<rss version="2.0" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Hacker News: codelion</title><link>https://news.ycombinator.com/user?id=codelion</link><description>Hacker News RSS</description><docs>https://hnrss.org/</docs><generator>hnrss v2.1.1</generator><lastBuildDate>Sun, 24 May 2026 07:27:30 +0000</lastBuildDate><atom:link href="https://hnrss.org/user?id=codelion" rel="self" type="application/rss+xml"></atom:link><item><title><![CDATA[New comment by codelion in "Velonus – Open-source AppSec scanner that deduplicates SAST noise"]]></title><description><![CDATA[
<p>You can consider using Frame for the SAST part - <a href="https://github.com/lambdasec/frame" rel="nofollow">https://github.com/lambdasec/frame</a></p>
]]></description><pubDate>Fri, 15 May 2026 04:35:40 +0000</pubDate><link>https://news.ycombinator.com/item?id=48144601</link><dc:creator>codelion</dc:creator><comments>https://news.ycombinator.com/item?id=48144601</comments><guid isPermaLink="false">https://news.ycombinator.com/item?id=48144601</guid></item><item><title><![CDATA[New comment by codelion in "Ollama is now powered by MLX on Apple Silicon in preview"]]></title><description><![CDATA[
<p>How does it compare to some of the newer mlx inference engines like optiq that support turboquantization - <a href="https://mlx-optiq.pages.dev/" rel="nofollow">https://mlx-optiq.pages.dev/</a></p>
]]></description><pubDate>Tue, 31 Mar 2026 04:50:16 +0000</pubDate><link>https://news.ycombinator.com/item?id=47582875</link><dc:creator>codelion</dc:creator><comments>https://news.ycombinator.com/item?id=47582875</comments><guid isPermaLink="false">https://news.ycombinator.com/item?id=47582875</guid></item><item><title><![CDATA[Zero-Dependency Programming]]></title><description><![CDATA[
<p>Article URL: <a href="https://conjure.pages.dev/">https://conjure.pages.dev/</a></p>
<p>Comments URL: <a href="https://news.ycombinator.com/item?id=47568297">https://news.ycombinator.com/item?id=47568297</a></p>
<p>Points: 2</p>
<p># Comments: 0</p>
]]></description><pubDate>Sun, 29 Mar 2026 22:53:42 +0000</pubDate><link>https://conjure.pages.dev/</link><dc:creator>codelion</dc:creator><comments>https://news.ycombinator.com/item?id=47568297</comments><guid isPermaLink="false">https://news.ycombinator.com/item?id=47568297</guid></item><item><title><![CDATA[New comment by codelion in "US Court of Appeals: TOS may be updated by email, use can imply consent [pdf]"]]></title><description><![CDATA[
<p>the key issue is the interpretation of "consent" when continued use is the only option. aree users truly consenting, or are they simply left with no alternative?</p>
]]></description><pubDate>Mon, 09 Mar 2026 07:53:39 +0000</pubDate><link>https://news.ycombinator.com/item?id=47305987</link><dc:creator>codelion</dc:creator><comments>https://news.ycombinator.com/item?id=47305987</comments><guid isPermaLink="false">https://news.ycombinator.com/item?id=47305987</guid></item><item><title><![CDATA[Scaling Pedagogical Pre-Training: From Optimal Mixing to 10B Tokens]]></title><description><![CDATA[
<p>Article URL: <a href="https://huggingface.co/blog/codelion/scaling-pedagogical-pretraining-10-billion-tokens">https://huggingface.co/blog/codelion/scaling-pedagogical-pretraining-10-billion-tokens</a></p>
<p>Comments URL: <a href="https://news.ycombinator.com/item?id=47305981">https://news.ycombinator.com/item?id=47305981</a></p>
<p>Points: 2</p>
<p># Comments: 0</p>
]]></description><pubDate>Mon, 09 Mar 2026 07:52:37 +0000</pubDate><link>https://huggingface.co/blog/codelion/scaling-pedagogical-pretraining-10-billion-tokens</link><dc:creator>codelion</dc:creator><comments>https://news.ycombinator.com/item?id=47305981</comments><guid isPermaLink="false">https://news.ycombinator.com/item?id=47305981</guid></item><item><title><![CDATA[From HashHop to Memory-Augmented Language Models]]></title><description><![CDATA[
<p>Article URL: <a href="https://huggingface.co/blog/codelion/reverse-engineering-magic-hashhop">https://huggingface.co/blog/codelion/reverse-engineering-magic-hashhop</a></p>
<p>Comments URL: <a href="https://news.ycombinator.com/item?id=46833832">https://news.ycombinator.com/item?id=46833832</a></p>
<p>Points: 2</p>
<p># Comments: 0</p>
]]></description><pubDate>Sat, 31 Jan 2026 05:40:42 +0000</pubDate><link>https://huggingface.co/blog/codelion/reverse-engineering-magic-hashhop</link><dc:creator>codelion</dc:creator><comments>https://news.ycombinator.com/item?id=46833832</comments><guid isPermaLink="false">https://news.ycombinator.com/item?id=46833832</guid></item><item><title><![CDATA[The Optimal Architecture for Small Language Models]]></title><description><![CDATA[
<p>Article URL: <a href="https://huggingface.co/blog/codelion/optimal-model-architecture">https://huggingface.co/blog/codelion/optimal-model-architecture</a></p>
<p>Comments URL: <a href="https://news.ycombinator.com/item?id=46390114">https://news.ycombinator.com/item?id=46390114</a></p>
<p>Points: 1</p>
<p># Comments: 0</p>
]]></description><pubDate>Fri, 26 Dec 2025 07:46:59 +0000</pubDate><link>https://huggingface.co/blog/codelion/optimal-model-architecture</link><dc:creator>codelion</dc:creator><comments>https://news.ycombinator.com/item?id=46390114</comments><guid isPermaLink="false">https://news.ycombinator.com/item?id=46390114</guid></item><item><title><![CDATA[Enhancing LLMs with LoRA – Standardized Recipes for Capability Enhancement]]></title><description><![CDATA[
<p>Article URL: <a href="https://huggingface.co/blog/codelion/ellora-lora-recipes">https://huggingface.co/blog/codelion/ellora-lora-recipes</a></p>
<p>Comments URL: <a href="https://news.ycombinator.com/item?id=46285260">https://news.ycombinator.com/item?id=46285260</a></p>
<p>Points: 3</p>
<p># Comments: 0</p>
]]></description><pubDate>Tue, 16 Dec 2025 05:56:21 +0000</pubDate><link>https://huggingface.co/blog/codelion/ellora-lora-recipes</link><dc:creator>codelion</dc:creator><comments>https://news.ycombinator.com/item?id=46285260</comments><guid isPermaLink="false">https://news.ycombinator.com/item?id=46285260</guid></item><item><title><![CDATA[OpenEvolve: Teaching LLMs to Discover Algorithms Through Evolution]]></title><description><![CDATA[
<p>Article URL: <a href="https://algorithmicsuperintelligence.ai/blog/openevolve-overview/index.html">https://algorithmicsuperintelligence.ai/blog/openevolve-overview/index.html</a></p>
<p>Comments URL: <a href="https://news.ycombinator.com/item?id=46211861">https://news.ycombinator.com/item?id=46211861</a></p>
<p>Points: 53</p>
<p># Comments: 9</p>
]]></description><pubDate>Tue, 09 Dec 2025 22:54:33 +0000</pubDate><link>https://algorithmicsuperintelligence.ai/blog/openevolve-overview/index.html</link><dc:creator>codelion</dc:creator><comments>https://news.ycombinator.com/item?id=46211861</comments><guid isPermaLink="false">https://news.ycombinator.com/item?id=46211861</guid></item><item><title><![CDATA[Ellora: Enhancing LLMs with LoRA Standardized Recipes for Capability Enhancement]]></title><description><![CDATA[
<p>Article URL: <a href="https://huggingface.co/blog/codelion/ellora-lora-recipes">https://huggingface.co/blog/codelion/ellora-lora-recipes</a></p>
<p>Comments URL: <a href="https://news.ycombinator.com/item?id=46130216">https://news.ycombinator.com/item?id=46130216</a></p>
<p>Points: 1</p>
<p># Comments: 0</p>
]]></description><pubDate>Wed, 03 Dec 2025 04:09:57 +0000</pubDate><link>https://huggingface.co/blog/codelion/ellora-lora-recipes</link><dc:creator>codelion</dc:creator><comments>https://news.ycombinator.com/item?id=46130216</comments><guid isPermaLink="false">https://news.ycombinator.com/item?id=46130216</guid></item><item><title><![CDATA[The 1B Token Challenge: Finding the Perfect Pre-Training Mix]]></title><description><![CDATA[
<p>Article URL: <a href="https://huggingface.co/blog/codelion/optimal-dataset-mixing">https://huggingface.co/blog/codelion/optimal-dataset-mixing</a></p>
<p>Comments URL: <a href="https://news.ycombinator.com/item?id=45937921">https://news.ycombinator.com/item?id=45937921</a></p>
<p>Points: 6</p>
<p># Comments: 0</p>
]]></description><pubDate>Sat, 15 Nov 2025 15:07:35 +0000</pubDate><link>https://huggingface.co/blog/codelion/optimal-dataset-mixing</link><dc:creator>codelion</dc:creator><comments>https://news.ycombinator.com/item?id=45937921</comments><guid isPermaLink="false">https://news.ycombinator.com/item?id=45937921</guid></item><item><title><![CDATA[The 1B Token Challenge: Finding the Perfect Pre-Training Mix]]></title><description><![CDATA[
<p>Article URL: <a href="https://huggingface.co/blog/codelion/optimal-dataset-mixing">https://huggingface.co/blog/codelion/optimal-dataset-mixing</a></p>
<p>Comments URL: <a href="https://news.ycombinator.com/item?id=45796734">https://news.ycombinator.com/item?id=45796734</a></p>
<p>Points: 3</p>
<p># Comments: 0</p>
]]></description><pubDate>Mon, 03 Nov 2025 07:42:34 +0000</pubDate><link>https://huggingface.co/blog/codelion/optimal-dataset-mixing</link><dc:creator>codelion</dc:creator><comments>https://news.ycombinator.com/item?id=45796734</comments><guid isPermaLink="false">https://news.ycombinator.com/item?id=45796734</guid></item><item><title><![CDATA[New comment by codelion in "Rats filmed snatching bats from air"]]></title><description><![CDATA[
<p>When mammals hunt other mammals strange things can happen.</p>
]]></description><pubDate>Sun, 02 Nov 2025 23:19:44 +0000</pubDate><link>https://news.ycombinator.com/item?id=45794294</link><dc:creator>codelion</dc:creator><comments>https://news.ycombinator.com/item?id=45794294</comments><guid isPermaLink="false">https://news.ycombinator.com/item?id=45794294</guid></item><item><title><![CDATA[New comment by codelion in "Don't Build Multi-Agents"]]></title><description><![CDATA[
<p>Whom to believe? Devin or Claude? - <a href="https://www.anthropic.com/engineering/multi-agent-research-system" rel="nofollow">https://www.anthropic.com/engineering/multi-agent-research-s...</a></p>
]]></description><pubDate>Mon, 01 Sep 2025 22:54:51 +0000</pubDate><link>https://news.ycombinator.com/item?id=45097335</link><dc:creator>codelion</dc:creator><comments>https://news.ycombinator.com/item?id=45097335</comments><guid isPermaLink="false">https://news.ycombinator.com/item?id=45097335</guid></item><item><title><![CDATA[Pivotal Token Search (PTS): Targeting Critical Decision Points in LLM Training]]></title><description><![CDATA[
<p>Article URL: <a href="https://huggingface.co/blog/codelion/pts">https://huggingface.co/blog/codelion/pts</a></p>
<p>Comments URL: <a href="https://news.ycombinator.com/item?id=44937442">https://news.ycombinator.com/item?id=44937442</a></p>
<p>Points: 2</p>
<p># Comments: 1</p>
]]></description><pubDate>Mon, 18 Aug 2025 04:33:43 +0000</pubDate><link>https://huggingface.co/blog/codelion/pts</link><dc:creator>codelion</dc:creator><comments>https://news.ycombinator.com/item?id=44937442</comments><guid isPermaLink="false">https://news.ycombinator.com/item?id=44937442</guid></item><item><title><![CDATA[New comment by codelion in "GPT-OSS vs. Qwen3 and a detailed look how things evolved since GPT-2"]]></title><description><![CDATA[
<p>It is by design. OpenAI is not going to reveal any architectural innovation they have made in their own commercial models.</p>
]]></description><pubDate>Mon, 11 Aug 2025 06:43:07 +0000</pubDate><link>https://news.ycombinator.com/item?id=44861346</link><dc:creator>codelion</dc:creator><comments>https://news.ycombinator.com/item?id=44861346</comments><guid isPermaLink="false">https://news.ycombinator.com/item?id=44861346</guid></item><item><title><![CDATA[Internal Coherence Maximization(ICM): Label-Free Unsupervised Training Framework]]></title><description><![CDATA[
<p>Article URL: <a href="https://github.com/codelion/icm">https://github.com/codelion/icm</a></p>
<p>Comments URL: <a href="https://news.ycombinator.com/item?id=44846304">https://news.ycombinator.com/item?id=44846304</a></p>
<p>Points: 1</p>
<p># Comments: 0</p>
]]></description><pubDate>Sat, 09 Aug 2025 13:29:10 +0000</pubDate><link>https://github.com/codelion/icm</link><dc:creator>codelion</dc:creator><comments>https://news.ycombinator.com/item?id=44846304</comments><guid isPermaLink="false">https://news.ycombinator.com/item?id=44846304</guid></item><item><title><![CDATA[Unsupervised Model Improvement via Internal Coherence Maximization]]></title><description><![CDATA[
<p>Article URL: <a href="https://huggingface.co/blog/codelion/internal-coherence-maximization">https://huggingface.co/blog/codelion/internal-coherence-maximization</a></p>
<p>Comments URL: <a href="https://news.ycombinator.com/item?id=44776598">https://news.ycombinator.com/item?id=44776598</a></p>
<p>Points: 1</p>
<p># Comments: 0</p>
]]></description><pubDate>Sun, 03 Aug 2025 13:53:15 +0000</pubDate><link>https://huggingface.co/blog/codelion/internal-coherence-maximization</link><dc:creator>codelion</dc:creator><comments>https://news.ycombinator.com/item?id=44776598</comments><guid isPermaLink="false">https://news.ycombinator.com/item?id=44776598</guid></item><item><title><![CDATA[Show HN: PTS Library – Analyze LLM reasoning through "thought anchors"]]></title><description><![CDATA[
<p>I built PTS (Pivotal Token Search), an open-source library for mechanistic interpretability analysis of language models. The core feature is generating "thought anchors" - identifying which specific sentences in a model's reasoning chain significantly impact task success.<p>What it does:<p>- Generates chain-of-thought reasoning traces from any LLM<p>- Uses counterfactual analysis to measure impact of each reasoning step<p>- Identifies critical sentences that make-or-break task completion<p>- Exports semantic embeddings for clustering analysis<p>- Provides systematic failure mode categorization<p>Example use case:<p>I used PTS to compare Qwen3-0.6B vs DeepSeek-R1-Distill-1.5B on math problems and discovered they have fundamentally different reasoning architectures:<p>- DeepSeek: concentrated reasoning (fewer, high-impact steps)<p>- Qwen3: distributed reasoning (impact spread across multiple steps)<p>Quick start:<p># Generate thought anchors<p>pts run --model="your-model" --dataset="gsm8k" --generate-thought-anchors<p># Export for analysis<p>pts export --format="thought_anchors" --output-path="analysis.jsonl"<p>The library implements the thought anchors methodology from Bogdan et al. (2025) with extensions for:<p>- Comprehensive metadata collection<p>- 384-dimensional semantic embeddings<p>- Causal dependency tracking<p>- Systematic failure analysis<p>Why this matters: Most interpretability tools focus on individual tokens or attention patterns. Thought anchors operate at the sentence level, revealing which complete reasoning steps actually matter for getting correct answers.<p>Limitations: Currently focused on mathematical reasoning tasks. Planning to extend to other domains and larger models.<p>Links:<p>- GitHub: <a href="https://github.com/codelion/pts">https://github.com/codelion/pts</a><p>- Research example: <a href="https://huggingface.co/blog/codelion/understanding-model-reasoning-thought-anchors" rel="nofollow">https://huggingface.co/blog/codelion/understanding-model-rea...</a><p>- Generated datasets: Available on HuggingFace<p>Would appreciate feedback on extending this to other reasoning domains or interpretability approaches.</p>
<hr>
<p>Comments URL: <a href="https://news.ycombinator.com/item?id=44655663">https://news.ycombinator.com/item?id=44655663</a></p>
<p>Points: 2</p>
<p># Comments: 0</p>
]]></description><pubDate>Wed, 23 Jul 2025 04:09:58 +0000</pubDate><link>https://news.ycombinator.com/item?id=44655663</link><dc:creator>codelion</dc:creator><comments>https://news.ycombinator.com/item?id=44655663</comments><guid isPermaLink="false">https://news.ycombinator.com/item?id=44655663</guid></item><item><title><![CDATA[Automated Discovery of High-Performance GPU Kernels with OpenEvolve]]></title><description><![CDATA[
<p>Article URL: <a href="https://huggingface.co/blog/codelion/openevolve-gpu-kernel-discovery">https://huggingface.co/blog/codelion/openevolve-gpu-kernel-discovery</a></p>
<p>Comments URL: <a href="https://news.ycombinator.com/item?id=44401609">https://news.ycombinator.com/item?id=44401609</a></p>
<p>Points: 4</p>
<p># Comments: 0</p>
]]></description><pubDate>Sat, 28 Jun 2025 01:03:46 +0000</pubDate><link>https://huggingface.co/blog/codelion/openevolve-gpu-kernel-discovery</link><dc:creator>codelion</dc:creator><comments>https://news.ycombinator.com/item?id=44401609</comments><guid isPermaLink="false">https://news.ycombinator.com/item?id=44401609</guid></item></channel></rss>