daily

University Hospital Waterford is to start providing 24/7 cardiac care after 10 years of campaigning by the public to have the service provided for the southeast region. From 8am on Monday, the new round-the-clock Primary PCI (Percutaneous Coronary Intervention) means patients suffering serious cardiac arrests can receive emergency treatment at any time of the day or night instead of having to make round-trip journeys of 246 kilometres to Cork or 340kms to Dublin. PPCI is an emergency procedure used to open a completely blocked coronary artery during a severe heart attack. The hospital has been providing a 12-hour service from 8 am to 8 pm, since March last year. There had been an 8 am to 5 pm service operating from UHW for several years. The HSE has confirmed that the service will be expanded from this month and will cover the HSE Dublin and the south-east region. Former independent TD for Waterford Matt Shanahan, who has been one of many campaigners calling for the round-the-clock service for nearly 15 years. Shanahan highlighted that data from 2015 to 2018 showed that less than three per cent of patients in the southeast region were getting to a cardiac centre within the clinical time frame. A 2016 report cited that "emergency patients could be adequately dealt with from this region by transporting them to either centres in Dublin and Cork", which he was "resisted forcibly" by clinicians in Waterford at the time also. Data from 2015 to 2018 showed that less than three per cent of patients in the southeast region were getting to a cardiac centre within the clinical time frame, Mr Shanahan said. Minister of State for Local Government and Planning John Cummins said the introduction of the new 24/7 service marks a “significant day” for Waterford and the southeast region. The Minister said the move marks a “new chapter for cardiac services” at UHW. “I want to acknowledge all who have campaigned tirelessly and worked hard to ensure this service has become a reality – from advocacy groups, to consultants, hospital management and all my government colleagues past and present,” Minister Cummins continued. He said that the provision of the service “is unquestionably an issue which has transcended political lines. The (addition) of the second cath lab was key, but so too was the move to a 8am to 8pm weekday service and then the extension to a 8am to 8pm weekend service.” The deputy acknowledged the work done by the Minister for Health, Jennifer Carroll MacNeill, and hospital management over the past twelve months to “ensure this final significant step to 24/7 was taken”. Local Sinn Fein councillor David Cullinane added that the new service will “save lives and give people across our region access to the emergency treatment they deserve, when they need it most”. Cullinane highlighted that “this is a victory for everyone who fought for it over so many years, patients, families, clinicians, healthcare workers, trade unions, campaigners and the wider community. “Together, we refused to accept second-class healthcare for the southeast”.

Homegames is a free and open-source game platform. Play, make and share games in your browser. No account required to play. - Make games in your browser with a simple code editor - Live game preview lets you test your changes with live multiplayer sessions - Manage game assets right from the studio. Upload your own files or draw/record something in the studio. - Publish games if you want to share them with the world. Or you can just keep them to yourself. Open Source - Everything is GPLv3 from the platform code to the games to this website. You can read the code and fork it if you want to. - Self-host on your own hardware. Homegames was designed to be self-hostable, including the API. - Preservation. If this website gets hit by a bus, your games can live on. Homegames has been in development as a (mostly) solo side project since 2018.

Computer Science > Software Engineering [Submitted on 19 May 2026] Title:Does Code Cleanliness Affect Coding Agents? A Controlled Minimal-Pair Study View PDF HTML (experimental)Abstract:As autonomous coding agents see rapid adoption, their evaluation has primarily focused on task completion rates holding the target codebase fixed. This leaves a critical question unanswered: does the structural and stylistic quality, or ``cleanliness'' of the underlying code affect an agent's ability to navigate and modify it? To isolate the effect of code cleanliness from agent capability, we introduce an evaluation protocol built around minimal pairs: repositories that match on architecture, dependencies, and external behaviour, but differ on static-analysis rule violations and cognitive complexity. The pairs are constructed in both directions, by agent pipelines that either degrade a clean repository or clean a messy one. We author 33 tasks across six such pairs, evaluated through hidden tests at the application's public surface. Across 660 trials with Claude Code, code cleanliness does not change the agent's pass rate. However, it substantially alters the agent's operational footprint: agents working on cleaner code use 7 to 8% fewer tokens and reduce file revisitations by 34%. Our findings suggest that traditional maintainability principles remain highly relevant in the era of AI-driven development, shaping the computational cost and navigational efficiency of coding agents. Code cleanliness joins model choice, harness, and prompting as a factor that materially affects agent behaviours. Submission history From: Priyansh Trivedi [view email][v1] Tue, 19 May 2026 16:06:26 UTC (1,094 KB) References & Citations Loading... Bibliographic and Citation Tools Bibliographic Explorer (What is the Explorer?) Connected Papers (What is Connected Papers?) Litmaps (What is Litmaps?) scite Smart Citations (What are Smart Citations?) Code, Data and Media Associated with this Article alphaXiv (What is alphaXiv?) CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub (What is DagsHub?) Gotit.pub (What is GotitPub?) Hugging Face (What is Huggingface?) ScienceCast (What is ScienceCast?) Demos Recommenders and Search Tools Influence Flower (What are Influence Flowers?) CORE Recommender (What is CORE?) arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

👋 Hey there, I’m Lenny. Each week, I answer reader questions about building product, driving growth, and accelerating your career. For more: Lenny’s Podcast | Lennybot | How I AI | My favorite AI/PM courses, public speaking course, and interview prep copilot

Subscribe now

P.S. Get a full free year of Google AI, Cursor, Lovable, Notion, Manus, Replit, Gamma, n8n, Canva, ElevenLabs, Factory, Wispr Flow, Fin, Supabase, Bolt, Linear, PostHog, Framer, Railway, Granola, Warp, Gumloop, Magic Patterns, Mobbin, Stripe Atlas, and ChatPRD, by becoming an Insider subscriber. Yes, this is for real.

For years, the PM job drifted toward coordinating and aligning people. That version of the role is fading. Today, the best PMs at the best companies are prototyping with real code, querying data conversationally with MCP, confidently running coding AI agents, and finding countless ways to increase their leverage with AI.

To help you navigate this shift, I’m excited to announce that I’ve co-developed a course with my frequent collaborator and new Head of Education, Colin Matthews. It’s called Become an AI-Native Builder, and Colin will teach you how to best use the latest AI tools—including Codex, Claude Code, Cursor, and many of the Product Pass products—in your day-to-day work as a PM. You’ll use skills and MCPs to support discovery, create prototypes using your real codebase, ship changes to production using GitHub, and set up evals to automate and improve the quality of your work. This is the course I’ve always wished existed. And it’s not just for PMs. It’s great for designers, ops, researchers, sales, and basically anyone who’s non-technical and wants to get their hands dirty.

The first cohort kicks off on July 13th. If you’re a Lenny’s Newsletter annual subscriber you’ll get $600 off (and Insiders get $1,000 off). Grab your discount code here and sign up today.

In addition to this course, Colin is hosting a handful of free workshops with leaders from OpenAI, Cursor, Linear, Replit, and Lovable. These will be live and hands-on, and you’ll learn and practice new skills alongside other people in the trenches. These workshops are exclusive to paid Lenny’s Newsletter subscribers, and you can sign up for them here.

Colin is one of the most talented instructors of AI that I’ve come across. We’ve done four guest posts together (one is my third-most-popular post of all time) and, like me, he’s low on hype and high on pragmatic advice. He’s taught AI and other technical skills to tens of thousands of PMs at leading companies like OpenAI, Google, Stripe, Figma, Microsoft, and more. He’s a longtime product leader, a founder, and has shipped more than 10 SaaS products solo.

To mark the launch of this course, Colin wrote a guest post that will help you understand what’s possible with AI right now and where you stand on the “ladders of leverage.”

Let’s get into it.

I’ve trained over 30,000 PMs on how to integrate AI into their workflows. Early this year, I noticed a shift. Whereas before I heard from executives that they expected teams to be using AI for just basic prototyping and general productivity, now they’re increasingly looking for their employees to use AI to complete entire tasks. After building bespoke training programs to level up product and design ICs across industries like healthcare, legal, and streaming, I saw two main gaps: knowing what level of AI to reach for and having the technical skills to leverage AI tools to their fullest.

I wanted to share a forward-looking framework on how the most AI-native PMs are operating in mid-2026, and how you can create much more leverage for yourself with AI.

Think of this framework as three ladders, each for a different type of leverage that AI can give you.

Personal leverage helps you check items off your own to-do list at work. Product leverage accelerates your ability to ship the right things more quickly. And systems leverage helps build repeatable steps to consistently outsource work to AI and get high-quality results.

As you ascend each ladder rung, you get an order of magnitude more leverage. On the first rung, you use AI for assistance in your own work. On the second rung, you pass tasks to AI and review the output. At the top of the ladder, AI completes multi-step tasks and checks its own results. You will always apply some level of review at the end, but increasing leverage frees you up for other work.

Not every task, workflow, or company demands that you move up to the highest rung to get the most out of AI. The right rung on each ladder is about the best use of AI for the work in front of you.

I’ll walk through all three ladders, with examples you can start using today. Let’s get into it.

The personal leverage ladder

This is the most common way we all use AI at work: drafting docs, researching, or creating small artifacts. Most PMs are already pretty capable here, but it’s worth detailing the rungs so that you can see where you’re at and where you might go.

• Rung 1: You use AI to write text. You’re using AI to help you with PRDs, Jira tickets, emails, etc. You then copy and paste answers into other tools. Most people are at this rung, or even lower. Don’t feel bad if you’re hanging out here. There’s so much opportunity!

• Rung 2: You use AI to create artifacts. Think slides, basic Excel models, or small prototypes. Instead of generating text for you to copy, AI generates the actual artifact.

• Rung 3: You get AI to complete a full to-do item for you. You’ve connected your LLM to external products like Amplitude, Google Drive, Notion, and Canva so it can pull and push information as needed. You run a prompt or skill to complete a task you might have handed off to a colleague before, like reading through customer support tickets or analyzing A/B test results.

Let’s walk through an example of each rung to illustrate baseline expectations.

At the beginning, you’re simply talking to the AI. For example, if I wanted to create a PRD, I might ask Claude to help me write it with a prompt like this:

The AI has very little context on your company or what a good PRD looks like. You’ll likely talk back and forth until you get a good enough result, then copy-paste to Google Docs or Word to improve it before sharing with your team.

Next is getting the AI to do work instead of just helping you do the work. Continuing with the last example, you could have Claude generate a financial model that shows the cost of hosting an agent yourself vs. using a managed service like Vercel.

Here’s a prompt I recently used:

Create a model that represents costs if we build and host ourselves vs. using managed agents. Do research on the engineering time saved and the compute costs in self-hosted vs. managed. Look at other vendors, like Cloudflare, Vercel, or E2B that provide sandboxes for agents for pricing. Demonstrate both the cost of the pilot and the cost at scale in the model, assuming we have 5M+ agent instances running annually (where an agent instance is per hour).

And here’s what the resulting model looks like:

As mentioned, you should expect the output to need significant revision at this rung, but it’s a step forward from copy-pasting text from AI into a separate document.

You’re at the highest rung of personal leverage if you are able to delegate complete to-do items to AI. To illustrate this, I’ll use a fictional product called Stride. Stride is a Strava clone, where athletes can share their performance for running, swimming, and other activities. Let’s say I wanted to do a retention analysis of users who share their exercises with an attached photo, and compare that against the cohort who don’t share photos. To complete this task, I’ll connect my LLM of choice (Claude) to my product analytics software (PostHog) and give it instructions to run this analysis for me. This was my prompt:

Use PostHog to check if users who use social share features have a higher 30d retention than those who don’t. Show me an html doc as a final output visualizing cohorts and any other useful data. Cite all your sources so I can validate.

And here’s the result:

In the past, this would have been a task you’d fit in between meetings. Instead, you’re handing it off to an LLM to complete end-to-end. I’d recommend including “cite your sources” so you can easily validate if the output is correct. In this case, Claude provides links directly to the source data in PostHog.

Pro tip: To allow models to complete tasks for you (and thus move to this rung in your personal work), you’ll need to connect your LLM to the products you use frequently via MCP. Claude Code, Codex, and Cursor can all connect to tools like Figma, Amplitude, PostHog, Pendo, and more via MCP. This may sound complicated, but it’s really easy to do, and once you create this connection, you’ll never have to touch it again. Simply navigate to your product’s connectors marketplace and add your tools (Claude, ChatGPT, Gemini).

Once you have your connectors set up, try completing a common task using your AI, like:

• Analyzing how a launch went by reviewing recent customer tickets and online sentiment

• Checking how many users actually use a feature through product analytics events

• Summarizing a recording from a customer call and creating a prototype based on their feedback

• Updating your next sprint based on a change in roadmap priorities

You’ll likely find the results disappointing at first, but that’s just because the model doesn’t know how to meet your standards yet. Continue iterating with the model until you have a good result, then lock in the workflow by creating a skill—just ask your LLM to create one in the same chat you completed the task in.

You can repeat this workflow to get reasonable first drafts for almost anything: PRDs, roadmaps, marketing assets, survey analysis, prototypes, Figma mocks, and more. I totally acknowledge that the quality may not be perfect, but we’ll come back to that when we talk about systems leverage.

The product leverage ladder

Product leverage closes the gap between what you want to build and what you can ship, even without strong design or engineering chops. The way to leverage AI for this ladder breaks down into three rungs:

• Rung 1: You create web-based prototypes. These communicate your ideas better than docs, but the prototype itself doesn’t have any value beyond communication. And your prototype’s code is independent from your product—it doesn’t use your existing codebase.

• Rung 2: You create code-based prototypes. Claude Code or Codex accesses your real codebase as the context for generating prototypes, instead of screenshots or complex prompts.

• Rung 3: You get an agent to ship changes to production as pull requests. An engineer picks up the PR, reviews, and merges your code into the product.

Let’s spend a bit more time with each of these.

Using AI tools like Lovable, Replit, and Magic Patterns is an amazing way to quickly and easily create a prototype. You can use this to share a concept or design with stakeholders, customers, or internal team members, allowing you to validate whether your solution is usable and solves the customer problem faster than ever.

Let’s revisit Stride as an example. Here’s what the profile page currently looks like:

Customers have been complaining that they don’t have clear cancellation paths and are confused about their subscription status after a free trial. You can use AI prototyping to create a quick mock to test with customers and stakeholders if one or more of your solutions address this problem:

This helps get to the right solution by testing more concepts faster, but typically the underlying code doesn’t have any value. Web AI prototyping tools have limited context on our real components, pages, and data models, so this prototype is removed from reality. That means that it will take more work to translate the tested prototype to real code.

The next rung up is to ask AI to prototype with your real product. This does not require running the full product on your laptop—or an expert-level coding ability—but you will need some technical skills. First, use a product like Claude Code or Codex to write code and run your app. Second, you’ll need a codebase that contains your UI but not your full backend; more on that later.

You can create the same prototype, this time asking Claude Code or Codex to use your existing codebase:

This time, the cancellation flow is added to the existing settings page, uses real components, and follows the design patterns of the actual product.

To create a codebase or repo that’s easy to use and doesn’t require running the full backend, pair with an engineer and have them use a prompt like this on your main codebase:

Create a new repo that contains all of the base UI elements, styles, routes, pages, and components for [list parts of the product you want included]. Create a mock data store that mimics the API data model and is stored locally. I should be able to run the resulting repo without any environment variables or backend services.

Once complete, clone the new repo to your computer. You should have an easy-to-run version of your UI that you can’t accidentally mess up. Sometimes your engineer will tell you this process is more complicated to get running than a simple prompt. It is very much worth the effort to build prototypes on your real styles and components, so do your best to get over the hump!

The last rung in product leverage is getting an agent to ship code to production. This is a great example of where your technical judgment as a PM is critical. It’s possible that your billing change is only in the UI, and all backend APIs, data elements, and events already exist. It’s also possible that this would require new infrastructure or integrations with another team to ship.

As a PM, it does not make sense to spend time being a worse engineer than the rest of your team. Knowing when you should write a doc, ship a prototype, or create a PR is as important as the technical ability to complete these tasks. PRs are great for copy changes, small UI/UX tweaks, and changes to views that use existing backend code.

The systems leverage ladder

Read more

6th July 2026 I hoped to release sqlite-utils 4.0 stable this weekend, but as I worked through the backlog of issues and PRs with a combination of Claude Fable 5 and GPT-5.5 the changelog since rc2 kept getting bigger. The biggest new feature is support for introspecting and creating compound foreign keys - a feature that involves a subtle breaking change to table.foreign_keys and hence needed to land for the 4.0 stable release. sqlite-utils also now follows SQLite's convention for case insensitive column names, which turned out to touch a bunch of different places at once.

Have your agent record video demos of its work with shot-scraper video 30th June 2026 shot-scraper video is a new command introduced in today’s shot-scraper 1.10 release which accepts a storyboard.yml file defining a routine to run against a web application and uses Playwright to record a video of that routine. I’ve written before about the importance of having coding agents produce demos of their work; this is my latest attempt at enabling them to do that. Here’s an example video created using shot-scraper video , exercising a still in development feature adding the ability to create new tables in Datasette from pasted CSV, TSV or JSON data: That video was created by running this command: shot-scraper video datasette-bulk-insert-storyboard.yml \ --auth datasette-demo-auth.json --mp4 (That --auth JSON file contains a cookie, as described here in the documentation.) Here’s the datasette-bulk-insert-storyboard.yml file: output: /tmp/datasette-bulk-insert-demo.webm server: - uv - --directory - /Users/simon/Dropbox/dev/datasette - run - datasette - -p - 6419 - --root - --secret - "1" - /tmp/demo.db url: http://127.0.0.1:6419/demo/tasks viewport: width: 1280 height: 720 cursor: true wait_for: 'button[data-table-action="insert-row"]' javascript: | (() => { let clipboardText = ""; Object.defineProperty(navigator, "clipboard", { configurable: true, get: () => ({ writeText: async (text) => { clipboardText = String(text); }, readText: async () => clipboardText, }), }); })(); scenes: - name: Bulk insert existing table rows do: - pause: 0.8 - click: 'button[data-table-action="insert-row"]' - wait_for: "#row-edit-dialog[open]" - pause: 0.5 - click: ".row-edit-bulk-insert" - wait_for: ".row-edit-bulk-textarea" - pause: 0.5 - click: ".row-edit-copy-template" - wait_for: "text=Copied" - pause: 0.8 - fill: into: ".row-edit-bulk-textarea" text: | title,owner,status,priority,notes Prepare release video,Ana,doing,1,Recorded with shot-scraper Check pasted CSV import,Ben,review,3,Previewed before inserting Share the branch demo,Chen,queued,2,Bulk insert creates three rows - pause: 0.8 - click: ".row-edit-save" - wait_for: "text=Previewing 3 rows." - pause: 1.2 - click: ".row-edit-save" - wait_for: "text=3 rows inserted." - pause: 1.0 - click: ".row-edit-cancel" - wait_for: "text=Prepare release video" - pause: 1.0 - name: Create a table from pasted CSV open: http://127.0.0.1:6419/demo wait_for: 'details.actions-menu-links summary' do: - pause: 0.8 - click: 'details.actions-menu-links summary' - click: 'button[data-database-action="create-table"]' - wait_for: "#table-create-dialog[open]" - pause: 0.5 - fill: into: ".table-create-table-name" text: "launch_metrics" - click: ".table-create-from-data" - wait_for: ".table-create-data-textarea" - pause: 0.5 - fill: into: ".table-create-data-textarea" text: | metric_id,name,score,recorded_on m001,Activation rate,87.5,2026-06-29 m002,Retention check,72.25,2026-06-30 m003,CSV import health,95,2026-07-01 - pause: 0.8 - click: ".table-create-save" - wait_for: "text=Previewing 3 rows." - pause: 1.2 - click: ".table-create-save" - wait_for_url: "**/demo/launch_metrics" - wait_for: "text=Activation rate" - pause: 1.2 The video command documentation includes simpler examples, but for the purpose of this post I thought I’d go with something more comprehensive. That demo YAML storyboard was constructed entirely by GPT-5.5 xhigh running in Codex Desktop, using the following prompt run inside my ~/dev/datasette checkout of this branch: Review the changes on this branch. cd to ~/dev/shot-scraper and run the command "uv run shot-scraper video --help" Now use that new video command to record a video demo of the new features from this branch, including running a "uv run datasette -p 6419 --root --secret 1 /tmp/demo.db" development server so you can record the video against a demo DB that you first create. Now that I’ve released the feature the prompt could say "run uvx shot-scraper video --help " instead and it should achieve the same result. I really like this pattern where the --help output for a command provides enough detail that a coding agent can use it—it works kind of like bundling a SKILL.md file directly inside the tool. I used the same pattern for showboat and rodney. How I built this shot-scraper video started as an experimental prototype. shot-scraper is built on top of Playwright, and the key feature it needed was for Playwright to be able to record video of browser sessions with enough control to create the desired demo. I first tried this a few years ago and found that the Playwright-produced videos included additional chrome that was useful for debugging a test failure but unwanted for a product demo. They fixed that a while ago, but there were still some minor blockers. In particular I was getting a few white frames at the start of the videos, since the recording mechanism kicked in before the first URL was loaded by the browser. Playwright 1.59 added a new screencast mechanism providing much more finely grained control over video recording. This was very nearly what I needed, but the resulting videos were fixed at 800px wide. I found a landed PR fixing that but it wasn’t yet in a release. Then yesterday they shipped it in playwright-python 1.61.0 and I was finally unblocked to finish implementing the feature! The code itself was all written by GPT-5.5 xhigh in Codex Desktop. I had it write the documentation as well which gave me a very useful frame for reviewing the design—much of the iteration on the feature came from reviewing that documentation, spotting things that were redundant, inconsistent or confusing, and requesting (or dictating) a better design. The YAML format itself was mostly defined by the coding agent. I had it use Pydantic to both define and validate the format, partly to make the design easier to review. This is a great example of the kind of feature that I almost certainly wouldn’t have taken on without coding agent support. I filed the original issue in February 2024, and had difficulty finding the necessary time to solve this in amongst all of my other projects.