like Grafana Loki. Understanding the spectrum from unstructured to fully structured logging is
Grafana Cloud is Grafana Labs' fully managed observability platform that provides hosted instances of Grafana, Mimir (metrics), Loki (logs), Tempo (traces), and additional services like Alerting, Incident, OnCall, and Synthetic Monitoring. For learning purposes, the free tier is more than
Grafana Loki is a horizontally-scalable, highly-available log aggregation system inspired by Prometheus. Unlike traditional log management systems that index the full text of every log line, Loki indexes only a small set of labels (key-value pairs) associated with each log stream. The actual log
PromQL (Prometheus Query Language) is the standard query language for time-series metrics in the Prometheus ecosystem. It powers dashboards in Grafana, alert conditions in Alertmanager, and recording rules in both Prometheus and Grafana Mimir. Whether you're querying a local Prometheus instance or
Distributed tracing is the observability signal that reveals how a request flows through your system — which services it touches, where latency accumulates, and where errors originate. While metrics tell you what is happening and logs tell you why , traces tell you the complete journey of every
Kubernetes is the dominant container orchestration platform, and monitoring it effectively requires collecting telemetry at multiple layers: node metrics, pod and container statistics, cluster-level events, and application-generated signals. The OpenTelemetry Collector provides a comprehensive set
Dashboards are the heart of Grafana — they transform raw telemetry data into actionable visual insights. A dashboard is a collection of panels arranged on a grid, each panel displaying a specific query result through a chosen visualization. Whether you’re monitoring infrastructure health,
The distinction between being alerted and being alarmed is fundamental to building sustainable on-call practices. An alert should inform you that something meaningful requires attention. An alarm — the visceral “wake me up at 3 AM” page — should be reserved for situations where immediate human
Managing observability infrastructure through manual UI interactions — what some call “click-ops” — is a pattern that scales poorly. When your Grafana stack exists only as configurations stored in a database, backed by memory and tribal knowledge, you inherit every risk of unversioned,
Building an observability platform is fundamentally different from deploying individual monitoring tools. A platform provides self-service capabilities to multiple teams, enforces consistent standards, abstracts infrastructure complexity, and scales gracefully as the organization grows. Think of it
Throughout this series, we’ve focused on backend telemetry — metrics from Prometheus, logs from Loki, traces from Tempo. But all of that infrastructure exists to serve users . Real User Monitoring (RUM) closes the observability loop by capturing what users actually experience in their browsers:
Traditional profiling is something developers do locally — attach a profiler, reproduce an issue, collect samples, analyze. Continuous profiling changes this by running profiling in production 24/7 with negligible overhead (~2–5% CPU). This means you can answer questions like “what function
The DevOps infinity loop (Plan → Code → Build → Test → Release → Deploy → Operate → Monitor) generates telemetry at every stage. The most effective engineering organizations use observability data not just for operations, but as the primary feedback mechanism that
The most effective troubleshooting follows a systematic narrowing pattern: start broad with metrics, narrow with logs, then pinpoint with traces. This is the “golden path” through the LGTM stack:
To understand where Prometheus fits in the monitoring landscape, we need to trace the lineage of monitoring systems from their earliest forms to the cloud-native era. Each generation solved the problems of its time while creating the constraints that the next generation would overcome. The history
Before deploying Prometheus, we need a Kubernetes cluster. For this track, we’ll use kind (Kubernetes in Docker) as our primary lab environment — it’s lightweight, fast to create, and closely mirrors production clusters. All examples in Parts 2–12 are tested against this lab setup. Our lab cluster
Every piece of data in Prometheus is a time series — a stream of timestamped values belonging to the same metric and label set. The data model is deceptively simple but immensely powerful: # A single sample (data point): # metric_name{label1="value1", label2="value2"} float64_value timestamp_ms #
An instant vector returns the most recent sample for each matching time series at a single point in time. This is what you get when you type a metric name into the Prometheus expression browser: # Instant vector - returns one sample per series at query evaluation time http_requests_total # Returns:
Prometheus needs to know where to scrape metrics. In static environments, you list targets manually. In dynamic environments (Kubernetes, cloud, service mesh), targets appear and disappear constantly — requiring automated discovery:
Prometheus alerting separates two concerns: alert evaluation (done by Prometheus itself) and alert notification (handled by Alertmanager). Prometheus periodically evaluates alert rules, fires alerts when conditions are met, and pushes them to Alertmanager for routing, deduplication, and delivery.
Prometheus was designed as a single-server monitoring system with a local TSDB. This design makes it simple to operate but introduces hard limits as your infrastructure grows. Understanding where those limits lie is essential before choosing a scaling strategy. A well-tuned Prometheus server on
Before optimizing, you need to understand how Prometheus consumes resources. Its performance characteristics are directly tied to four primary factors: active time series count, ingestion rate, query complexity, and TSDB operations (compaction, WAL replay).
The Prometheus Node Exporter exposes hardware and OS-level metrics from *nix kernels. It reads from /proc , /sys , and other kernel pseudo-filesystems to provide hundreds of metrics covering CPU, memory, disk, network, filesystem, and more.
Prometheus’s local TSDB is optimized for recent data queries. It excels at last-few-hours dashboards but has inherent limitations for enterprise use cases:
Thanos is a CNCF Incubating project that extends Prometheus with long-term storage and global querying capabilities. Unlike Mimir or VictoriaMetrics, Thanos doesn’t replace Prometheus — it augments existing Prometheus deployments by: flowchart TD subgraph Cluster1["Cluster: US-East"] P1[Prometheus
As Prometheus deployments grow beyond a handful of alert rules and dashboards, managing YAML files manually becomes untenable. Teams need version-controlled, reviewable, DRY configuration that can be templated, tested, and deployed consistently across environments. Jsonnet is a data templating
promtool is Prometheus’s built-in CLI for validating configuration and rules. It catches syntax errors, invalid PromQL, label conflicts, and structural issues before deployment: # Validate prometheus.yml configuration promtool check config prometheus.yml # Output: Checking prometheus.yml # SUCCESS:
Implement Service Level Objectives with Prometheus using multi-window multi-burn-rate alerting, error budgets, and the Sloth SLO generator. Learn the SLI/SLO/SLA hierarchy, calculate error budgets, build burn-rate alerts, and create SLO dashboards for reliability engineering.
Prometheus and OpenTelemetry started as separate projects with different models — pull vs push, metrics-focused vs multi-signal. Today, they’re converging: timeline title Prometheus + OpenTelemetry Convergence 2012 : Prometheus created at SoundCloud 2015 : Prometheus open-sourced 2019 :