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    <title>DEV Community: Fardin Sabid</title>
    <description>The latest articles on DEV Community by Fardin Sabid (@fardinsabid).</description>
    <link>https://dev.to/fardinsabid</link>
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      <title>DEV Community: Fardin Sabid</title>
      <link>https://dev.to/fardinsabid</link>
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    <item>
      <title>🚀 GQLSA v1.0.0 — Officially Released!</title>
      <dc:creator>Fardin Sabid</dc:creator>
      <pubDate>Thu, 10 Sep 2026 21:57:10 +0000</pubDate>
      <link>https://dev.to/fardinsabid/gqlsa-v100-officially-released-49mm</link>
      <guid>https://dev.to/fardinsabid/gqlsa-v100-officially-released-49mm</guid>
      <description>&lt;p&gt;🚀 GQLSA v1.0.0 — Officially Released!&lt;/p&gt;

&lt;p&gt;Grouped-Query Latent Sparse Attention&lt;/p&gt;

&lt;p&gt;A hardware-native attention mechanism built on one principle:&lt;/p&gt;

&lt;p&gt;"All tokens don't need compute. Compute only where it matters."&lt;/p&gt;

&lt;p&gt;━━━━━━━━━━━━━━━━━━&lt;/p&gt;

&lt;p&gt;📊 Performance:&lt;/p&gt;

&lt;p&gt;⚡ 3.8× faster than Multi-Head Attention&lt;br&gt;
💾 2.2× less memory at T=4096&lt;br&gt;
📦 16× smaller KV cache (2 KB vs 32 KB)&lt;br&gt;
🎯 Quality on par with dense attention&lt;br&gt;
🔒 Verified causal correctness — 11/11 tests passed&lt;/p&gt;

&lt;p&gt;━━━━━━━━━━━━━━━━━━&lt;/p&gt;

&lt;p&gt;🔬 The Three Techniques:&lt;/p&gt;

&lt;p&gt;1️⃣ Latent Compression&lt;br&gt;
→ Reduces KV from 4096 to 512 dims&lt;/p&gt;

&lt;p&gt;2️⃣ Grouped-Query Sharing&lt;br&gt;
→ 4 KV groups across 32 query heads&lt;/p&gt;

&lt;p&gt;3️⃣ Block-Sparse Selection&lt;br&gt;
→ 192 tokens per query instead of all T&lt;/p&gt;

&lt;p&gt;━━━━━━━━━━━━━━━━━━&lt;/p&gt;

&lt;p&gt;💡 Key Innovation:&lt;/p&gt;

&lt;p&gt;Unlike previous sparse attention methods that use slow Python loops, GQLSA formulates everything as a single batched matrix multiplication — fully utilizing GPU tensor cores.&lt;/p&gt;

&lt;p&gt;This is why theoretical O(T) complexity translates to real 3.8× speedup.&lt;/p&gt;

&lt;p&gt;━━━━━━━━━━━━━━━━━━&lt;/p&gt;

&lt;p&gt;📜 License: CC BY-NC-SA 4.0&lt;/p&gt;

&lt;p&gt;✅ Academic research&lt;br&gt;
✅ Education&lt;br&gt;
✅ Personal use&lt;br&gt;
❌ Commercial — requires permission&lt;/p&gt;

&lt;p&gt;━━━━━━━━━━━━━━━━━━&lt;br&gt;
📄 Research Paper: &lt;a href="https://doi.org/10.5281/zenodo.22658037" rel="noopener noreferrer"&gt;https://doi.org/10.5281/zenodo.22658037&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;💻 GitHub: &lt;a href="https://github.com/fardinsabid/gqlsa" rel="noopener noreferrer"&gt;https://github.com/fardinsabid/gqlsa&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;🏷️ Release v1.0.0: &lt;a href="https://github.com/fardinsabid/gqlsa/releases/tag/v1.0.0" rel="noopener noreferrer"&gt;https://github.com/fardinsabid/gqlsa/releases/tag/v1.0.0&lt;/a&gt;&lt;br&gt;
&lt;a href="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.us-east-2.amazonaws.com%2Fuploads%2Farticles%2Ft3ke2tq866hgibuzu3a1.png" class="article-body-image-wrapper"&gt;&lt;img src="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.us-east-2.amazonaws.com%2Fuploads%2Farticles%2Ft3ke2tq866hgibuzu3a1.png" alt=" " width="800" height="1002"&gt;&lt;/a&gt;&lt;/p&gt;

</description>
      <category>ai</category>
      <category>machinelearning</category>
      <category>performance</category>
      <category>softwaredevelopment</category>
    </item>
    <item>
      <title>🎲 Aleam — A True Random Number Generator built for AI.</title>
      <dc:creator>Fardin Sabid</dc:creator>
      <pubDate>Wed, 01 Apr 2026 12:04:37 +0000</pubDate>
      <link>https://dev.to/fardinsabid/aleam-a-true-random-number-generator-built-for-ai-20bn</link>
      <guid>https://dev.to/fardinsabid/aleam-a-true-random-number-generator-built-for-ai-20bn</guid>
      <description>&lt;p&gt;&lt;a href="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2F19sgacx14pz8vxa1uoab.jpg" class="article-body-image-wrapper"&gt;&lt;img src="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2F19sgacx14pz8vxa1uoab.jpg" alt=" " width="800" height="884"&gt;&lt;/a&gt;&lt;br&gt;
Here's the truth:&lt;/p&gt;

&lt;p&gt;Python's &lt;code&gt;random&lt;/code&gt; is actually &lt;strong&gt;pseudo-random&lt;/strong&gt;.  &lt;/p&gt;

&lt;p&gt;Aleam delivers &lt;strong&gt;real randomness&lt;/strong&gt; — straight from your system, hashed with BLAKE2s.  &lt;/p&gt;

&lt;p&gt;No tricks. No fakes. Just like nature intended.&lt;/p&gt;

&lt;p&gt;✨ What it can do:&lt;/p&gt;

&lt;p&gt;• 15+ statistical distributions  &lt;/p&gt;

&lt;p&gt;• Works with PyTorch, TensorFlow, JAX, CuPy  &lt;/p&gt;

&lt;p&gt;• GPU acceleration with CUDA  &lt;/p&gt;

&lt;p&gt;• 81 tests passing — 100% production ready&lt;/p&gt;

&lt;p&gt;📦 Install it:&lt;/p&gt;

&lt;p&gt;pip install aleam&lt;/p&gt;

&lt;p&gt;🇧🇩 Built in Bangladesh. Open source. For everyone.&lt;/p&gt;

&lt;p&gt;🚀 Want true randomness in your project? Start today.&lt;/p&gt;

&lt;p&gt;Github:&lt;a href="https://github.com/fardinsabid/aleam" rel="noopener noreferrer"&gt;https://github.com/fardinsabid/aleam&lt;/a&gt;&lt;br&gt;
PYPI:&lt;a href="https://pypi.org/project/aleam/" rel="noopener noreferrer"&gt;https://pypi.org/project/aleam/&lt;/a&gt;&lt;/p&gt;

</description>
      <category>ai</category>
      <category>opensource</category>
      <category>python</category>
      <category>showdev</category>
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