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Interactively learn to process real-time data with Fenwick trees and segment trees
https://www.reddit.com/r/programming/comments/1muncbb/interactively_learn_to_process_realtime_data_with/

<!-- SC_OFF -->some time i thinks, how big ecommerce platforms and IOT devices and algo trading handles and query millions of data points effectively without any hurdles think if platform like amazon which have to update the discount price of the all the products at a time and run a query for that it would take so long, (sorting the product, finding the product, updating it) the real time trading terminals which have to update the real time price have to be efficient or it may cause huge financial losses so according to me the Fenwick trees and segment trees can be good or potential solution for this kind of situation, this two algorithms are super powerful for querying, replacing index values with extreme efficiency and time complexity meanwhile thinking it i have tried to experiment with it and wrote something about it , if someone interested learning this algorithms and how they works can check it out here (https://beyondit.blog/blogs/segment-fenwick-trees-real-time-analytics) <!-- SC_ON --> submitted by /u/BeyondITBlog (https://www.reddit.com/user/BeyondITBlog)
[link] (https://beyondit.blog/blogs/segment-fenwick-trees-real-time-analytics) [comments] (https://www.reddit.com/r/programming/comments/1muncbb/interactively_learn_to_process_realtime_data_with/)
Drag-and-drop TUI designer: Design once, deploy to Python, Go, Rust, or Node.js terminal frameworks
https://www.reddit.com/r/programming/comments/1mv6550/draganddrop_tui_designer_design_once_deploy_to/

<!-- SC_OFF -->I was tired of manually coding terminal UIs and switching between different TUI frameworks for different projects. Each framework has its own patterns, and building dashboards from scratch takes forever. The generated code follows each framework's idioms and best practices. You get proper async patterns in Python, channels in Go, ownership in Rust, and event handling in Node.js. Connect to system metrics (CPU, memory, disk, network), APIs, log files, or command output. Generates professional terminal applications you can deploy anywhere. Currently working on plugin architecture and real-time collaboration. Would love feedback from the community. Hope it helps others too. <!-- SC_ON --> submitted by /u/catalysed (https://www.reddit.com/user/catalysed)
[link] (https://github.com/aerocyberlabs/jackal_cli) [comments] (https://www.reddit.com/r/programming/comments/1mv6550/draganddrop_tui_designer_design_once_deploy_to/)
I built a Spring Boot starter for MapStruct to remove boilerplate – feedback welcome
https://www.reddit.com/r/programming/comments/1mv8kwj/i_built_a_spring_boot_starter_for_mapstruct_to/

<!-- SC_OFF -->Hello everyone, While working with Spring Boot + MapStruct, I noticed one recurring pain: Repeatedly writing @Mapper(componentModel = "spring") in every mapper Manual configuration boilerplate just to get things wired up It may look like a small at the first… but across multiple projects and dozens of mappers, this repetitive setup becomes a bigger productivity drag and an easy source of mistakes. So, I created a MapStruct Spring Boot Starter. What it does: Auto-detects and registers MapStruct mappers in your Spring Boot app No need to specify componentModel = "spring" everywhere in the mappers Simple dependency + optional application.yml config → ready to go Installation: 1) Dependency: Maven: com.codestackfoundry.starters mapstruct-spring-boot-starter 1.0.1 Gradle: dependencies { implementation 'com.codestackfoundry.starters:mapstruct-spring-boot-starter:1.0.1' } Configuration(Optional): mapstruct: base-packages: - com.example.demo.mapper - com.shared.mappers fail-if-no-mappers: true 🔗 GitHub: https://github.com/codestackfoundry/spring-boot-starters
📄 Docs: https://codestackfoundry.com/docs/mapstruct-spring-boot-starter.html I’d love to hear your feedback. <!-- SC_ON --> submitted by /u/effortlesscodeLearn (https://www.reddit.com/user/effortlesscodeLearn)
[link] (https://codestackfoundry.com/docs/mapstruct-spring-boot-starter.html) [comments] (https://www.reddit.com/r/programming/comments/1mv8kwj/i_built_a_spring_boot_starter_for_mapstruct_to/)
Vibe Coding and AI Agents Redefine How Web Apps Are Built in 2025 – [Research Results]
https://www.reddit.com/r/programming/comments/1mv9beq/vibe_coding_and_ai_agents_redefine_how_web_apps/

<!-- SC_OFF -->We just wrapped up our fourth annual “Starting Web App” research, and the shift we’re seeing this year feels like a real breaking point in software development. Some highlights: AI app generators exploded — they jumped to 38% adoption in just one year. “Vibe coding” (tools like Lovable, Bolt, Base44, etc.) went from experimental to mainstream, letting devs “chat” an app into existence. AI agents are starting to handle not just coding, but requirements gathering, schema changes, and even version control. Traditional dev + low-code are still here, but the balance tilts hard toward AI-first approaches. Full write-up, data, and charts are here:
👉 https://flatlogic.com/starting-web-app-in-2025-research-results Curious to hear what you think: Are these AI-first tools production-ready, or still toys? Will devs trust agents to handle critical backend + data work, or will it stay a frontend toy for now? How will this reshape SaaS startups over the next 2–3 years? <!-- SC_ON --> submitted by /u/flatlogic-generator (https://www.reddit.com/user/flatlogic-generator)
[link] (https://flatlogic.com/starting-web-app-in-2025-research-results) [comments] (https://www.reddit.com/r/programming/comments/1mv9beq/vibe_coding_and_ai_agents_redefine_how_web_apps/)
From single data query agent to MCP (Model Context Protocol) AI Analyst
https://www.reddit.com/r/programming/comments/1mvbq5w/from_single_data_query_agent_to_mcp_model_context/

<!-- SC_OFF -->We started with a simple AI agent for data queries but quickly realized we needed more: root cause analysis, anomaly detection, and new functionality. Extending a single agent for all of this would have made it overly complex. So instead, we shifted to MCP (Model Context Protocol). This turned our agent into a modular AI Analyst that can securely connect to external services in real time. Here’s why MCP beats a single-agent setup: 1. Flexibility Single Agent: Each integration is custom-built → hard to maintain. MCP: Standard protocol for external tools → plug/unplug tools with minimal effort. This is the only code your would need to post to add MCP server to your agent Sample MCP configuration "playwright": { "command": "npx", "args": [ "@playwright/mcp@latest" ] } 2. Maintainability Single Agent: Tightly coupled integrations mean big updates if one tool changes. MCP: Independent servers → modular and easy to swap in/out. 3. Security & Governance Single Agent: Permissions can be complex and less controllable (agent gets too much permissions compared to what is needed. MCP: standardized permissions and easy to review (read-only/write). ​ "servers": { "filesystem": { "permissions": { "read": [ "./docs", "./config" ], "write": [ "./output" ] } } } 👉 You can try out to connect MCP servers to data agent to perform tasks that were commonly done by data analysts and data scientists: GitHub — datu-core (https://github.com/Datuanalytics/datu-core). The ecosystem is growing fast and there are a lot of ready made MCP servers mcp.so — a large directory of available MCP servers across different categories. MCPLink.ai — a marketplace for discovering and deploying MCP servers. MCPServers.org — a curated list of servers and integrations maintained by the community. MCPServers.net — tutorials and navigation resources for exploring and setting up servers. Has anyone here tried building with MCP? What tools would you want your AI Analyst to connect to? <!-- SC_ON --> submitted by /u/shalinga123 (https://www.reddit.com/user/shalinga123)
[link] (https://github.com/Datuanalytics/datu-core) [comments] (https://www.reddit.com/r/programming/comments/1mvbq5w/from_single_data_query_agent_to_mcp_model_context/)