llm 0.30
<p><strong>Release:</strong> <a href="https://github.com/simonw/llm/releases/tag/0.30">llm 0.30</a></p> <blockquote> <ul> <li>The <a href="http://llm.datasette.io/en/stable/plugins/plugin-hooks.html#plugin-hooks-register-models">register_models() plugin hook</a> now takes an optional <code>model_aliases</code> parameter listing all of the models, async models and aliases that have been registered so far by other plugins. A plugin with <code>@hookimpl(trylast=True)</code> can use this to take previously registered models into account. <a href="https://github.com/simonw/llm/issues/1389">#1389</a></li> <li>Added docstrings to public classes and methods and included those directly in the documentation.</li> </ul> </blockquote> <p>Tags: <a href="https://simonwillison.net/tags/llm">llm</a></p>
Release
llm 0.30 — Access large language models from the command-line
The register_models() plugin hook now takes an optional model_aliases parameter listing all of the models, async models and aliases that have been registered so far by other plugins. A plugin with @hookimpl(trylast=True) can use this to take previously registered models into account. #1389
Added docstrings to public classes and methods and included those directly in the documentation.
Sign in to highlight and annotate this article
Conversation starters
Daily AI Digest
Get the top 5 AI stories delivered to your inbox every morning.
More about
modelreleasegithub
From Direct Classification to Agentic Routing: When to Use Local Models vs Azure AI
<p>In many enterprise workflows, classification sounds simple.</p> <p>An email arrives.<br><br> A ticket is created.<br><br> A request needs to be routed.</p> <p>At first glance, it feels like a straightforward model problem:</p> <ul> <li>classify the input</li> <li>assign a category</li> <li>trigger the next step</li> </ul> <p>But in practice, enterprise classification is rarely just about model accuracy.</p> <p>It is also about:</p> <ul> <li>latency</li> <li>cost</li> <li>governance</li> <li>data sensitivity</li> <li>operational fit</li> <li>fallback behavior</li> </ul> <p>That is where the architecture becomes more important than the model itself.</p> <p>In this post, I want to share a practical way to think about classification systems in enterprise environments:</p> <ul> <li>when <str
Single-Cluster Duality View 🃏
<p>In DynamoDB, a <em><a href="https://aws.amazon.com/blogs/database/single-table-vs-multi-table-design-in-amazon-dynamodb/" rel="noopener noreferrer">single-table design</a></em> stores one-to-many relationships in a single physical block while still following relational-like normal form decomposition. In MongoDB, the <em><a href="https://www.mongodb.com/docs/manual/data-modeling/design-patterns/single-collection/" rel="noopener noreferrer">Single Collection Pattern</a></em> unnests relationships from a single document, but goes against the general recommendation as it sacrifices one of MongoDB’s key advantages—keeping a document in a single block. In Oracle Database and MySQL, JSON-relational <a href="https://oracle-base.com/articles/23/json-relational-duality-views-23" rel="noopener nor
Axios npm Package Compromised: Supply Chain Attack Delivers Cross-Platform RAT
<p>On March 31, 2026, two malicious versions of <a href="https://snyk.io/advisor/npm-package/axios" rel="noopener noreferrer">axios</a>, the enormously popular JavaScript HTTP client with over 100 million weekly downloads, were briefly published to npm via a compromised maintainer account. The packages contained a hidden dependency that deployed a cross-platform remote access trojan (RAT) to any machine that ran <code>npm install</code> (or equivalent in other package managers like Bun) during a two-hour window.</p> <p>The malicious versions (<code>1.14.1</code> and <code>0.30.4</code>) were removed from npm by 03:29 UTC. But in the window they were live, anyone whose CI/CD pipeline, developer environment, or build system pulled a fresh install could have been compromised without ever touc
Knowledge Map
Connected Articles — Knowledge Graph
This article is connected to other articles through shared AI topics and tags.
More in Models
AI 週報：2026/3/27–4/1 Anthropic 一週三震、Arm 首顆自研晶片、Oracle 裁三萬人押注 AI
<blockquote> <p><strong>本週一句話摘要：</strong> Anthropic 成為全場焦點——但聚光燈有一半來自意外。</p> </blockquote> <h2> 一、最重要事件：Anthropic 的三重震盪 </h2> <p>本週沒有一家公司比 Anthropic 更頻繁登上頭條——但三次曝光中有兩次並非計畫內。</p> <h3> 1. IPO 計畫曝光（3/27） </h3> <p>Bloomberg 報導，Anthropic 正考慮<strong>最快十月掛牌上市</strong>，募資規模可能超過 <strong>600 億美元</strong>。消息指出公司已與 Goldman Sachs、JPMorgan、Morgan Stanley 進行初步接洽。</p> <p>背景脈絡：</p> <div class="table-wrapper-paragraph"><table> <thead> <tr> <th>指標</th> <th>數字</th> </tr> </thead> <tbody> <tr> <td>二月融資輪估值</td> <td>3,800 億美元</td> </tr> <tr> <td>付費訂閱成長</td> <td>2026 年以來翻倍以上</td> </tr> <tr> <td>企業客戶首次採購勝率</td> <td>約 70%（vs. OpenAI）</td> </tr> <tr> <td>每日新增免費用戶</td> <td>超過 100 萬人</td> </tr> </tbody> </table></div> <p>這不只是一場 IPO——這是 AI 產業從「誰的模型更強」轉向「誰先成為上市公司」的訊號。Anthropic 與 OpenAI 同時在準備 2026 年上市，資本市場正在為 AI 雙雄的對決定價。
New build
<table> <tr><td> <a href="https://www.reddit.com/r/LocalLLaMA/comments/1s95cpa/new_build/"> <img src="https://preview.redd.it/6gcwubqi5hsg1.jpeg?width=640&crop=smart&auto=webp&s=dcc11e379b3473a61e23b0d2d398400393fef9b4" alt="New build" title="New build" /> </a> </td><td> <!-- SC_OFF --><div class="md"><p>Seasonic 1600w titanium power supply</p> <p>Supermicro X13SAE-F</p> <p>Intel i9-13900k</p> <p>4x 32GB micron ECC udimms</p> <p>3x intel 660p 2TB m2 ssd</p> <p>2x micron 9300 15.36TB u2 ssd (not pictured)</p> <p>2x RTX 6000 Blackwell max-q</p> <p>Due to lack of pci lanes gpus are running at x8 pci 5.0</p> <p>I may upgrade to a better cpu to handle both cards at x16 once ddr5 ram prices go down.</p> <p>Would upgrading cpu and increasing ram channels matter really that much?</p> <
1-bit llms on device?!
<!-- SC_OFF --><div class="md"><p>everyone's talking about the claude code stuff (rightfully so) but <a href="https://github.com/PrismML-Eng/Bonsai-demo/blob/main/1-bit-bonsai-8b-whitepaper.pdf">this paper</a> came out today, and the claims are pretty wild:</p> <ul> <li>1-bit 8b param model that fits in 1.15 gb of memory ...</li> <li>competitive with llama3 8B and other full-precision 8B models on benchmarks</li> <li>runs at 440 tok/s on a 4090, 136 tok/s on an M4 Pro</li> <li>they got it running on an iphone at ~40 tok/s</li> <li>4-5x more energy efficient</li> </ul> <p>also it's up on <a href="https://huggingface.co/prism-ml/Bonsai-8B-gguf">hugging face</a>! i haven't played around with it yet, but curious to know what people think about this one. caltech spinout from a famous professor
Me: avoiding r/LocalLLaMA on April Fools’ Day so I don’t fall for fake model releases.
<table> <tr><td> <a href="https://www.reddit.com/r/LocalLLaMA/comments/1s973u2/me_avoiding_rlocalllama_on_april_fools_day_so_i/"> <img src="https://preview.redd.it/km4rhb1djhsg1.gif?width=320&crop=smart&s=5f34e0e2ec7deee2ee9daa7eb56bc4b0d0ccbaf6" alt="Me: avoiding r/LocalLLaMA on April Fools’ Day so I don’t fall for fake model releases." title="Me: avoiding r/LocalLLaMA on April Fools’ Day so I don’t fall for fake model releases." /> </a> </td><td> <!-- SC_OFF --><div class="md"><p>See y’all April 2nd. </p> </div><!-- SC_ON -->   submitted by   <a href="https://www.reddit.com/user/Porespellar"> /u/Porespellar </a> <br/> <span><a href="https://i.redd.it/km4rhb1djhsg1.gif">[link]</a></span>   <span><a href="https://www.reddit.com/r/LocalLLaMA/comments/1s973u2/me_avoiding_
Discussion
Sign in to join the discussion
No comments yet — be the first to share your thoughts!