
Neuralens
Neuralens scans your website and tells you what to improve.
Neuralens launched on May 14, 2026 with 7 votes, #1,574 of 20,538 launches that month and more than 90% of that year's launches.
What it does
Neuralens analyzes your website to uncover what hurts clarity, user experience, and conversions. Enter any URL and get clear, prioritized recommendations on security, performance and conversion blockers. Neuralens embeds RedMachine, Neuralens' phishing detection model. RedMachine powers Neuralens’ phishing detection to help identify suspicious links and security risks. Built for founders, marketers, and agencies who want to know exactly what to improve.
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Testing HN titles against a neural network2019 · github.com · ▲285Test your HN title against a neural network. Contribute to victorqribeiro/hntitlenator development by creating an account on GitHub.



Phishing Risk Assessment Tool2025 · phishingrisk.com · ▲12Instantly assess your email security risk with AI
- CVComputer vision to detect phishing attacks2016 · ▲12
A few months ago, the Exploratorium in San Francisco had its network compromised due to a phishing attack. An employee hurriedly filled out her credentials in what looked like a Google Docs sign-in but was actually a hacker's fake site. The hacker then used her credentials to steal 54 other employee passwords. Current solutions to phishing are limited to forcing employees to attend training to spot these hacks and attempting to judge the URL's reputation. People don't have time to pay attention to every url they click. This is especially true when faced with deadlines. Even the technical…
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Source: Product Hunt launch ↗
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Hey HN, Henry here from Cactus. We open-sourced Needle, a 26M parameter function-calling (tool use) model. It runs at 6000 tok/s prefill and 1200 tok/s decode on consumer devices. We were always frustrated by the little effort made towards building agentic models that run on budget phones, so we conducted investigations that led to an observation: agentic experiences are built upon tool calling, and massive models are overkill for it. Tool calling is fundamentally retrieval-and-assembly (match query to tool name, extract argument values, emit JSON), not reasoning. Cross-attention…
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