Machine Learning World
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Contacts: @anikishaev | creotiv@gmail.com
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Крутой курс за бесплатно. рекомендую
https://stanford-cs336.github.io/spring2025/
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🏭 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
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Forwarded from Devs World
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
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Interesting topic on LoRA finetuning
https://huggingface.co/blog/flux-qlora
Forwarded from Devs World
Приклад того як використання тулів в яких ви не розумієтеся та ще й без перевірки може призвести жо суттєвих проблем в бізнесі.

Ось приклад тексту що я написав для минулого поста, своїми руками без ШІ.

Перевіркана багатьох сервісах, що наче заявляють що вони можуть відрізняти ШІ контент, показав що текст сгенерований.

А тепер уявіть що ви використали подібний сервіс для фільтраціх кандидатів, чи контрагентів? Уявили?

Як людина що не один рік працював в Machine Learning, можу вас запевнити, що майже будьякий контент, шо не написаний дуже погано буде роспізнаний як ШІ. Саме тому що завдання ШІ писати якісно. А тому подібні тули не більше ніж кидок на бабки.
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Forwarded from Andrey Nikishaev
Нажаль завтра тварини будуть без їжі( Зібрали лише 5к грн з необхідних 20к.

Прошу закрийте збір, життя тварин залежать від цих зброві

https://send.monobank.ua/jar/6NekQ6ChYd
Forwarded from Devs World
🚀 As i always say - every business steals.

Here is a typical example of "innovation". The company used the design of Mig-21, to build new 155mm shells. Nothing new, nothing created from scratch. Just old technologies that worked put together and tested.

Also SpaceX rockets were developed in the same way, they just couldn't be built because at the time of when they were created there were no computers capable of such computation.

Almost all things that you think are NEW, were created ~50years ago
Друзі я тут отримав доступ до Newsletter на LinkedIn. Тож прошу підписатися

https://www.linkedin.com/newsletters/software-engineering-world-7350235171381567489/
Forwarded from Devs World
Comparative Analysis: Software Engineer vs. ML Engineer vs. ML Researcher (US Market 2025–2027)

With AI reshaping the tech landscape, many developers are asking: Should I continue as a software engineer or pivot into ML engineering or research? Here’s a breakdown of how these paths compare in the US job market over the next 2 years — covering salary, demand, skills, and long-term growth.


https://www.linkedin.com/pulse/comparative-analysis-software-engineer-vs-ml-us-andrew-nikishaev-ua-llgje