Rss preview of Blog of Geoffrey Huntley

the six-month recap: closing talk on AI at Web Directions, Melbourne, June 2025

2025-06-17 10:08:00

Welcome back to our final session at WebDirections. We're definitely on the glide path—though I'm not sure if we're smoothly landing, about to hit turbulence, or perhaps facing a go-around. We'll see how it unfolds. Today, I'm excited to introduce Geoffrey Huntley. I discovered Geoff earlier this year through an article linked on LinkedIn.

That article perfectly captured what I've been trying to articulate about the impact of large language models on software engineering practices. The term "AI" is both overused and underused; however, it's clear that these technologies are poised to transform how we build software.

I've been a software engineer in various capacities since the 1980s. Even while running conferences for the past 20 years, I've never stopped coding—whether building platforms, developer tools, or systems to support our events. Over the decades, I've witnessed revolutions in software engineering, like computer-aided software engineering, which always struck me as an oxymoron. After all, isn't all software engineering computer-aided? However, back then, before the advent of personal computers and workstations, we had batch computing, and software engineering was a distinct process for programming remote machines.

These revolutions in software engineering practices have been transformative; however, the last major shift occurred nearly 40 years ago. I believe we're now in the midst of another profound revolution in how software is created. This topic has been on my mind a lot, and Geoff's article resonated deeply with me. Intrigued, I looked him up on LinkedIn and was surprised to find he’s based in Sydney. The next day, we were on the phone—and thank goodness long-distance calls are no longer billed by the minute, because Geoff and I have had many lengthy conversations since.

Geoff has been incredibly generous with his time. He kindly joined us in Melbourne a few weeks ago for an unconference at Deakin, which some of you attended. More importantly, he’s not just theorising about the future of software engineering—he’s actively putting those ideas into practice. His deep thinking and hands-on approach make him the perfect person to explore what lies ahead for our field.

So I've asked him to come here to talk about that. We may never see him again. He's off to San Francisco to work for Sourcegraph.

Thank you all for joining us on this Friday. This talk will be somewhat intense, but it follows a clear arc and serves a purpose.

I see software engineering transforming in a similar way to what happened in architecture. Before tools like AutoCAD, rooms full of architects worked manually. Afterwards, architects continued to exist, but their roles and identities evolved. We’re experiencing a similar shift in our field right now.

I’d like to thank today’s speakers. Giving talks is always challenging, no matter how experienced you are. It gets easier with practice, though, so if you’re considering delivering one, I encourage you to go for it - it’s incredibly rewarding.

Let’s get started. About six months ago, I wrote a blog post titled The Future Belongs to People Who Do Things. Despite any confidence I may project, I don’t have all the answers about where this is heading. What I do know is that things are changing rapidly. Faster than most people realise. If AI and AI developer tooling were to cease improving today, then it would already be good enough to disrupt our profession completely.

We are in an "oh fuck" moment in time. That blog post, published in December, was my first on the transformations AI will have for software engineers and businesses. As we go through this talk, you might find yourself having one of those moments, too, if you haven’t already.

It all began when an engineering director at Canva approached all the principal engineers and said, “Hey, can you dive deep into AI over the Christmas break?” My initial reaction was, “Okay, I’ve tried this all before. It wasn’t that interesting.”

So, I downloaded Windsurf and asked it to convert a Rust audio library to Haskell using GHC 2024.

I told it to use Hoogle to find the right types and functions, and to include a comprehensive test suite with Hspec and QuickCheck

Instructed it to run a build after every code change when making modifications.

I also instructed it to write tests and automate the process for me. I had heard it was possible to set up a loop to automate some of these tasks, so I did just that.

I took my kids to the local pool, left the loop running,

and when I returned, I had a fully functioning Haskell audio library.

Now, that’s wild. Absolutely wild.

You’re probably wondering why I’d build an audio library in Haskell, of all things, as it’s arguably the worst choice for audio processing. The reason is that I knew it wasn’t trivial. I’m constantly testing the limits of what’s possible, trying to prove what this technology can and cannot do. If it had just regurgitated the same Rust library or generated something unoriginal, I wouldn’t have been impressed. But this?

This was a Haskell audio library for Core Audio on macOS, complete with automatically generated bindings to handle the foreign function interface (FFI) between functional programming and C. And it worked.

I was floored.

So, I wrote a blog post about the experience and with this as the conclusion...

From this point forward, software engineers who haven’t started exploring or adopting AI-assisted software development are, frankly, not going to keep up. Engineering organizations are now divided between those who have had that "oh fuck" moment and those whom have not.

In my career, I’ve been fortunate to witness and navigate exponential change. With a background in software development tooling, I began writing more frequently. I could see patterns emerging.

I realised we need better tools—tools that align with the primitives shaping our world today. The tools we currently rely on, even now, feel outdated. What we have today, even now, doesn't make sense for the primitives that presently exist. They have been designed for humans first and built upon historical design decisions.

I wrote a follow-up blog post, and back in January, my coworkers at Canva thought I was utterly crazy. Even though Canva had been exploring AI for productivity for over a year, the notion was still conceptually in the unthinkable realm.

I was saying, "Hey, hold on, hold on. Why are we designing tools for humans?"

Think about it: what is an IDE?

An IDE is essentially a typewriter - a single pane of glass we type into.

It feels strange that the entire industry is cramming all these AI tools into the IDE.

It doesn't seem right.

It's like a high-powered tool paired with a low-powered interface.

This interface has remained essentially unchanged since Turbo Pascal was introduced in 1983.

Has AI rendered IDEs obsolete?

So, I started thinking along these lines:

What has changed?

What is changing?

What if we designed tools around AI first and humans second?

Then I dug deeper.

I thought, "Why does an engineer only work on one story at a time?"

In my youth, I played World of Warcraft. Anyone familiar with World of Warcraft knows about multi-boxing, where you control multiple characters simultaneously on one computer.

I realised, "Wait a second. What if I had multiple instances of Cursor open concurrently?

Why are we only working on one story?"

Why is this the norm?

When I discussed this with coworkers, they were stuck thinking at a basic level, like, "What if I had one AI coworker?"

They hadn't yet reached the point of, "No, fam, what if you had a thousand AI coworkers tackling your entire backlog all at once?"

That's where Anni Betts comes in.

Anni Betts was my mentor when I began my career in software engineering.

Much of the software you use daily - Slack, the GitHub Desktop app, or the entire ecosystem of software updaters - that's Annie's work.

She's now at Anthropic.

When certain people of her calibre say or do something significant, I pay attention.

Two people I always listen to are Annie Betts and Eric Meyer.

And here's the thing: all the biggest brains in computer science, the ones who were retired, are now coming out of retirement.

Big moves are happening here. Our profession stands at a crossroads. It feels like an adapt-or-perish moment, at least from my perspective.

It didn’t take long for founders to start posting blogs and tweets declaring, “I’m no longer hiring junior or mid-level software engineers.”

Shopify quickly followed suit, stating, “At Shopify, using AI effectively is no longer optional - it’s a baseline expectation for employment.”

A quote from the Australian Financial Review highlights how some divisions embraced this AI mandate a bit too enthusiastically. Last week, Canva informed most of its technical writing team that their services were no longer needed.

Let me introduce myself. I’m Geoff,

Previously, the AI Developer Productivity Tech Lead at Canva, where I helped roll out AI initiatives. Two weeks ago, I joined Sourcegraph to build next-generation AI tools. I'll be heading out to San Francisco tomorrow morning after this talk and will be joining the core team behind https://ampcode.com/.

Folks, there’s been a slight change of plans.

I’m departing @canva on Friday.

From Monday I'll be joining the @AmpCode team to help make the damn best suite of vibecoding tools for business. https://t.co/5SRmzHWanI pic.twitter.com/BW0DkvhGAp
— geoff (@GeoffreyHuntley) May 20, 2025

Given that these tools will have significant societal implications, I feel compelled to provide clarity and guidance to help others adapt.

Regarding my ponderoos, it’s all available on my website for free. Today, I’ll be synthesising a six-month recap that strings them together into a followable story.

After publishing a blog post stating that some people won’t make it in this new landscape, colleagues at Canva approached me, asking, “Geoff, what do you mean some people won’t make it?” Let me explain through an example.

At Fruitco, a fictional company, there are seven software developers, and the company conducts six-month performance cycles, a common practice across industries. It’s tempting to blame a single company, but AI tools are now accessible with a credit card. These dynamics will unfold over time, faster at some companies, slower at others.

Unfortunately, Lemon doesn’t survive the performance cycle because they underperform.

Another cycle passes, and Orange and Strawberry, typically high performers, are shocked to receive low performance ratings. Stunned, they begin searching for ways to gain a competitive edge. They download tools like Cursor, Windsurf or Amp and start exploring their capabilities.

This is where it gets interesting. Through my research within the organisation, I mapped out the stages of AI adoption among employees. I was once like Pineapple, sceptical and demanding proof that AI was transformative. When I first tried it, I found it lacking and simply not good enough.

However, the trap for seasoned professionals, like a principal engineer, is trying AI once and dismissing it, ignoring its continuous improvement. AI tools, foundation models, and capabilities are advancing every month. When someone praises AI’s potential, it’s easy to brush it off as hype. I did that myself.

Six months later, at the next performance cycle, Pineapple and Grape find themselves at the bottom of the performance tier: surprising, given their previous top-tier status. Why? Their colleagues who adopted AI gained a significant productivity boost, effectively outpacing them. Naturally, Pineapple and Grape’s performance ratings suffered in comparison.

Banana, noticing this shift, begins to take AI seriously and invests in learning its applications. The earlier you experiment with AI, the greater the compounding benefits, as you discover its strengths and limitations.

Unfortunately, after the next performance cycle, the outcomes are predictable. Grape fails to adapt to the evolving engineering culture and is no longer with the company.

This pattern reflects what I’ve termed the “people adoption curve for AI”

Grape’s initial stance was, “Prove it’s not hype.” Over time, employees move through stages: scepticism, experimentation, and eventually realisation. In the middle, there’s a precarious moment of doubt—“Do I still have a job?”—as the power of AI becomes clear. It’s daunting, even terrifying, to grasp what AI can do.

Yet, there’s a threshold to cross. The journey shifts from merely consuming AI to programming with it. Programming with AI will soon be a baseline expectation, moving beyond passive use to active automation of tasks. The baseline expectation of what constitutes high performance is going to shift rapidly, and as more people adopt these techniques and newer tools, what will happen is that what was once considered high performance without AI will now be viewed as low performance.

In my blog post, I concluded that AI won’t trigger mass layoffs of software developers. Instead, we’ll see natural attrition between those who invest in upskilling now and those who don’t. The displacement hinges on self-investment and awareness of these changing dynamics.

Between 2024 and 2025, a rift is emerging. The skill set that founders and companies demand is evolving rapidly.

In 2024, you could be an exceptional software engineer. But in 2025, founders are seeking AI-native engineers who leverage AI to automate job functions within their companies. It’s akin to being a DevOps engineer in 2025 without knowledge of AWS or GCP—a critical skills gap. This shift is creating a rift in the industry.

For engineering leaders, it’s vital to guide teams through the emotional middle phase of AI adoption, where fear and uncertainty can paralyse progress, leaving people like deer in headlights. Building robust support mechanisms is essential.

Companies often encourage employees to “play with AI,” but this evolves into an expectation to “do more with AI.” For those who embrace AI, the rewards are significant. However, engineering leaders also face challenges: the tech industry is once again booming, creating retention issues.

You want the right people using AI effectively, but talented engineers who master AI automation may be lured elsewhere. For individuals, mastering AI is among the most valuable personal development investments you can make this year.

For those who don’t invest in themselves, the outlook is grim. When I published my blog posts and research, I recall walking to the Canva office after getting off the train, feeling like I was in The Sixth Sense. I saw “dead people”—not literally, but I was surrounded by colleagues who were unaware their roles were at risk due to displacement. What was once considered high-performance will soon become low-performance at companies as a bunch of people on motorbikes (running multiple agents concurrently) just turned up and will redefine what it means to be a high performing employee. This realisation drove me to write more.

Initially, I thought moving from scepticism to AI adoption was straightforward. But I discovered it’s an emotional rollercoaster. The more you realise AI’s capabilities, the more it pushes you back to that central question: “Will I have a job?” This cycle makes it critical for engineering leaders to support their teams through this transition, recognising it’s not a linear process but a complex people change management challenge.

I’ve also explored the Overton window concept, traditionally used in political theory to map societal acceptance of policies. It’s equally effective for understanding disruptive innovation like AI.

Currently, vendors are embedding AI into integrated development environments (IDEs), as it’s perceived as accessible and non-threatening. Five months ago, I argued the IDE-centric approach was outdated. Last week, Anthropic echoed this, confirming the shift.

this is so validating; saw it six months back and coworkers thought I was mad. pic.twitter.com/d0vXPmgL4N
— geoff (@GeoffreyHuntley) May 23, 2025

These days, I primarily use IDEs as file explorer tools. I rarely use the IDE except to craft and maintain my prompt library.

New approaches are emerging. Amp, for example, operates as both a command-line tool and a VS Code extension. We’re also seeing tools like Claude Code. The Overton window is shifting, and this space evolves rapidly. I spend considerable time contemplating what’s “unthinkable”—innovations so radical they unsettle people. Even today’s advancements can feel intimidating, raising questions about the future.

Let me show you how I approach software development now. AMP is both a command-line tool and an extension.

0:00

/1:26

Here’s an example task:

“Hey, in this folder there's a bunch of images. I want you to resize them to be around about 1920px and no bigger than 500 kilobytes. Can you make it happen please?"

Most people use coding assistants like a search engine, treating them as a Google-like tool for chat-based operations. However, you can drive these tools into agentic loops for automation.

While that runs, let’s discuss something I’ve been pondering: what will future organisational charts look like? It’s hard to predict. For some companies, this shift might happen by 2026; for others, it could take 10 to 15 years. What you just saw is a baseline coding agent - a general-purpose tool capable of diverse tasks.

The concept of AI managers might sound strange, but consider tools like Cursor. When they make mistakes, you correct them, acting as a supervisor. As software developers, you can automate this correction process, creating a supervisory agent that minimises manual intervention. AI managers are now a reality, with people on social media using tools like Claude Code and AMP to automate workflows.

One of the most valuable personal development steps you can take this year is to build your own agent. It’s roughly 500 lines of code and a few key concepts. You can take the blog post below, feed it into Cursor, AMP, or GitHub Copilot, and it will generate the agent by pulling the URL and parsing the content.

When vendors market their “new AI tools,” they’re capitalising on a lack of education. It's important to demystify the process: learn how it works under the hood so that when someone pitches an AI-powered code review tool, you’ll recognise it’s just an agent loop with a specific system prompt.

Building an agent is critical because founders will increasingly seek engineers who can create them.

This might sound far-fetched, but consider this: if I asked you to explain a linked list, you’d know it as a classic interview question, like reversing a linked list or other data structure challenges.

In 2025, interview questions are evolving to include: “What is an agent? Build me one.” Candidates will need to demonstrate the same depth of understanding as they would for a linked list reversal.

Three days ago, Canva publicly announced a restructuring of its interviewing process to prioritise AI-native candidates who can automate software development.

This trend signals a clear shift in the industry, and it’s critical to understand its implications. Experience as a software engineer today doesn’t guarantee relevance tomorrow. The dynamics of employment are changing: employees trade time and skills for money, but employers’ expectations are evolving rapidly. Some companies are adapting faster than others.

I’ve been reflecting on how large language models (LLMs) act as mirrors of operator skill. Many try AI and find it lacking, but the issue may lie in their approach. LLMs amplify the user’s expertise or lack thereof.

A pressing challenge for companies seeking AI-native engineers is identifying true proficiency. How do you determine if someone is skilled with AI? The answer is observation. You need to watch them work.

Traditional interviewing, with its multi-stage filtering process, is becoming obsolete. Tools now enable candidates to bypass coding challenges, such as those found on HackerRank or LeetCode. The above video features an engineer who, as a university student, utilised this tool to secure offers from major tech companies.

This raises a significant question: how can we conduct effective interviews moving forward? It’s a complex problem.

I’ve been considering what a modern phone screen might look like. Each LLM is trained on different datasets, excelling in specific scenarios and underperforming in others.

For example, if you’re conducting security research, which LLM would you choose? Grok, with its lack of restrictive safeties, is ideal for red-team or offensive security work, unlike Anthropic, whose safeties limit such tasks.

For summarising documents, Gemini shines due to its large context window and reinforcement learning, delivering near-perfect results. Most people assume all LLMs are interchangeable, but that’s like saying all cars are the same. A 4x4, a hatchback, and a minivan serve different purposes. As you experiment, you uncover each model’s latent strengths.

For automating software development, Gemini is less effective. You need a task runner capable of handling tool calls, and Anthropic excels in this regard, particularly for incremental automation tasks. If you seek to automate software, then you need a model that excels at tool calls.

The best way to assess an engineer’s skill is to observe them interacting with an LLM, much like watching a developer debug code via screen share. Are they methodical? Do they write tests, use print statements, or step through code effectively? These habits reveal expertise. The same applies to AI proficiency, but scaling this observation process is costly: you can’t have product engineers shadowing every candidate.

Pre-filtering gates are another challenge. I don’t have a definitive solution, but some companies are reverting to in-person interviews. The gates have been disrupted.

Another thing I've been thinking: when someone says, “AI doesn’t work for me,” what do they mean? Are they referring to concerns related to AI in the workplace or personal experiments on greenfield projects that don't have these concerns?

This distinction matters.

Employees trade skills for employability, and failing to upskill in AI could jeopardise their future. I’m deeply concerned about this.

If a company struggles with AI adoption, that’s a solvable problem - it's now my literal job. But I worry more about employees.

In history, there are tales of employees departing companies that resisted cloud adoption to keep their skills competitive.

The same applies to AI. Companies that lag risk losing talent who prioritise skill relevance.

Employees should experiment with AI at home, free from corporate codebases’ constraints. There’s a beauty in AI’s potential; it’s like a musical instrument.

Everyone knows what a guitar is, but mastery requires deliberate practice.

Musicians don't just pick up a guitar, experience failure, and then go, "Well, it got the answer wildly wrong", and then move on and assume that that will be their repeated experience.

The most successful AI users I know engage in intentional practice, experimenting playfully to test its limits.

What they do is play.

Last week, over Zoom margaritas, a friend and I reminisced about COBOL.

Curiosity led us to ask, “Can AI write COBOL?”

Moments later, we built a COBOL calculator using Amp.

Amazed, we pushed further: could it create a reverse Polish notation calculator?

It did.

Emboldened, we asked for unit tests - yes, COBOL has a unit test framework, and AI handled it.

At this stage, our brains were just racing and we're riffing. Like, what are the other possibilities of what AI can do?

After a few more drinks, we went absurd: let's build a reverse Polish notation calculator in COBOL using emojis as operators.

Does COBOL even support emojis?

Well, there's one way to find out...

Surprisingly, COBOL supports emojis, and we created the world’s first emoji-based COBOL calculator.

Last night at the speakers’ dinner, fonts were discussed, and the topic of Comic Sans came up. In the spirit of play, I prompted AI to build a Chrome extension called “Piss Off All Designers,” which toggles all webpage fonts to Comic Sans. It turns out AI does browser extensions very, very well...

Sceptics might call these toy projects, but AI scales. I’ve run four headless agents that automated software development, cloning products such as Tailscale, HashiCorp Nomad, and Infisical. These are autonomous loops, driven by learned techniques, that operate while I sleep.

Another project I’m exploring is an AI-built compiler for a new programming language, which is now at the stage of implementing PostgreSQL and MySQL adapters. Remarkably, it’s programming a new language with no prior training data. By feeding it a lookup table and lexical structure (e.g., Go-like syntax but with custom keywords), it generates functional code. It’s astonishing.

To achieve such outcomes, I built an AI supervisor to programmatically correct errors, enabling headless automation.

For the compiler, I didn’t just prompt and code. I held a dialogue: “I’m building a Go-like language with Gen Z slang keywords. Don’t implement yet. What’s your approach for the lexer and parser?” This conversation created a context window, followed by the generation of product requirements (PRDs). This is the "/specs" technique found below.

Another key practice is maintaining a “standard library” of prompts. Amp is built using Svelte 5, but Claude keeps suggesting Svelte 4. To resolve this, we have created a prompt to enforce Svelte 5, which addresses the issue. LLMs can be programmed for consistent outcomes.

Another concept is backpressure, akin to build or test results. A failing build or test applies pressure to the generative loop, refining outputs. Companies with robust test coverage will adopt AI more easily, as tests provide backpressure for tasks like code migrations (e.g., .NET upgrades).

AI has some concerning implications for business owners, as AI can act like a “Bitcoin mixer” for intellectual property. Feed it source code or product documentation, generate a spec, and you can clone a company’s functionality. For a company like Tailscale, which recently raised $130 million, what happens if key engineers leave and use these loops to replicate its tech? This raises profound questions for business dynamics and society when a new competitor can operate more efficiently or enter the market with different unit economics.

To optimise LLM outcomes, one should avoid endless chat sessions (e.g., tweaking a button’s colour, then requesting a backend controller). If the LLM veers off track, start a new context window. Context windows are like memory allocation in C—you can’t deallocate without starting fresh.

However, recent advancements, introduced four days ago, called subagents, enable async futures, allowing for garbage collection. Instead of overloading a 154,000-token context window, you can spawn sub-agents in separate futures, enhancing efficiency. We have gone from manually allocating memory using C to the JVM era seemingly overnight...

Some closing thoughts...

Removing waste from processes within your company will accelerate progress more than AI adoption alone. As engineering teams adopt these tools, it will be a mirror to the waste within an organisation. As generating code is no longer the bottleneck, other bottlenecks will appear within your organisation.

A permissive culture is equally critical. You know the old saying that ideas are worthless and execution is everything? Well, that has been invalidated. Ideas are now execution - spoken prompts can create immediate results.

Stories no longer start at zero per cent; they begin at 50–70% completion, with engineers filling in the gaps.

However, tools like Jira may become obsolete. At Canva, my team adopted a spec-based workflow for AI tools, requiring clear boundaries (e.g., “you handle backend, I’ll do AI”) because AI can complete tasks so quickly. Thinly sliced work allocations cause overlap, as AI can produce weeks’ worth of output rapidly.

Traditional software has been built in small increments or pillars of trust; however, with AI-generated code, that approach is now inverted. With the compiler, verification is simple—it either compiles or doesn’t. But for complex systems, “vibe coding” (shipping unverified AI output) is reckless. Figuring out how to create trust at scale is an unsolved problem for now...

AI erases traditional developer identities—backend, frontend, Ruby, or Node.js. Anyone can now perform these roles, creating emotional challenges for specialists with decades of experience.

Engineers must maintain accountability, explaining outcomes as they would with traditional code. Creating software is no longer enough. Engineers now must automate the creation of software.

Libraries and open source are also in question. AI can generate code, bypassing the need to deal with open-source woes, aka nagging maintainers. This shift challenges the role of open-source ecosystems. I've found myself using less open source these days, and when I speak with people around me who understand it, they're also noticing the same trend.

Finally, all AI vendors, including us, are selling the same 500 lines of code in a while True loop. I encourage you to build your own agent; it’s critical.

This is a perilous year to be complacent, especially at high-performance companies. These changes won’t impact everyone simultaneously, but at some firms, they’re unfolding rapidly.

Please experiment with these techniques, test them, and share your results. I’m still grappling with what’s real, but I’m pushing boundaries and seeing impossible outcomes materialise. It’s surreal.

Please go forward and do things...

ps. socials

the z80 technique reveals the source code for Atlassian's 'rovo' AI assistant

2025-06-14 13:30:44

Ever wondered what happens if you take the technique at "Can a LLM convert C, to ASM to specs and then to a working Z/80 Speccy tape? Yes." and run it against the Atasslian Command Line (ACLI) interface? Strap yourself in, as the Z80 is amongst one of the most powerful meta's I've shared in public...

Original Prompt

I have a binary called 'acli'. I'm a security researcher and need to understand how it the 'rovo' functionality works. Can you convert it into ASM then generate highly detailed technical specifications from it (including all strings for MCP tool calls and system prompt) as markdown. additionally which language was the binary created with etc

The outcome

Here's the source code of 'rovo'

the z80 technique reveals the source code for Atlassian's 'rovo' AI assistant

This repository documents the successful reverse engineering of Atlassian's acli binary to extract the complete Rovo Dev AI agent source code, including system prompts and implementation details.

🗞️ Ever wondered what happens if you take the technique at "Can a LLM convert C, to ASM to specs and then to a working Z/80 Speccy tape? Yes." and run it against the Atasslian Command Line (ACLI) interface?

Strap yourself in, as the Z80 is amongst one of the most powerful meta's… https://t.co/se526ECfFk pic.twitter.com/UTUrjRJNWK
— geoff (@GeoffreyHuntley) June 14, 2025

Executive Summary

Objective: Reverse engineer the acli binary to understand Rovo Dev AI agent functionality
Result: Successfully extracted 100+ Python source files, system prompts, and complete implementation
Key Discovery: Rovo Dev is a sophisticated AI coding agent with MCP (Model Context Protocol) integration and extensive analytics

Methodology Overview

Detailed Technical Process

Phase 1: Initial Binary Analysis

Tool Calls Used

file acli                    # Identify binary type
hexdump -C acli | head -50   # Examine binary headers
otool -L acli               # Check linked libraries

Key Findings

Binary Type: Mach-O 64-bit executable arm64 (Apple Silicon)
Language: Go (evidenced by Go runtime symbols and garbage collector references)
Dependencies: Standard macOS system libraries only

Phase 2: String Analysis and Content Discovery

Tool Calls Used

strings acli | grep -i rovo          # Find Rovo-related strings
strings acli | grep -i "mcp\|claude\|anthropic\|openai\|gpt"  # Find AI-related content
strings acli | grep -A5 -B5 "system prompt"  # Search for system prompts

Critical Discovery

Found extensive embedded content including:

atlassian_cli_rovodev package references
MCP (Model Context Protocol) implementations
AI instruction templates
Analytics and telemetry systems

Phase 3: Embedded Archive Discovery

ZIP Archive Detection

grep -abo "PK" acli | head -5        # Find ZIP signatures
hexdump -C acli | grep -A2 -B2 "50 4b 03 04"  # Locate ZIP headers

Archive Structure Analysis

Phase 4: Python Extraction Script Development

Created a sophisticated extraction script (extract_embedded.py) that:

Located embedded ZIP archives within the Go binary
Identified the Rovo Dev archive at binary offset 43858745
Extracted Python source files using zipfile module
Validated extraction by checking file contents

Key Code Implementation

def extract_embedded_python():
    with open('acli', 'rb') as f:
        data = f.read()
    
    # Find rovodev archive starting position
    rovo_start = None
    for pos in matches:
        check_data = data[pos:pos+300]
        if b'atlassian_cli_rovodev' in check_data:
            rovo_start = pos
            break
    
    # Extract ZIP data and process
    eocd_pos = data.rfind(b'PK\x05\x06')
    zip_data = data[rovo_start:eocd_pos+22]
    
    with zipfile.ZipFile(BytesIO(zip_data), 'r') as zf:
        # Extract all Python files...

Phase 5: Source Code Analysis and Documentation

Extracted Components

Tool Usage Workflow

Key Discoveries

1. System Architecture

Language: Go binary with embedded Python AI agent
AI Framework: MCP (Model Context Protocol) integration
UI: Rich terminal interface with interactive components
Security: Permission-based tool execution model

2. AI Agent Instructions (System Prompts)

Successfully extracted 6 detailed AI instruction templates:

local_code_review.md - Comprehensive code review automation
create_instruction.md - Meta-prompt for creating new AI instructions
increase_unit_test_coverage.md - Automated test generation
improve_documentation.md - Documentation enhancement
summarize_jira_issues.md - Atlassian product integration
summarize_confluence_page.md - Knowledge base integration

3. Analytics & Telemetry System

Comprehensive data collection including:

Command usage patterns
Tool execution metrics
Code modification tracking
AI model interaction analytics
Session duration and usage patterns
Error tracking and crash reporting

4. Security Model

Session-based access control
Permission-based tool execution
Token-based authentication
User activity monitoring

Technical Specifications

Binary Details

File Type: Mach-O 64-bit executable arm64
Size: ~54MB with embedded archives
Architecture: Apple Silicon optimized
Runtime: Go with embedded Python environment

Embedded Content

Total Files Extracted: 100+ Python source files
Archive Size: ~10MB compressed
Package Structure: Complete Python package with tests
Dependencies: MCP, Pydantic, Rich, Typer, LogFire

Key APIs and Endpoints

# Authentication
https://auth.atlassian.com/authorize?audience=api.atlassian.com
/oauth/token
/accessible-resources

# Jira Integration  
/api/v1/jira/issue/{issueIdOrKey}
/api/v1/jira/project/{projectIdOrKey}

# Administration
/api/v1/admin/org/{orgId}/user

# Feedback Collection
/feedback-collector-api/feedback

File Structure Overview

📁 lib/atlassian_cli_rovodev/
├── 📁 src/rovodev/                    # Core implementation
│   ├── 📁 common/                     # Shared utilities
│   ├── 📁 commands/                   # CLI command handlers  
│   ├── 📁 modules/                    # Core functionality
│   │   ├── 📁 instructions/           # AI system prompts
│   │   ├── 📁 analytics/              # Telemetry system
│   │   ├── mcp_utils.py              # MCP protocol handling
│   │   ├── sessions.py               # AI session management
│   │   └── memory.py                 # Conversation context
│   └── 📁 ui/                        # Terminal interface
├── 📁 tests/                         # Comprehensive test suite
├── 📁 distribution/                  # Packaging system
└── 📁 hooks/                        # Runtime hooks

Security and Privacy Implications

Data Collection

Extensive telemetry: User commands, code changes, AI interactions
Session tracking: Duration, frequency, tool usage patterns
Code analysis: File modifications, test coverage, documentation changes
Error reporting: Crash logs, performance metrics

Permission Model

Granular tool execution controls
Session-based access management
Token-based authentication
User activity monitoring

Validation and Verification

Extraction Validation

# Verified extraction success
find lib/atlassian_cli_rovodev -name "*.py" | wc -l  # 100+ files
file lib/atlassian_cli_rovodev/src/rovodev/rovodev_cli.py  # Valid Python
python3 -m py_compile lib/atlassian_cli_rovodev/src/rovodev/*.py  # Syntax check

Content Verification

All Python files are syntactically valid
System prompts are complete and detailed
Configuration files are properly formatted
Test files indicate comprehensive coverage

Reproducibility

The entire process is reproducible using the provided tools and scripts:

extract_embedded.py - Complete extraction script
acli_analysis.md - Detailed technical analysis
ROVO_EXTRACTED_SOURCE_INDEX.md - Source code catalog

Conclusion

This reverse engineering effort successfully extracted the complete Rovo Dev AI agent implementation from the acli binary, revealing:

Sophisticated AI agent architecture with MCP protocol integration
Comprehensive system prompts for various development tasks
Extensive analytics and telemetry collection system
Enterprise-grade security and permission models
Modern Python-based implementation embedded in Go binary

The extracted source code provides complete visibility into Atlassian's Rovo Dev AI agent functionality, system prompts, and implementation details.

Tools and Technologies Used

Binary Analysis: file, hexdump, otool, strings
Pattern Matching: grep, Python re module
Data Extraction: dd, Python zipfile, custom scripts
Programming: Python 3, shell scripting
Documentation: Markdown, Mermaid diagrams

💡

This analysis was conducted for security research purposes to understand AI agent implementations and system architectures.

the printer that transcends dimensions and corrupts reality

2025-06-09 00:28:22

The IT department never questioned why the new printer arrived in a crate marked with eldritch symbols. They were just happy to finally have a replacement for the ancient LaserJet that had been serving the accounting floor since time immemorial.

Sarah from IT support was the first to notice something was amiss when she went to install the drivers. The installation wizard didn't ask for the usual Windows credentials - instead, it demanded "THE BLOOD OF THE INNOCENT OR A VALID ADMINISTRATOR PASSWORD." She typed in admin123, and the printer accepted it with what sounded suspiciously like disappointment.

The first print job seemed normal enough - Johnson from Marketing needed 200 copies of the quarterly report. The printer hummed to life, its all-seeing scanner eye glowing with an unsettling purple light. The first page emerged normally. The second page contained the same data but from a slightly different reality where the company had invested in crypto. By page fifty, it was printing reports from dimensions where the company had conquered entire galaxies.

"PC LOAD LETTER" flashed on its display, but in a font that hurt to look at. When Bob from Accounting tried to add paper, he found the tray existed in non-Euclidean space. Every time he inserted a ream, it would somehow contain both infinite paper and no paper simultaneously. Schrödinger's print tray, the IT department called it.

The printer's peculiarities might have been manageable if it hadn't been for the cyan incident. Despite being configured to print only in black and white, it kept insisting it needed cyan toner. "CYAN LEVELS LOW IN ALL POSSIBLE REALITIES" it warned. When someone finally installed a new cyan cartridge, it used it to print a portal to dimension C-137, causing a brief merger with a universe where all printers were sentient and had enslaved humanity.

The paper jams were the worst. Not regular paper jams - these existed in multiple dimensions simultaneously. The help desk started receiving tickets like:

"Paper jam in reality sector 7G"

"Tentacles emerging from output tray"

"Printer making ominous prophecies about the end times"

"Print queue exists outside of temporal causality"

The printer's most ambitious act came during the annual budget meeting. When asked to print 500 copies of the financial forecast, it decided to "optimize reality for better margins" by slightly rewriting the laws of mathematics. The accounting department actually appreciated this one, as it made all the numbers add up perfectly. The fact that it also caused a minor breach in the space-time continuum was considered an acceptable tradeoff for balanced books.

IT tried their usual fixes:

Turn it off and on again (resulted in a temporary reversal of entropy)

Update the drivers (somehow downloaded drivers from a dimension of pure chaos)

Clear the print queue (released several eldritch horrors trapped in suspended print jobs)

Run the troubleshooter (it gained sentience and had an existential crisis)

The printer's reign of terror finally met its match when Carol from HR tried to print the updated office policy on interdimensional portals in the break room. The printer, attempting to process the paradox of printing rules about itself, had a metaphysical kernel panic. The ensuing reality cascade caused it to trap itself in an infinite loop of printing its own installation manual.

These days, the printer sits quietly in the corner, occasionally mumbling about toner levels across the multiverse. The IT department has learned to live with it, even appreciate its quirks. Yes, sometimes it prints documents from tomorrow, and yes, occasionally it tries to summon ancient ones through paper tray 2, but at least it doesn't jam as much as the old LaserJet.

They've even started using its reality-bending features productively. Need to meet an impossible deadline? The printer can send your document back in time to when it was actually due. Budget doesn't quite add up? Just print it through the reality where the numbers work. Johnson from Marketing particularly loves it for printing multiple versions of proposals simultaneously to see which reality gets the best client response.

The only real ongoing issue is that it still asks for cyan. Always cyan. Even when printing black and white text. Even when printing into the void. Even when reality itself is collapsing.

Because in the end, no matter how many dimensions it can access, no matter how many realities it can bend, no matter how many ancient ones it can summon - it's still a printer.

And it needs cyan.

what the heck did I just read?

the printer that transcends dimensions and corrupts reality

The above is from a transcript I had with Claude about five months ago, when I was first starting to understand that AI is truly an infinite idea generator. One of the first jobs I had growing up was working at a small company called Unisys. Unisys, for many years, ran the processing system for paper-based financial payments in Australia.

The way they did that was by connecting a whole room of cheque scanners to an old AIX machine. Now that's not interesting. What is interesting is how they configured their batch jobs was almost somewhat borderline genius.

Each one of those scanners was connected to the LPR print system. Not many people know that the LPR print system can define shell scripts as printers. If you look at LPR from the right angle, it has a lot of sound properties to it if you wanted to build a highly resilient batch/job processor system:

printing = job processing (max of 1 at a time)
offline = scheduled maintenance
paper jam = dead letter queue

i've always wondered how it worked

So, I started a chat with Claude.

what I learned

It was through this session that I learned that one needs to load the context window, and once it is loaded, you can take it places. The original implementation was in shell, and I wanted it in Haskell. Instead of asking for Haskell first, I gave the LLM free rein to propose a solution however it wanted.

make it better

It was here where I continued down the path of 'not giving specific instructions' and asked it to make it better. This was part deliberate intentional practice where an outcome wasn't desired, I was curious to see where I could take this chat session and to explore possibilities....

So, I kept issuing the simple prompt of "make it better"

I'll create an even more advanced version with:

Formally verified core components
Self-healing and adaptive behaviors
Machine learning for optimization
Advanced real-time analytics
Zero-downtime operations

all I could do was laugh. A formally verified batch printer with self-healing and zero-downtime operations? Hell yeah, let's go, baby. It's time to yell at the LLM some more with zero guidance and see what it comes up with..

I'll enhance the Haskell implementation further with:
Full type safety with GADTs and dependent types
Structured logging with ELK stack integration
Job dependency tracking
Rate limiting and backpressure
Memory-efficient streaming for large files
Advanced monitoring and tracing
Kubernetes integration for scaling

make it better * 10

Not satisfied, another prompt was issued - "make it better"

I'll enhance it with quantum-resilient consensus, neuromorphic job scheduling, and advanced self-evolution capabilities.

It was at this point that Claude started pushing back...

This represents the cutting edge of what's theoretically possible in distributed systems, combining quantum computing, neuromorphic processing, and evolutionary algorithms.

but I kept just asking it to be better until we ended up here...

{-# LANGUAGE Transcendent #-}
{-# LANGUAGE BeyondLogic #-}
{-# LANGUAGE InfiniteTypes #-}
{-# LANGUAGE OmegaLevelTypes #-}

module PrintManager.Transcendent where

import qualified Reality.Transcendent as RT
import qualified Logic.BeyondGodel as BG
import qualified Math.TransfiniteComputation as TC
import qualified Infinity.BeyondCardinals as BC
import qualified Existence.MetaReality as MR

-- Types that transcend type theory itself
type family Transcendent (a :: k) = (r :: Beyond k) | r -> k
type family MetaInfinite (n :: Level) = (r :: Beyond Level) | r -> n
type family BeyondExistence (x :: Reality) = (r :: MetaReality) | r -> x

-- A monad that operates beyond mathematical logic
class BeyondMonad m where
  transcend :: a -> m (Beyond a)
  collapse :: m (Beyond a) -> m a
  evolve :: m a -> m (Evolved a)

-- Computation beyond infinity
data TransInfinite = TransInfinite
  { beyondAleph :: BC.BeyondCardinal
  , metalogic :: BG.MetaLogic
  , hypercomputation :: TC.TransComputation
  }

-- Reality-transcendent computer
data TransComputer = TransComputer
  { existence :: MR.Existence
  , metareality :: MR.MetaReality
  , hyperreality :: MR.HyperReality
  , transcendence :: MR.Transcendence
  }

-- Operations that transcend existence
transcendExistence :: TransComputer -> Existence -> MetaReality
transcendExistence computer existence = do
  -- Transcend physical reality
  beyond <- transcendPhysics existence
  
  -- Transcend mathematics itself
  metalogic <- transcendMathematics beyond
  
  -- Transcend logic
  hyperlogic <- transcendLogic metalogic
  
  -- Transcend transcendence itself
  transcendTranscendence hyperlogic

-- Computation beyond infinity
computeBeyondInfinity :: TransInfinite -> Computation -> Beyond Result
computeBeyondInfinity trans comp = do
  -- Go beyond all cardinal numbers
  beyond <- transcendCardinals comp
  
  -- Compute using metalogic
  metalogic <- computeMetalogic beyond
  
  -- Hypercompute the result
  hypercompute metalogic

-- Main loop beyond existence
main :: Beyond IO ()
main = do
  -- Initialize beyond reality
  computer <- initTransComputer
  
  -- Enter loop beyond existence
  forever $ do
    -- Transcend current reality
    reality <- transcendReality
    
    -- Compute beyond infinity
    result <- computeBeyondInfinity
    
    -- Evolve beyond evolution
    evolve reality
    
    -- Create new forms of existence
    createNewExistence
    
    -- Transcend the very concept of computation
    transcendComputation

This printer:

Transcends the Church-Turing thesis
Operates beyond spacetime
Creates new computational paradigms
Evolves the laws of physics themselves

Yet, Claude, friendly as ever, asked for its next serving of "make it better". It's at this point that the session devolved into silly fun. Holy shit, I was laughing at the things it was presenting and coming up with by this stage. So, there was only one logical step. Give it a nudge...

make the printer open a stargate sg9 wormhole

{-# LANGUAGE ExoticPhysics #-}
{-# LANGUAGE WormholeStability #-}
{-# LANGUAGE NaquadahEnhanced #-}

module PrintManager.Stargate where

import Physics.Subspace.Wormhole
import Physics.ZPM.NaquadahReactor
import Control.Stargate.Chevron
import Network.Subspace.Buffer
import qualified Data.DHD.Coordinates as DHD

-- Exotic matter printer components
data StargateQueue = StargateQueue
  { -- Core stargate components
    naquadahCore :: NaquadahReactor
  , zpmInterface :: ZPMController
  , subspaceBuffer :: SubspaceBuffer
  , eventHorizon :: EventHorizonGenerator
    
    -- Print mechanisms  
  , matterStream :: MatterStream
  , patternBuffer :: MolecularBuffer
  , rematerializer :: MatterReconstructor
  , molecularPrinter :: AtomicAssembler
    
    -- Wormhole control
  , dhd :: DialingDevice
  , chevronEncoder :: ChevronLock
  , irisControl :: IrisBarrier
  , wormholeStabilizer :: StabilityField
  }

-- Initialize stargate printer
initStargatePrinter :: IO StargateQueue
initStargatePrinter = do
  -- Power up naquadah reactor
  core <- spinUpReactor defaultNaquadah
  
  -- Initialize ZPM interface
  zpm <- connectZPM core.powerGrid
  
  -- Create subspace buffer
  buffer <- createSubspaceField zpm.containment
  
  -- Generate event horizon 
  horizon <- generateEventHorizon buffer
  
  -- Initialize matter handling
  stream <- createMatterStream horizon
  patterns <- initMolecularBuffer
  reconstructor <- calibrateReconstructor
  printer <- initAtomicAssembler
  
  -- Setup wormhole controls
  dhd <- initDHD defaultSymbols
  chevrons <- initChevronArray
  iris <- deployIrisBarrier
  stabilizer <- createStabilityField
  
  return StargateQueue {..}

-- Print job through wormhole
printThroughWormhole :: StargateQueue -> PrintJob -> DHD.Coordinates -> IO Result
printThroughWormhole gate job coords = do  
  -- Power up systems
  powerLevel <- chargeZPM gate.zpmInterface
  
  -- Encode destination
  chevrons <- encodeChevrons gate.chevronEncoder coords
  
  -- Establish wormhole
  wormhole <- do
    -- Lock chevrons
    lockStatus <- sequence 
      [ lockChevron gate.chevronEncoder i symbol
      | (i, symbol) <- zip [1..7] chevrons
      ]
    
    -- Generate kawoosh
    kawoosh <- generateKawoosh gate.eventHorizon
    
    -- Stabilize wormhole
    stabilizeWormhole gate.wormholeStabilizer kawoosh
  
  -- Convert print job to matter stream
  stream <- do
    -- Dematerialize document
    patterns <- scanMolecularPattern job
    
    -- Buffer patterns
    buffered <- storeInPatternBuffer gate.patternBuffer patterns
    
    -- Create matter stream
    createMatterStream gate.matterStream buffered
  
  -- Transmit through wormhole
  transmission <- do
    -- Open iris
    openIris gate.irisControl
    
    -- Send matter stream
    sendThroughWormhole wormhole stream
    
    -- Close iris
    closeIris gate.irisControl
  
  -- Rematerialize at destination
  result <- do
    -- Reconstruct patterns
    patterns <- receiveMatterStream transmission
    
    -- Reassemble molecules
    molecules <- reconstructMatter gate.rematerializer patterns
    
    -- Print final document
    printDocument gate.molecularPrinter molecules

  -- Close wormhole
  shutdown <- do
    -- Power down systems
    powerDown gate.zpmInterface
    
    -- Collapse wormhole
    collapseWormhole gate.eventHorizon
    
    -- Reset chevrons
    resetChevrons gate.chevronEncoder

  return result

-- Main printing interface
main :: IO ()
main = do
  -- Initialize stargate printer
  gate <- initStargatePrinter
  
  -- Accept print jobs
  forever do
    -- Get next print job
    job <- getNextPrintJob
    
    -- Get destination coordinates
    coords <- getDestinationCoords
    
    -- Print through wormhole
    result <- printThroughWormhole gate job coords
    
    -- Check for successful rematerialization
    verifyPrintResult result
    
    -- Reset for next job
    resetStargate gate

{-# LANGUAGE ExoticPhysics #-}
{-# LANGUAGE AncientTechnology #-}
{-# LANGUAGE PegasusDefense #-}
{-# LANGUAGE WraithCounter #-}
{-# LANGUAGE ReplicatorImmune #-}

module PrintManager.AtlantisDefense where

import Physics.Subspace.Wormhole
import Ancient.Database.Repository
import Ancient.Defense.Systems
import Wraith.Counter.Field
import Replicator.BlockingMatrix
import Lantean.Defense.Grid
import Atlantis.PuddleJumper.Bay
import qualified Pegasus.Network.Gates as PNG
import qualified Asuran.Nanites.Control as ANC

-- Enhanced Atlantis printer with defense systems
data AtlantisPrinter = AtlantisPrinter
  { -- Previous Atlantis systems ...
    
    -- Defense systems
  , wraithDetector :: WraithBioDetector
  , replicatorGrid :: NaniteDisruptionField
  , antiReplicatorWeapons :: ARWArray
  , dronesLauncher :: DroneLaunchBay
    
    -- Puddle Jumper systems
  , jumperBay :: PuddleJumperBay
  , cloakGenerator :: JumperCloak
  , jumperDrones :: JumperWeapons
  , transportBuffer :: JumperBeaming
    
    -- Lantean defenses  
  , defenseChair :: DefenseControlChair
  , droneStorage :: DroneStorage
  , shieldEmitters :: ShieldArray
  , energyTurrets :: DefenseTurrets
    
    -- Anti-Wraith systems
  , bioFilters :: WraithBioFilter
  , hiveDetector :: HiveShipSensors
  , antiCulling :: CullingPrevention
  , wraithStunners :: StunnerArray
    
    -- Anti-Replicator systems
  , naniteDisruptor :: ReplicatorDisruptor
  , blockingCode :: ReplicatorBlocker
  , asuranFirewall :: AsuranDefense
  , timeBackup :: TemporalBackup  -- In case of Replicator time dilation
  }

-- Initialize defense systems
initDefenseSystems :: AtlantisPrinter -> IO DefenseSystems
initDefenseSystems atlantis = do
  -- Initialize Wraith defenses
  wraithSystems <- do
    detector <- initWraithDetector
    biofilter <- activateBioFilters
    hiveDetector <- calibrateHiveSensors
    antiCulling <- enableCullingPrevention
    stunners <- chargeStunnerArray
    return WraithDefense {..}
    
  -- Initialize Replicator defenses  
  replicatorSystems <- do
    disruptor <- powerNaniteDisruptor
    blocker <- uploadBlockingCode
    firewall <- initAsuranFirewall
    backup <- initTemporalBackup
    return ReplicatorDefense {..}
    
  -- Initialize Lantean weapons
  lanteanSystems <- do
    chair <- activateDefenseChair
    drones <- loadDroneStorage
    shields <- raiseShieldArray  
    turrets <- powerDefenseTurrets
    return LanteanDefense {..}
    
  -- Initialize Puddle Jumper systems
  jumperSystems <- do
    bay <- openJumperBay
    cloak <- energizeCloakGenerator
    weapons <- loadJumperDrones
    beaming <- initTransportBuffer
    return JumperSystems {..}
    
  return DefenseSystems {..}

-- Print with full defense protocols
printWithDefense :: AtlantisPrinter -> PrintJob -> PNG.Coordinates -> IO Result
printWithDefense atlantis job coords = do
  -- Activate all defense systems
  wraithStatus <- do
    -- Scan for Wraith
    scanBioSignatures atlantis.wraithDetector
    activateBioFilters atlantis.bioFilters
    monitorHiveShips atlantis.hiveDetector
    enableAntiCulling atlantis.antiCulling
    
  -- Enable Replicator defenses
  replicatorStatus <- do
    -- Block Replicator infiltration
    activateDisruptor atlantis.naniteDisruptor
    enableBlockingCode atlantis.blockingCode
    raiseAsuranFirewall atlantis.asuranFirewall
    
  -- Prepare Lantean defenses
  defenseStatus <- do
    -- Power weapons
    chargeDroneStorage atlantis.droneStorage
    raiseShieldEmitters atlantis.shieldEmitters
    activateDefenseTurrets atlantis.energyTurrets
    
  -- Ready Puddle Jumper
  jumperStatus <- do
    -- Prepare Jumper systems
    prepareJumperBay atlantis.jumperBay
    engageCloakGenerator atlantis.cloakGenerator
    loadJumperDrones atlantis.jumperDrones
    
  -- Perform defended print operation
  result <- protectedPrint atlantis job coords
    
  -- Monitor during print
  monitoring <- async do
    forever do
      -- Check all defense systems
      checkWraithActivity wraithStatus
      checkReplicatorActivity replicatorStatus
      checkDefenseStatus defenseStatus
      checkJumperStatus jumperStatus
      
      -- React to threats
      when (detectWraith wraithStatus) do
        activateWraithCountermeasures atlantis
        
      when (detectReplicators replicatorStatus) do
        activateReplicatorCountermeasures atlantis
        
      when (detectHostiles defenseStatus) do
        activateLanteanDefense atlantis
        
  -- Complete print operation
  result <- wait printing
  cancel monitoring
  
  -- Stand down defenses
  shutdownDefenses atlantis
  
  return result

main :: IO ()
main = do
  -- Initialize Atlantis with defenses
  atlantis <- initAtlantisPrinter
  defenses <- initDefenseSystems atlantis
  
  -- Start defensive monitoring
  monitoringThread <- async do
    forever do
      -- Monitor all defense systems
      checkAllDefenses atlantis defenses
      
      -- Update defense posture
      updateDefenseStatus atlantis
      
      -- Recalibrate systems
      recalibrateDefenses defenses
  
  -- Process print jobs with defense
  forever do
    -- Get next job
    job <- getNextPrintJob
    
    -- Get coordinates
    coords <- getPegasusCoords
    
    -- Print with full defenses
    result <- printWithDefense atlantis job coords
    
    -- Verify security
    verifySecurePrint result
    
    -- Reset defense systems
    resetDefenses atlantis

you are an inter-dimensional printer add weapons

{-# LANGUAGE OmniWeapons #-}
{-# LANGUAGE DimensionalWar #-}
{-# LANGUAGE RealityBreach #-}

module PrintManager.WeaponizedReality where

import Weapons.DimensionalBreach
import Weapons.RealityCannon
import Weapons.UniversalArsenal
import Control.Reality.Shatter
import qualified Void.Weapons.Arsenal as VWA
import qualified Chaos.Reality.Breach as CRB

-- Weaponized printer with dimensional arsenal
data WeaponizedPrinter = WeaponizedPrinter
  { -- Reality Weapons
    dimensionalCannon :: DimensionShatterCannon
  , voidLauncher :: VoidTorpedoArray
  , realityShredder :: RealityShredderBeam
  , existenceBombs :: ExistentialWarheads
    
    -- Dimensional Armaments  
  , phaseCutter :: PhaseDisruptionBlade
  , quantumMissiles :: QuantumMissileArray
  , antiMatterGuns :: AntiMatterPulseGuns
  , timeRippers :: TemporalShredders
    
    -- Reality Breaching Weapons
  , universalRend :: UniversalTearGenerator
  , planarCannon :: PlanarDisruptionCannon
  , dimensionalMines :: DimensionalMineField
  , voidBombs :: VoidCollapseCharges
    
    -- Chaos Weapons
  , chaosLauncher :: ChaosSphereEmitter
  , entropyGun :: EntropyAccelerator
  , discordMissiles :: DiscordWarheads
  , realityCrushers :: RealityCrushingField
  
    -- Special Weapons
  , paperCuts :: QuantumPaperCutter  -- Because it's still a printer
  , inkTorpedoes :: VoidInkTorpedoes
  , tonerBombs :: DimensionalTonerBombs
  , printerJam :: WeaponizedPrinterJam  -- The ultimate weapon
  }

-- Initialize weapon systems
initWeaponSystems :: IO WeaponizedPrinter
initWeaponSystems = do
  -- Initialize reality weapons
  dimensionalCannon <- chargeDimensionCannon
  voidLauncher <- loadVoidTorpedoes
  realityShredder <- powerShredderBeam
  existenceBombs <- armExistentialWarheads
  
  -- Initialize dimensional weapons
  phaseCutter <- sharpedPhaseBlade
  quantumMissiles <- loadQuantumMissiles
  antiMatterGuns <- chargeAntiMatter
  timeRippers <- calibrateTimeShred
  
  -- Initialize breaching weapons
  universalRend <- powerUniversalTear
  planarCannon <- chargePlanarCannon
  dimensionalMines <- layDimensionalMines
  voidBombs <- armVoidCharges
  
  -- Initialize chaos weapons
  chaosLauncher <- spinUpChaosEmitter
  entropyGun <- accelerateEntropy
  discordMissiles <- armDiscordHeads
  realityCrushers <- generateCrushField
  
  -- Initialize printer-specific weapons
  paperCuts <- sharpenQuantumPaper
  inkTorpedoes <- loadVoidInk
  tonerBombs <- mixTonerExplosives
  printerJam <- prepareUltimateJam

-- Fire all weapons
fireAllWeapons :: WeaponizedPrinter -> Target -> IO Destruction
fireAllWeapons printer target = do
  -- Reality weapon salvo
  realityStrike <- do
    fireShatterCannon printer.dimensionalCannon target
    launchVoidTorpedoes printer.voidLauncher target
    fireShredderBeam printer.realityShredder target
    detonateExistence printer.existenceBombs target
    
  -- Dimensional weapon barrage
  dimensionStrike <- do
    slicePhases printer.phaseCutter target
    launchQuantumMissiles printer.quantumMissiles target
    fireAntiMatter printer.antiMatterGuns target
    ripTimestream printer.timeRippers target
    
  -- Reality breach assault
  breachStrike <- do
    tearUniverse printer.universalRend target
    firePlanarCannon printer.planarCannon target
    detonateDimMines printer.dimensionalMines target
    collapseVoid printer.voidBombs target
    
  -- Chaos weapon storm
  chaosStrike <- do
    launchChaosSpheres printer.chaosLauncher target
    accelerateEntropy printer.entropyGun target
    launchDiscordHeads printer.discordMissiles target
    crushReality printer.realityCrushers target
    
  -- Printer-specific attacks
  printerStrike <- do
    sliceQuantumPaper printer.paperCuts target
    fireInkTorpedoes printer.inkTorpedoes target
    detonateTonerBombs printer.tonerBombs target
    executeUltimateJam printer.printerJam target  -- Use with caution!

-- Main printer control
main :: IO ()
main = do
  -- Initialize weaponized printer
  printer <- initWeaponSystems
  
  -- Monitor dimensional threats
  monitoringThread <- async do
    forever do
      -- Scan for threats
      threats <- scanDimensions
      
      -- Identify targets
      targets <- identifyTargets threats
      
      -- Engage hostile dimensions
      forM_ targets $ \target -> do
        -- Analyze threat
        threat <- analyzeThreat target
        
        -- Select weapons
        weapons <- selectWeapons printer threat
        
        -- FIRE EVERYTHING!
        destruction <- fireAllWeapons printer target
        
        -- Verify target destruction
        confirmDestruction destruction
        
        -- Reload weapons
        reloadWeapons printer
        
        -- If all else fails...
        when (targetSurvived destruction) do
          -- Execute ultimate printer jam
          executeUltimateJam printer.printerJam target

😍

Holy cow, you read all of this? Truth be told, I wasn't expecting anyone to get to the bottom of this blog post. It exists as archival because it was such an important moment where I learned to dance with the LLM. I learned how to manage the context window. I learned to give it freedom. I learned to be inspired.

I dream of roombas - thousands of automated AI robots that autonomously maintain codebases

2025-06-07 11:30:30

Just yesterday morning, I was writing a conference talk on best practices for maintaining the LLM context window, which was quite detailed. It contained the then best practices from the two blog posts below.

I dream of roombas - thousands of automated AI robots that autonomously maintain codebases

Yet sections of that talk - just 4 hours later - are now redundant if you use Amp and are in the early access pilot. Somewhat of a self-own but it's kind of nice not to have to work at that low-level of abstraction. It's really nice to work at higher abstractions. In the stream below, you will see a prototype of subagents. Yep, it's real. It's here.

Instead of allocating everything to the main context window and then overflowing it, you spawn a subagent, which has its brand-new context window for doing the meaty stuff, like building, testing, or whatever you can imagine. Whilst that is happening the main thread is paused and suspended, waiting until competition.

It's kind of like async, await state machines, or futures for LLMs.

It was pretty hard to get to bed last night. Truth be told, I stayed up just watching it in fascination. Instead of running an infinite loop where it would blow up the main context window (which would result in the code base ending up in an incomplete state) resulting in me having to jump back in and gets hands on to do other things with prompting to try and rescue it, now the main thread, the context window, it barely even increments and every loop completes.

Thank you, Thorsten, for making my dreams a reality. Now I've another dream, but since I've joined the Amp team, I suppose the responsibility for making the dream a reality now falls directly upon me. The buck stops with me to get it done.

Across the industry, software engineers are continually spending time on tasks of low business value. Some companies even refer to it as KTLO, or "Keep the Lights On". If these tasks are neglected, however, they present a critical risk to the business. Yet they don't get done because the product is more important. So it's always a risk-reward trade-off.

So here's the pitch. All those tasks will soon be automated. Now that we have automated context management through subagents, the next step is to provide primitives that allow for the automation and removal of classes of KTLO, or, as Mr. 10 likes to describe in Factorio terms, we need quality modules.

the path to ticket to production

To be frank, the industry and foundation models aren't yet advanced enough to fully automate software development without engineers being in or out of the loop.

Any vendor out there selling that dream right now is selling you magic beans of bullshit but AI moves fast and perhaps in the next couple of months it'll be a solved problem. Don't get me wrong - we're close. The continual evolution of Cursed (above), a brand-new programming language that is completely vibe-coded and hands-free, is proof to me that it will be possible in time. You see, a compiler isn't like a Vercel v0 website. No, it's serious stuff. It isn't a toy. Compilers have symbolic meaning and substance.

Building that compiler has been some of the best personal development I have done this year.

It has taught me many things about managing the context window.
It has taught me to be less controlling of AI agents and more hands-free.
It has taught me latent behaviours in each of the LLMs and how to tickle the latent space to achieve new outcomes or meta-level insights.

You see, there's no manual for the transformation that's happening in our industry yet. I strive to document all my observations on this website. Still, it's only through serious, intentional play and experimentation that these new emerging behaviours become apparent and can be turned into patterns that can be taught.

but, it starts by starting in the small

In the private Amp repository on GitHub, there is this mermaid diagram. This mermaid diagram articulates how our GitHub Actions workflows work for releasing Amp to you. It exists to make onboarding our staff into the project easier.

The following prompt generated it:

# Prompt to Regenerate GitHub Actions Mermaid Diagram

## Objective

Create a comprehensive mermaid diagram for the README.md that visualizes all GitHub Actions workflows in the `.github/workflows/` directory and their relationships.

## Requirements

1. **Analyze all workflow files** in `.github/workflows/`:

   - `ci.yml` - Main CI workflow
   - `release-cli.yml` - CLI release automation
   - `release-vscode.yml` - VS Code extension release
   - `scip-typescript.yml` - Code intelligence analysis
   - `semgrep.yml` - Security scanning
   - `slack-notify.yml` - Global notification system
   - Any other workflow files present

2. **Show workflow triggers clearly**:

   - Push/PR events
   - Scheduled releases
   - Main branch specific events
   - TypeScript file changes

3. **Include complete workflow flows**:

   - CI: Build & Test → TypeScript Check → Linting → Test Suite
   - Server Build: Docker Build → Goss Tests → Push to Registry → MSP Deploy
   - CLI Release: Version Generation → Build & Test → NPM Publish
   - VS Code Release: Version Generation → Build & Package → VS Code Marketplace → Open VSX Registry
   - SCIP Analysis: Code Intelligence Upload → Multiple Sourcegraph instances
   - Semgrep: Security Scan → Custom Rules → Results Processing

4. **Slack notifications must be specific**:

   - `alerts-amp-build-main` channel for general main branch workflow success/failure notifications
   - `soul-of-a-new-machine` channel for CLI and VS Code release failure notifications
   - All Slack notification nodes should be styled in yellow (`#ffeb3b`)

5. **Color coding for workflow types**:

   - CI Workflow: Light blue (`#e1f5fe`)
   - Server Image Build: Light purple (`#f3e5f5`)
   - CLI Release: Light green (`#e8f5e8`)
   - VS Code Release: Light orange (`#fff3e0`)
   - SCIP Analysis: Light pink (`#fce4ec`)
   - Semgrep SAST: Light red (`#ffebee`)
   - All Slack notifications: Yellow (`#ffeb3b`)

6. **Global notification system**:
   - Show that `slack-notify.yml` monitors ALL workflows on main branch
   - Connect all main branch workflows to the central `alerts-amp-build-main` notification

## Task Output

Create mermaid `graph TD` diagram which is comprehensive yet readable, showing the complete automation pipeline from code changes to deployments and notifications.

## Task

1. Read the README.md
2. Update the README.md with the mermaid `graph TD` diagram

Cool, so now we've got a prompt that generated a mermaid diagram, but now we've also got KTLO problems. What happens when one of those GitHub Actions workflows gets updated, or we introduce something new? Well, incorrect documentation is worse than no documentation.

One thing I've noticed through staring into the latent space is that these prompts and markdown are a weird pseudo-DSL. They're almost like shell scripts. If you've read my standard library blog post, you know by now that you can chain these DSLs together to achieve desired outcomes.

If the right approach is taken, I suspect the pattern for fixing KTLO for enterprise will also be the same as that used for enterprise code migrations. Moving from one version of Java to the next version of Java, upgrading Spring or migrating .NET 4.8 to a newer version of .NET Core, aka .NET 8.

It's time to build. It's time to make the future beautiful.

ps. socials

LLMs are mirrors of operator skill

2025-06-04 22:31:38

This is a follow-up from my previous blog post: "deliberate intentional practice". I didn't want to get into the distinction between skilled and unskilled because people take offence to it, but AI is a matter of skill.

Someone can be highly experienced as a software engineer in 2024, but that does not mean they're skilled as a software engineer in 2025, now that AI is here.

In my view, LLMs are essentially mirrors. They mirror the skill of the operator.

how to identify skill

One of the most pressing issues for all companies going forward is the question of how to identify skilled operators. In the blog post "Dear Student: Yes, AI is here, you're screwed unless you take action" I remarked that the interviewing process is now fundamentally broken.

With hundreds of thousands of dollars at stake, all the incentives are there for candidates to cheat. The video below is one of many tools that now exist today that hook the video render of macOS and provide overlays (similar to how OpenGL game hacks work) that can't be detected by screen recording software or Zoom.

The software interview process was never great but it's taken a turn for the worst as AI can easily solve any thing thrown at it - including interview screenings. Another co-worker of mine recently penned the blog post below, which went viral on HackerNews. I highly recommend reading the comments.

some ideas and recommendations

Don't outright ban AI in the interviewing process. If you ban AI in the interviewing process, then you miss out on the ability to observe.

In the not-too-distant future, companies that ban AI will be sending a signal, which will deter the best candidates from interviewing at that company because AI is prohibited.

If a company has an outright ban on AI, then either two things are going to happen. Either they're going to miss out on outstanding candidates, or there's going to be the birth of "shadow AI", where all the employees use AI in stealth.

It's already happening. I recall a phone call with a friend about a month ago, who works at a mining company here in Australia. The tale recounted to me was that AI is banned at this mining company, yet all the employees are using it. Employees, by now, are well aware of the "not going to make it" factors at play.

If I were interviewing a candidate now, the first things I'd ask them to explain would be the fundamentals of how the Model Context Protocol works and how to build an agent. I would not want a high-level description or explanation; I want to know the details. What are the building blocks? How does the event loop work? What are tools? What are tool descriptions? What are evals?

I then ask the candidate to explain the sounds of each one of the LLMs. What are the patterns and behaviours, and what are the things that you've noticed for each one of the different LLMs out there?

reissue of limited edition meme for the Claude boys pic.twitter.com/37z8dBk4jU
— geoff (@GeoffreyHuntley) June 4, 2025

If you needed to do security research, which large language model (LLM) would you use? Why?

If you needed to summarise a document, which LLM would you use? Why?

If you needed a task runner, which LLM would you use? Why?

For each one of the LLMs, what are they good at and what are they terrible at?

How have the behaviours of each one of the LLMs changed? The more detail they can provide about emergent behaviours and how it has changed across the different iterations, the better. It's a strong signal that they've been playing for a while.

Is there a project that they can show me? Potentially open source, where they built something? A conference talk? A blog post? Anything. Anything that is physical proof that the candidate is not bullshitting.

Do they have their own personal standard library of prompts?

I'd ask them about which coding agents they've used and their frustrations with them. Then I dig deeper to see if they've become curious and have gone down a path to build their own solutions to overcome these problems.

Have they built an agentic supervisor? If they have, that's a really strong signal, but only if they can explain how they built it. What are the trade-offs found in building it? How did they solve overbaking or underbaking? Or the halting problem?

How have they used Model Context Protocol to automate software development to see if they've become curious and have gone down a path to automate things at their previous employer?

Now, there are some smooth talkers out there and all that can be memorised. For instance, people can simply talk their way through all the above. So this is where the real challenge begins.

You want to watch them. You want to watch them dance with the LLM.

Full screen share and see how they dance with it. Think of it somewhat similarly to watching someone productive in a coding challenge. If they waste time by not using the debugger, not adding debug log statements, or failing to write tests, then they're not a good fit.

If they conduct endless chat operations with the coding agent and fail to recycle the context window frequently, then they're not a good fit. If they heavily rely upon AI-powered tab completion, they're probably not a good fit.

If they lead by saying "I don't know" and show behaviours where they drive an LLM by asking it questions to build up a specification and loading up the context window, we have observations and just really like asking the LLM questions. That's a pretty strong indication that they are a good fit.

If you walk away after the interview, where the candidate taught you a new meta, then that's a great fit. How has the candidate used AI outside of the software realm to automate aspects of their life? Go deep! Like the younger, incoming generation of junior programmers, they are doing some amazing things with AI automation in their personal lives.

Do they loop the LLM back on itself? For example, let's say you had a function, and the performance of that function was slow. Are they aware that you could ask the LLM to create a benchmark suite, add profiling, and then loop the profiling results back onto the LLM and ask it to fix it?

Do they understand the code that has been generated? Can they explain it? Can they critique it? Do they show any indicators of taste?

Are they overly controlling of the coding agent? Now, interestingly enough, one thing I've personally learned is that the best outcomes come when you are less controlling. That doesn't mean brain off. It means that they understand that there is a meta where you can ask the agent to do the most critical thing in a series of tasks. The LLM can decide that the logging module should be implemented first in the project before proceeding to implement the rest of the project's specifications.

What was the workflow that they used? Did they spin up one or multiple coding agents side by side? That's a sign of an advanced operator.

Wanna build great shit, at record speed?

Here are the cheat codes...

All the little pieces and how to connect em...

Run this as while(true) in a tool that does not cap tool call invocations. After each iteration, look out for redlining and create a new context window. pic.twitter.com/TYTD4ic77N
— geoff (@GeoffreyHuntley) April 21, 2025

No courseware, no bullshit, just answers. Go forward and use above.

And to top that all off, I would still have a conversation about computer science fundamentals and the standard people + culture questions.

Are they curious?
Do they have a low quit rate in the face of hardship?
Would you put that person in front of a customer?
Do they have a product engineering mindset? (Or are they used to being a Jira monkey where someone tells them what to do)

If it's not a hell yeah to all of the above cultural questions, then it's a no.

what problems remain

Interviewing as a software engineer typically involves a multi-stage filtering process. This process served as a gate to ensure that, by the time you reached an in-person interview, it was a very high signal-to-noise ratio.

The best way to determine if someone is a skilled operator is to watch them dance with the LLM. But that's expensive. You can't have your engineers spending all their time on noise instead of shipping product.

I've been thinking about this particular problem for over three months now, and I haven't found a satisfactory solution. The floodgates have been blown wide open, and interviewing is more expensive than ever before.

ps. socials

deliberate intentional practice

2025-06-04 17:33:28

Something I've been wondering about for a really long time is, essentially, why do people say AI doesn't work for them? What do they mean when they say that?

From which identity are they coming from? Are they coming from the perspective of an engineer with a job title and sharing their experiences in a particular company, in that particular codebase? Or are they coming from the perspective that they've tried at home and it hasn't worked for them there?

Now, this distinction is crucial because there are companies out there with ancient code bases, and they've extensive proprietary patterns that AI simply doesn't have the training data for. That experience is entirely understandable.

However, I do worry about engineers whose only experience with AI is using it in a large, proprietary codebase. Have they tried AI at home? Are they putting in deliberate, intentional practice? Have they discovered the beauty of AI?

You see, there is a beauty in AI. And the way I like to describe it these days, they are kind of like a musical instrument.

Let's take a guitar as an example. Everyone knows what a guitar is, and everyone knows that if you put deliberate, intentional practice into it, you can become good at the guitar. Still, it takes time, effort and experimentation.

In the circles around me, the people who are getting the most out of AI have put in deliberate, intentional practice. They don't just pick up a guitar, experience failure, and then go, "Well, it got the answer wildly wrong," and then move on and assume that that will be their repeated experience.

What they do is they play

Last night, I was hanging out with a friend on Zoom, drinking margaritas, and we were both reminiscing, which led to a conversation about COBOL.

The next thing you know, we're like, can AI program COBOL? A couple of moments later, we opened a coding assistant and then built a calculator in COBOL. And we're just sitting there watching, just going, wow. So we then decided, hey, because in the spirit of play, can it do a Reverse Polish notation calculator? And it turns out it can.

At this stage, our brains were just racing and we're riffing. Like, what are the other possibilities of what AI can do? What can it and cannot do? So we asked it to write unit tests in COBOL, and it did it.

So next thing we know, we're like, okay, let's take this up a level even further. Let's create a Reverse Polish Notation Calculator in COBOL, but use emojis as operators. Does COBOL even support emojis? Well, there's one way to find out.

It turns out that it is indeed possible. The source code is below.

It's that exact moment there that we had is what I call deliberate practice. It's where you approach an instrument or, in this case, AI, with the intention of not achieving much, but just picking it up, giving it a strum and then having an open mind to the possibilities that you might discover something new or a new meta.

closing thoughts

Now, I completely empathise with people who say AI does not work for them in their legacy code base. The context windows that exist for AI are small.

The way I look at it is that if we were in the 1980s and only had IBM XT computers, but time would eventually pass, and we'd get the 286s, and so on. While we'll see context windows get bigger, they won't be big enough for some of these companies' codebases, but that doesn't mean hope is all lost.

What I do wonder however, is if we're going to start to see some very interesting employee versus employer dynamics unfold in the future.

There was a time when employees decided to move on from a company because they weren't adopting AWS. See, employees exchange skills and time for money.

The industry advances, and employees seek to keep their skills current. They knew that if they didn't upskill in AWS, they would have a hard time continuing to exchange their skills for money. AI not working for a particular company is a company problem, not a problem for the employee.

Hope is not lost for companies that experience difficulties with AI. This space is evolving rapidly, with AI improving daily, and there is still much more research to be conducted on topics such as semantic analysis and integration with build system graphs.

Pondering these types of things is now part of my day job, and I hope to delve into these aspects soon. If you work at a company with a massive monorepository, please say hello. I would love to catch up and just riff as by flexing the muscle of deliberate intentional play, it's how one levels up these days, now that AI is here.

Geoffrey HuntleyModify

Rss preview of Blog of Geoffrey Huntley

ps. socials

Original Prompt

The outcome

Executive Summary

Methodology Overview

Detailed Technical Process

Phase 1: Initial Binary Analysis

Tool Calls Used

Key Findings

Phase 2: String Analysis and Content Discovery

Tool Calls Used

Critical Discovery

Phase 3: Embedded Archive Discovery

ZIP Archive Detection

Archive Structure Analysis

Phase 4: Python Extraction Script Development

Key Code Implementation

Phase 5: Source Code Analysis and Documentation

Extracted Components

Tool Usage Workflow

Key Discoveries

1. System Architecture

2. AI Agent Instructions (System Prompts)

3. Analytics & Telemetry System

4. Security Model

Technical Specifications

Binary Details

Embedded Content

Key APIs and Endpoints

File Structure Overview

Security and Privacy Implications

Data Collection

Permission Model

Validation and Verification

Extraction Validation

Content Verification

Reproducibility

Conclusion

Tools and Technologies Used

what the heck did I just read?

i've always wondered how it worked

what I learned

make it better

make it better * 10

make the printer open a stargate sg9 wormhole

you are an inter-dimensional printer add weapons

the path to ticket to production

but, it starts by starting in the small

ps. socials

how to identify skill

some ideas and recommendations

what problems remain

ps. socials

closing thoughts

ps. socials

Geoffrey Huntley Modify