PRIMENTAL/index.html at main · WiktorLigeza/PRIMENTAL · GitHub

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<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <title>Primental</title>
    <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/6.4.0/css/all.min.css">
    <link rel="stylesheet" href="style.css">
</head>

<script>
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    varying vec3 vPos;

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      vPos = position;
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      gl_Position = projectionMatrix * mvPosition;
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  `,

  fragmentShader: `
    vec4 permute(vec4 x){return mod(((x*34.0)+1.0)*x, 289.0);}
    vec4 taylorInvSqrt(vec4 r){return 1.79284291400159 - 0.85373472095314 * r;}
    vec3 fade(vec3 t) {return t*t*t*(t*(t*6.0-15.0)+10.0);}

    float cnoise(vec3 P){
      vec3 Pi0 = floor(P); // Integer part for indexing
      vec3 Pi1 = Pi0 + vec3(1.0); // Integer part + 1
      Pi0 = mod(Pi0, 289.0);
      Pi1 = mod(Pi1, 289.0);
      vec3 Pf0 = fract(P); // Fractional part for interpolation
      vec3 Pf1 = Pf0 - vec3(1.0); // Fractional part - 1.0
      vec4 ix = vec4(Pi0.x, Pi1.x, Pi0.x, Pi1.x);
      vec4 iy = vec4(Pi0.yy, Pi1.yy);
      vec4 iz0 = Pi0.zzzz;
      vec4 iz1 = Pi1.zzzz;

      vec4 ixy = permute(permute(ix) + iy);
      vec4 ixy0 = permute(ixy + iz0);
      vec4 ixy1 = permute(ixy + iz1);

      vec4 gx0 = ixy0 / 7.0;
      vec4 gy0 = fract(floor(gx0) / 7.0) - 0.5;
      gx0 = fract(gx0);
      vec4 gz0 = vec4(0.5) - abs(gx0) - abs(gy0);
      vec4 sz0 = step(gz0, vec4(0.0));
      gx0 -= sz0 * (step(0.0, gx0) - 0.5);
      gy0 -= sz0 * (step(0.0, gy0) - 0.5);

      vec4 gx1 = ixy1 / 7.0;
      vec4 gy1 = fract(floor(gx1) / 7.0) - 0.5;
      gx1 = fract(gx1);
      vec4 gz1 = vec4(0.5) - abs(gx1) - abs(gy1);
      vec4 sz1 = step(gz1, vec4(0.0));
      gx1 -= sz1 * (step(0.0, gx1) - 0.5);
      gy1 -= sz1 * (step(0.0, gy1) - 0.5);

      vec3 g000 = vec3(gx0.x,gy0.x,gz0.x);
      vec3 g100 = vec3(gx0.y,gy0.y,gz0.y);
      vec3 g010 = vec3(gx0.z,gy0.z,gz0.z);
      vec3 g110 = vec3(gx0.w,gy0.w,gz0.w);
      vec3 g001 = vec3(gx1.x,gy1.x,gz1.x);
      vec3 g101 = vec3(gx1.y,gy1.y,gz1.y);
      vec3 g011 = vec3(gx1.z,gy1.z,gz1.z);
      vec3 g111 = vec3(gx1.w,gy1.w,gz1.w);

      vec4 norm0 = taylorInvSqrt(vec4(dot(g000, g000), dot(g010, g010), dot(g100, g100), dot(g110, g110)));
      g000 *= norm0.x;
      g010 *= norm0.y;
      g100 *= norm0.z;
      g110 *= norm0.w;
      vec4 norm1 = taylorInvSqrt(vec4(dot(g001, g001), dot(g011, g011), dot(g101, g101), dot(g111, g111)));
      g001 *= norm1.x;
      g011 *= norm1.y;
      g101 *= norm1.z;
      g111 *= norm1.w;

      float n000 = dot(g000, Pf0);
      float n100 = dot(g100, vec3(Pf1.x, Pf0.yz));
      float n010 = dot(g010, vec3(Pf0.x, Pf1.y, Pf0.z));
      float n110 = dot(g110, vec3(Pf1.xy, Pf0.z));
      float n001 = dot(g001, vec3(Pf0.xy, Pf1.z));
      float n101 = dot(g101, vec3(Pf1.x, Pf0.y, Pf1.z));
      float n011 = dot(g011, vec3(Pf0.x, Pf1.yz));
      float n111 = dot(g111, Pf1);

      vec3 fade_xyz = fade(Pf0);
      vec4 n_z = mix(vec4(n000, n100, n010, n110), vec4(n001, n101, n011, n111), fade_xyz.z);
      vec2 n_yz = mix(n_z.xy, n_z.zw, fade_xyz.y);
      float n_xyz = mix(n_yz.x, n_yz.y, fade_xyz.x);
      return 2.2 * n_xyz;
    }

    varying vec3 vPos;
    uniform float skyRadius;
    uniform vec3 noiseOffset;

    uniform vec3 env_c1;
    uniform vec3 env_c2;

    uniform float clusterSize;
    uniform float clusterStrength;

    uniform float starSize;
    uniform float starDensity;
    uniform float time;

    void main() {
      float freq = 1.1/skyRadius;
      float noise = cnoise(vPos * freq);
      vec4 backgroundColor = vec4(mix(env_c1, env_c2, noise), 1.0);

      float scaledClusterSize = (1.0/clusterSize)/skyRadius;
      float scaledStarSize = (1.0/starSize)/skyRadius;

      float cs = pow(abs(cnoise(scaledClusterSize*vPos+noiseOffset)),1.0/clusterStrength) + cnoise(scaledStarSize*vPos);

      float c =clamp(pow(abs(cs), 1.0/starDensity),0.0,1.0);
      vec4 starColor = 0.5*vec4(c, c, c, 1.0);

      vec3 p = vPos * mod(time * 2.5, 10.);
      //p.y += time;
      float sn = sin(time * 2.) * 0.5 + 0.5;
      float starNoise = (cnoise(p * freq) * 0.5 + 0.5) * sn;

      gl_FragColor = backgroundColor;
      gl_FragColor += starColor * starNoise;
    }
  `,

};
</script>


<body>
    <div id="container">
        <header id="mainHeader">
            <div class="header-left">
                <div class="logo">
                </div>
            </div>
            <div class="header-right">

            </div>
        </header>

        <!-- Content wrapper with text and image grid -->
        <div class="content-wrapper">
            <div class="left-section">
                <div class="image-grid animate-from-left" id="imageGrid">
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                <!-- <div class="text-container animate-from-right" style="width: 100%; margin-top: 5%;">

                </div> -->
            </div>
            <div class="text-container animate-from-right">
                <h2>About Primental</h2>
                <p>Primental is a startup at the forefront of AI, large language models (LLMs), and computer vision technologies. We specialize in developing cutting-edge solutions with a strong focus on low-effort anomaly detection systems.</p>

                <p>Our innovative approach enables rapid deployment of intelligent systems that can identify anomalies and deviations with minimal training data. By combining modern machine learning techniques with self-supervised learning, we deliver robust solutions that are accurate, scalable, and easy to integrate in industrial environments.</p>

                <h3>Anomaly Detection Solution</h3>
                <p>Our flagship anomaly detection solution revolutionizes quality control and predictive maintenance by working with minimal input and limited sensor access. Unlike traditional systems that require extensive datasets and full system visibility, our technology can map and model complex systems using only a few samples. By leveraging artificial dataset generation and advanced self-supervised learning, we reduce the need for extensive real-world data collection while maintaining high accuracy and reliability.</p>

                <h4>Key Features:</h4>
                <ul>
                    <li><span class="feature-emoji">🎯</span> Low-effort deployment with minimal sample requirements</li>
                    <li><span class="feature-emoji">🤖</span> Artificial dataset generation reduces data collection overhead</li>
                    <li><span class="feature-emoji">🧠</span> Modern ML and self-supervised learning for robust detection</li>
                    <li><span class="feature-emoji">📈</span> Rapid deployment and high scalability</li>
                </ul>
            </div>
        </div>


    </div>


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