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Lauren Begins Guide to Pstack Agent Engineering Workflow

The Complete Guide to pstack Pt. 1

Engineer Lauren, known as @poteto, begins a multi-part guide to pstack, her set of skills for rigorous agent-assisted engineering, claiming it enabled about 2,000 PRs a month. Part one centers on verification skills that let agents check their own work.

Original post · 12 min read
I'm writing a guide to pstack! Here's part one.
X ArticleThe Complete Guide to pstack Pt. 1
In this series of posts, I'm going to show you how I use pstack, my personal set of skills for doing rigorous engineering work. It's allowed me to ship 2,000 PRs a month to production with high confidence.

Personally, I have never put much emphasis into how many lines of code or how many PRs I was landing. Before agents, no one cared, and rightfully so, as raw productivity did not always equate to quality or a visible outcome for users. It was simply a vanity metric.
But I've discovered through the course of building pstack that volume does matter, especially when you are able to maintain or even increase the level of quality of the product with agents. For example, I started working on Grok @Bot about 2 months ago, when it was still in its early days and the codebase was fresh but starting to grow. Despite the team growing and now landing hundreds of PRs a day into the Grok @Bot codebase, pstack has allowed me to keep the quality of the code high for everyone as I constantly monitor code, refactor, add new lints and checks, and also work on features.

Being Grok @Bot's gardener and maintainer is something I was only able to do through pstack. Our early momentum after building the prototype was very high and many people were joining the team. I had a critical moment of opportunity to refactor the whole codebase, while it was being built and extended and with no downtime, into something with strong foundations. A codebase with high quality that scales no matter how many engineers (and most importantly, non-engineers) contribute to it. All of this work requires me to refactor and improve the foundations of Grok Bot as it's being built, and you can only do that when the foundations can keep up with the number of contributions.

The proof is in Grok @Bot itself. Over the next few weeks, I'll tell you everything you need to know to be able to build and maintain a high quality app using pstack.
Part 1 – Verification is all you need
The most critical skill to have in your toolbox is a high quality verification skill. This skill is so important to have and maintain that I think of it more like critical infrastructure rather than "just" a skill. A good one will amplify the output of your whole team, including non-engineers. Done well, you will 100-1000x your whole team's output.
If you're not familiar with the term, verification means that an agent can verify its own work. It can keep going until it succeeds at its task, because it can now close the loop without you being the bottleneck. If you're interested to know more of the story of how I created my first verification skill for Cursor, check out my previous post Loops You Can Trust.
Let's build a verification skill together
To start, install pstack and then run /create-verification-skill. I also recommend adding Dr Eggbot, my bot that helps you create high quality bots, to your roster. Dr Eggbot ships with pstack. It’ll teach coding bots how to use it, and it can also make non-coding bots with the same rigor.
You can ask Dr Eggbot to create an engineer bot for you that you can then ask to run /create-verification-skill and set up a daily routine to run /maintain-verification-skill.

While that runs, let's walk through what the skill does and how it makes a high quality verification skill for you.
I distilled all of our verification skills that we use to build Grok @Bot and Cursor into this skill as a sort of meta-skill. It teaches your agent how to create a high quality one for your own app.
Now this is where choice of tech stack is important. If you're building an app in Electron or for the web for example, you can take advantage of the rich debugging tools available for the JS ecosystem. For example, the Chrome DevTools Protocol (CDP) allows you to use the same tooling available in your browser's developer tools. Or if you're building an iOS app, making use of the simulator.
You ideally want the ability to interact with your app, debug it, take perf traces, and any other debugging and development tooling that you might typically use if you were developing the app by hand. If you don't have a rich runtime to make use of, you may need to ask your agent to create tools for you (eg using lldb, or a custom package that runs as a sidecar in dev environments), or just make use of what you have available.
I personally feel that agentic verification is so important that I would unironically suggest building your own rich debugging tools, or even choosing a different tech stack, in order to have unfair advantages and extreme productivity in building software. As I mentioned earlier, giving agents the ability to verify their own work unlocks everyone in your organization to be able to contribute and validate that their changes actually work. The harder your tech stack is to debug and control, the more difficult it will be to use agents productively.
Make it Reproducible
In pstack, we have a principle called "Build the Lever". What this means in the context of c… continue on X ↗
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More in Agents & Dev Tools

Developer Rebuilds Seven Adobe Apps in Rust Using Opus 5.5

Peter Yang highlights a developer who reimplemented seven Adobe apps, including Photoshop, Premiere and Lightroom, in Rust with Claude Opus 5.5 and open-sourced them. The developer believes they can match Adobe's features within months, against Adobe's $840 yearly all-apps plan.

Original post · 1 min read
It's insane to watch AI blow apart closed source software and games.

4 examples from the past month:

1. 7 of Adobe's biggest apps, including Photoshop, Premiere, and Lightroom, have been partially rebuilt in Rust with Opus 5.5 and open sourced. It's still early, but the developer thinks they can match Adobe's features within months. Adobe's all-apps plan costs $840/year.
Miguel Ángel Durán @midudev
Todos los productos de Adobe reimplementados desde cero, gratuitos y de código abierto

→ getartcraft.com/apps
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Vercel's Guillermo Rauch Explains Turborepo's Migration From Go to Rust

Guillermo Rauch says Vercel moved Turborepo from Go to Rust, a migration that was controversial internally due to human costs. He argues that with AI agents the calculus has changed, so what is best for humans is no longer necessarily best for business.

Original post · 1 min read
DHH is fundamentally right about Rust. For context, Vercel has been undergoing a Rust-ification (carcinization, technically 🦀) for a while.

One of the first projects we migrated was Turborepo, from Go to Rust¹. The migration completed, but the RoI was actually quite controversial internally.

While Rust was in our eyes better for low-level OS access, something crucial for a build system like Turbo, the human migration costs were very sustantive.

Go is very fast. It's beautifully designed. It's easy to iterate on. We were very conflicted about the migration, because it was *humans* writing the code, *even if we knew Rust was a better choice*.

The calculus has now changed. What's "best for humans" is no longer necessarily "best for business".

FWIW, it's also quite unlikely that Rust is the end-all-be-all toolchain. I'm quite certain there's greener pasture ahead, because Rust itself was designed before the 'supersonic tsunami' of agents hit.

¹ https​://vercel.com/blog/how-turborepo-is-porting-from-go-to-rust
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Integer Multiplication Algorithm Bound Tightened Repeatedly With Astra

A post reports that a user running ChatGPT Astra in a loop is repeatedly breaking records for integer multiplication algorithms. It quotes an update to OpenAI problem #109 that tightens the constant from 2^-182 to 2^-59, a roughly 500,000-fold improvement over the previous result.

Original post · 1 min read
This guy has 6.1 Astra running in a loop and is breaking the record for integer multiplication algorithms every few hours lmaooooo.
Doug Colkitt @0xdoug
We are publishing an update to OpenAI problem #109 Integer multiplication) with another substantial further tightening:

κ = 2⁻⁵⁹ (from OpenAI’s original κ = 2⁻¹⁸²)

Approximately 500 thousand fold improvement over our previous result and a 2¹²³ fold improvement over the original OAI result.

The latest redesigned the finite network to share intermediate computations and scratch space, then tightened the recursion and Gaussian estimates.
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Boris Cherny Says Prompting Claude Should Feel Like Talking to a Coworker

Boris Cherny explains his approach to prompting Claude, advising users to give clear goals, specify effort level and verification steps rather than relying on heavy scaffolding.

Original post · 1 min read
I am surprised that people are surprised this is how I prompt Claude.

Talk to Claude the way you would a coworker. There's no secret to prompting. There's no need to be overly scaffolded or prescriptive for most tasks -- give Claude a goal, and it will figure it out.

Back in the Sonnet 3.5 days, your prompt mattered a lot. Nowadays, it's much more important to communicate to the model:

1. What you want it to do
2. How much effort you want it to spend
3. How it should verify that it did the right thing
Boris Cherny @bcherny
Prompt
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Eric Raymond Highlights Open-Source Rust Clone of Photoshop Built via LLM

Eric S. Raymond shares the photocraft GitHub project, a clean-room open-source reimplementation of Photoshop that he says was likely generated by decompiling the app, converting it to a spec and prompting an LLM for Rust. He argues this threatens closed-source software.

Original post · 1 min read
This is the doom I predicted a few days ago, coming for Photoshop. A clean-room open-source reimplementation.

No prizes for guessing that they decompiled Photoshop to source code, processed that to some kind of non-code specification language, then fed the spec to an LLM with an instruction to generate Rust.

Adobe just got nuked. And closed source is dead, dead, dead.

github.com/storytold/photocraft
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Nat Eliason Details Fourteen Ways His Bot Setup Automates Work

Nat Eliason lists fourteen functions of his bot setup, including a chief-of-staff agent that drafts emails, specialist agents per work lane, and cloud coding agents that open pull requests from Linear issues. He notes GrokBot as a substantial improvement over his previous OpenClaw setup.

Original post · 2 min read
Things my @bot setup does that still blow my mind:

1. A Chief of Staff who opens the day pulling open loops from email & tasks and suggesting things it can knock out before 7am.

2. After every meeting, decisions get folded into Notion, Linear, and Todoist — not left rotting in Granola

3. Every email starts as a draft. The CoS bot scans my email every ~2hr and drafts replies to nearly everything — including checking my cal for availability and finding requested attachments / links

4. A specialist for each lane: curriculum, engineering, coaching, hiring, content, ops, and one for every single piece of software

5. Routines that keep running while I’m offline (e.g. monitoring Sentry errors in our apps and proactively fixing things)

6. Group rooms where 2–4 bots share one project thread instead of me copy-pasting context

7. Cloud coding agents that pick up Linear issues and open PRs after running the list of open work by me EoD — then squash-merge to main when it’s done

8. Meeting prep briefs pulled from Granola + Notion before I walk in

9. A growing shareable knowledge base in Notion + a GitHub repo that we update daily based on what happens at school

10. Student progress look-up across Expertise, Followers, and CoFounder without inventing numbers — chat anytime to see where a student is on their business work

11. Mentor Mind that coaches me on how to hold the bar without inventing doctrine

12. Todoist as a central task list where it logs things it’s blocked on for me, or from meetings / emails — and I can paste links into chat to direct it how to solve them

13. Engineering work is automatically tracked in Linear so my and the product teams’ bots don’t collide with each other

14. Presentations spun up in Gamma / Claude Design without me opening a slide tool

15. Plaud / live capture → notes the bots can actually act on

Probably more but these were the immediate ones we thought of.
Nat Eliason @nateliason
GrokBot feels like absolute magic at this point, a meaningful leg up on my previous OpenClaw etc. setups.

And with how easy it is to setup, there's really no excuse now.
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