Forem: Durgesh

Kubernetes Architecture

Durgesh — Tue, 28 Apr 2026 14:00:18 +0000

Million -- token Context Window

Durgesh — Tue, 28 Apr 2026 13:52:09 +0000

Model-by-model takeaway
Claude Opus 4.7 had the strongest baseline reasoning, but it suffered the sharpest evidence-quality erosion under heavy context and got worse even when given more thinking budget.

Claude Sonnet 4.6 was the surprise winner on heavy-fill pairwise tests, but it paid for that with very high reasoning-token usage and long latency.

GPT-5.5 was the safest against hallucinations and cross-contamination, but it lost reasoning depth as context filled and showed a cliff drop at very high fill.

Gemini 3.1 Pro had the flattest drift and the fastest runtime, but its baseline reasoning ceiling was lower than the others.

DeepSeek V4 Pro looked stable on absolute scores, but pairwise testing revealed the steepest hidden losses and the highest variance.

Software architecture

Durgesh — Sat, 23 Aug 2025 05:07:09 +0000

Prompt engineering concepts

Durgesh — Sat, 23 Aug 2025 05:04:06 +0000

OSI Model

Durgesh — Sat, 23 Aug 2025 04:57:00 +0000

Application Layer
Presentation Layer
Session Layer
Transport Layer
Network Layer
Data Link Layer
Physical Layer

Kubernetes vs conventional

Durgesh — Sat, 23 Aug 2025 04:54:32 +0000

Use IAM roles to connect GitHub Actions to actions in AWS

Durgesh — Sat, 23 Aug 2025 00:48:17 +0000

https://aws.amazon.com/blogs/security/use-iam-roles-to-connect-github-actions-to-actions-in-aws/

AWS = Azure: Same difference

Durgesh — Fri, 22 Aug 2025 20:30:23 +0000

Top Cloud Careers

Durgesh — Fri, 22 Aug 2025 20:29:19 +0000

Devops Burger

Durgesh — Fri, 22 Aug 2025 20:28:46 +0000

List of all LLM models

Durgesh — Fri, 09 May 2025 21:43:56 +0000

Major LLMs

GPT-4.5 / GPT-4o / GPT-o3 / GPT-o4-mini (OpenAI)
Gemini 2.5 Pro / Gemini 2.0 / Gemini 1.5 (Google DeepMind)
Claude 3.7 Sonnet / Claude 3.5 Sonnet (Anthropic)
Grok-3 / Grok-2 / Grok-1 (xAI)
Llama 3.1 / Llama 3 / Llama 2 (Meta AI)
Mistral Large 2 / Mistral 7B / Mixtral 8x22B (Mistral AI)
Qwen 3 / Qwen 2.5-Max (Alibaba)
DeepSeek R1 / DeepSeek-V3 / DeepSeek-V2.5 (DeepSeek)
Falcon 180B / Falcon 40B (Technology Innovation Institute)
PaLM 2 (Google)
Nova (Amazon)
DBRX (Databricks' Mosaic ML)
Command R (Cohere)
Inflection-2.5 (Inflection AI)
Gemma (Google DeepMind)
Stable LM 2 (Stability AI)
Nemotron-4 340B (NVIDIA)
XGen-7B (Salesforce)
Alpaca 7B (Stanford CRFM)
Pythia (EleutherAI)
Phi-3 (Microsoft)
Jamba (AI21 Labs)
Ernie (Baidu)
Granite (IBM)
BERT (Google, foundational transformer model)

Introducing AWS CloudFormation Stack Refactoring

Durgesh — Tue, 11 Feb 2025 00:46:39 +0000

As your cloud infrastructure grows and evolves, you may find the need to reorganize your AWS CloudFormation stacks for better management, for improved modularity, or to align with changing business requirements. CloudFormation now offers a powerful feature that allows you to move resources between stacks. In this post, we’ll explore the process of stack refactoring and how it can help you maintain a well-organized and efficient cloud infrastructure.

Read the whole article here