SShortSingh.
Back to feed

Developer Releases Free 9-Module Apache Kafka Course With 470 Minutes of Content

0
·1 views

A software developer has published a free Apache Kafka course comprising 9 modules and 470 minutes of content, with no paywall or premium tier. The course was built to address a gap the author identified in existing learning resources, which either assumed prior knowledge or skipped foundational concepts. Unlike typical tutorials, the first two modules focus on the architectural problems that event streaming solves before introducing Kafka itself. The curriculum uses a consistent e-commerce scenario to build context, and includes working Python code along with a minimal Docker Compose setup. The author also shares mistakes made during curriculum development to help future learners and educators avoid similar pitfalls.

Read the full story at DEV Community

This is an AI-generated summary. ShortSingh links to the original source for the complete article.

Discussion (0)

Log in to join the discussion and vote.

Log in

Related stories

0
ProgrammingHacker News ·

Developer Uses Claude AI to Analyze Personal MRI Scan for Second Opinion

A developer shared their experience using Anthropic's Claude Code with the Opus model to analyze their own MRI scan results. The experiment involved feeding medical imaging data into the AI tool to obtain an independent interpretation alongside a professional diagnosis. The post, shared on Hacker News, attracted 15 comments and 19 upvotes from the tech community. The author documented the process on their personal website, highlighting both the capabilities and limitations of using AI for personal medical review. The experiment reflects a growing trend of individuals turning to large language models for supplementary health information, raising questions about AI's role in personal healthcare.

0
ProgrammingHacker News ·

Daisugi: The Ancient Japanese Technique of Growing Trees From Trees

Daisugi is a traditional Japanese forestry technique that involves growing multiple straight shoots from the base of a single cedar tree. Originating in the Kitayama region of Japan, the method was developed to produce high-quality, uniform timber on limited land. By carefully pruning lateral branches, foresters guide vertical shoots to grow perfectly straight, yielding usable wood without felling the parent tree. This approach maximizes timber output per unit of land while preserving the original tree for future harvests. The technique has drawn renewed global interest for its sustainable and space-efficient qualities.

0
ProgrammingHacker News ·

Tokenmaxxing Evolves: What the Shift Means for AI Agent Design

A new piece published on 12gramsofcarbon.com argues that the concept of 'tokenmaxxing' — optimizing AI prompts to maximize token usage — is undergoing a significant transformation. The article connects this shift to broader developments in agentic AI systems, where token strategies must adapt to more complex, multi-step workflows. The author suggests that while the original tokenmaxxing approach may be fading, its core principles are being reimagined rather than abandoned. The post gained traction on Hacker News, sparking early discussion among developers and AI practitioners.

0
ProgrammingDEV Community ·

Context Engineering Emerges as the New Standard for Production AI Systems

As AI systems grow more complex, experts argue that prompt engineering — the practice of refining text inputs to a model — is no longer sufficient for building reliable production-grade applications. Unlike simple single-turn tasks, modern AI systems involve multi-step reasoning, memory, tool calls, and retrieval from external sources, making the broader information environment more critical than prompt wording alone. Most failures in production AI are attributed not to the model itself but to poor context design, where relevant information is missing, buried, or diluted within the context window. A 2026 arXiv paper introduced the concept of 'context rot,' finding that model performance degrades as uncurated information accumulates in the context window. Context engineering addresses this by treating the full stack of inputs — system prompts, retrieved documents, memory summaries, and conversation history — as a structured pipeline to optimize at inference time.

Developer Releases Free 9-Module Apache Kafka Course With 470 Minutes of Content · ShortSingh