TownSquare Tool Lets Website Visitors See and Interact With Each Other

·1 views

A developer named Caue Napier has released a tool called TownSquare that adds social presence features to any website. The tool allows visitors to see other users currently browsing the same site, enabling spontaneous interactions similar to bumping into someone in a public space. TownSquare is designed to make websites feel more like shared community spaces rather than isolated browsing experiences. The release was shared on Hacker News, where it attracted early attention and discussion from developers.

Read the full story at Hacker News

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

Discussion (0)

Developer Launches Softchic, a Premium Template Marketplace Built on Next.js 14

A developer known as Delight has begun building Softchic, a premium website template and ready-made site marketplace targeting developers and businesses. The platform is being built using Next.js 14, TypeScript, Tailwind CSS v4, and shadcn/ui components, with payments handled via Lemon Squeezy and Paystack. In its first week, the project has completed a waitlist page, responsive navbar, email capture form, brand identity, and payment architecture. The founder aims to attract 200 waitlist subscribers before officially opening the store, with a planned launch window of July 18–20, 2026. A product listing page, template preview system, and a first SaaS landing page template are among the next milestones planned.

0 comments Read more at DEV Community

ProgrammingHacker News ·

Michigan's $1.8B Economic Development Spend Yielded Only 602 Jobs

Michigan spent $1.8 billion in economic development funds but managed to create only 602 jobs, raising serious questions about the efficiency of the state's spending. The figures suggest a cost of roughly $3 million per job created, far exceeding typical benchmarks for public investment returns. The report has drawn attention to how the state allocates taxpayer money toward job creation initiatives. Critics are likely to scrutinize the programs responsible for the spending and demand greater accountability. The findings underscore a broader debate about the effectiveness of government-led economic development efforts.

0 comments Read more at Hacker News

ProgrammingDEV Community ·

Active Inference Technique Lets AI Agents Develop Curiosity Without Explicit Programming

A developer built a small AI agent using active inference, a brain-inspired approach where the agent tries to minimize surprise rather than simply chase rewards. Unlike conventional reward-seeking agents, this agent independently chooses to gather information before acting, because uncertainty itself carries a cost in its decision-making. In a simple door-choice task where a hint reveals the correct answer, the active inference agent achieved 100% success compared to 48% for a standard reward-chasing agent across 400 attempts. The agent was never instructed to check the hint — it did so because resolving uncertainty was inherently valuable to its goal. The developer notes this approach could address a longstanding AI challenge of getting agents to explore new situations without manually programming exploration incentives.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

Developer Uses Classic Child Psychology Test to Show AI Agents Need Theory of Mind

Software engineer Shridhar Shah built two AI agents to demonstrate how 'theory of mind' — the ability to track what others believe versus what is actually true — affects agent performance. The experiment is based on the Sally-Anne false-belief test, a well-known child psychology benchmark in which children must distinguish their own knowledge from another person's mistaken belief. Shah's first agent, which only tracks objective reality, incorrectly predicts where Sally will look for a moved marble, mirroring the reasoning of a three-year-old. His second agent maintains separate belief states for each person, updating them only when that person is present to witness an event, allowing it to answer correctly. Shah argues this capability is foundational for AI agents working collaboratively with humans or other agents, enabling better task delegation, targeted explanations, and fewer faulty assumptions.

0 comments Read more at DEV Community