Semantic Search Is No Longer an ML Problem
Why your search system is failing, and why it is probably not the model’s fault Continue reading on Medium »
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Google’s TurboQuant Is Quietly Rewriting the Rules of AI Memory
Google’s TurboQuant shrinks AI’s working memory by up to 10x A new compression algorithm from Google Research shrinks AI’s working memory by up to 10x — with near-zero accuracy loss. Here is how it works, and why it matters. Every time you have a long conversation with an AI, ask it to summarize a document, or run a complex semantic search, the model is quietly filling up a working memory called the key-value cache . It is the model’s fast-access notepad — storing what it has already processed so it does not have to recompute everything from scratch with each new word. And at scale, it is enormously expensive. The reason comes down to what is actually being stored. For every single token the model processes — every word, every punctuation mark — it stores two high-dimensional vectors: a ke
Measured post-2050 input anchors and PNG review artifacts for the TD2 SDL port
<h1> Measured post-2050 input anchors and PNG review artifacts for the TD2 SDL port </h1> <p>The current TD2 SDL runtime already had scripted input and a first menu handoff mutator, but it still flattened the post-<code>2050</code> default-rival corridor into a mostly generic rail.</p> <p>This checkpoint moves one step deeper into a SNES-mimetic path:</p> <ul> <li>promoted exact no-input scheduler anchors at frames <code>2052</code>, <code>2053</code>, <code>2083</code>, <code>2104</code>, and <code>2125</code> </li> <li>overlaid the traced default-rival <code>A</code> route on top of those anchors</li> <li>carried measured fields instead of heuristics: <code>state_09a2</code>, <code>state_09a8</code>, <code>state_137c</code>, <code>dp_0020</code>, <code>dp_0022</code>, <code>dp_0053</code
Telepage – I built a self-hosted PHP app that turns any Telegram channel into a website
<p>If you run a Telegram channel, you already know the problem: your content is invisible to Google, there's no search, old posts are buried, and readers need the app just to see your work.</p> <p>I built <strong>Telepage</strong> to fix that.</p> <h2> What it does </h2> <p>Telepage connects to your Telegram channel via a bot webhook and turns every post into a searchable web card — automatically, in real time.</p> <p><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%2Ft0zcxkaob0c3c5sglju1.png" class="article-body-image-wrapper"><img src="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uplo
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Google’s TurboQuant Is Quietly Rewriting the Rules of AI Memory
Google’s TurboQuant shrinks AI’s working memory by up to 10x A new compression algorithm from Google Research shrinks AI’s working memory by up to 10x — with near-zero accuracy loss. Here is how it works, and why it matters. Every time you have a long conversation with an AI, ask it to summarize a document, or run a complex semantic search, the model is quietly filling up a working memory called the key-value cache . It is the model’s fast-access notepad — storing what it has already processed so it does not have to recompute everything from scratch with each new word. And at scale, it is enormously expensive. The reason comes down to what is actually being stored. For every single token the model processes — every word, every punctuation mark — it stores two high-dimensional vectors: a ke
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