Работа:

МЛ архитект до 60евро/час, до 20 часов в неделю.
https://jobs.ashbyhq.com/gt-hq/99f890e3-7069-439c-8952-3df52b3c3247

💥 🧠 Нарешті чуваки зробили відео про симуляцію навколишньго середовища в мозку.


Це відео пояснить:
- Чому те що ви бачете це іллюзія
- Чому неможливо мати миттеву реакцію
- Чому ваша віра впливає на те що ви бачите
- Чому погане середовище під час розвитку дитини так негативно впливає на її майутне.
... та ще багато всього цікавого.
Тому дуже рекомендую подивитися

https://www.youtube.com/watch?v=wo_e0EvEZn8

Ісусе.. воно ще в МЛ полізло.. де ті местники ганула коли вони так потрібні країні https://forbes.ua/ru/innovations/ukrainska-llm-ta-shi-asistent-v-dii-golovne-z-vistupu-kerivnika-mintsifri-mikhayla-fedorova-na-ai-day-vid-forbes-ukraine-28032025-28409

Friends ill be very grateful for sharing this post
https://www.linkedin.com/posts/creotiv_friends-im-currently-looking-for-a-new-activity-7332685780118552577-iQef?utm_source=share&utm_medium=member_desktop&rcm=ACoAAAPl0X4BWZSqccqAVcirdBAwe5jWKVOQ9fI

Крутой курс за бесплатно. рекомендую
https://stanford-cs336.github.io/spring2025/

мл локальные модели для андроид
https://github.com/google-ai-edge/gallery

🏭 I built a small #tutorial that illustrates how sidecar containers can transform observability and control in any Kubernetes‐based service. Instead of embedding metrics, logging, and networking logic directly into your app, you simply “bolt on” three dedicated sidecars:

1. Prometheus – Collects application and infrastructure metrics out of the box.

2. Promtail → Grafana Loki – Streams logs in real time without changing your app code.

3. Envoy Proxy – Handles TLS termination, request filtering, and even simulates network failures.

✅ Why this Matters

- Separation of Concerns: All observability (metrics + logs) and networking (TLS, filters, chaos testing) live outside the main container. Your app stays lightweight and focused solely on business logic.

- Consistency Across Services: You can reuse the exact same Prometheus/Promtail/Envoy configuration for every microservice. No more “one‐off” instrumentation code in each repo.

- Faster Development & Onboarding: Developers don’t need to spend time wiring in monitoring libraries or custom proxy code—sidecars handle it for you automatically.

- Easier Upgrades and Troubleshooting: When you need to tweak logging formats or experiment with TLS settings, you update the sidecar image, not dozens of disparate applications.

⚠️ Considerations

- Resource Overhead: Each sidecar adds CPU/memory usage. In lightweight deployments, be mindful of your node capacity and pod limits.

- Operational Complexity: More containers per Pod means more YAML to manage and additional network hops. You’ll need to ensure your CI/CD and Helm charts stay up to date.

- Sidecar Lifecycle: When you roll out a new version of your app, the sidecar versions may need to stay in sync (e.g., Promtail pipelines). Establish clear versioning and testing practices.

Ultimately, moving observability and networking logic into reusable sidecars makes your main application image lighter, speeds up developer onboarding, and enforces a consistent control plane across all services. If you’re evaluating how to standardize monitoring, logging, and network policies at scale, consider a sidecar‐centric approach. It’s a small shift in architecture that can pay big dividends in maintainability and speed.

https://github.com/creotiv/sidecar-demo

5min to write AI YouTube moderator for streams)

https://www.linkedin.com/posts/creotiv_people-saying-vibe-coding-is-a-bad-thing-activity-7336053511316455426-VzAF?utm_source=share&utm_medium=member_desktop&rcm=ACoAAAPl0X4BWZSqccqAVcirdBAwe5jWKVOQ9fI

Modern generative #AI and large language model (#LLM) services create unique traffic-routing challenges on #Kubernetes. Unlike typical short-lived, stateless web requests, LLM inference sessions are often long-running, resource-intensive, and partially stateful. For example, a single #GPU-backed model server may keep multiple inference sessions active and maintain in-memory token caches.

Traditional load balancers focused on HTTP path or round-robin lack the specialized capabilities needed for these workloads. They also don’t account for model identity or request criticality (e.g., interactive chat vs. batch jobs). Organizations often patch together ad-hoc solutions, but a standardized approach is missing.

And here comes the new #Gateway API Inference Extension in #K8S

https://kubernetes.io/blog/2025/06/05/introducing-gateway-api-inference-extension/

Bonhoeffer’s Theory of Stupidity: A Tool for Control?

Dietrich Bonhoeffer, a theologian executed for resisting the Nazis, had a haunting insight: stupidity is more dangerous than malice. Why? Because stupid people can be manipulated without knowing they’re being used — and worse, they often become fanatically loyal to ideas they don’t understand.

Bonhoeffer observed that stupidity is not a lack of intelligence but a moral failure, a refusal to think critically. And this, he warned, flourishes most in times of social upheaval and under authoritarian regimes.

Why does this matter today?

Because those in power — whether governments or media empires — often rely on this passive obedience. It’s easier to rule a population that feels informed but doesn’t question narratives, doesn’t challenge contradictions, and fears being isolated for thinking differently.

True resistance doesn’t always start with shouting — it starts with thinking. And that’s exactly what oppressive systems try to prevent.

Bonhoeffer’s warning wasn’t just about Nazi Germany. It’s about every society that starts silencing thought under the guise of unity or safety.

Critical thinking is not rebellion — it’s responsibility.

Watch this video to not regret later
https://youtu.be/Sfekgjfh1Rk?si=kWTd2r7OH8tqwCxT

Подключаемся на стрим
https://youtube.com/live/ERrRBBYIQe8?feature=share

Interesting topic on LoRA finetuning
https://huggingface.co/blog/flux-qlora