<!DOCTYPE html><html lang="en"><head><meta http-equiv="Content-Type" content="text/html charset=UTF-8"><meta charset="UTF-8"><meta name="viewport" content="width=device-width"><meta name="x-apple-disable-message-reformatting"><title>TLDR AI</title><meta name="color-scheme" content="light dark"><meta name="supported-color-schemes" content="light dark"><style type="text/css">
:root {
color-scheme: light dark; supported-color-schemes: light dark;
}
*,
*:after,
*:before {
-webkit-box-sizing: border-box; -moz-box-sizing: border-box; box-sizing: border-box;
}
* {
-ms-text-size-adjust: 100%; -webkit-text-size-adjust: 100%;
}
html,
body,
.document {
width: 100% !important; height: 100% !important; margin: 0; padding: 0;
}
body {
-webkit-font-smoothing: antialiased; -moz-osx-font-smoothing: grayscale; text-rendering: optimizeLegibility;
}
div[style*="margin: 16px 0"] {
margin: 0 !important;
}
table,
td {
mso-table-lspace: 0pt; mso-table-rspace: 0pt;
}
table {
border-spacing: 0; border-collapse: collapse; table-layout: fixed; margin: 0 auto;
}
img {
-ms-interpolation-mode: bicubic; max-width: 100%; border: 0;
}
*[x-apple-data-detectors] {
color: inherit !important; text-decoration: none !important;
}
.x-gmail-data-detectors,
.x-gmail-data-detectors *,
.aBn {
border-bottom: 0 !important; cursor: default !important;
}
.btn {
-webkit-transition: all 200ms ease; transition: all 200ms ease;
}
.btn:hover {
background-color: #f67575; border-color: #f67575;
}
* {
font-family: Arial, Helvetica, sans-serif; font-size: 18px;
}
@media screen and (max-width: 600px) {
.container {
width: 100%; margin: auto;
}
.stack {
display: block!important; width: 100%!important; max-width: 100%!important;
}
.btn {
display: block; width: 100%; text-align: center;
}
}
body,
p,
td,
tr,
.body,
table,
h1,
h2,
h3,
h4,
h5,
h6,
div,
span {
background-color: #FEFEFE !important; color: #010101 !important;
}
@media (prefers-color-scheme: dark) {
body,
p,
td,
tr,
.body,
table,
h1,
h2,
h3,
h4,
h5,
h6,
div,
span {
background-color: #27292D !important; color: #FEFEFE !important;
}
}
a {
color: inherit !important; text-decoration: underline !important;
}
</style><!--[if mso | ie]>
<style type="text/css">
a {
background-color: #FEFEFE !important; color: #010101 !important;
}
@media (prefers-color-scheme: dark) {
a {
background-color: #27292D !important; color: #FEFEFE !important;
}
}
</style>
<![endif]--></head><body class="">
<div style="display: none; max-height: 0px; overflow: hidden;">Anthropic appears to have started a fresh round of red teaming on a new build. The company is set to host its Code with Claude developer conference </div>
<div style="display: none; max-height: 0px; overflow: hidden;">
<br>
</div>
<table align="center" class="document"><tbody><tr><td valign="top">
<table align="center" border="0" cellpadding="0" cellspacing="0" class="container" width="600"><tbody><tr class="inner-body"><td>
<table align="center" border="0" cellpadding="0" cellspacing="0" width="100%"><tbody><tr class="header"><td bgcolor="" class="container">
<table width="100%"><tbody><tr><td class="container">
<table align="center" bgcolor="" border="0" cellpadding="0" cellspacing="0" style="margin-top: 0px;" width="100%"><tbody><tr><td style="padding: 0px;">
<table align="center" bgcolor="" border="0" cellpadding="0" cellspacing="0" width="100%"><tbody><tr><td class="container" style="padding: 15px 15px;">
<div style="text-align: center;">
<span style="margin-right: 0px;"><a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Ftldr.tech%2Fai%3Futm_source=tldrai/1/0100019df32340e9-a0ca27a1-a8aa-48d0-9216-dd5b8e119031-000000/Q96NVj8ae6MvS6Lyi9njvIZWz9InujT5Ryi8jGBURF8=452" rel="noopener noreferrer" target="_blank"><span>Sign Up</span></a>
|<span style="margin-right: 2px; margin-left: 2px;"><a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fadvertise.tldr.tech%2F%3Futm_source=tldrai%26utm_medium=newsletter%26utm_campaign=advertisetopnav/1/0100019df32340e9-a0ca27a1-a8aa-48d0-9216-dd5b8e119031-000000/y4RxiBthq4-zXNR__hOP1AMisG3xLKOXKD7RcGakCAc=452" rel="noopener noreferrer" target="_blank"><span>Advertise</span></a></span>|<span style="margin-left: 2px;"><a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fa.tldrnewsletter.com%2Fweb-version%3Fep=1%26lc=1571a9f6-84b7-11f0-b464-47c5c04ad337%26p=3c455ea4-47b4-11f1-8a50-b96a4f78d0f7%26pt=campaign%26t=1777900667%26s=2372df0bbe0ac1c4dac61b86a9ce313ce261512ae2c56a2ba41f0bd35ce3159c/1/0100019df32340e9-a0ca27a1-a8aa-48d0-9216-dd5b8e119031-000000/LpDlBiaLvY1t_jrItBras-YozBZHk11ktiDEqEQZw44=452"><span>View Online</span></a></span>
<br>
</span></div>
</td></tr></tbody></table>
<table align="center" bgcolor="" border="0" cellpadding="0" cellspacing="0" width="100%"><tbody><tr><td class="container" style="text-align: center;"><span data-darkreader-inline-color="" style="--darkreader-inline-color:#3db3ff; color: rgb(51, 175, 255) !important; font-size: 30px;">T</span><span style="font-size: 30px;"><span data-darkreader-inline-color="" style="color: rgb(232, 192, 96) !important; --darkreader-inline-color:#e8c163; font-size:30px;">L</span><span data-darkreader-inline-color="" style="color: rgb(101, 195, 173) !important; --darkreader-inline-color:#6ec7b2; font-size:30px;">D</span></span><span data-darkreader-inline-color="" style="--darkreader-inline-color:#dd6e6e; color: rgb(220, 107, 107) !important; font-size: 30px;">R</span>
<br>
</td></tr></tbody></table>
<br>
<table align="center" bgcolor="" border="0" cellpadding="0" cellspacing="0" width="100%"><tbody><tr id="together-with"><td align="center" height="20" style="vertical-align:middle !important;" valign="middle" width="100%"><strong style="vertical-align:middle !important; height: 100%;">Together With </strong>
<a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fworkos.com%2Fblog%2Fwriting-my-first-evals%3Futm_source=tldrai%26utm_medium=newsletter%26utm_campaign=q22026/1/0100019df32340e9-a0ca27a1-a8aa-48d0-9216-dd5b8e119031-000000/IzyV-P3EV6d_PItn1FlUyc_YQ8Ota-E9FnIXAfCWf3U=452"><img src="https://images.tldr.tech/workos50.png" valign="middle" style="vertical-align: middle !important; height: 100%;" alt="WorkOS"></a></td></tr></tbody></table>
<table style="table-layout: fixed; width:100%;" width="100%"><tbody><tr><td style="padding:0;border-collapse:collapse;border-spacing:0;margin:0;">
<div style="text-align: center;">
<h1><strong>TLDR AI <span id="date">2026-05-04</span></strong></h1>
</div>
</td></tr></tbody></table>
<table style="table-layout: fixed; width:100%;" width="100%"><tbody><tr id="sponsy-copy"><td class="container" style="padding: 15px 15px;">
<div class="text-block">
<span>
<a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fworkos.com%2Fblog%2Fwriting-my-first-evals%3Futm_source=tldrai%26utm_medium=newsletter%26utm_campaign=q22026/2/0100019df32340e9-a0ca27a1-a8aa-48d0-9216-dd5b8e119031-000000/f8L6E3vkdXM8SaFBbPkg9kL4LDNdpzS5g6qwmc7lPNU=452">
<span>
<strong>Why the Same AI Prompt Gives Different Answers (And How Teams Are Fixing It) (Sponsor)</strong>
</span>
</a>
<br>
<br>
<span style="font-family: "Helvetica Neue", Helvetica, Arial, Verdana, sans-serif;">
Same input. Same prompt. Different output. That's the reality of testing AI agents that write code, and most teams are shipping without solving it.<br><br>Nick Nisi from <a class="in-cell-link" href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fworkos.com%2F%3Futm_source=tldrai%26utm_medium=newsletter%26utm_campaign=q22026/1/0100019df32340e9-a0ca27a1-a8aa-48d0-9216-dd5b8e119031-000000/9Yq1n1lbQGRSJj6H1MFzO0gmwwJRV1VPjsSP31fjSAI=452" rel="noopener noreferrer nofollow" target="_blank"><span>WorkOS</span></a> tackled this by building eval systems for two AI tools:
<br>- <strong>npx workos@latest</strong>, <a class="in-cell-link" href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fworkos.com%2Fdocs%2Fauthkit%2Fcli-installer%3Futm_source=tldrai%26utm_medium=newsletter%26utm_campaign=q22026/1/0100019df32340e9-a0ca27a1-a8aa-48d0-9216-dd5b8e119031-000000/gs_7XNUolqSxWM0OWsoUS5l3DleslenAGGbgpvFXp58=452" rel="noopener noreferrer nofollow" target="_blank"><span>a CLI agent that installs AuthKit</span></a> into your project
<br>- WorkOS agent skills that power LLM responses about SSO, directory sync, and RBAC.
<br>
<br>The post covers how to test against real project structures, score output that's different every time, and catch when your agent makes up methods that don't exist.
<br>
<br><a class="in-cell-link" href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fworkos.com%2Fblog%2Fwriting-my-first-evals%3Futm_source=tldrai%26utm_medium=newsletter%26utm_campaign=q22026/3/0100019df32340e9-a0ca27a1-a8aa-48d0-9216-dd5b8e119031-000000/uiu0Wdnwe2WSld-8ZQ6AT-rMG_EBkvn4zf9rOe7_2mM=452" rel="noopener noreferrer nofollow" target="_blank"><span>Learn more about evals →</span></a>
</span>
</span>
</div>
</td></tr></tbody></table>
</td></tr></tbody></table>
</td></tr></tbody></table>
</td></tr>
<tr bgcolor=""><td class="container">
<table align="center" bgcolor="" border="0" cellpadding="0" cellspacing="0" width="100%"><tbody><tr><td style="padding: 0px;">
<table align="center" bgcolor="" border="0" cellpadding="0" cellspacing="0" width="100%"><tbody><tr><td class="container" style="padding-top: 0px; padding-bottom: 0px;">
<div class="text-block">
<div style="text-align: center;"><span style="font-size: 36px;"><span style="font-size:36px;">🚀</span></span></div></div>
</td></tr></tbody></table>
<table align="center" bgcolor="" border="0" cellpadding="0" cellspacing="0" width="100%"><tbody><tr><td class="container" style="padding-top: 0px; padding-bottom: 0px;">
<div class="text-block">
<div style="text-align: center;">
<h1><strong>Headlines & Launches</strong></h1>
</div>
</div>
</td></tr></tbody></table>
<table style="table-layout: fixed; width: 100%;" width="100%"><tbody><tr><td style="padding:0;border-collapse:collapse;border-spacing:0;margin:0;" valign="top">
<table align="center" border="0" cellpadding="0" cellspacing="0" width="100%"><tbody><tr><td class="container" style="padding: 15px 15px;">
<div class="text-block">
<span>
<a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fwww.testingcatalog.com%2Fanthropic-tests-jupiter-v1-p-before-potential-launch-on-may-6%2F%3Futm_source=tldrai/1/0100019df32340e9-a0ca27a1-a8aa-48d0-9216-dd5b8e119031-000000/7YAMdZmua3kukzQqAjqf2MLsLlV5A90io-l2qQsxI_8=452">
<span>
<strong>Anthropic tests Jupiter-v1-p ahead of its developer conference (2 minute read)</strong>
</span>
</a>
<br>
<br>
<span style="font-family: "Helvetica Neue", Helvetica, Arial, Verdana, sans-serif;">
Anthropic appears to have started a fresh round of red teaming on a new internal build. The company is set to host its Code with Claude developer conference in San Francisco on May 6. The timing suggests that the model is being hardened ahead of an announcement timed to the event. The red team round is consistent with the company's responsible scaling policy, which calls for jailbreak probes and constitutional classifier stress tests before any frontier-class deployment.
</span>
</span>
</div>
</td></tr></tbody></table>
<table align="center" border="0" cellpadding="0" cellspacing="0" width="100%"><tbody><tr><td class="container" style="padding: 15px 15px;">
<div class="text-block">
<span>
<a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fwww.testingcatalog.com%2Fgoogle-is-testing-new-omni-model-for-video-generation-ahead-of-i-o%2F%3Futm_source=tldrai/1/0100019df32340e9-a0ca27a1-a8aa-48d0-9216-dd5b8e119031-000000/uWlq1BhYvK6arf83XvuNowlHHchj3qqKz7rmjB63_LA=452">
<span>
<strong>Google is testing new Omni model for video generation (2 minute read)</strong>
</span>
</a>
<br>
<br>
<span style="font-family: "Helvetica Neue", Helvetica, Arial, Verdana, sans-serif;">
Google is testing a new Omni model for video generation, potentially unifying its video and image-generation tools. The Omni model appears in Gemini's video generation UI, suggesting it might become a public product name. A launch during Google I/O 2026 is possible amidst increasing AI video competition.
</span>
</span>
</div>
</td></tr></tbody></table>
<table align="center" border="0" cellpadding="0" cellspacing="0" width="100%"><tbody><tr><td class="container" style="padding: 15px 15px;">
<div class="text-block">
<span>
<a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fwww.testingcatalog.com%2Fopenai-adds-animated-pets-and-config-imports-to-codex%2F%3Futm_source=tldrai/1/0100019df32340e9-a0ca27a1-a8aa-48d0-9216-dd5b8e119031-000000/sNPy0JcL8q01f4G1Rf44fk2Q3cPAmEvh6nbKR5GU1Yo=452">
<span>
<strong>OpenAI adds animated Pets and config imports to Codex (2 minute read)</strong>
</span>
</a>
<br>
<br>
<span style="font-family: "Helvetica Neue", Helvetica, Arial, Verdana, sans-serif;">
OpenAI updated Codex with animated Pets, which appear as overlays on the screen and interact via short message bubbles. Codex also now auto-imports configuration files from other coding agents and features a new dictation dictionary to improve voice input accuracy. These updates aim to enhance Codex's usability and appeal as a comprehensive desktop application.
</span>
</span>
</div>
</td></tr></tbody></table>
</td></tr></tbody></table>
<table align="center" bgcolor="" border="0" cellpadding="0" cellspacing="0" width="100%"><tbody><tr><td class="container" style="padding-top: 0px; padding-bottom: 0px;">
<div class="text-block">
<div style="text-align: center;"><span style="font-size: 36px;">🧠</span></div>
</div>
</td></tr></tbody></table>
<table align="center" bgcolor="" border="0" cellpadding="0" cellspacing="0" width="100%"><tbody><tr><td class="container" style="padding-top: 0px; padding-bottom: 0px;">
<div class="text-block">
<div style="text-align: center;">
<h1><strong>Deep Dives & Analysis</strong></h1>
</div>
</div>
</td></tr></tbody></table>
<table style="table-layout: fixed; width: 100%;" width="100%"><tbody><tr><td style="padding:0;border-collapse:collapse;border-spacing:0;margin:0;" valign="top">
<table align="center" border="0" cellpadding="0" cellspacing="0" width="100%"><tbody><tr><td class="container" style="padding: 15px 15px;">
<div class="text-block">
<span>
<a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fsimonwillison.net%2F2026%2FApr%2F24%2Fdeepseek-v4%2F%3Futm_source=tldrai/1/0100019df32340e9-a0ca27a1-a8aa-48d0-9216-dd5b8e119031-000000/lB0DgPo6lXNi5d9GISTS5PbIxcxihR9WFMDl1eN0ZM4=452">
<span>
<strong>DeepSeek V4—almost on the frontier, a fraction of the price (4 minute read)</strong>
</span>
</a>
<br>
<br>
<span style="font-family: "Helvetica Neue", Helvetica, Arial, Verdana, sans-serif;">
DeepSeek's latest preview models, DeepSeek-V4-Pro and DeepSeek-V4-Flash, are both one-million-token Mixture of Experts models. Pro has a total of 1.6 trillion parameters, with 49 billion active, while Flash has 284 billion total, with 13 billion active. DeepSeek-V4-Pro is now the largest open weights model. It is also a very cheap model to run.
</span>
</span>
</div>
</td></tr></tbody></table>
<table align="center" border="0" cellpadding="0" cellspacing="0" width="100%"><tbody><tr><td class="container" style="padding: 15px 15px;">
<div class="text-block">
<span>
<a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fsites.diy%2Fblog%2F2026-05-01-coding-plan-comparisons%2F%3Futm_source=tldrai/1/0100019df32340e9-a0ca27a1-a8aa-48d0-9216-dd5b8e119031-000000/LClYeLiCyU-BYqXzdDF6wSCSAHIhcduleG49p1uAgZ4=452">
<span>
<strong>Coding plan comparisons based on actual usage (4 minute read)</strong>
</span>
</a>
<br>
<br>
<span style="font-family: "Helvetica Neue", Helvetica, Arial, Verdana, sans-serif;">
This post looks at how pricing compares across coding plans and APIs. Codex is heavily subsidized compared to others, but most others are still subsidized. Claude Pro costs around 10 times more per token than the rest.
</span>
</span>
</div>
</td></tr></tbody></table>
<table align="center" border="0" cellpadding="0" cellspacing="0" width="100%"><tbody><tr><td class="container" style="padding: 15px 15px;">
<div class="text-block">
<span>
<a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fwww.greaterwrong.com%2Fposts%2FgcKhnqysxj9bBvbWD%2Fhow-did-large-language-models-get-that-way-the-role-of%3Futm_source=tldrai/1/0100019df32340e9-a0ca27a1-a8aa-48d0-9216-dd5b8e119031-000000/znmDLrxQkAG4wDMLsMHkQRP0jmlZyPDDl2X5deI7TBw=452">
<span>
<strong>How did ‘large' language models get that way? The role of Transformers and Pretraining in GPT (7 minute read)</strong>
</span>
</a>
<br>
<br>
<span style="font-family: "Helvetica Neue", Helvetica, Arial, Verdana, sans-serif;">
Large language models are set to be one of the largest computing infrastructure projects ever. This post is the first part of a series about LLM architecture and its implications for reasoning. It looks at why the transformer architecture was so impactful for LLMs.
</span>
</span>
</div>
</td></tr></tbody></table>
<table align="center" border="0" cellpadding="0" cellspacing="0" width="100%"><tbody><tr><td class="container" style="padding: 15px 15px;">
<div class="text-block">
<span>
<a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fresearch.perplexity.ai%2Farticles%2Fdesigning-refining-and-maintaining-agent-skills-at-perplexity%3Futm_source=tldrai/1/0100019df32340e9-a0ca27a1-a8aa-48d0-9216-dd5b8e119031-000000/321vgeVGYFBD7HVpGE8EPLnjWqMX2jEPIZVajfZDorE=452">
<span>
<strong>Designing, Refining, and Maintaining Agent Skills at Perplexity (20 minute read)</strong>
</span>
</a>
<br>
<br>
<span style="font-family: "Helvetica Neue", Helvetica, Arial, Verdana, sans-serif;">
Perplexity emphasizes modular Agent Skills for enhancing its frontier agent products, with specific designs and hierarchies to ensure high-quality user experiences. Unlike traditional software, Skill development prioritizes detailed, context-specific design principles where real queries and evaluations shape their necessity and content. Maintaining these Skills involves constant iteration, testing across multiple models, and prioritizing efficiency and simplicity due to the inherent 'cost' each Skill introduces.
</span>
</span>
</div>
</td></tr></tbody></table>
</td></tr></tbody></table>
<table align="center" bgcolor="" border="0" cellpadding="0" cellspacing="0" width="100%"><tbody><tr><td class="container" style="padding-top: 0px; padding-bottom: 0px;">
<div class="text-block">
<div style="text-align: center;"><span style="font-size: 36px;">🧑💻</span></div>
</div>
</td></tr></tbody></table>
<table align="center" bgcolor="" border="0" cellpadding="0" cellspacing="0" width="100%"><tbody><tr><td class="container" style="padding-top: 0px; padding-bottom: 0px;">
<div class="text-block">
<div style="text-align: center;">
<h1><strong>Engineering & Research</strong></h1>
</div>
</div>
</td></tr></tbody></table>
<table style="table-layout: fixed; width: 100%;" width="100%"><tbody><tr><td style="padding:0;border-collapse:collapse;border-spacing:0;margin:0;" valign="top">
<table align="center" border="0" cellpadding="0" cellspacing="0" width="100%"><tbody><tr><td class="container" style="padding: 15px 15px;">
<div class="text-block">
<span>
<a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Finfo.notion.so%2Fdownload-ultimate-ai-buyers-guide%3Futm_source=tldr%26utm_medium=newsletter%26utm_content=ai-buyers-guide%26utm_campaign=2026Q2_ai-buyers-guide/1/0100019df32340e9-a0ca27a1-a8aa-48d0-9216-dd5b8e119031-000000/bIPxAO9dYdgqzwCDiXX-COvyymExYmo9fdz83kqHyYk=452">
<span>
<strong>Most teams are approaching AI adoption backwards (Sponsor)</strong>
</span>
</a>
<br>
<br>
<span style="font-family: "Helvetica Neue", Helvetica, Arial, Verdana, sans-serif;">
The question isn't “which tool has the best model?” It's “which solution will our team actually use?” This <a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Finfo.notion.so%2Fdownload-ultimate-ai-buyers-guide%3Futm_source=tldr%26utm_medium=newsletter%26utm_content=ai-buyers-guide%26utm_campaign=2026Q2_ai-buyers-guide/2/0100019df32340e9-a0ca27a1-a8aa-48d0-9216-dd5b8e119031-000000/0-3JrkRmf8X0aDNwgMVh_nrmJ2vAYzREtNiHuELfHf4=452" rel="noopener noreferrer nofollow" target="_blank"><span>Notion guide</span></a> breaks down the 5 critical jobs AI should solve at work and how to evaluate tools for adoption and integration, not just capabilities. <a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Finfo.notion.so%2Fdownload-ultimate-ai-buyers-guide%3Futm_source=tldr%26utm_medium=newsletter%26utm_content=ai-buyers-guide%26utm_campaign=2026Q2_ai-buyers-guide/3/0100019df32340e9-a0ca27a1-a8aa-48d0-9216-dd5b8e119031-000000/B1uoiuQ6tfdE9fSFUDH43fqQzLVlRjQQM9rJwURlz0U=452" rel="noopener noreferrer nofollow" target="_blank"><span>Get the guide</span></a>
</span>
</span>
</div>
</td></tr></tbody></table>
<table align="center" border="0" cellpadding="0" cellspacing="0" width="100%"><tbody><tr><td class="container" style="padding: 15px 15px;">
<div class="text-block">
<span>
<a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fgithub.com%2Fintel%2Fauto-round%3Futm_source=tldrai/1/0100019df32340e9-a0ca27a1-a8aa-48d0-9216-dd5b8e119031-000000/p6CTa-LQRijuOznG8HA2nEf8FV25e5vtQphwnZai0Ks=452">
<span>
<strong>AutoRound (GitHub Repo)</strong>
</span>
</a>
<br>
<br>
<span style="font-family: "Helvetica Neue", Helvetica, Arial, Verdana, sans-serif;">
AutoRound is an advanced quantization toolkit designed for large language models and vision-language models. It achieves high accuracy at ultra-low bit widths with minimal tuning. AutoRound seamlessly works with Transformers, vLLM, SGLang, and more. It can quantize 7B models in 10 minutes on a single GPU.
</span>
</span>
</div>
</td></tr></tbody></table>
<table align="center" border="0" cellpadding="0" cellspacing="0" width="100%"><tbody><tr><td class="container" style="padding: 15px 15px;">
<div class="text-block">
<span>
<a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Farxiv.org%2Fabs%2F2604.28181%3Futm_source=tldrai/1/0100019df32340e9-a0ca27a1-a8aa-48d0-9216-dd5b8e119031-000000/xTNsomMGws4Nb9GRpXX21rqHW7ybBZ6pdmJmEV564eQ=452">
<span>
<strong>Synthetic Computer Environments for Agent Training (44 minute read)</strong>
</span>
</a>
<br>
<br>
<span style="font-family: "Helvetica Neue", Helvetica, Arial, Verdana, sans-serif;">
A scalable method generates realistic virtual computer environments and long-horizon simulations, producing rich training signals that improve agent performance across productivity tasks.
</span>
</span>
</div>
</td></tr></tbody></table>
<table align="center" border="0" cellpadding="0" cellspacing="0" width="100%"><tbody><tr><td class="container" style="padding: 15px 15px;">
<div class="text-block">
<span>
<a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Farxiv.org%2Fabs%2F2604.27505%3Futm_source=tldrai/1/0100019df32340e9-a0ca27a1-a8aa-48d0-9216-dd5b8e119031-000000/BDoen1zjSsNC1O0YS38SjqQ1G3ewXz4brCdAPpLuuDQ=452">
<span>
<strong>Reasoning-Based Rewards for Image Editing (18 minute read)</strong>
</span>
</a>
<br>
<br>
<span style="font-family: "Helvetica Neue", Helvetica, Arial, Verdana, sans-serif;">
Edit-R1 introduced a chain-of-thought reward model that evaluates image edits through structured reasoning, improving alignment and performance in text-guided editing tasks.
</span>
</span>
</div>
</td></tr></tbody></table>
</td></tr></tbody></table>
<table align="center" bgcolor="" border="0" cellpadding="0" cellspacing="0" width="100%"><tbody><tr><td class="container" style="padding-top: 0px; padding-bottom: 0px;">
<div class="text-block">
<div style="text-align: center;"><span style="font-size: 36px;">🎁</span></div></div>
</td></tr></tbody></table>
<table align="center" bgcolor="" border="0" cellpadding="0" cellspacing="0" width="100%"><tbody><tr><td class="container" style="padding-top: 0px; padding-bottom: 0px;">
<div class="text-block">
<div style="text-align: center;"><strong><h1>Miscellaneous</h1></strong></div>
</div>
</td></tr></tbody></table>
<table bgcolor="" style="table-layout: fixed; width: 100%;" width="100%"><tbody><tr><td style="padding:0;border-collapse:collapse;border-spacing:0;margin:0;" valign="top">
<table align="center" border="0" cellpadding="0" cellspacing="0" width="100%"><tbody><tr><td class="container" style="padding: 15px 15px;">
<div class="text-block">
<span>
<a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Ftechcrunch.com%2F2026%2F05%2F01%2Freplits-amjad-masad-on-the-cursor-deal-fighting-apple-and-why-hed-rather-not-sell%2F%3Futm_source=tldrai/1/0100019df32340e9-a0ca27a1-a8aa-48d0-9216-dd5b8e119031-000000/FtLEjGwbE_g3MOrkhlkyoOTuKcwYMmXuXKUSAV9KFF4=452">
<span>
<strong>Replit's Amjad Masad on the Cursor deal, fighting Apple, and why he'd rather not sell (8 minute read)</strong>
</span>
</a>
<br>
<br>
<span style="font-family: "Helvetica Neue", Helvetica, Arial, Verdana, sans-serif;">
Replit's Amjad Masad highlights strong growth, nearing a billion-dollar run rate, and boasts a 300% net revenue retention rate. Unlike Cursor, which struggles with negative margins, Replit maintains gross margin positivity and appeals to non-technical users with its secure, end-to-end platform. While Masad remains committed to Replit's independence, he acknowledges open discussions with potential acquirers and expresses frustration with Apple's alleged discriminatory App Store practices, suggesting possible legal action.
</span>
</span>
</div>
</td></tr></tbody></table>
<table align="center" border="0" cellpadding="0" cellspacing="0" width="100%"><tbody><tr><td class="container" style="padding: 15px 15px;">
<div class="text-block">
<span>
<a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Flinks.tldrnewsletter.com%2FgahXlw/1/0100019df32340e9-a0ca27a1-a8aa-48d0-9216-dd5b8e119031-000000/Vi365UwRRC0dc6ztUxlFcxoEb9nL-SZWk_zzRh4f99I=452">
<span>
<strong>How LLM Inference Works (8 minute read)</strong>
</span>
</a>
<br>
<br>
<span style="font-family: "Helvetica Neue", Helvetica, Arial, Verdana, sans-serif;">
This post walks through the inference pipeline from tokenization and embeddings through stacked self-attention layers, then splits generation into two distinct phases on the same GPU: compute-bound prefill that processes all input tokens in parallel and memory-bound decode that emits one token at a time.
</span>
</span>
</div>
</td></tr></tbody></table>
<table align="center" border="0" cellpadding="0" cellspacing="0" width="100%"><tbody><tr><td class="container" style="padding: 15px 15px;">
<div class="text-block">
<span>
<a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fwww.lesswrong.com%2Fposts%2FvKtuRbo4e3ffixmee%2Fyou-are-not-immune-to-mode-collapse%3Futm_source=tldrai/1/0100019df32340e9-a0ca27a1-a8aa-48d0-9216-dd5b8e119031-000000/DFvZLUJ6TPCZmlzyBh9TPzmjT-5nXL4sGBk4JUc2Fj4=452">
<span>
<strong>You Are Not Immune To Mode Collapse (8 minute read)</strong>
</span>
</a>
<br>
<br>
<span style="font-family: "Helvetica Neue", Helvetica, Arial, Verdana, sans-serif;">
Mode collapse occurs when models repeatedly generate the most common outputs, leading to homogenous results, exemplified by AI generating more dogs over cats with unbalanced training data. It similarly impacts various domains like grant-making and music, as systems become increasingly specialized over time based on prior outputs and successes. To counteract, introduce variability or change external pressures to diversify and prevent over-specialization.
</span>
</span>
</div>
</td></tr></tbody></table>
</td></tr></tbody></table>
<table align="center" bgcolor="" border="0" cellpadding="0" cellspacing="0" width="100%"><tbody><tr><td class="container" style="padding-top: 0px; padding-bottom: 0px;">
<div class="text-block">
<div style="text-align: center;"><span style="font-size: 36px;">⚡</span></div></div>
</td></tr></tbody></table>
<table align="center" bgcolor="" border="0" cellpadding="0" cellspacing="0" width="100%"><tbody><tr><td class="container" style="padding-top: 0px; padding-bottom: 0px;">
<div class="text-block">
<div style="text-align: center;">
<h1><strong>Quick Links</strong></h1>
</div>
</div>
</td></tr></tbody></table>
<table bgcolor="" style="table-layout: fixed; width: 100%;" width="100%"><tbody><tr><td style="padding:0;border-collapse:collapse;border-spacing:0;margin:0;" valign="top">
<table align="center" border="0" cellpadding="0" cellspacing="0" width="100%"><tbody><tr><td class="container" style="padding: 15px 15px;">
<div class="text-block">
<span>
<a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fgo.granola.ai%2Fadops-tldr-tech%3Futm_source=tldrai/1/0100019df32340e9-a0ca27a1-a8aa-48d0-9216-dd5b8e119031-000000/t5JVnvETi_O8_49oq8WnWf7EpY60Vn7_MmnHhdgsnQA=452">
<span>
<strong>TLDR readers: Get 1 month of the AI notetaker everyone's talking about for free (Sponsor)</strong>
</span>
</a>
<br>
<br>
<span style="font-family: "Helvetica Neue", Helvetica, Arial, Verdana, sans-serif;">
Granola takes your raw meeting notes and makes them awesome, no awkward transcription bot required. Now TLDR readers get one month free with code: <strong>TLDR1MO</strong>. <a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fgo.granola.ai%2Fadops-tldr-tech/1/0100019df32340e9-a0ca27a1-a8aa-48d0-9216-dd5b8e119031-000000/K2OhHeykw5_cAFg1-OggE27_lMKPdfYuu9VSdBbdotA=452" rel="noopener noreferrer nofollow" target="_blank"><span>See why people love Granola</span></a>
</span>
</span>
</div>
</td></tr></tbody></table>
<table align="center" border="0" cellpadding="0" cellspacing="0" width="100%"><tbody><tr><td class="container" style="padding: 15px 15px;">
<div class="text-block">
<span>
<a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fwww.turingpost.com%2Fp%2Fclem-delangue-hugging-face-ai-builders%3Futm_source=tldrai/1/0100019df32340e9-a0ca27a1-a8aa-48d0-9216-dd5b8e119031-000000/U_FbL9d-C98ibsPwZb6T1kaGLb8dUEXEaN7sr77u6c4=452">
<span>
<strong>Hugging Face's Clem Delangue: Stop Comparing Engines to Cars (29 minute read)</strong>
</span>
</a>
<br>
<br>
<span style="font-family: "Helvetica Neue", Helvetica, Arial, Verdana, sans-serif;">
Comparing open-source models to closed APIs is flawed, as they serve different purposes.
</span>
</span>
</div>
</td></tr></tbody></table>
<table align="center" border="0" cellpadding="0" cellspacing="0" width="100%"><tbody><tr><td class="container" style="padding: 15px 15px;">
<div class="text-block">
<span>
<a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Flinks.tldrnewsletter.com%2FTaoEzW/1/0100019df32340e9-a0ca27a1-a8aa-48d0-9216-dd5b8e119031-000000/bgtI_Zk9CUwFg-8OKE2N1-Hrivbg9-Er50gaYRY8NDM=452">
<span>
<strong>Top AI Companies Agree to Pentagon Deals for Classified Work (6 minute read)</strong>
</span>
</a>
<br>
<br>
<span style="font-family: "Helvetica Neue", Helvetica, Arial, Verdana, sans-serif;">
Many of the companies have said their deals with the Department of War include commitments that their tools wouldn't be used for mass surveillance or autonomous weapons.
</span>
</span>
</div>
</td></tr></tbody></table>
<table align="center" border="0" cellpadding="0" cellspacing="0" width="100%"><tbody><tr><td class="container" style="padding: 15px 15px;">
<div class="text-block">
<span>
<a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fjobs.ashbyhq.com%2Ftldr.tech%2F038c4419-5b48-4279-a75e-6f7a0afdb240%3Futm_source=tldrai/1/0100019df32340e9-a0ca27a1-a8aa-48d0-9216-dd5b8e119031-000000/JMPmDG1AGqW2bqtWvRa9gVMVz8rIBmANyTbsa9N4gsw=452">
<span>
<strong>Become a curator for TLDR AI (3-5 hrs/week)</strong>
</span>
</a>
<br>
<br>
<span style="font-family: "Helvetica Neue", Helvetica, Arial, Verdana, sans-serif;">
TLDR is looking for an engineer/researcher at a major AI lab or startup to help write for 1M+ subscribers. Our curators have been invited to Google I/O and OpenAI DevDay, scouted for Tier 1 VCs, and get early access to unreleased TLDR products. <a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fjobs.ashbyhq.com%2Ftldr.tech%2F038c4419-5b48-4279-a75e-6f7a0afdb240/1/0100019df32340e9-a0ca27a1-a8aa-48d0-9216-dd5b8e119031-000000/6qyUglC6TGR1bfBBNcUHOx39KtXPrN3yJGTpUUxNRWI=452" rel="noopener noreferrer" target="_blank"><span>Learn more</span></a>.
</span>
</span>
</div>
</td></tr></tbody></table>
<table align="center" border="0" cellpadding="0" cellspacing="0" width="100%"><tbody><tr><td class="container" style="padding: 15px 15px;">
<div class="text-block">
<span>
<a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Flinks.tldrnewsletter.com%2FPLJK7R/1/0100019df32340e9-a0ca27a1-a8aa-48d0-9216-dd5b8e119031-000000/2iCOQwyLx6S3KFZUfPTbFnm7tTGyJsghekorAtMBxig=452">
<span>
<strong>Anthropic Nears $1.5 Billion Joint Venture With Wall Street Firms (3 minute read)</strong>
</span>
</a>
<br>
<br>
<span style="font-family: "Helvetica Neue", Helvetica, Arial, Verdana, sans-serif;">
The investors want to create a company that helps teach businesses how to incorporate AI across their operations.
</span>
</span>
</div>
</td></tr></tbody></table>
<table align="center" border="0" cellpadding="0" cellspacing="0" width="100%"><tbody><tr><td class="container" style="padding: 15px 15px;">
<div class="text-block">
<span>
<a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Favkcode.github.io%2Fblog%2Fhow-vllm-works.html%3Futm_source=tldrai/1/0100019df32340e9-a0ca27a1-a8aa-48d0-9216-dd5b8e119031-000000/crIER3RE2Py6KQLdVeZS_alLRdE2HcnwPd6UopMUhXU=452">
<span>
<strong>vLLM routing and KV (16 minute read)</strong>
</span>
</a>
<br>
<br>
<span style="font-family: "Helvetica Neue", Helvetica, Arial, Verdana, sans-serif;">
One global vLLM pool is a poor default for mixed traffic.
</span>
</span>
</div>
</td></tr></tbody></table>
</td></tr></tbody></table>
<table align="center" bgcolor="" border="0" cellpadding="0" cellspacing="0" width="100%"><tbody><tr><td align="left" style="word-break: break-word; vertical-align: top; padding: 5px 10px;">
<p style="padding: 0; margin: 0; font-size: 22px; color: #000000; line-height: 1.6; font-weight: bold;">
Love TLDR? Tell your friends and get rewards!
</p>
</td></tr>
<tr><td class="container" style="padding: 0px 10px 15px;">
<div class="text-block">
Share your referral link below with friends to get free TLDR swag!
</div>
</td></tr>
<tr><td align="left" style="padding: 10px;">
<div class="text-block">
<a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Frefer.tldr.tech%2F51d793d6%2F2/1/0100019df32340e9-a0ca27a1-a8aa-48d0-9216-dd5b8e119031-000000/w7C8lsz0tOd7BnUILRIGIZBfDDek8-U2hwaqi0_Abzw=452" style="color: #464ba4; text-decoration: underline;">https://refer.tldr.tech/51d793d6/2</a>
</div>
</td></tr>
<tr></tr>
<tr><td align="left" style="padding:5px 10px;">
<a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fhub.sparklp.co%2Fsub_9a4c16be99eb%2F2/1/0100019df32340e9-a0ca27a1-a8aa-48d0-9216-dd5b8e119031-000000/dtLkPrr3-IndFSBlKxbGwx6OG0tyC2o3L2GpQv0kuFk=452" style="font-size: 16px; line-height: 1.6; padding: 10px 0; display: inline-block; text-decoration: underline;"><span style="mso-text-raise:13pt; text-decoration: underline;">Track your referrals here.</span></a>
</td></tr></tbody></table>
<table align="center" bgcolor="" border="0" cellpadding="0" cellspacing="0" width="100%"><tbody><tr><td align="left" style="word-break: break-word; vertical-align: top; padding: 5px 10px;">
<p style="padding: 0; margin: 0; font-size: 22px; color: #000000; line-height: 1.6; font-weight: bold;">
Want to advertise in TLDR? 📰
</p>
<div class="text-block" style="margin-top: 10px;">
If your company is interested in reaching an audience of AI professionals and decision makers, you may want to <a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fadvertise.tldr.tech%2F%3Futm_source=tldrai%26utm_medium=newsletter%26utm_campaign=advertisecta/1/0100019df32340e9-a0ca27a1-a8aa-48d0-9216-dd5b8e119031-000000/R8sKFKZyeVldvr6DBaN_7yiUz41_-gtMwVDJ4_cV5WU=452"><strong><span>advertise with us</span></strong></a>.
</div>
<br>
<!-- New "Want to work at TLDR?" section -->
<p style="padding: 0; margin: 0; font-size: 22px; color: #000000; line-height: 1.6; font-weight: bold;">
Want to work at TLDR? 💼
</p>
<div class="text-block" style="margin-top: 10px;">
<a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fjobs.ashbyhq.com%2Ftldr.tech/1/0100019df32340e9-a0ca27a1-a8aa-48d0-9216-dd5b8e119031-000000/OmqIed5okzHeXXPFH9Tx1nDGfXe47SECTVCexZ99rDE=452" rel="noopener noreferrer" style="color: #0000EE; text-decoration: underline;" target="_blank"><strong>Apply here</strong></a>,
<a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fjobs.ashbyhq.com%2Ftldr.tech%2Fc227b917-a6a4-40ce-8950-d3e165357871/1/0100019df32340e9-a0ca27a1-a8aa-48d0-9216-dd5b8e119031-000000/uTWhXOO3j7vmKXhN7GQwAhOjNv5_Pt8SIgg3enL8Nf4=452" rel="noopener noreferrer" style="color: #0000EE; text-decoration: underline;" target="_blank"><strong>create your own role</strong></a> or send a friend's resume to <a href="mailto:jobs@tldr.tech" style="color: #0000EE; text-decoration: underline;">jobs@tldr.tech</a> and get $1k if we hire them! TLDR is one of <a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fwww.linkedin.com%2Ffeed%2Fupdate%2Furn:li:activity:7401699691039830016%2F/1/0100019df32340e9-a0ca27a1-a8aa-48d0-9216-dd5b8e119031-000000/gdeIeW4mK5mB0cY-yN-mMRRIDDvQNBRTPo6rkIJJgHM=452" rel="noopener noreferrer" style="color: #0000EE; text-decoration: underline;" target="_blank"><strong>Inc.'s Best Bootstrapped businesses</strong></a> of 2025.
</div>
<br>
<div class="text-block">
If you have any comments or feedback, just respond to this email!
<br>
<br> Thanks for reading,
<br>
<a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Ftwitter.com%2Fandrewztan/1/0100019df32340e9-a0ca27a1-a8aa-48d0-9216-dd5b8e119031-000000/nUhjNnBi9B_YS8vMeZB9HgXoNjcKokmEJG5oNKc9vXQ=452"><span>Andrew Tan</span></a>, <a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fwww.linkedin.com%2Fin%2Faliiaminian%2F/1/0100019df32340e9-a0ca27a1-a8aa-48d0-9216-dd5b8e119031-000000/JCxKcbywRTxQ-icxK96OC8C_bHJuTc9Pq8pLerGohKI=452"><span>Ali Aminian</span></a>, & <a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fwww.linkedin.com%2Fin%2Fjacob-turner-7521a8198%2F/1/0100019df32340e9-a0ca27a1-a8aa-48d0-9216-dd5b8e119031-000000/EjoP54i45uY39NTr1C3iIKwCV59TMSmgI-2Qd610CK8=452"><span>Jacob Turner</span></a>
<br>
<br>
</div>
<br>
</td></tr></tbody></table>
<table align="center" bgcolor="" border="0" cellpadding="0" cellspacing="0" width="100%"><tbody><tr><td class="container" style="padding: 15px 15px;">
<div class="text-block" id="testing-id">
<a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Ftldr.tech%2Fai%2Fmanage%3Femail=silk.theater.56%2540fwdnl.com/1/0100019df32340e9-a0ca27a1-a8aa-48d0-9216-dd5b8e119031-000000/IoM8X2uh-cy5qZQxnxZR-pEAo6R1ffNrMPZcJJkZAL8=452">Manage your subscriptions</a> to our other newsletters on tech, startups, and programming. Or if TLDR AI isn't for you, please <a href="https://tracking.tldrnewsletter.com/CL0/https:%2F%2Fa.tldrnewsletter.com%2Funsubscribe%3Fep=1%26l=eedf6b14-3de3-11ed-9a32-0241b9615763%26lc=1571a9f6-84b7-11f0-b464-47c5c04ad337%26p=3c455ea4-47b4-11f1-8a50-b96a4f78d0f7%26pt=campaign%26pv=4%26spa=1777899671%26t=1777900667%26s=bc3d3c30d22d96032c8e350c169f251bd13e80ea42bb008494c8d8027e9e4c16/1/0100019df32340e9-a0ca27a1-a8aa-48d0-9216-dd5b8e119031-000000/EjDD3Um8nmSly8uPWJ7Tl9fmNj9G58roG7UGMMcwJkQ=452">unsubscribe</a>.
<br>
</div>
</td></tr></tbody></table>
</td></tr></tbody></table>
</td></tr></tbody></table>
</td></tr></tbody></table>
</td></tr></tbody></table>
<img alt="" src="http://tracking.tldrnewsletter.com/CI0/0100019df32340e9-a0ca27a1-a8aa-48d0-9216-dd5b8e119031-000000/Moi35j3Pb4NNMMBe30CWnIvpdn6F7iLHf49FkQdObOI=452" style="display: none; width: 1px; height: 1px;">
</body></html>