<?xml version="1.0" encoding="UTF-8"?><rss xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:content="http://purl.org/rss/1.0/modules/content/" xmlns:atom="http://www.w3.org/2005/Atom" version="2.0"><channel><title><![CDATA[Kniffy's DevLog]]></title><description><![CDATA[A personal devlog on Machine Learning, cognitive models, systems design, and vibe coding experiments.]]></description><link>https://kniffy.hashnode.dev</link><image><url>https://cdn.hashnode.com/uploads/logos/6a87f924122cfc81962c9543/95a5d8e7-1358-4fcb-9f42-072d94177f41.png</url><title>Kniffy&apos;s DevLog</title><link>https://kniffy.hashnode.dev</link></image><generator>RSS for Node</generator><lastBuildDate>Sat, 05 Sep 2026 17:59:38 GMT</lastBuildDate><atom:link href="https://kniffy.hashnode.dev/rss.xml" rel="self" type="application/rss+xml"/><language><![CDATA[en]]></language><ttl>60</ttl><item><title><![CDATA[Building a Personalized Cognitive Extender: Edge AI with Smart Glasses & On-Device LLMs]]></title><description><![CDATA[Here is the current project I’m working on: a customizable “Cognitive Extender” tailored to my exact needs.
It’s no secret that Google makes its own Pixel smartphone lineup. Opinions on them vary, and]]></description><link>https://kniffy.hashnode.dev/building-a-personalized-cognitive-extender-edge-ai-with-smart-glasses-on-device-llms</link><guid isPermaLink="true">https://kniffy.hashnode.dev/building-a-personalized-cognitive-extender-edge-ai-with-smart-glasses-on-device-llms</guid><dc:creator><![CDATA[Никита Арсёнов]]></dc:creator><pubDate>Mon, 24 Aug 2026 09:56:46 GMT</pubDate><enclosure url="https://cdn.hashnode.com/uploads/covers/6a87f924122cfc81962c9543/a63af417-ed25-40ba-8b90-28594cfacf3c.jpg" length="0" type="image/jpeg"/><content:encoded><![CDATA[<p>Here is the current project I’m working on: a customizable “Cognitive Extender” tailored to my exact needs.</p>
<p>It’s no secret that Google makes its own Pixel smartphone lineup. Opinions on them vary, and I won’t praise the series unconditionally—it often lacks stability and optimization—but one thing is certain: it’s an incredible playground for experimentation thanks to Google’s open platform approach. My hardware stack: Even G2 + Pixel 9 Pro + Even R1 / Midra link.</p>
<p>Even’s native implementation of the AI assistant, translator, teleprompter, and other features is decent, but it is clearly designed as a generic, out-of-the-box solution that relies heavily on cloud processing. The Pixel 9, on the other hand, packs a TPU core inside the Tensor G4 ("Rio") paired with LPDDR5X RAM (~45 TOPS INT8/INT4, up to 8,533 Mbps on a quad-channel bus). This architecture allows you to run on-device inference with the Gemini Nano v3 model (4B+, mixed INT4/INT2 hybrid quantization) virtually instantaneously—and completely LOCALLY.</p>
<p>Since I'm starting this blog mid-development, some of the groundwork is already laid. First off, I quickly abandoned the idea of writing fully custom firmware for the glasses. Even's engineering team did some impressive heavy lifting under the hood: a 4-MEMS microphone array handles initial ADC (PDM/I2S) -&gt; the uncompressed audio stream routes to an integrated ARM Cortex-M for conversion to linear PCM, noise reduction, and voice isolation -&gt; feeds into an LC3 encoder -&gt; and the resulting LC3 frames are transmitted over BLE 5.4 to the official Even Hub.</p>
<p>Right around that point, I realized that writing my own LC3-to-PCM decoder was a fool’s errand—it’s already implemented natively inside the official Even Hub. As a result, at the entry point of our custom ehpk plugin, we are already working with raw, uncompressed 16-bit Mono LE PCM bytes.</p>
]]></content:encoded></item><item><title><![CDATA[Hello World: From Sysadmin to Reinforcement Learning]]></title><description><![CDATA[Hey everyone, I'm Nikita.
I'm starting this blog as a home for my technical notes, side projects, and random deep dives. To be completely honest, I don't have a rigid roadmap for where this blog is he]]></description><link>https://kniffy.hashnode.dev/hello-world-from-sysadmin-to-reinforcement-learning</link><guid isPermaLink="true">https://kniffy.hashnode.dev/hello-world-from-sysadmin-to-reinforcement-learning</guid><dc:creator><![CDATA[Никита Арсёнов]]></dc:creator><pubDate>Fri, 21 Aug 2026 08:01:21 GMT</pubDate><content:encoded><![CDATA[<p>Hey everyone, I'm Nikita.</p>
<p>I'm starting this blog as a home for my technical notes, side projects, and random deep dives. To be completely honest, I don't have a rigid roadmap for where this blog is heading—part of it is simply wanting a space for creative and technical self-expression.</p>
<h3>A Quick Background</h3>
<p>I’m a Ukrainian-Russian engineer by training, with around 4 years of experience in system administration. Over the last 1.5 years, I’ve been actively diving into Machine Learning, with a particular interest in cognitive science and reinforcement learning.</p>
<h3>The Spark</h3>
<p>Like many hobbies that turn into obsessions, it started with a single project: building and training a small-scale, AlphaZero-inspired model combining <strong>CNN + DQN + LSTM</strong> architectures.</p>
<p>It wasn't an overnight success. It took roughly three months of relentless tweaking—redesigning the architecture, fixing gradient issues, and fine-tuning reward functions—before the model finally started showing real, intelligent results. That breakthrough moment was when everything clicked: I realized just how much I love this field, and experimenting with ML officially became my main passion.</p>
<h3>What to Expect Here</h3>
<p>Going forward, this blog will be a mix of:</p>
<ul>
<li><p>Ongoing ML and reinforcement learning experiments</p>
</li>
<li><p>Notes and thoughts on cognitive architectures</p>
</li>
<li><p>Practical takeaways, debugging stories, and engineering logs</p>
</li>
</ul>
<p>Thanks for stopping by! If you're interested in ML, cognitive systems, or just like following along with hands-on technical experiments, feel free to connect or drop a comment.</p>
<p><strong>Tags:</strong> <code>#MachineLearning</code> <code>#DeepLearning</code> <code>#ReinforcementLearning</code> <code>#DevLog</code> <code>#Beginner</code></p>
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