Video summary:

• Core idea: People default to one of four communication “colors” and connect best when adapting to the other person’s style.
• The four colors: Red (power/results), Green (peace/stability), Blue (logic/structure), Yellow (fun/connection).

• Framework origin: Model popularized by Thomas Erikson’s “Surrounded by Idiots,” mapping to DISC-like styles via colors.

• Identify own color with 3 questions: 1) introverted (blue/green) vs extroverted (red/yellow); 2) logical (blue/red) vs emotional (green/yellow); 3) deliberate (green/blue) vs fast speaker (yellow/red); combine answers to narrow to one color.

• Everyone is a mix, but most have a dominant default; conflicts arise when speaking only in one’s own color.

• Matching styles creates instant rapport; mismatch leads to misjudgments (e.g., reds seen as abrasive by others, blues as nitpicky).

• How others may perceive each color:

  • Red: pushy to greens, too serious to yellows, reckless with details to blues.
  • Yellow: unfocused to reds, chaotic to greens, superficial to blues.
  • Green: indecisive to reds, boring to yellows, non-committal to blues.
  • Blue: too slow for reds, overly critical for yellows, nitpicky for greens.

• How to talk to each color:

  • To Red: be direct, decisive, confident; use frameworks; focus on outcomes, not fluff.
  • To Yellow: be enthusiastic and story-driven; keep it light and fun; don’t drown them in manuals.
  • To Green: be calm, patient, supportive; move at a comfortable pace; avoid rushing change.
  • To Blue: be precise, structured, factual; bring data and step-by-step plans; avoid exaggeration.

• Practical tip: Keep the 3 self-check questions visible during calls to rapidly gauge color and adjust delivery.

• Mindset shift: Don’t “change who you are”; expand range to “speak all four languages” to connect with 95% of people.
• Personal example: Vinh (Yellow) adapted to spouse (Red) by cutting fluff, adding clarity and speed to avoid overwhelm.
• Goal: Become dynamic “like water,” meeting people where they are without losing identity; communication skill lifts every area of life.

MIT report: 95% of companies see no profit from investments in generative AI, which amounted to approximately $35 billion.

Most AI pilots have no measurable impact on company profits. Attempts to implement tools like ChatGPT into the workplace primarily increase the productivity of individual employees, not the earnings of the entire company.

• DRA goes mainstream: stable core with ResourceClaim/DeviceClass and structured parameters — finally flexible, sane GPU/accelerator management without node‑selector tricks or quota hacks.
• Ops bonuses: production‑ready kubelet/API Server tracing (end‑to‑end debugging), new Deployment pod replacement policies (faster vs. resource‑conservative), PreferSameNode/SameZone traffic distribution, KYAML output in kubectl, and per‑HPA configurable tolerance.
• Zero breaking changes: no deprecations or removals — smooth upgrade for production teams.
• Release planned for late August

lol

Summary: Navigating Failures in Pods With Devices

This article examines the unique challenges Kubernetes faces in managing specialized hardware (e.g., GPUs, accelerators) within AI/ML workloads, and explores current pain points, DIY solutions, and the future roadmap for more robust device failure handling.

Why AI/ML Workloads Are Different

• Heavy Dependence on Specialized Hardware: AI/ML jobs require devices like GPUs, with hardware failures causing significant disruptions.
• Complex Scheduling: Tasks may consume entire machines or need coordinated scheduling across nodes due to device interconnects.
• High Running Costs: Specialized nodes are expensive; idle time is wasteful.
• Non-Traditional Failure Models: Standard Kubernetes assumptions (like treating nodes as fungible, or pods as easily replaceable) don’t apply well; failures can trigger large-scale restarts or job aborts.

Major Failure Modes in Kubernetes With Devices

1. Kubernetes Infrastructure Failures

   • Multiple actors (device plugin, kubelet, scheduler) must work together; failures can occur at any stage.
   • Issues include pods failing admission, poor scheduling, or pods unable to run despite healthy hardware.
   • Best Practices: Early restarts, close monitoring, canary deployments, use of verified device plugins and drivers.

2. Device Failures

   • Kubernetes has limited built-in ability to handle device failures—unhealthy devices simply reduce the allocatable count.
   • Lacks correlation between device failure and pod/container failure.
   • DIY Solutions:
     • Node Health Controllers: Restart nodes if device capacity drops, but these can be slow and blunt.
     • Pod Failure Policies: Pods exit with special codes for device errors, but support is limited and mostly for batch jobs.
     • Custom Pod Watchers: Scripts or controllers watch pod/device status, forcibly delete pods attached to failed devices, prompting rescheduling.

3. Container Code Failures

   • Kubernetes can only restart containers or reschedule pods, with limited expressiveness about what counts as failure.
   • For large AI/ML jobs: Orchestration wrappers restart failed main executables, aiming to avoid expensive full job restart cycles.

4. Device Degradation

   • Not all device issues result in outright failure; degraded performance now occurs more frequently (e.g., one slow GPU dragging down training).
   • Detection and remediation are largely DIY; Kubernetes does not yet natively express "degraded" status.

Current Workarounds & Limitations

• Most device-failure strategies are manual or require high privileges.
• Workarounds are often fragile, costly, or disruptive.
• Kubernetes lacks standardized abstractions for device health and device importance at pod or cluster level.

Roadmap: What’s Next for Kubernetes

SIG Node and Kubernetes community are focusing on:

• Improving core reliability: Ensuring kubelet, device manager, and plugins handle failures gracefully.
• Making Failure Signals Visible: Initiatives like KEP 4680 aim to expose device health at pod status level.
• Integration With Pod Failure Policies: Plans to recognize device failures as first-class events for triggering recovery.
• Pod Descheduling: Enabling pods to be rescheduled off failed/unhealthy devices, even with restartPolicy: Always.
• Better Handling for Large-Scale AI/ML Workloads: More granular recovery, fast in-place restarts, state snapshotting.
• Device Degradation Signals: Early discussions on tracking performance degradation, but no mature standard yet.

Key Takeaway

Kubernetes remains the platform of choice for AI/ML, but device- and hardware-aware failure handling is still evolving. Most robust solutions are still "DIY," but community and upstream investment is underway to standardize and automate recovery and resilience for workloads depending on specialized hardware.

A Harvard Law graduate who argued before the Supreme Court fed his case briefs into Claude 4 Opus and had it answer the same questions the Justices posed to him. The AI delivered what he called an "outstanding oral argument" with coherent answers and clever responses he hadn't considered, leading him to conclude that AI lawyers could soon outperform even top human advocates at oral argument.

• Sauna use induces mild heat stress, activating natural repair processes in the body such as heat shock proteins, improved blood flow, antioxidant activity, metabolism optimization, reduced inflammation, and enhanced immunity, all contributing to whole-body health benefits.

• Regular sauna sessions (4–7 times/week, at least 19 minutes) are strongly associated with improved heart health, significantly lowering the risk of heart disease (by up to 48%) and high blood pressure (by up to 47%).

• Mental health benefits include better sleep (over 80% report improvement), reduced muscle pain, improved mood, and a lower risk of dementia (up to 48% reduction) and psychotic disorders (up to 77% reduction) with frequent use.

• Sauna use reduces systemic inflammation, with frequent sessions leading to up to 31.5% lower inflammation markers and easing symptoms of chronic inflammatory and autoimmune conditions like rheumatoid arthritis.

• Metabolic improvements include decreased bad cholesterol (LDL), transiently lower triglycerides, and small increases in good cholesterol (HDL); some studies show increased muscle mass and bone density after intensive sauna protocols.

• Saunas may aid detoxification by promoting sweat-induced elimination of heavy metals and environmental toxins, with longer sessions (15–20+ minutes) enhancing this effect.

• Frequent sauna use is linked to increased longevity, with studies showing up to 40% lower all-cause mortality and up to 70% lower risk of dying from heart disease for those using saunas 4–7 times per week.

• Bryan Johnson’s personal protocol involves daily 20-minute dry sauna sessions at 93°C (200°F), with hydration and protective measures, and he tracks various biomarkers to measure health impacts.

• Recommended sauna practice: 3–5 sessions per week, 15–20 minutes each at 80–90°C (175–194°F), ideally post-workout; always hydrate and avoid sauna use if dehydrated, unwell, or with certain medical conditions.

• Results from Bryan Johnson’s ongoing self-experimentation are forthcoming, but current evidence supports sauna as a promising intervention for detox, longevity, and overall health.

• NVIDIA’s latest earnings highlight a dramatic surge in AI demand, driven by a shift from simple one-shot inference to more complex, compute-intensive reasoning tasks.
• Reasoning models require hundreds to thousands of times more computational resources and tokens per task, significantly increasing GPU usage, especially for AI coding agents and advanced applications.
• Major hyperscalers like Microsoft, Google, and OpenAI are experiencing exponential growth in token generation, with Microsoft alone processing over 100 trillion tokens in Q1—a fivefold year-over-year increase.
• Hyperscalers are deploying nearly 1,000 NVL72 racks (72,000 Blackwell GPUs) per week, and NVIDIA-powered “AI factories” have doubled year-over-year to nearly 100, with the average GPU count per factory also doubling.
• To meet this unprecedented demand, more than $300 billion in capital expenditure is being invested this year in data centers (rebranded by NVIDIA as “AI factories”), signaling a new industrial revolution in AI infrastructure.

That's even too much for me.

• 🦶 10,000 steps a day is the minimum<br /> Regular physical activity, even in the form of a 15–30 minute walk daily, can extend life by 3–5 years. The exact number of steps doesn't matter—what counts is consistent movement.

• 🔁 Day of effort, day of rest<br /> Daily intense exercise, such as running 10 km, can lead to physiological stress. A recommended rhythm is one day of activity followed by one day of recovery, which supports the creation of new mitochondria.

• 🌬️ What happens during the break<br /> On the rest day, the body prepares for the next activity: it creates mitochondria, replenishes energy, and regenerates cellular structures.

• 🧠 Physical activity affects the brain and hormones<br /> Movement reduces cortisol, which increases levels of testosterone and estrogen. This influences libido and overall mood.

• 💧 Hydration is essential<br /> Activity increases water loss—you should drink at least 1.5–2.5 liters of water daily. Avoid dehydration caused by coffee, alcohol, and tea.

• 🌲 Best activity is outdoors<br /> Walking in the forest is more beneficial than exercising in the city—better microbiota, fewer toxins, and cleaner air.

📊 Insights Based on Numbers

• 🚶‍♀️ 15-minute walk daily = +3 years of life
• 🏃‍♂️ 30 minutes daily = +5 years of life
• 💧 Body loses about 2.5 liters of water daily through sweat, breath, and urine
• 🏃‍♂️ 4 km walk to and from work = full daily dose of activity

• Average BMI in Poland: The average BMI of Poles is 26.33, which falls into the overweight category (BMI ≥ 25). Approximately 60% of Poles are classified as overweight.
• Comparison with Hungary: Poland and Hungary have the highest proportion of overweight individuals in Europe.
• Physical Activity in Poland:
  • Poland ranks second to last in Europe regarding physical activity levels.
  • Over one-third of Poles hardly engage in sports.
  • The average number of weekly physical activities has dropped from 3 to 2.7, the second-lowest in Europe (Hungary ranks lowest at 2.4).
• BMI Trends in Europe: In the past decade, BMI has increased across most European countries.
• Countries with the Fewest Overweight Issues: Switzerland, Austria, France, and Italy have the lowest prevalence of overweight individuals.

• Revolut accounts are difficult for Polish bailiffs to seize because the bank operates under a Lithuanian license and does not have a branch or headquarters in Poland. It is not part of the OGNIVO system, which facilitates communication between banks and enforcement authorities.

• Revolut operates legally in Poland through cross-border services under the "single passport" rule. Deposits are protected up to €100,000 by the Bank of Lithuania, ensuring client security.

• Polish IBANs are available through Revolut via cooperation with Aion Bank, enabling users to receive salaries, make transfers, and use payment systems like BLIK.

Sounds like it's better to look for Bosch engines than Shimano in e-bikes

See the summary below this highlight

• Introduction

  • Containers are widely used for scalable applications but face challenges with startup times for large images (e.g., AI/ML workloads).
  • Pulling large images from Amazon Elastic Container Registry (ECR) can take several minutes, impacting performance.
  • Bottlerocket, an AWS open-source Linux OS optimized for containers, offers a solution to reduce container startup time.

• Solution Overview

  • Bottlerocket's data volume feature allows prefetching container images locally, eliminating the need for downloading during startup.
  • Prefetching is achieved by creating an Amazon Elastic Block Store (EBS) snapshot of Bottlerocket's data volume and mapping it to new Amazon EKS nodes.
  • Steps to implement:
  • Spin up an Amazon EC2 instance with Bottlerocket AMI.
  • Pull application images from the repository.
  • Create an EBS snapshot of the data volume.
  • Map the snapshot to Amazon EKS node groups.

• Benefits of Bottlerocket

  • It separates OS and container data volumes, ensuring consistency and security during updates.
  • Prefetched images significantly reduce startup times for large containers.

• Implementation Walkthrough

  • Step 1: Build EBS Snapshot
    • Automate snapshot creation using a script.
    • Prefetch images like Jupyter-PyTorch and Kubernetes pause containers.
    • Export the snapshot ID for use in node group configuration.
  • Step 2: Setup Amazon EKS Cluster
    • Create two node groups:
    • no-prefetch-mng: Without prefetched images.
    • prefetch-mng: With prefetched images mapped via EBS snapshot.
  • Step 3: Deploy Pods
    • Test deployment on both node groups.
    • Prefetched nodes start pods in just 3 seconds, compared to 49 seconds without prefetching.

• Results

  • Prefetching reduced container startup time from 49 seconds to 3 seconds, improving efficiency and user experience.

• Further Enhancements

  • Use Karpenter for automated scaling with Bottlerocket nodes.
  • Automate snapshot creation in CI pipelines using GitHub Actions.

• Cleaning Up

  • Delete AWS resources (EKS cluster, Cloud9 environment, EBS snapshots) to avoid charges after testing.

• Conclusion

  • Bottlerocket's data volume prefetching dramatically enhances container startup performance for large workloads on Amazon EKS.

• Net Profit:

  • In 2024, Orlen's net profit amounted to 7.95 billion PLN, compared to 20.97 billion PLN in 2023.

• Sales Revenue:

  • Dropped to 296.95 billion PLN in 2024 from 372.77 billion PLN in 2023.

• Dividend:

  • The board recommends a dividend payout of 6 PLN per share for 2024, an increase from 4.15 PLN per share in 2023.
  • The total amount allocated for dividends is 6.966 billion PLN.

• Business Segments:

  • Refining: Revenue decreased by 16.89 billion PLN (5% year-over-year), mainly due to lower gasoline (-9%), diesel (-10%), jet fuel (-10%), and light heating oil (-9%) prices.
  • Energy: Revenue fell by 10.04 billion PLN, impacted by a 12% drop in sales volume to 28.5 TWh and a 19% lower electricity price on the Polish Power Exchange.
  • Petrochemicals: Revenue increased by 760 million PLN due to a 9% growth in sales volume to 4.788 million tons and higher prices for key products such as ethylene (+1%), benzene (+11%), polyethylene (+5%), and polypropylene (+3%).
  • Retail: Revenue rose by 4.65 billion PLN as fuel sales volume increased by 11% to 11.308 million tons.
  • Upstream (Oil & Gas Production): Revenue grew by 882 million PLN due to a 23% increase in sales volume, driven by higher hydrocarbon production and the consolidation of the KUFPEC Norway AS acquisition.

• Overall Takeaway:

  • Despite a significant decline in net profit and revenue, Orlen plans to distribute a record dividend, reflecting its strategy of sharing profits with shareholders.

Claude 3.7 Sonnet again excels at coding, as verified by multiple teams

About Claude 3.7 Sonnet

• Why use Kubernetes

  • Best for running multiple processes/servers/jobs with redundancy and load balancing
  • Enables infrastructure-as-code configuration for service relationships
  • Outsourced infrastructure management via cloud providers (e.g., Google Kubernetes Engine)

• What they use

  • Core resources: Deployments (with rolling updates), Services (ClusterIP/LoadBalancer), CronJobs
  • Configuration: ConfigMaps and Secrets via Pulumi (TypeScript) instead of raw YAML
  • Cautious adoptions: StatefulSets for limited persistence, RBAC only when necessary

• What they avoid

  • Hand-written YAML and Helm charts ("fragility for no gain")
  • Operators, custom resources, service meshes, and most third-party controllers
  • Local k8s stack replication (prefer Docker Compose for local dev)

• Key insights

  • "A human should never wait for a pod" - unsuitable for interactive workloads requiring fast startup
  • Use managed databases/storage for critical data instead of k8s volumes
  • Alternatives like Railway/Render may be better for simpler SaaS apps
  • Recently adopted Ingress controllers for Cloud Armor integration despite initial reservations

Chinese cars with record sales growth in Poland, in January 1000% increase in registrations year-on-year (2 thousand), and their market share jumped to 5%; the most popular brand is MG (1 thousand units), ahead of e.g. Fiat, Citroën, Suzuki and Opel.

In the whole of 2024, 11 thousand Chinese cars were registered, and in 2023 only 160.

Prices of the cheapest Chinese cars:

• MG3 PLN 73.5 thousand, 1.5 l engine
• BAIC Beijing 3: PLN 79 thousand, 1.5 l engine
• MG ZS Classic PLN 80 thousand, 1.5 l engine

The cheapest hybrid: MG ZS Hybrid+: PLN 99 thousand, 1.5 l engine.

• Daily intake of 1g of omega-3 fatty acids can slow down aging, especially when combined with vitamin D and exercise - as demonstrated by a 3-year study on 777 Swiss seniors[1][2].

• Epigenetic clocks were used to measure the pace of aging, showing up to 4 months of biological age reduction[1][3].

• Additional benefits included:

  • 61% reduction in invasive cancer risk[4]
  • 10% reduction in falls[4]
  • 13% decrease in infection rates[4]

• Sources of omega-3: fish, seafood, rapeseed and flaxseed oil, walnuts, supplements[7][9]

Citations: [1] https://www.sci.news/medicine/omega-3-supplementation-biological-aging-13635.html [2] https://www.news-medical.net/news/20250204/Omega-3-fatty-acids-and-vitamin-D-slow-biological-aging-in-older-adults.aspx [3] https://www.nature.com/articles/s43587-024-00793-y [4] https://www.medonet.pl/leki-od-a-do-z/witaminy-i-mineraly,wystarczy-gram-dziennie--spowolnisz-starzenie-nawet-o-cztery-miesiace,artykul,86261609.html [5] https://www.eurekalert.org/news-releases/1072552 [6] https://www.national-geographic.pl/nauka/kwasy-omega-3-to-pigulka-mlodosci-zwlaszcza-w-polaczeniu-z-witamina-d/ [7] https://dzienniknaukowy.pl/kwasy-tluszczowe-omega-3-wydaja-sie-spowalniac-procesy-starzenia [8] https://translate.google.com/?prev=_t&hl=pl&ie=UTF-8&eotf=1&sl=en&tl=zh-CN [9] https://www.businessinsider.com/taking-omega-3-supplements-may-slow-down-aging-study-2025-2 [10] https://pubmed.ncbi.nlm.nih.gov/36629088/ [11] https://www.sciencedaily.com/releases/2025/02/250204132423.htm

• Figma's Initial Infrastructure Challenges:

  • Figma's monolithic architecture struggled with resource allocation inefficiencies and limited scalability.
  • High traffic spikes from collaborative design workflows required more robust solutions for resource autoscaling and failover.

• Why Kubernetes Was Chosen:

  • Kubernetes' container orchestration capabilities promised better resource management and service isolation.
  • Features like Horizontal Pod Autoscaling (HPA), robust networking via Kubernetes Services, and support for StatefulSets made it an ideal fit for Figma’s needs.
  • The platform also wanted better alignment with cloud-native practices and modern CI/CD workflows.

• Incremental Migration Approach:

  • Step 1: Non-Critical Services: Figma migrated stateless services first, allowing experimentation without risking core functionality.
  • Step 2: Custom Tooling: Internal tooling was built to manage Kubernetes manifests and automate Helm chart creation for standardization.
  • Step 3: Stateful Services: For databases and other stateful components, Figma relied on Kubernetes' StatefulSets and persistent volumes (PVs) to ensure data integrity during the migration.
  • Step 4: Observability Enhancements: Kubernetes-native tools like Prometheus and Grafana were integrated to provide detailed metrics and system insights.

• Key Technical Adjustments During Migration:

  • Service Discovery: Transitioned to Kubernetes-native DNS for internal service communication, replacing legacy methods.
  • Load Balancing: Leveraged Kubernetes Ingress and external load balancers (e.g., NGINX or cloud-native solutions) for traffic routing.
  • Networking Complexity: Resolved challenges around multi-cluster networking using Kubernetes CNI plugins like Calico.
  • Resource Management: Used Resource Quotas and Limits to prevent pod overcommitment and optimize cluster utilization.

• Challenges Faced:

  • Stateful Services: Ensuring zero-downtime migration for databases required careful orchestration of PersistentVolumeClaims (PVCs) and StatefulSets.
  • Networking: Handling cross-region traffic and external dependencies required tweaking Kubernetes Ingress configurations.
  • Resource Constraints: Balancing costs and performance involved tuning cluster-autoscaler configurations and evaluating node pool setups.

• Benefits Realized Post-Migration:

  • Scalability: Kubernetes' HPA allowed Figma to scale pods dynamically based on traffic patterns, ensuring consistent performance.
  • Deployment Efficiency: CI/CD pipelines integrated seamlessly with Kubernetes, enabling faster and more reliable rollouts using tools like Argo CD.
  • Reliability: Self-healing capabilities, such as pod restarts and node failover, reduced downtime during failures.
  • Observability: Improved system monitoring with Kubernetes' native metrics server and integrations with Prometheus and Grafana.

• Future Enhancements Planned:

  • Service Mesh Integration: Adoption of Istio or Linkerd to enhance observability, security (e.g., mutual TLS), and traffic management.
  • Cost Optimization: Further tuning autoscaling policies and resource limits to minimize waste.
  • Edge Improvements: Deploying Kubernetes clusters closer to end-users for reduced latency, potentially using Kubernetes' Cluster Federation.

• Overview of Tesla's Data Infrastructure Challenges:

  • Modern Tesla vehicles generate an enormous volume of telemetry data related to sensor readings, driver behavior, energy consumption, and more.
  • The primary challenge is ingesting, processing, and analyzing this data at scale while maintaining real-time capabilities.

• Kubernetes for Orchestration:

  • Tesla uses Kubernetes to manage containerized microservices across a distributed cloud environment.
  • Kubernetes ensures dynamic scaling to handle fluctuating workloads, providing high availability for critical services.
  • Each microservice is encapsulated in its own container, improving isolation and deployability.

• Kafka for Real-Time Event Streaming:

  • Apache Kafka is the backbone of Tesla’s data pipeline, managing trillions of events daily from globally distributed vehicles.
  • Kafka topics are structured to partition and replicate data efficiently, ensuring fault tolerance and high throughput.
  • Producers (vehicles) send data to Kafka brokers, while consumers (analytics systems, data lakes) process these streams in real-time.

• Data Processing Pipelines:

  • Data from Kafka is ingested into processing systems for real-time analytics, anomaly detection, and predictive maintenance.
  • Stream processing frameworks (e.g., Apache Flink or Kafka Streams) analyze data for immediate feedback.
  • Batch systems handle aggregation and storage in Tesla’s data lake for long-term insights.

• Key Technical Advantages:

  • Scalability: Kubernetes dynamically allocates resources based on the volume of incoming data and computational requirements.
  • Resilience: Kafka’s replication factor ensures that no single broker failure impacts the system.
  • Low Latency: Data streams from Kafka enable Tesla to act on insights in milliseconds, critical for safety and performance monitoring.

• Simplified Management:

  • The platform supports multi-cluster Kubernetes configurations for geographic data segregation.
  • A central control plane monitors system health, manages deployments, and ensures compliance with data regulations.

• Future Goals and Improvements:

  • Enhancing AI-driven analytics to derive deeper insights from vehicle data.
  • Further optimizing Kafka’s cluster topology to improve fault tolerance and reduce operational costs.
  • Expanding edge processing capabilities in vehicles to pre-filter data, reducing bandwidth requirements to the cloud.

• Kubernetes Configuration Drift Issue:

  • Reddit experienced a significant outage on March 13, 2022, during a Kubernetes upgrade from version 1.23 to 1.24.
  • The outage was caused by configuration drift, where unintended changes accumulated over time, leading to inconsistencies across clusters.

• New Platform Abstraction with Declarative APIs:

  • Reddit adopted a declarative approach, leveraging Kubernetes controllers to manage configurations and enforce consistency.
  • The implementation of these controllers enabled Reddit to abstract platform complexities and ensure a uniform deployment environment.

• Centralized Control Plane for Multi-Cluster Management:

  • The team built a centralized control plane to manage multiple Kubernetes clusters effectively.
  • Cluster provisioning time was drastically reduced from over 30 hours to approximately 2 hours.
  • Centralization facilitated standardized configurations and reduced operational overhead.

• Development of Achilles SDK:

  • Achilles, an SDK developed in-house by Reddit, simplified the creation of Kubernetes controllers.
  • It allowed infrastructure engineers to automate and manage resources programmatically without deep Kubernetes expertise.
  • The SDK supported a more proactive approach to problem-solving, preventing drift by design.

• Benefits and Lessons Learned:

  • The new system ensured robust monitoring, minimized manual intervention, and improved scalability.
  • Configuration drift was effectively mitigated, providing a more stable and predictable infrastructure.
  • The experience highlighted the importance of using Kubernetes-native solutions and declarative configurations for managing large-scale deployments.

• Future Goals:

  • Further refinement of the platform to address edge cases and improve developer experience.
  • Continued investment in tools and processes to maintain infrastructure consistency at scale.

MLOps prediction for 2025

MLOps prediction for 2025

MLOps prediction for 2025

RAG will shine even more in 2025

Simple definition of RAG

• Finding Wins Above Replacement: Look for candidates who made a significant impact in their past roles, distinguishing their actions from team efforts. This highlights their talent, agency, and individuality.

• Key Traits to Identify:

  • Creativity: Ability to identify bigger problems and opportunities rather than merely following orders.
  • Resourcefulness & Follow-through: Persistence to complete tasks even when facing obstacles.
  • Vision: A thoughtful, original future vision with coherent life choices.

• Effective Questions to Ask:

  • "Tell me about your best [accomplishment/task]."
  • "If your life was a book, give me the chapter titles from birth till now."

• Chip on the Shoulder: Candidates with a drive to prove themselves can be valuable. Look for those with optimism and resilience rather than deep insecurities or negativity.

• Challenges as Motivation: Present company challenges and assess if candidates are excited by the opportunity to make an impact rather than deterred by imperfection.

• High EQ & Persuasion: Evaluate their ability to adapt communication and influence effectively, especially in group interactions.

• Theory for Excellence: Past excellence in any field (sports, arts, academics) suggests transferable drive and discipline for unrelated roles.

• Understanding Competence: Candidates should know their strengths, weaknesses, and preferred tools or methods.

• Spike Potential: Tailor questions to their profile and assess specific strengths that hint at future excellence.

• Coachability & Openness: Look for candidates open to feedback, willing to improve, and capable of committing even when they disagree.

• Unpretentiousness: Seek candidates who are self-aware, lighthearted, and pleasant to work with.

• Assessment Methods:

  • Use scenario-based interviews, group exercises, or role-playing to evaluate real-time reactions.
  • Gather backchannel references for additional insights.

Participants were asked: "Is your walking speed faster than people of your gender and age?" Based on their answers, they were categorized as "fast walkers" or "slow walkers."

The study included:

• 8,578 obese individuals,
• 9,626 individuals with a large waist circumference,
• 6,742 individuals meeting both criteria.

Summary:

• Overweight individuals who perceive their walking speed as fast have a 30% lower risk of diabetes, along with reduced risks of hypertension and dyslipidemia.
• Subjective assessment of walking speed can serve as a simple and cost-effective tool to identify metabolic health risks.
• Fast walking indicates the good condition of muscles, joints, and the cardiovascular and respiratory systems.
• Previous research has linked slow walking speed to an increased risk of cardiovascular diseases and higher mortality rates in older adults.

• Regular coffee consumption can extend life by an average of 1.8 years, according to researchers from the University of Coimbra.
• Findings are based on a meta-analysis of over 50 studies.
• Benefits of coffee consumption include:
  • Reduced risk of heart disease, type 2 diabetes, and obesity.
  • Improved circulation.
  • Relief from muscle pain.
  • Anti-inflammatory effects due to chlorogenic acids, which act as antioxidants combating free radicals responsible for cellular aging.
• Recommendations:
  • Limit intake to no more than 4 cups per day.
  • Avoid adding sugar and cream.

Summary

🌍 Avoiding Tax Legally in Other Countries

Changing tax residency for half a year may help avoid taxes (but it;s complex and rather recommended for very high earnings). You can move for example to: - United Arab Emirates: No crypto tax. - Germany: No tax on crypto sold after holding for 12 months. - Portugal: Similar tax-free policies for holding over a year.

🔄 What Is Taxed?

Transactions converting cryptocurrency into fiat currency (e.g., PLN, USD) or purchasing goods like property or pizza with cryptocurrency are taxable. Pure crypto-to-crypto transactions are not taxable.

📈 How to Calculate Tax

Calculate the difference between the amount spent to acquire cryptocurrency and the amount earned from its sale. Example: If Bitcoin was bought at $30,000 and sold at $40,000, the taxable income is $10,000.

💰 Tax Rate Overview

• Income up to 1 million PLN is taxed at 19%.
• Income exceeding 1 million PLN incurs an additional 4% solidarity tax, totaling 23% for high earners.

📃 Keeping Records

Tax obligations arise at the moment of converting crypto to fiat or goods, not at the time of withdrawal from an exchange. Keep detailed records to avoid issues during audits or when exchanges request proof of funds.

⚠️ Challenges and Advice

Tax laws in Poland are comprehensive and offer few loopholes. Engaging tax professionals is strongly advised to ensure compliance and minimize errors.

🔄 Deferring Tax Payments in Poland Using Stablecoins

One strategy involves converting crypto profits into stablecoins at year-end and selling them in the following year to postpone taxation. Below is a detailed breakdown of how the strategy works and its limitations.

The Concept

Stablecoins are cryptocurrencies pegged to fiat currencies (e.g., USD or EUR) and have stable values. Using stablecoins in Poland offers a way to legally defer tax payments:

1. End-of-Year Transaction:

   • Convert your cryptocurrency gains (e.g., Bitcoin) into stablecoins like Tether (USDT) or USD Coin (USDC) at the end of the tax year.
   • These transactions, as crypto-to-crypto conversions, are not taxable in Poland.

2. Start-of-Year Sale:

   • In the new tax year, sell the stablecoins for fiat currency (e.g., PLN).
   • The taxable event occurs in the following year, deferring the tax obligation.

Benefits of This Strategy

1. Tax Payment Deferral:

   • Delays the payment of taxes on your crypto gains by shifting the taxable event to the next year.

2. Inflation Advantage:

   • Inflation reduces the real value of money over time, decreasing the actual financial burden of the deferred tax.

3. Liquidity Management:

   • Funds remain accessible as stablecoins, which can be reinvested or used in decentralized finance (DeFi) during the deferral period.

How Long Can This Be Done in Poland?

• The strategy can be legally repeated annually for up to 15 years.
• After 15 years, the deferred gains may be treated differently or trigger tax liabilities due to long-term reporting requirements. It's important to monitor evolving tax regulations in Poland to ensure compliance.

Key Considerations

1. Documentation:

   • Maintain detailed records of all transactions, including dates, values, and stablecoin transfers, for tax compliance and audits.

2. Stablecoin Selection:

   • Choose stablecoins with strong pegs to fiat currencies to avoid price fluctuations that may affect gains or losses.

3. Regulatory Changes:

   • Polish tax laws are subject to change. Always confirm that the strategy remains valid before executing it.

Example

• Year 1 (2023):

  • Bitcoin bought for PLN 50,000.
  • Sold for PLN 150,000 at the end of 2023.
  • Proceeds converted to USDT (not taxable in Poland).

• Year 2 (2024):

  • USDT sold for PLN 150,000 in January.
  • Taxable gain of PLN 100,000 is reported in the 2024 tax year.

Limitations

1. Short-Term Transactions:

   • If stablecoins are sold within the same year as the crypto-to-stablecoin conversion, the tax deferral benefit is lost.

2. Exchange Fees:

   • Frequent crypto-stablecoin conversions may incur exchange fees, slightly reducing net gains.

3. Regulatory Risks:

   • Future changes to tax laws or stablecoin regulations could impact the strategy's viability.

This strategy allows you to legally defer cryptocurrency taxes for a significant period, maximizing your financial flexibility and leveraging stablecoin stability. Always consult a tax professional for tailored advice and compliance.

Why Gitpod is Leaving Kubernetes

Gitpod has decided to transition away from Kubernetes for managing cloud development environments, opting instead for a custom-built solution better suited to their needs. While Kubernetes is powerful for orchestrating stateless application workloads, Gitpod identified several challenges that made it less ideal for their dynamic, stateful workloads.

Key Challenges of Kubernetes

• Resource Overhead: Kubernetes introduces significant complexity and resource consumption, which is inefficient for scaling ephemeral development environments.

• Latency in Scaling: The time required to scale pods and handle stateful workloads can slow down developer workflows that demand near-instant provisioning.

• Stateful Workloads: Kubernetes is designed for stateless applications, and adapting it for stateful environments adds operational complexity.

• Cost Inefficiency: Running dynamic workloads on Kubernetes incurs higher operational costs due to the constant need for scaling and resource orchestration.

• Security Concerns: Managing multi-tenant security on Kubernetes is challenging, requiring considerable effort to ensure workload isolation and permission control.

• Operational Complexity: Maintaining Kubernetes clusters at scale involves a significant operational burden, including updates, monitoring, and configuration management.

Gitpod is now focusing on Gitpod Flex, a new solution tailored to better meet the demands of developers, offering improved scalability, efficiency, and simplicity.

The introduction of the Image Volume Source feature in Kubernetes 1.31 allows MLOps practitioners to mount OCI-compatible artifacts, such as large language model weights or machine learning models, directly into pods without embedding them in container images. This streamlines model deployment, enhances efficiency, and leverages OCI distribution mechanisms for effective model management.

Article about remote work

In essence, this article is about:

1) Training a sample model and uploading it to an S3 bucket:

```python from sklearn.datasets import load_iris from sklearn.model_selection import train_test_split from sklearn.linear_model import LogisticRegression import joblib

Load the Iris dataset

iris = load_iris() X, y = iris.data, iris.target

Split the data into training and testing sets

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

Train the logistic regression model

model = LogisticRegression(max_iter=200) model.fit(X_train, y_train)

Save the trained model to a file

joblib.dump(model, 'model.pkl') ```

1. Creating a sample Zappa config, because AWS Lambda doesn’t natively support Flask, we need to use Zappa, a tool that helps deploy WSGI applications (like Flask) to AWS Lambda:

```json { "dev": { "app_function": "app.app", "exclude": [ "boto3", "dateutil", "botocore", "s3transfer", "concurrent" ], "profile_name": null, "project_name": "flask-test-app", "runtime": "python3.10", "s3_bucket": "zappa-31096o41b" },

"production": {
    "app_function": "app.app",
    "exclude": [
        "boto3",
        "dateutil",
        "botocore",
        "s3transfer",
        "concurrent"
    ],
    "profile_name": null,
    "project_name": "flask-test-app",
    "runtime": "python3.10",
    "s3_bucket": "zappa-31096o41b"
}

} ```

1. Writing a sample Flask app:

```python import boto3 import joblib import os

Initialize the Flask app

app = Flask(name)

S3 client to download the model

s3 = boto3.client('s3')

Download the model from S3 when the app starts

s3.download_file('your-s3-bucket-name', 'model.pkl', '/tmp/model.pkl') model = joblib.load('/tmp/model.pkl')

@app.route('/predict', methods=['POST']) def predict(): # Get the data from the POST request data = request.get_json(force=True)

# Convert the data into a numpy array
input_data = np.array(data['input']).reshape(1, -1)

# Make a prediction using the model
prediction = model.predict(input_data)

# Return the prediction as a JSON response
return jsonify({'prediction': int(prediction[0])})

if name == 'main': app.run(debug=True) ```

1. Deploying this app to production (to AWS):

bash zappa deploy production

and later eventually updating it:

bash zappa update production

1. We should get a URL like this:

https://xyz123.execute-api.us-east-1.amazonaws.com/production

which we can query:

curl -X POST -H "Content-Type: application/json" -d '{"input": [5.1, 3.5, 1.4, 0.2]}' https://xyz123.execute-api.us-east-1.amazonaws.com/production/predict

You may use uv in production, but there may be still some undiscovered quirks.

About uv Python packaging tool

The blog post by Cliff Malmborg from Loft Labs discusses optimizing Kubernetes costs using multi-tenancy and virtual clusters. With Kubernetes expenses rising rapidly at scale, traditional cost-saving methods like autoscaling, resource quotas, and monitoring tools help but are not enough for complex environments where underutilized clusters are common. Multi-tenancy enables resource sharing, reducing the number of clusters and, in turn, management and operational costs.

A virtual cluster is a fully functional Kubernetes cluster running within a larger host cluster, providing better isolation and flexibility than namespaces. Unlike namespaces, each virtual cluster has its own Kubernetes control plane, so resources like statefulsets and webhooks are isolated within it, while only core resources (like pods and services) are shared with the host cluster. This setup addresses the "noisy neighbor" problem, where workloads in a shared environment interfere with each other due to resource contention.

Virtual clusters offer the isolation benefits of individual physical clusters but are cheaper and easier to manage than deploying separate physical clusters for each tenant or application. They also support "sleep mode," automatically scaling down unused resources to save costs, and allow shared use of central tools (like ingress controllers) installed in the host cluster. By transitioning to virtual clusters, companies can balance security, isolation, and cost-effectiveness, reducing the need for multiple physical clusters and making Kubernetes infrastructure scalable for modern, resource-demanding applications.

Gold has risen in price by 33% since the beginning of the year and is now worth PLN 10,780 per ounce. This is almost the best year for this precious metal in 45 years.

It may be over of IP BOX (5%) in 2025.

This article (which can be viewed using a paywall remover) shows that even if someone does not deal directly with code, the court may think otherwise:

Fiskus i NSA nie pozwalają na niższą stawkę ryczałtu W sprawie, która doszła do NSA, spór ze skarbówką zaczął się od wniosku o interpretację informatyka prowadzącego jednoosobową działalność. Napisał, że zajmuje się projektowaniem i rozwojem technologii informatycznych dla sieci i systemów komputerowych lub ich poszczególnych składowych/komponentów. Świadczy też usługi pomocy technicznej. Szczegółowo opisał wszystkie czynności. Podkreślił też, że do zakresu jego obowiązków nie należy tworzenie oprogramowania. Dlatego uważa, że ma prawo do niższej stawki ryczałtu. Fiskus miał jednak inne zdanie. Uznał, że informatyk powinien płacić 12 proc. ryczałt, ponieważ „okoliczność, że do zakresu obowiązków podatnika nie należy tworzenie oprogramowania komputerowego, nie jest wystarczającą przesłanką do uznania braku związku świadczonych usług z oprogramowaniem”.

Informatyk zaskarżył interpretację, przegrał jednak zarówno w pierwszej, jak i drugiej instancji. Wojewódzki Sąd Administracyjny w Gliwicach zauważył, że z wniosku o interpretację wynika, iż świadczone usługi są związane z rozwojem systemu SAP. Ten system jest oprogramowaniem wspomagającym prowadzenie przedsiębiorstwa. Opisane usługi są więc niewątpliwie związane z oprogramowaniem – uznał gliwicki WSA. Za szerokim rozumieniem pojęcia „usługi związane z oprogramowaniem” opowiedział się też NSA. Podkreślił, że nie chodzi tylko o programowanie, ale również inne czynności. Także te, które wykonuje przedsiębiorca.

Sąd wymienił wszystkie obowiązki informatyka i po prostu stwierdził, że są to usługi związane z oprogramowaniem. Po NSA spodziewałbym się głębszej analizy i dokładnego określenia, które z wymienionych czynności spełniają to kryterium. Miejmy nadzieję, że kolejne orzeczenia będą wnikliwsze. I po głębszych rozważaniach może okaże się, że to jednak podatnicy mają rację – podsumowuje Piotr Sekulski

In September, employers in Poland published 12% more job offers y/y (288 thousand).

The increase has been going on for 4 months, and now it has reached the highest level since March 2022. The largest increase in the number of job offers is in medical professions (24%) and manual workers (13%). Decrease among financiers (-15%), HR specialists (-10%) and lawyers (-7%). In IT, the number of offers increased by 5%, and in marketing and sales by 1%.

Good advice for the presenters using macOS

Mental disorders are indirectly contagious – i.e. negative emotional and behavioral patterns that cause illness are transferred even to friends of people with disorders, a study in Finland involving 700,000 people has shown.

The data showed that having friends diagnosed with mental disorders in the 9th grade of secondary school increased the risk of developing mental disorders later in life, such as mood swings, anxiety and eating disorders, by up to 18%.

Drinking 3 cups of coffee or 200-300 mg of caffeine a day can halve the risk of diseases such as type 2 diabetes, coronary artery disease and stroke, researchers from Suzhou University in China, in collaboration with Swedish scientists, have shown.

Moderate caffeine consumption may protect cardiovascular health, regardless of age, gender, smoking or diet. The study is based on data from over 300,000 people from the UK Biobank, collected over 11 years.

The essence of cutting AWS data transfer costs by 99% is to use Amazon S3 as an intermediary for data transfers between EC2 instances in different Availability Zones (AZs). Instead of direct transfers, which incur significant costs, you upload the data to S3 (free upload), and then download it within the same region (free download). By keeping the data in S3 only temporarily, you minimize storage costs, drastically reducing overall transfer expenses.

Infopiguła:

Liczba Polaków zarabiających ponad 10 tys. zł / mc wzrosła w 2022 r. o 51% rdr., do 1,5 mln osób. Na koniec 2022 r. było w Polsce 90 tys. milionerów dolarowych, 10% mniej niż rok wcześniej.

Pensje zarabiających ponad 10 tys. wzrosły o 10% do ok. 375 mld zł. Głównym czynnikiem była wysoka inflacja, ale też napływ Ukraińców z dobrymi zarobkami w korporacjach.

Liczba osób z zarobkami 20-50 tys. zł / mies. wzrosła o 38% do 440 tys., tych z zarobkami między 50 a 83 tys. zł wzrosła o 1% do 84 tys., a osób z zarobkami ponad 83 tys. zł (czyli 1 mln rocznie) spadła o 5% do 35 tys. O prawie 2% wzrosła liczba osób z majątkiem ponad 50 mln $.

In this case, IaC is favored over using EKS directly or manually deploying on EC2

Why use EKS

Cost of EKS

Benefits of using Kubernetes on AWS: - scalability - cost efficiency - high availability

special methods = magic methods = dunder methods

Opening URL using Python's webbrowser module

Sample .devcontainer/devcontainer.json:

json { "name": "Global", "build": { "context": "..", "dockerfile": "Dockerfile" }, "containerEnv": { "PYTHONPATH": "." }, "customizations": { "vscode": { "settings": { "extensions.verifySignature": false }, "extensions": [ "GitHub.copilot", "ms-python.vscode-pylance", "ms-python.python", "eamodio.gitlens" ] } }, "initializeCommand": "/bin/bash -c '[[ -d ${HOME}/.aws ]] || { echo \"Error: ${HOME}/.aws directory not found.\"; exit 1; }; [[ -f ${HOME}/.netrc ]] || { echo \"Error: ${HOME}/.netrc file not found.\"; exit 1; }; [[ -d ${HOME}/.ssh ]] || { echo \"Error: ${HOME}/.ssh directory not found.\"; exit 1; }; echo \"\n> All required mounts found on the host machine.\"'", "onCreateCommand": { "hadolint": "apt-get update && apt-get install wget -y && wget -O /bin/hadolint https://github.com/hadolint/hadolint/releases/download/v2.12.0/hadolint-Linux-x86_64 && chmod u+x /usr/bin/hadolint", "precommit": "pip install pre-commit" }, "updateContentCommand": "/bin/bash -c 'if grep -A 2 \"machine gitlab.com\" ~/.netrc | grep -q \"password\" && GITLAB_TOKEN=$(grep -A 2 \"machine gitlab.com\" ~/.netrc | grep -oP \"(?<=password ).*\" | tr -d \"\\n\") && [ -n \"$GITLAB_TOKEN\" ]; then echo \"\n> Token found in ~/.netrc\"; else read -sp \"\n> Enter your GitLab token: \" GITLAB_TOKEN && echo; fi; echo \"export GITLAB_TOKEN=$GITLAB_TOKEN\" >> ~/.bashrc && . ~/.bashrc && poetry config http-basic.abc __token__ $GITLAB_TOKEN'", "postCreateCommand": ". ~/.bashrc && curl -s --location 'https://gitlab.com/api/v4/projects/12345/repository/files/.pre-commit-config.yaml/raw?ref=main' --header \"PRIVATE-TOKEN: $GITLAB_TOKEN\" -o .pre-commit-config.yaml", "postAttachCommand": "/bin/bash -c '. ~/.bashrc && read -p \"\n> Do you want to update the content of devcontainer.json? (y/n): \" response; if [[ \"$response\" == \"y\" ]]; then curl -s --location \"https://gitlab.com/api/v4/projects/12345/repository/files/devcontainer.json/raw?ref=main\" --header \"PRIVATE-TOKEN: $GITLAB_TOKEN\" -o .devcontainer/devcontainer.json; else echo \"\n> Skipping update of devcontainer.json\"; fi'", "mounts": [ "source=${localEnv:HOME}/.aws/,target=/root/.aws/,type=bind,readonly", "source=${localEnv:HOME}/.netrc,target=/root/.netrc,type=bind,readonly", "source=${localEnv:HOME}/.ssh/,target=/root/.ssh/,type=bind,readonly" ] }

Some more of my recent learning with devcontainer.json (its Dev Container metadata):

• Interactive commands (those waiting for user input like read) do not display the input request in (at least onCreateCommand and postCreateCommand sections), so it is better to keep them in updateContentCommand or postAttachCommand.
• If there are 2 read commands in a single section, like updateContentCommand, only the 1st one is displayed to the user, and the 2nd one is ignored.
• When I put a read command within a dictionary (with at lest 2 key/values) of postAttachCommand, the interactive command wasn't being displayed.
• We need to use /bin/bash -c to be able to use read -s (the -s flag) which allows for securely passing the password so that it does not stay in the VS Code console. Also, I had trouble with interactive commands and if statements without it.
• Using "GITLAB_TOKEN": "${localEnv:GITLAB_TOKEN}" does not easily work as it is looking for GITLAB_TOKEN env variable set locally on our host computers, and I believe no one does it.
• The dictionary seems to be executing its scripts in parallel; therefore, it is not easily possible to break down long lines which have to execute in a chronological sequence.
• JSON does not allow for human-readable line breaks; therefore, indeed, it seems impossible to improve the long one-liners.
• The files/folders mentioned within mounts need to exist locally (otherwise, Docker container build fails). They are mounted before any other section. Technically, we can protect ourselves with the following command to find an extra message in VS Code container logs:

json "initializeCommand": "/bin/bash -c '[[ -d ${HOME}/.aws ]] || { echo \"Error: ${HOME}/.aws directory not found.\"; exit 1; }; [[ -f ${HOME}/.netrc ]] || { echo \"Error: ${HOME}/.netrc file not found.\"; exit 1; }; [[ -d ${HOME}/.ssh ]] || { echo \"Error: ${HOME}/.ssh directory not found.\"; exit 1; }'",

Other option is to get rid of the error completely, but this creates files on the host machine; therefore, it is not an ideal solution:

json "initializeCommand": "mkdir -p ~/.ssh ~/.aws && touch ~/.netrc",

I would also simply put the following:

bash /bin/bash -c 'read -p 'Type a message: ' -t 10 && echo Attach $REPLY'

I also found that updateContentCommand allows for the user interaction (it displays interactive command in the VS Code console).

Explanation of 6 available devcontainer.json (Dev Container in VS Code) hooks.

Why sleep tracking may not make any sense

List of reasons (below) why I may be paying for BIK alerts (36 (if extended before 07/2024) or 42 PLN per year, although I used to pay 24 PLN).

I may generally consider it while taking a housing loan.

The author was charged over $1300 after two days of using an S3 bucket, because some OS tool stored a default bucket name in the config, which was the same as his bucket name.

Luckily, after everything AWS made an exception and he did not have to pay the bill.

Explanation why Chrome lock icon was replaced with tune icon

Karpenter

Key limitation of CAS

:D

Framework for message framing

Recommendations for doing git rebase (see bullet points below annotation)

Core soft skills in IT

The core software development process

:)

git bisect run ./calc.py 14 0 ← example of running git bisect automatically. * If the commit is good, then the command should return 0; * If the commit is bad, then the command should return anything between 1 and 127, inclusive, except 125; * If it is not possible to tell if this commit is good or bad, then it need to be ignored, and the command should return 125.

Git Bisect can be run manually or automatically

6 differen debugging tools

While today’s LMs agents don't pose a serious risk, we should be on the lookout for improved autonomous capabilities as LMs get more capable and reliable.

~$50m = estimated training cost of GPT-4

Programming with LLM = less stress

In a study, students who learned to code with AI made more progress during training sessions, had significantly higher correctness scores, and retained more of what they learned compared to students who didn't learn with AI.

OpenAI’s voice cloning AI model only needs a 15-second sample to work

Brief explanation why not to change Wordpress login URL

Comparison of the frequency of posting by the most popular polish cybersecurity blogs: 1. Sekurak 2. Niebezpiecznik 3. Z3S

docker init

See (above this annotation) the most optimized & CI friendly Python Docker build with Poetry (until this issue gets resolved)

Combining Buildkit cache and Poetry cache

ADR sections from the example: * Title * Status * Date * Context * Decision * Consequences * Compliance * Notes

They went through 310 role descriptions and, even though role descriptions may vary significantly, they found 3 core skills that a large percentage of MLOps roles required:

📦 Docker and Kubernetes 🐍 Python 🌥 Cloud

Command to get all the links of a website

https://www.localstack.cloud/

Popularity of Python package managers in 2024

:)

I used to work in such a project :)

MLX (high overview)