Indian Student Builds AI Tools Platform Xedge in 3 Months With Near-Zero Budget

·3 views

Harsha, a college student from India, launched Xedge, an AI tools platform, after feeling overwhelmed by the fragmented and disorganized state of existing AI tool directories. He envisioned a single ecosystem where users could discover, buy, sell, and build AI tools, templates, prompts, and automations in one place. With no prior web development experience beyond C language, he self-taught HTML, CSS, JavaScript, Python, and MySQL in under a month, then used AI coding assistants to accelerate development while manually reviewing the output. The platform has since attracted over 600 users, with Harsha spending virtually nothing beyond the cost of a domain name. Despite not yet generating revenue, he completed the project within three months and continues working toward a fully monetized version of Xedge.

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)

Operant Conditioning Explained: How Rewards and Punishments Shape Behavior

Operant conditioning is a learning process in which voluntary behaviors are shaped by their consequences, including reinforcements that increase a behavior and punishments that decrease it. Psychologist B.F. Skinner demonstrated this through experiments with rats, showing that animals reliably learn actions when rewarded with food. Different reinforcement schedules produce distinct behavioral patterns — variable ratio rewards, as seen in slot machines, generate the most persistent responses and underlie addictive behaviors. The principles apply broadly to parenting, workplace management, and addiction, where immediate and unpredictable rewards tend to have the strongest influence on behavior. Research consistently shows that reinforcement is more effective than punishment for building lasting behavioral change, as punishment suppresses actions without teaching alternatives.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

ByteDance Finds AI Agents Improve Predictably the Longer They Operate in Real World

ByteDance Seed, the core AI research team behind TikTok's parent company, published a study called EdgeBench on July 2, 2026, introducing a new scaling law for AI agents. The research analyzed over 38,000 hours of AI agent interactions across 134 real-world tasks in six categories, involving models such as Claude Opus 4.8, GPT 5.5, and DeepSeek V4 Pro. Researchers found that agent performance improves in a mathematically predictable pattern, with learning speed roughly doubling every three months of real-world deployment. This post-deployment scaling is distinct from traditional pre-training scaling, which has faced diminishing returns due to rising compute costs and shrinking high-quality data supplies. The findings suggest that competitive advantage in AI may shift from raw compute resources toward having large, active user bases that generate real-world feedback.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

Awesome-Claude-Code Repository Hits 47K Stars as Go-To Hub for Power Users

A GitHub repository called awesome-claude-code, maintained by user hesreallyhim, has emerged as the leading community resource for users of Anthropic's Claude Code agentic coding assistant. The hand-curated collection includes skills, hooks, slash commands, agent orchestrators, workflows, and plugins that extend Claude Code's capabilities. Unlike auto-scraped lists, the repository applies editorial judgment — removing tools that don't work and tagging those that do based on practical utility. It has accumulated approximately 47,000 stars and nearly 4,000 forks, reflecting rapid growth among developers. The project addresses a core challenge: Claude Code's feature set is expanding faster than official documentation can track, making community-driven curation increasingly valuable.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

Unity, Godot or Unreal in 2026: A Practical Guide for Indie Game Developers

The 2023 Unity Runtime Fee controversy prompted many indie developers to reconsider their engine choices, ultimately benefiting Godot's growth while Unity reversed its unpopular changes and Unreal maintained dominance in high-fidelity projects. A Unity-specialist studio with 12 years of game development experience has outlined practical guidance for small teams choosing between the three engines. Unity is recommended for one-to-five-person teams focused on 2D or mobile games, citing its large contractor pool, asset ecosystem, and monetisation tools. Godot suits developers who prioritise open licensing or are building 2D-first projects, while Unreal is advised only for small teams with strong C++ skills where visual fidelity is a core selling point. Key factors influencing the recommendation include engine cost, iteration speed, hiring flexibility, pre-built asset availability, and the stability of licence terms.

0 comments Read more at DEV Community