2025-11-13 01:35:58
Toroidal CTC sim is a Python-based wave simulation that models directional delay in a closed-loop waveguide.
It’s built to explore what happens when you drag a refractive index perturbation around a torus—and how that messes with time-of-flight.
I’m not a physicist. I’m a shed enthusiast. But I wanted to see if I could build a desktop analogue for closed timelike curves (CTCs) using classical wave mechanics. Turns out, you can.
parameters.json
timing_data.csv

Arrival time vs. Ω — directional delay emerges as the perturbation spins
concept_paper.pdf in the repo
animation.gif in the repoBecause time loops are cool.
Because reproducible metaphysics is underrated.
Because sometimes the best place to simulate a paradox is a shed.
If you're on arXiv or ResearchGate and resonate with delay-based logic, wave simulations, or metaphysical modeling—I'd love an endorsement or a nudge. This project is open-source, reproducible, and built for curious minds. Ping me or star the repo if it made you think weird thoughts.
2025-11-13 01:33:19
Our phone is a cognitive dumpster. How intent-centric architectures will replace app-based clutter.
Our phones have become cognitive dumpsters. 127 icons, each a gateway to a separate universe with its own rules, logins, loyalty cards, and push notifications. We’ve reached peak absurdity: our pockets hold supercomputers, while our brains perform the chores of digital handymen — searching, comparing, downloading, logging in, paying.
Today’s tech giant presentations, with Apple as the prime example, increasingly resemble Nokia in its decline: endless iterations of cases, slightly brighter screens, marginally faster chips. This is an evolutionary dead end masking a crisis of ideas. Innovation has been reduced to swapping aluminum for titanium and adding another sensor. The specs race has exhausted itself because it no longer transforms how we interact with the world. Platforms have matured, hardened, and stagnated. It seems the next revolution won’t come from Silicon Valley, but from those daring to offer not a new product, but a new logic of interaction.
Imagine a platform where the central element isn’t your home screen, but your goal. You don’t open a taxi app. You say: “To the airport by 5 PM.” Your agent — your digital self — consults an open registry, finds everyone who can solve this task, chooses the best based on your criteria, and directly, via API, books the ride. No downloading. No logins. No pizza discount notifications.
We’re accustomed to voice assistants promising such scenarios, but their fundamental limitation is that they’re merely add-ons to an outdated architecture dominated by monolithic apps and walled gardens. They can only work with what the platform explicitly allows them. The main question isn’t what we want to achieve, but what architecture our devices must have to make it truly possible?
The answer lies not in updating software, but in rethinking the very core of digital interaction. We must elevate the agent from a beggar knocking on closed app doors to a full-fledged orchestrator with direct access to an open market of machine-readable services. This requires a new operating environment where the central process isn’t a window manager, but a task scheduler working with a global skill registry. Where security is ensured not by app store reviews, but by execution isolation and cryptographic trust protocols. Where the device isn’t a program repository, but an intelligent gateway to the world of services.
This isn’t an improved voice assistant. It’s a paradigm shift in control. I call it the i*ntent-centric operating environment (ICOE).* Its core isn’t an operating system, but your personal proactive intelligent agent (PIA) — a digital extension of your will.
Your agent is your cognitive immune system. It must be proactive, run locally, and be your only interface. It operates exclusively on your device. Its task is to understand your request’s essence, not to sell you a partner’s service. Its loyalty is solely to you. The interface ceases to be an icon forest — your main interlocutor becomes an intelligent agent.
An open global skill registry — a decentralized catalog where any company can publish machine-readable descriptions of its services. No need to download apps — services become virtual and available to your agent. Operators can earn money not from selling apps or in-app advertising, but by providing specific functions and API calls — via micropayments, subscriptions, or pay-per-result models. This creates competition at the API quality level, not the app level.
Tasks are executed in isolated containers. The agent orchestrates micro-services from the registry to achieve your goal, not the goal of an advertising algorithm.
This might seem like a bold idea, but all the necessary technologies have matured for its implementation. Modern AI systems can run on-device, understanding context and request semantics. Decentralized registry technologies have been proven for creating trusted systems and secure data storage. Microservice architecture has become the standard for modern IT infrastructures, ensuring flexibility and scalability. All these components exist and are ready for integration into a new paradigm of digital interaction. A new intention-based OS might not arrive tomorrow, but within a 10–15 year horizon, such a transition is inevitable.
For me, this is deeper than just architecture or technology. It’s a matter of digital sovereignty and cognitive ecology. We spend energy managing interfaces instead of spending it on creativity, communication, and decision-making. We will stop thinking about “apps.” We will simply live in a digital world that understands our intentions and responds with quiet, effective action.
The presented concept of an intent-centric operating environment (ICOE) describes a paradigm shift from managing applications to declaring target states. The system’s key elements are an AI agent (PIA) for semantic analysis and planning, a decentralized machine-readable operator registry, and an isolated execution environment.
In the context of current developments, partial analogs should be noted. For example, the Rabbit R1 project uses a Large Action Model to simulate human actions in existing interfaces, representing a workaround that doesn’t require service cooperation. Humane AI Pin focuses on alternative I/O interfaces. Voice assistants (Siri, Alexa) and automation platforms (IFTTT) work as add-ons over traditional OSs, maintaining dependence on GUI apps and manual configuration. In any case, these are all layers on top of a monolithic OS, attempting to reconcile the old world of apps with the world of automation.
ICOE’s fundamental difference lies in proposing a fundamentally new architecture based on a machine-readable skill registry and decentralized service orchestration. Instead of simulating human actions in outdated interfaces or creating another abstraction layer, the system presupposes providers transitioning to a new interaction standard. This ensures semantic compatibility, security, and scalability absent in existing solutions.
Beyond the App Store and Google Play lies a world without apps. A future where our digital experience is defined not by downloaded programs, but by our own intentions.
But for this world to become reality, key challenges must be solved: who will architect the universal language of digital intentions, and who will create the trusted environment for their execution? This isn’t a theoretical question — it’s the space for the next revolution.
The question is whether one of the current corporate digital giants will do this, or a new promising startup, free from the burden of outdated paradigms. The answer will determine who builds the next big AI platform — replacing obsolete app stores.
2025-11-13 01:19:54
Schnelle Lösung zum Hinzufügen von Eigenschaftsinformationen zu Ihren Blechteil-Zeichnungen in SOLIDWORKS.
// Rechtsklick auf Abwicklungsansicht
// Anmerkung > Schnittlisteneigenschaften wählen
// Für Eigenschaftszuordnung: $PRPWLD:"PropertyName"
$PRPWLD:"Bounding Box Length" - Begrenzungsbox-Länge$PRPWLD:"Sheet Metal Thickness" - Blechdicke$PRPWLD:"Cutting Length-Outer" - Äußere Schnittlänge$PRPWLD:"Bends" - Anzahl der BiegungenWeitere Tipps und technische Informationen zu SOLIDWORKS finden Sie in unserem Artikel „Anleitung zum Hinzufügen von Blech-Eigenschaften zu SOLIDWORKS-Zeichnungen“.
2025-11-13 01:12:38
El hype de la IA generativa para código ha pasado a la fase de Productividad Profesional. SuperClaude es la prueba: no es solo un chatbot que te escupe funciones, sino un meta-programming framework que transforma a herramientas como Claude Code en un socio de desarrollo especializado y consciente del contexto. Esto significa que, por fin, la IA está aprendiendo a pensar como un ingeniero senior.
SuperClaude introduce Personas Cognitivas como el "System Architect" y el "Security Engineer". Esto es crucial porque el framework se enfoca en corregir la tendencia de la IA a saltarse pasos críticos de planning, arquitectura y testing. Ya no es solo "hazme un componente", sino "diseña la API con enfoque DDD". Utiliza más de 22 comandos *slash* para tareas específicas, desde análisis de seguridad (/user:analyze --security) hasta la solución de problemas en producción (/user:troubleshoot --prod).
El framework dota al agente de una capacidad de razonamiento iterativo (Multi-Hop Reasoning) de hasta 5 saltos, permitiéndole profundizar en conceptos y seguir cadenas causales (e.g., Causa → Efecto → Prevención). Más importante aún, utiliza un Quality Scoring (Puntuación de Calidad) para validar la confianza de la fuente y la coherencia de la síntesis. Si el resultado es una alucinación, SuperClaude tiene un built-in para detectarlo.
SuperClaude implementa un mecanismo de Auditoría de Razonamiento que rastrea el proceso lógico de la IA, lo cual es esencial para la confianza y la depuración. Además, utiliza el Aprendizaje Basado en Casos (Case-Based Learning) para guardar las estrategias de éxito o fracaso en el prompt inicial. Esto significa que, si le enseñas cómo abordar un problema de React-Native en una sesión, aplicará esa estrategia y mejorará las futuras soluciones, optimizando su comportamiento.
Esta herramienta marca el pivot de la IA en la industria: de ser una muleta para novatos a ser una herramienta de delegación para seniors.
Moraleja Dev: Ya no es suficiente con que tu agente sepa escribir una función. En la fase 2.0 de la IA, lo que importa es que sepa planificar, auditar su proceso y aprender de sus errores para que la próxima vez sea más eficiente.
2025-11-13 01:12:24
When every Pod screams for CPU and memory, who decides who lives, who waits, and who gets evicted?
Kubernetes isn't just a scheduler — it's a negotiator of fairness and efficiency.
Every second, it balances hundreds of workloads, deciding what runs, what waits, and what gets terminated — while maintaining reliability and cost efficiency.
This article unpacks how Quality of Service (QoS), Priority Classes, Preemption, and Bin-Packing Scoring come together to keep your cluster stable and fair.
⚙️ The Challenge: Competing Workloads in Shared Clusters
When multiple workloads share cluster resources, conflicts are inevitable:
Kubernetes addresses this by applying a layered decision-making model — QoS, Priority, Preemption, and Scoring.
🧭 QoS (Quality of Service): Who Gets Evicted First
Each Pod belongs to a QoS class based on CPU and memory configuration:
| QoS Class | Description | Eviction Priority |
|---|---|---|
| Guaranteed | Requests = Limits for all containers | Evicted last |
| Burstable | Requests < Limits | Evicted after BestEffort |
| BestEffort | No requests/limits set | Evicted first |
💡 Lesson: Always define requests and limits — QoS decides who survives under node pressure.
🧱 Priority Classes: Who Runs First
QoS defines who stays, while Priority Classes define who starts.
Assigning PriorityClass values (integer-based) helps rank workloads during scheduling.
apiVersion: scheduling.k8s.io/v1
kind: PriorityClass
metadata:
name: critical-services
value: 100000
description: Critical platform workloads
💡 Lesson: Reserve high priorities for mission-critical services.
Overusing "high" priority leads to chaos — not resilience.
⚔️ Preemption: Controlled Sacrifice, Not Chaos
When a high-priority Pod can't be scheduled:
This is guided by PodDisruptionBudgets (PDBs) to avoid excessive collateral damage.
💡 Lesson: Preemption is controlled resilience — ensuring important workloads run while maintaining order.
⚖️ Scoring & Bin-Packing: Finding the Right Home
Once eligible nodes are filtered, Kubernetes enters the scoring phase to find the best fit.
Plugins involved:
Each node receives a score (0–100) from multiple plugins.
Weighted scores are combined:
final_score = (w1*s1) + (w2*s2) + ...
How weights work:
Scheduler plugins have default weights that you can customize via the scheduler configuration. For example:
LeastRequestedPriority: weight 1 (default) — spreads pods across nodesBalancedResourceAllocation: weight 1 (default) — prevents CPU/memory imbalanceImageLocalityPriority: weight 1 (default) — prefers nodes with cached imagesNodeAffinityPriority: weight 2 (default) — stronger preference for affinity matchesYou can adjust these weights in the kube-scheduler config to prioritize different strategies. Higher weights mean that plugin's score has more influence on the final decision.
QoS defines survivability.
Priority defines importance.
Scoring defines placement.
Together, they shape a stable and efficient cluster.
📖 Real-World Example: Critical Service Under Pressure
Imagine your payment service needs to scale during a traffic spike:
value: 100000) ensures the payment pod is considered before batch jobs.Without these mechanisms:
With proper configuration:
🧩 Visual Flow: Kubernetes Scheduling & Bin-Packing
🔧 Troubleshooting Common Issues
"Why is my high-priority pod still pending?"
kubectl describe nodes to see available CPU/memorykubectl get pod <pod-name> -o jsonpath='{.spec.priorityClassName}'
kubectl logs -n kube-system <scheduler-pod> for preemption attempts"My Guaranteed QoS pod got evicted — why?"
kubectl get nodes -o wide for DiskPressure or MemoryPressure
kubectl describe pod <pod-name> to confirm Guaranteed class"Pods are scheduling to the wrong nodes"
kubectl get nodes --show-labels
kubectl get events --field-selector involvedObject.kind=Pod
"Preemption isn't working"
kubectl get priorityclass
🧠 Key Lessons for SREs & Platform Teams
✅ Always define CPU/memory requests & limits.
✅ Use PriorityClasses sparingly.
✅ Test evictions under simulated stress.
✅ Combine QoS + PDB + Priority for controlled resilience.
✅ Observe scheduling metrics (kube_pod_status_phase, scheduler_score) regularly.
🚀 Takeaway
Kubernetes doesn't just schedule Pods — it negotiates priorities.
Reliability doesn't come from overprovisioning, but from predictable, fair, and disciplined scheduling.
Resilience = Consistency in scheduling decisions.
💬 Connect with Me
✍️ If you found this helpful, follow me for more insights on Platform Engineering, SRE, and CloudOps strategies that scale reliability and speed.
🔗 Follow me on LinkedIn if you’d like to discuss reliability architecture, automation, or platform strategy.
Images are generated using Gemini-AI
2025-11-13 01:07:36
CREATE OR REPLACE FUNCTION blob_to_text_large(p_blob BLOB) RETURN VARCHAR2 IS
l_text VARCHAR2(32767);
l_raw RAW(32767);
l_blob_length NUMBER;
l_max_safe_bytes NUMBER := 32000; -- Conservative safe limit
BEGIN
IF p_blob IS NULL THEN
RETURN '';
END IF;
l_blob_length := DBMS_LOB.GETLENGTH(p_blob);
IF l_blob_length IS NULL OR l_blob_length = 0 THEN
RETURN '';
END IF;
-- Handle large BLOBs by truncating to safe size
IF l_blob_length > l_max_safe_bytes THEN
-- Extract only safe portion and indicate truncation
l_raw := DBMS_LOB.SUBSTR(p_blob, l_max_safe_bytes, 1);
BEGIN
l_text := UTL_RAW.CAST_TO_VARCHAR2(UTL_RAW.CONVERT(l_raw, 'AL32UTF8', 'AL32UTF8'));
l_text := SUBSTR(l_text, 1, 32000) || '... [TRUNCATED: ' || l_blob_length || ' bytes]';
EXCEPTION
WHEN OTHERS THEN
l_text := '[BINARY_DATA_TRUNCATED: ' || l_blob_length || ' bytes]';
END;
ELSE
-- Handle normal-sized BLOBs
l_raw := DBMS_LOB.SUBSTR(p_blob, l_blob_length, 1);
BEGIN
l_text := UTL_RAW.CAST_TO_VARCHAR2(UTL_RAW.CONVERT(l_raw, 'AL32UTF8', 'AL32UTF8'));
EXCEPTION
WHEN OTHERS THEN
l_text := '[BINARY_DATA: ' || l_blob_length || ' bytes]';
END;
END IF;
RETURN l_text;
EXCEPTION
WHEN OTHERS THEN
RETURN 'ERROR: ' || SUBSTR(SQLERRM, 1, 100);
END blob_to_text;
/