A naukri.com initiative
Programming News
Dev
2w
370
Image Credit: Dev
Telegram AI Agent step by step (my first experience)
- A science enthusiast built an AI agent using Telegram bot to automate staying updated with scientific news and reminders, making it both fun and practical.
- Tech stack used: Telegram Chatbot, OpenAI Assistant API, APScheduler, and SerpAPI.
- Capabilities of the AI agent include composing search queries, conducting scheduled web searches, sending results to a Telegram channel, delivering reminders, and working autonomously.
- Step-by-step implementation involved creating the Telegram bot, getting API keys, setting up the project in the IDE, writing Python scripts, testing the bot, and deploying to production.
Read Full Article
22 Likes
Medium
2w
384
Image Credit: Medium
Metaverse Economics 101: Part 3 – How to Turn an In-Game Item Marketplace Into a Profit Center in…
- In Web2 gaming, in-game marketplaces allow players to trade in-game loot and gear using play money which has no real value.
- Web3 gaming ties in-game currency to cryptocurrencies, turning the marketplace into a real-money economy for players and developers.
- Every trade in Web3 gaming involves real value transactions, with a percentage tax that contributes to a reward pool, developer income, and game vault.
- Monetizing in-game item transactions without restricting player freedom creates a revenue-generating profit center that benefits from the player base's growth.
Read Full Article
23 Likes
Medium
2w
91
Image Credit: Medium
The Day I Finally Abandoned ChatGPT — And Why You Should Too
- The author canceled their ChatGPT Plus subscription not due to cost but due to OpenAI's degradation of the product's performance.
- Despite being a devoted user who restructured their professional life around ChatGPT, the author felt let down by OpenAI's prioritization of growth metrics over user experience.
- Stanford and UC Berkeley research showed empirical evidence of ChatGPT's systematic degradation while GPT-3.5 saw improvement, suggesting conscious design choices prioritizing speed and cost over capability.
- OpenAI possesses superior technology like GPT-4.1 but restricts it to API while limiting ChatGPT Plus subscribers, leading to dissatisfaction among users and a shift towards other AI providers.
Read Full Article
5 Likes
Medium
2w
288
I HATE UPWORK
- Ibrohim is a Software Engineering graduate and AI/ML specialist with a successful track record of delivering impactful solutions globally.
- Specialization in AI/ML Development & Integration, Full-Stack Development, and Business Process Automation.
- Noteworthy achievements include 70+ hackathon participations, 10+ wins, and published research with an 87.5% accuracy in medical image classification.
- Experienced in mentoring developers, founding tech communities, and speaking at industry conferences. Also known for rapid project delivery and clear communication through technical tutorials.
Read Full Article
17 Likes
Discover more
- Software News
- Web Design
- Devops News
- Open Source News
- Databases
- Cloud News
- Product Management News
- Operating Systems News
- Agile Methodology News
- Computer Engineering
- Startup News
- Cryptocurrency News
- Technology News
- Blockchain News
- Data Science News
- AR News
- Apple News
- Cyber Security News
- Leadership News
- Gaming News
- Automobiles News
Dev
2w
220
Image Credit: Dev
Balancing Code and Crayons: A Mom’s Tech Tale
- Motherhood taught a software developer new problem-solving skills and patience while raising children and building a career in tech.
- The journey from a love for code to motherhood brought a shift in priorities, requiring adaptability and time management skills.
- Adaptability over perfection became the new superpower, as the author learned to balance career and motherhood, and prioritize effectively.
- The author's message to women in tech is to embrace the marathon of their career, find balance, redefine success, and know that parenthood enhances their skills and resilience.
Read Full Article
13 Likes
Dev
2w
384
Image Credit: Dev
Personal CLI assistant on Linux
- A programmer has developed a terminal assistant powered by an open-source llms hosted locally for a customized and always ready-to-go assistant like a desktop Jarvis.
- The development process involved getting the Ollama server running locally, preparing the program to run on a dedicated terminal session, and programming the llm client and its interactions.
- Different models were tried for the assistant, with qwen3 of 8b and 14b parameters providing the best results.
- Future enhancements include improving memory between sessions, more system interaction, and compatibility with other operating systems.
Read Full Article
23 Likes
Dev
2w
235
Image Credit: Dev
100 days of Coding! Day 11
- Today was a productive day for system design learning with insights on CAP Theorem, Consistency Models, and Load Balancing.
- Understanding the trade-offs between Consistency, Availability, and Partition Tolerance offered new perspectives on distributed systems.
- The distinction between Strong and eventual consistency shed light on operations of applications like WhatsApp and Google Docs.
- Additionally, the concept of Load Balancing in distributing traffic across servers was grasped, with insights on different methods like round robin and least connections.
Read Full Article
14 Likes
Unite
2w
405
Image Credit: Unite
Guide to Understanding, Building, and Optimizing API-Calling Agents
- API-calling agents are AI tools that leverage Large Language Models (LLMs) to interact with software systems via APIs, transforming them into useful intermediaries.
- Companies use API-calling agents in consumer applications, enterprise workflows, and data retrieval and analysis to automate tasks and enhance efficiency.
- The article focuses on understanding, building, and optimizing API-calling agents using an engineering-centric approach.
- Key definitions include API, Agent, API-Calling Agent, and MCP (Model Context Protocol) for effective development of AI agents.
- The core task of an API-calling agent involves translating natural language into precise API actions, requiring intent recognition and parameter extraction.
- Architecting the solution involves defining tools for the agent, using Model Context Protocol (MCP), and selecting agent frameworks for implementation.
- Engineering for reliability and performance necessitates creating high-quality datasets, validating datasets, and optimizing agent prompts and logic.
- A systematic workflow is recommended for developing effective API agents, including clear API definitions, standardizing tool access, implementation, dataset creation, and optimization.
- The article provides an illustrative example of the workflow, highlighting steps from API definitions to agent implementation and dataset curation for evaluation.
- By integrating structured API definitions, standardized tool protocols, meticulous data practices, and systematic optimizations, engineering teams can enhance their API-calling AI agents.
Read Full Article
24 Likes
Javacodegeeks
2w
135
Image Credit: Javacodegeeks
Running LLMs Locally: Using Ollama, LM Studio, and HuggingFace on a Budget
- Running LLMs locally using tools like Ollama, LM Studio, and HuggingFace has become more accessible on consumer-grade hardware.
- Benefits of running LLMs locally include privacy, cost savings, customization, and offline access, making it ideal for developers, researchers, and businesses.
- Options for running LLMs locally include Ollama for simple setup, LM Studio for GUI interface, and HuggingFace Transformers for flexibility for Python developers.
- Hardware requirements, quantization tips, fine-tuning guide with QLoRA, and performance benchmarks for models like Mistral, LLaMA 3, and Gemma are covered in the guide.
Read Full Article
8 Likes
The Pragmatic Engineer
2w
321
Image Credit: The Pragmatic Engineer
Real-world engineering challenges: building Cursor
- Cursor, an AI-powered IDE by Anysphere, has gained popularity among engineers and recently raised $900M in a Series C round, valuing the company at $9.9B.
- Anysphere's Cursor is used by over half of the top 500 tech companies on the Fortune 500.
- The latest major release, Cursor 1.0, includes AI code review, background agents, and memory support for past chats.
- Cursor's tech stack includes TypeScript, Rust, Turbopuffer, Datadog, and more.
- Engineering challenges faced by Cursor include scaling problems, cold start issues, sharding challenges, and database migration to Turbopuffer.
- Anysphere's engineering culture involves regular releases, conservative feature flagging, dedicated infra team, and experimentation processes.
- Cursor employs 50 engineers, processes 1M transactions per second, and generates over $500M in annual revenue.
- Cursor uses TypeScript, Electron, Rust, and databases like Turbopuffer and Pinecone in its tech stack.
- The autocomplete feature in Cursor uses a low-latency sync engine and encrypted context for server-side inference.
- Cursor's chat feature works without storing code by utilizing codebase indexes and Merkle trees for efficient searches.
Read Full Article
15 Likes
Medium
2w
106
Image Credit: Medium
# Why I'm Excited About IHerta API: A Developer's Perspective on Simple, Powerful Integration
- IHerta API stands out for its promise of comprehensive functionality combined with genuine simplicity.
- The API follows a JSON-first design philosophy, with clean, predictable JSON responses and user-friendly documentation.
- Real-world integration experience highlights seamless webhook support, effective error handling, and free usage without hidden costs.
- IHerta API is ideal for developers building MVPs, small to medium projects, and teams valuing simplicity over complexity.
Read Full Article
6 Likes
Dev
2w
117
Image Credit: Dev
Android Studio: Stockholm Syndrome Disguised as an IDE
- Android Studio is often referred to as a Stockholm Syndrome Simulator for Mobile Developers due to its challenging aspects.
- The launch time of Android Studio is notably slow, making users wait for extended periods before it fully loads.
- The IDE is a memory hog, requiring substantial RAM and CPU resources, especially when working with emulators.
- The emulator in Android Studio is criticized for its slow performance and frequent failures to start.
- Gradle, the build system used in Android Studio, is known for its complex processes and error-prone nature, often causing delays in project development.
- The UI designer in Android Studio is critiqued for being difficult to use and sometimes causing elements to behave unexpectedly.
- Android Studio updates are known to introduce new issues and incompatibilities, sometimes making the development experience even more challenging.
- Autocomplete features in Android Studio can be unreliable, sometimes failing to suggest relevant code completions.
- Debugging in Android Studio is described as sluggish and prone to connectivity issues, impacting the developer's ability to inspect variables accurately.
- Despite its shortcomings, Android Studio remains indispensable for Android development, providing essential tools and functionalities for developers.
Read Full Article
7 Likes
Dev
2w
275
Image Credit: Dev
Js interview #1 : var, let, and const in JavaScript – What's the Difference?
- JavaScript variables can be declared using var, let, or const, with differences in scope, hoisting, reassignment, and more.
- 1. Scope: var is function-scoped, while let and const are block-scoped.
- 2. Hoisting: var is hoisted and initialized, while let and const are hoisted but not initialized.
- 3. Redeclaration & Reassignment: var allows both redeclaration and reassignment, let allows reassignment but not redeclaration, and const allows neither.
- 4. Temporal Dead Zone (TDZ): Variables declared with let and const have a Temporal Dead Zone where they cannot be accessed before declaration.
Read Full Article
16 Likes
Dev
2w
157
Image Credit: Dev
Your Ultimate Dev Server Setup: With Tailscale, Caddy, and Docker
- Shrijith Venkatrama is building LiveAPI, a tool for indexing API endpoints across repositories.
- Combining Tailscale, Caddy, and Docker enables secure, scalable dev server setups.
- Tailscale provides a secure private VPN network, Caddy offers automatic HTTPS and reverse proxying, and Docker ensures consistency.
- Tailscale setup involves installation and configuration, creating a private mesh network for secure access.
- Installing Docker on Ubuntu allows containerized app deployment with consistent environments.
- Caddy simplifies serving apps with automatic HTTPS and reverse proxying, enhancing server security.
- The integration of Tailscale, Caddy, and Docker allows seamless connection and secure access to applications.
- Scaling with Docker Compose simplifies managing multiple apps within containers and through Caddy's reverse proxy.
- Further securing the setup can be done with Tailscale ACLs, Caddy security measures, and Docker best practices.
- Common troubleshooting issues include Tailscale connection failures, Caddy HTTPS failures, and Docker container crashes.
Read Full Article
9 Likes
Logrocket
2w
78
Image Credit: Logrocket
I asked ChatGPT to help me design — here’s what worked
- Understanding the language of AI in design can set designers ahead by leveraging AI prompts effectively.
- Crafting quality AI prompts improves workflow in tasks like generating briefs, choosing color palettes, and creating wireframes.
- The AI prompt clarity framework involves defining role, context, task, and tone to structure prompts effectively.
- By structuring prompts intentionally, designers can obtain high-quality output tailored to their specific needs.
- Examples include crafting UX design briefs, writing microcopy, generating color palettes, and structuring websites.
- Clarity and specificity in prompts are crucial to receive relevant and useful AI-generated content.
- Defining audience, setting tone, providing context, offering examples, and refining feedback loops are best practices for writing AI prompts.
- ChatGPT is favored for design prompts, although other tools like Claude, Gemini, Jasper AI, and Notion AI serve different functions.
- AI tools empower designers and the framework shared (Role, Context, Task, Tone) ensures consistent and useful results.
- Overall, AI enhances design processes when designers understand how to effectively communicate with AI tools.
Read Full Article
4 Likes
For uninterrupted reading, download the app