Why an orchestrator?

Because Fable is EXPENSIVE. If you have Fable do planning and orchestration, it can delegate implementation to cheaper models.

I’ll walk you through this concept, then show you how I used it to add a pretty significant feature to a Next.js web app.

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Why Fable requires special strategy on our part

Fable is an incredibly capable model, but it’s too expensive for practical daily driving.

We’re seeing this pattern emerge where frontier labs are releasing parallel models of varying size. Fable is Anthropic’s most useful model, but it’s also it’s slowest and most expensive.

But smaller models (even Sonnet 5!) are useful to the point that you can use them for a huge percentage of software engineering.

Anthropic themselves is pushing the orchestrator pattern:

In fact, they shared that this pattern gives remarkable cost savings at minimal performance cost:

The Fable 5 orchestrator achieves 96% of Fable 5 performance at 46% of the price.

The change we’re making with Fable

I recently built a landing page for an info product using Claude Code, but the site only had a waitlist signup.

The AI-Augmented Intelligence report is a monthly report for software engineering leaders to keep them up to date on AI.

AI is moving quickly, and keeping up to date with trends, risks, and workflows can be a full-time job. If you resonated with that, I think you’d love the product.

Try Reports

I didn’t want to overengineer a product that didn’t have demand, so I just shipped it with a waitlist signup.

Well, the waitlist is overflowing. I’m only opening 25 spots and there were multiples of that that signed up for access.

So it’s time to actually open the product for signup!

The core of Reports is information. We need to let users sign up, pay, view an index of past reports, and get emails for new reports.

For today’s article, we’re going to add signup and billing.

How to use Fable as an orchestrator to make this change

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Anthropic released Claude Fable 5 on June 9. Three days later, the United States government placed the model under export controls, forcing Anthropic to disable it for everyone because the company had no practical way to verify each user’s nationality in real time.

The restrictions were lifted on June 30, and Fable returned globally on July 1. Anthropic initially included the model in paid Claude subscriptions for only a few days, after which subscribers would need to purchase usage credits at API rates. That temporary access has since been extended twice, most recently through July 19.

It has been a strange launch, even by frontier-model standards.

The regulatory story here has attracted most of the attention, but the product story may be more relevant to the average software engineer. During the few weeks that Fable was disappearing and reappearing, OpenAI released the GPT-5.6 family and Cursor released Grok 4.5 with SpaceXAI. Both releases made the market for expensive frontier intelligence considerably more competitive.

Once another model offers comparable performance inside a normal subscription, charging every Claude subscriber metered API rates for Fable becomes difficult to defend.

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The frontier is now crowded

Results of independent evaluations from Artificial Analysis currently place the major new models surprisingly close together.

The results show Fable holding a narrow general-intelligence lead, GPT-5.6 taking the lead in coding agents, and Grok delivering near-frontier coding performance at a much lower cost.

The answer to the model choice question now depends heavily on what you are building, how many times you need to run it, and how easily you can verify the result.

Fable 5 is an exceptional model when you can actually use it

Fable 5 is Anthropic’s most capable generally available model. It uses the same underlying model as the more restricted Mythos 5, with additional safeguards that sometimes route sensitive requests to Opus 4.8. (I think this happens way too often tbh)

Anthropic built Fable around long-running autonomous work. It has a one-million-token context window, supports outputs of up to 128,000 tokens, and uses adaptive reasoning rather than requiring users to select a fixed thinking budget. In one early test, Stripe reportedly used it to perform a migration across a 50-million-line Ruby codebase in a day, work that Stripe estimated would have occupied an engineering team for more than two months.

The independent results support Anthropic’s positioning. Fable scores 60 on the Artificial Analysis Intelligence Index, the highest current score, and it leads the AA-Briefcase knowledge-work benchmark. Its advantage there is substantial: a 56 percent rubric score compared with 42 percent for GPT-5.6 Sol, plus an Analytical Quality Elo of 1,764 compared with Sol’s 1,592.

For an engineer, Fable is most interesting when the work is large, ambiguous, and expensive to get wrong. Think architecture investigations, unfamiliar codebases, difficult migrations, incident analysis, or changes that require the agent to maintain a plan across a long session.

The downside is cost. Fable is priced at $10 per million input tokens and $50 per million output tokens. Artificial Analysis also measured a time to first token of roughly 117 seconds at maximum reasoning effort. This is a model for tasks where answer quality is worth waiting and paying for, rather than something I would casually put behind every agent loop.

Fable’s included access on Claude subscriptions currently runs through July 19. After that, Anthropic still says the model will move to usage credits, but I suspect the company extends the promotion again. Still, this a prudent time to run your own difficult tasks through it and figure out where the quality difference is real.

This post is sponsored by Cosmos, the agent orchestration platform for AI-native engineering teams.

Cosmos is a shared system where agents work across triage, spec, implementation, review, testing, deployment, and feedback with the context, memory, and controls teams need. Humans steer, agents do the implementation, and the system gets better as the team uses it.

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GPT-5.6 is just as good but at lower cost

OpenAI did more than release a new flagship. GPT-5.6 is a family of three models:

• Sol is the highest-capability model.

• Terra is positioned as the balanced option.

• Luna is the cheapest and fastest model.

All three are available in the API, ChatGPT Work, and Codex. Paid ChatGPT and Codex plans include access to the family, while free users receive access to Terra in supported products. API prices range from $5 input and $30 output for Sol down to $1 input and $6 output for Luna.

Sol’s raw intelligence score is barely behind Fable, but it costs approximately one-third as much per task in the Artificial Analysis evaluation. It also takes the lead on the Coding Agent Index with a score of 80, ahead of Fable’s 77.2. Artificial Analysis found that Sol led all three coding evaluations in the index while costing around 40 percent less per task than Fable in Claude Code.

OpenAI also reported a large advantage on longer professional workflows. GPT-5.6 Sol scored 52.7 percent on Agents’ Last Exam compared with 40.5 percent for Fable. On the independent Intelligence Index, however, Fable still held the one-point lead. The reasonable conclusion is that both models are frontier-class, with Sol currently offering the stronger overall price-to-performance ratio.

Luna may be the most practically important release in the family. It scores 51 on the Intelligence Index and 75 on the Coding Agent Index while costing about 80 percent less per evaluated task than Sol. For workflows that launch several agents, retry failed work, or process large queues, those economics can be more valuable than squeezing out the final few percentage points of performance.

At every tested Terra reasoning level, either Sol or Luna provided more intelligence for the same cost, or similar intelligence for less.

So there’s a free tip! In the GPT-5.6 family, avoid Terra!

Grok 4.5 is surprisingly good

Grok 4.5 was jointly trained by Cursor and SpaceXAI. Cursor says its training included trillions of tokens from interactions with codebases, tools, and developer agents, along with broader data from STEM, research, and professional knowledge work.

This is the first model from the partnership intended for more than software engineering. It was trained through reinforcement learning on realistic environments that required the model to investigate problems, use tools, recover from mistakes, and verify its work.

Its independent results are impressive. Grok 4.5 improved 16 points over Grok 4.3 on the Artificial Analysis Intelligence Index, reaching 54. In Cursor’s Grok Build harness, it scored 76 on the Coding Agent Index, effectively matching GPT-5.5 and landing just behind Fable.

Grok does not beat Fable or GPT-5.6 Sol in overall intelligence. It does crush them on cost.

Artificial Analysis measured a cost of $2.49 per coding-agent task for Grok 4.5, compared with $11.80 for Fable in Claude Code and $5.07 for GPT-5.5 in Codex. Grok used about 1.9 million tokens across the Coding Agent Index, compared with 7.2 million for Fable and 6.2 million for GPT-5.5. Its API price is $2 per million input tokens and $6 per million output tokens.

Those numbers make Grok attractive for code-generation loops, terminal agents, and workloads where you can run automated tests or another reliable verifier afterward.

There is an important caveat. On Artificial Analysis’s knowledge-reliability evaluation, Grok’s accuracy increased from 35 to 52 percent, but its hallucination rate also rose from 25 to 54 percent. It knows considerably more than its predecessor, while also becoming more willing to answer incorrectly. Grok looks strongest in environments where its work receives direct feedback from tests, compilers, databases, or other deterministic systems.

Google is still playing a different game

Gemini 3.5 Flash does not currently lead the broad intelligence rankings, but it remains a serious frontier option because Google has optimized around a different set of constraints.

It accepts text, images, audio, and video, supports a one-million-token context window, and is designed for high-throughput agentic work. Google reports scores of 76.2 percent on Terminal-Bench 2.1, 83.6 percent on MCP Atlas, and 83.6 percent on MMMU-Pro. Artificial Analysis currently measures output speeds around 162 tokens per second.

For agents that need to inspect video, process audio, understand large visual documents, or return results with low latency, Gemini can be the better engineering choice even when another model has a higher composite intelligence score.

This is another reason permanent model rankings have become less useful. Google is competing on speed and multimodality. Anthropic is competing on sustained analytical quality. OpenAI is competing aggressively on coding performance and cost. Grok is competing on agent economics.

How I choose models right now

I would avoid standardizing your entire workflow or an entire engineering organization around one model.

Route work according to what you observe:

• Use Fable 5 for your hardest open-ended investigations and long-running work where analytical quality is the primary concern.

• Use GPT-5.6 Sol for great coding agents and professional workflows where you want frontier performance without Fable’s full cost.

• Test GPT-5.6 Luna as the default for high-volume agents, background tasks, and parallel subagents.

• Use Grok 4.5 when cost efficiency is important and the output can be verified automatically.

• Keep Claude Sonnet 5 in consideration as Anthropic’s practical daily model. It is included across Claude plans and launches at $2 per million input tokens and $10 per million output tokens through August 31.

Want more practical tips like these to help you stay on top of a quickly-changing AI toolset? I’d love to have you join us for the paid membership, where you’ll unlock our full archive of deeply technical guides.

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Competition is doing its job

Anthropic released an extraordinary model and planned to charge subscribers separately for meaningful usage. OpenAI then shipped a model that is nearly tied with Fable on independent intelligence evaluations, beats it on a major coding-agent index, costs much less, and is included inside its existing products. Grok arrived with near-frontier coding performance at a fraction of Fable’s cost.

So who wins? Not investors :)

Maybe Anthropic would have made the same decision without those releases. From a customer’s perspective, the cause is less important than the result.

The leading labs are now close enough that each can expose a weakness in another company’s offering. A model can have the highest intelligence score and still lose on coding, latency, multimodality, access, or price. Developers have credible alternatives, and switching costs remain low enough to keep the pressure on.

That pressure is giving us more capable models, cheaper inference, better subscription access, and several genuinely good choices instead of one default provider.

The frontier has rarely been more confusing. It has also rarely been this favorable to the people actually using the models.

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I don’t think that’s true, but I do think understanding loops and how to use them in Claude Code has the potential to make you more productive. Initially, they may sound intimidating because people are hyping up a relatively straightforward automation concept.

Making loops is essentially replacing yourself as the person who prompts the agent. When you make a loop, you make a system that prompts the agent.

In today’s edition, I’ll show you:

• What a loop is

• What makes up a loop

• How to make a loop in Claude Code (with example prompts)

• How loops can make you more productive

What is a loop?

Even the OpenClaw guy is pushing loops now

This post is actually pretty explanatory for loops.

Loops prompt themselves to write code so that agents can do long tasks in parallel without much involvement from you.

One important concept that makes loops powerful is the idea that an agent can have its own agents. An orchestrator agent can perform the loop and spin off subagents to actually accomplish a task.

What makes up a loop?

Loops are essentially recursive goals where you define a purpose and the agent iterates until it meets that purpose/goal.

Sound familiar? Maybe you read the newsletter’s posts on /goal:

In fact, Anthropic calls goals “goal-based loops”:

Generally speaking, a loop needs 4 things:

1. A goal (scope of what you want agents to do)

2. Context (don’t let your agents fly blind!)

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Codex and Claude Code are incredible tools for creating software. Each takes advantage of state-of-the-art models to read and understand your entire codebase before making changes on your behalf.

If you’re looking to use AI to accelerate creating meaningful software, you’ll probably end up picking one of these. But choosing Codex vs Claude Code isn’t easy.

I’ll walk you through how to pick the right tool for you, but I won’t hide my opinion. Both are great, Codex is my preference, and Claude Code has a better CLI.

But don’t conflate choosing a model family with choosing a harness!

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Separating the model from the harness

If you’re going to compare Codex and Claude Code, it’s really important to understand the difference between a model and a harness.

The model is the actual brains of the operation. Models have weird names, like OpenAI’s GPT series:

• GPT-5.4

• GPT-5.4-Codex

• GPT-5.5

Or Anthropic’s Claude models:

• Claude Haiku 4.6

• Claude Opus 4.8

• Claude Sonnet 4.7

The harness is the tool that sits between you and the model. ChatGPT, Claude, Claude Code, Codex, Cursor, Intent. All of these are harnesses.

Many tools, like Cursor, allow you to choose the model that the tool/harness uses under the hood.

Obviously that creates a lot of competition for model providers, so they have some incentive to want you to use tools that lock you into their models. Claude Code is Anthropic’s leading harness for developers. Codex is OpenAI’s harness for developers.

This article will mostly be about comparing the harnesses to each other, not the models. Model comparison is often done with evals, which are published with each model release in a system card.

What is Codex?

Codex is OpenAI’s flagship agentic product. Historically, its been their agentic software development product. Recently, they’ve taken steps to make it useful for general use, but if you’re here, you’re probably interested in the software engineering piece.

When someone say’s “Codex”, they most often are referring to the Codex app. There’s also a Codex CLI, but the app is a lot more popular.

OpenAI fine-tunes some of its models for programing, and appends -Codex to the model name, but don’t let that confuse you. There are Codex models, the Codex CLI, and the Codex app.

What is Claude Code?

Claude Code is the Anthropic equivalent to the above. Meaning, Claude Code is Anthropic’s flagship agentic software engineering tool.

Many of you are already familar with Claude, which generally refers to the web app that lets you interact with Claude models (Haiku, Sonnet, Opus, Fable, etc.)

Claude Code is a Command Line Interface (CLI) tool primarily meant for agent-first software development. You run Claude Code inside a programming project, instruct/prompt it on what you need to do, and it uses tools to read, understand, and make changes to your codebase.

Most people use Claude Code in its original form, the CLI/TUI. While there’s a Claude Desktop app, most people find the CLI better to use.

The opposite is actually true of Codex. More people use the Codex app than Codex-CLI, their terminal interface.

We already ran a great detailed comparison of using the Claude Code TUI for the same task as the Codex app, so check that out!

Overall, my preference is for the Codex app since it makes parallel agent orchestration more straightforward. For frontend-heavy tasks, the built-in browser is incredible.

This post is sponsored by Cosmos, the agent orchestration platform for AI-native engineering teams.

Cosmos is a shared system where agents work across triage, spec, implementation, review, testing, deployment, and feedback with the context, memory, and controls teams need. Humans steer, agents do the implementation, and the system gets better as the team uses it.

Try Cosmos

How to make your decision

There’s an insane amount of competition in this space, and that’s good. I actually have a plan with OpenAI for Codex, Anthropic for Claude Code, and another for Cursor. This is helps me stay on top of all the tools for the newsletter, but it also helps me evade rate limits!

Whichever tools and models are best changes frequently, so keeping a subscription to both Claude Code and Codex isn’t a bad idea.

Here are some rules to help you decide:

• Lots of frontend design? → I suggest Claude models → I suggest Claude Code in CLI

• Lots of long-running tasks → I suggest Codex models → I suggest the Codex app

• Really prefer a CLI? → I suggest Claude Code

• Really prefer an app? → I suggest Codex

Do you have a strong preference? I’d love to hear about it in a comment or reply to this email.

Software development is moving out of the IDE. Well, it has moved out of the IDE.

Intent, Codex, Claude Code - all of these are agent interfaces that abstract code editing away.

For decades, the IDE was where software happened. You read code there. You wrote code there. You debugged code there. You ran tests there. You sat in front of the editor, typed the implementation, and used the rest of the toolchain to support that work.

Then, AI started working its way into our workflows.

First, it was autocomplete. Then chat in the sidebar. Then, inline edits. Then, agents that could inspect files, modify code, run commands, and loop until they got something working.

That was the first phase of AI-assisted development: make the individual engineer faster inside the environment they already use.

The next phase is different.

Once agents are doing meaningful work, the bottleneck stops being “can the tool write code?” and becomes “can I trust this work enough to ship it?”

But teams have a harder problem.

An individual can build a personal workflow. A team needs an operating model.

That is where Cosmos provides value.

The problem is no longer code generation

The first time you watch an agent make a non-trivial change, it feels like the future.

The twentieth time, you start noticing the mess. When you multiply this across entire teams, the mess gets pretty disruptive.

One engineer on your team might have a carefully tuned prompt for backend migrations. Another has a Claude Code skill that works great for tests. Someone else has a local script that spins up three agents in separate worktrees for adversarial reviews.

Agentic engineering is a paradigm shift, and it requires new tools.

The name Cosmos is hinting at something. Cosmos is Greek for order, specifically the ordered universe. It stands in contrast to chaos, which is the problem it seeks to solve.

AI-native teams have team-level systems

Most AI coding tools are still designed around the individual developer, which makes sense. Individual adoption is where the market started. A single engineer installs Cursor, Claude Code, Codex, Intent, or another coding agent and immediately gets leverage.

But individual leverage does not automatically become organizational leverage.

In fact, it can make some organizational problems worse.

If agents make it easy to generate code, PR volume goes up. If PR volume goes up, review becomes the bottleneck. If review becomes the bottleneck, teams either slow down or start rubber-stamping changes. If they rubber-stamp changes, they ship bugs, regressions, and architectural drift faster than before.

The same pattern shows up in incident management.

If agents help teams ship more code, production changes faster. If production changes faster, on-call has more context to reconstruct. The on-call engineer now has to correlate deploys, logs, metrics, Slack threads, dashboards, ownership, past incidents, and recent PRs under pressure.

Human attention is necessary, but a bottleneck. We shouldn’t be seeking to pull humans out of the loop, but that doesn’t mean we should hide from tools that make that human’s job easier.

But at this point, these tools need to be built for whole teams.

Intent is single-player, Cosmos is multiplayer

The distinction between Intent and Cosmos is helpful.

Intent is about the individual developer’s workflow after the IDE stops being the center of gravity.

You define a task. Agents coordinate inside a workspace. A spec gives the work shape. The tool helps you answer the key question: “Can I trust that the agents did the thing I asked them to do?”

That is a single-player problem.

Cosmos is aimed at the team version of the problem.

How does an organization make sure the best way to do something is not locked inside one engineer’s head?

How does a team turn a successful agent workflow into a reusable pattern?

How do you make sure code review, incident response, testing, and remediation all improve over time instead of starting from scratch every time a new agent session begins?

How do you keep humans in the loop for judgment without forcing them to inspect every line of agent-written code?

That is a multiplayer problem.

Cosmos is a bet that AI-native engineering teams need a shared system of record for agentic work. Not just prompts. Not just agents. Not just a dashboard. A coordination layer.

Experts are specialized agents in Cosmos

Experts are specialized agents you can define in markdown and reuse across your entire team.

Here’s a sneak peek of how Augment is using experts internally to do code review.

Here’s another example of how they’re using experts for automated incident response.

Agents gather evidence, summarize, propose, and remember. Humans decide, approve, and handle the parts where judgment matters.

You’ll notice in this flow that these “experts” don’t actually pull humans out of the loop completely. The human still makes the judgment call, but the human is no longer starting from zero.

Augment is reporting that these agent workflow triages 81% of incidents now! Agents aren’t new to Augment, so it should be surprising that the number before introducing Cosmos was 0.4%.

That’s how big a difference this tool makes for teams.

Want to set up your own experts? Cosmos advisor helps you set them up with natural language so you don’t have to learn a new tool from scratch. If you want a jumpstart, you can use the library of experts that ships with Cosmos.

The model router is built for pro teams

One underrated part of Cosmos is Prism, the model router. Prism handles model selection so you don't have to be an expert on model capabilities to get the most for your tokens.

Cosmos is intentionally model-agnostic. You can choose your own model, or get the model router to do it for you.

Historically, I haven’t really trusted model routers, under suspicion that they route you to the cheapest model.

This isn’t the case with Prism, because the tool is complexity-aware.

For each input, a small (read: inexpensive) planner model reads the request and decides which of the underlying models should handle it. From your perspective, Prism behaves like any other model in the picker. You pick Prism, then Prism picks the model.

Choosing a model based on complexity is actually the easier part. The harder part is caching. Modern coding agents lean heavily on prompt caching to reduce cost and latency. Naively switching models mid-session throws the savings from the cache out the door. A naive router that re-routes every turn would burn most of its potential savings on cache misses, which could make the session more expensive than just staying on one model.

Prism’s only switches models when the expected win from a different model exceeds the cost of the cache eviction, which makes it an effective means of staying in your token budget without getting sub-optimal results.

Cosmos meets you where your work already happens

Cosmos works on your local machine, in containers, in codespaces, in Augment’s managed cloud, or on your own cloud infrastructure.

That makes it easy to run Cosmos, but you also don’t have to move all your work inside of Cosmos. It has robust integrations to bring it into the work you already have going on. It integrates into Slack, Linear, a CLI, web, and mobile apps.

It also has MCP support and webhooks so building your own custom integrations is easy.

If you’re an engineering team looking to actually multiply your impact with AI, give Cosmos a try.

Try Cosmos

That app, Simple Meds, still gets used every day!

But it’s ugly! It works exactly as expected, but has no design frills whatsoever.

So today, I’m going to show you how I used Claude Code to make it much more delightful to use. Here’s what we’re turning it into:

AI tools are so much better than a year ago

When I made this app, I used Cursor’s chat to write every single line - I showed you how in what ended up being one of my most popular posts ever:

SO MUCH has changed since then. First, Cursor is no longer the leading AI tool for developers. It’s a great tool, but it’s been surpassed a bit by Claude Code and Codex.

Why? Agents.

Agents are the primary mechanism to use AI for software engineering now. Cursor isn’t oblivious to this - they completely redesigned Cursor around agents. So if you’re using Cursor today, you’re not behind. I still keep a subscription and use it from time to time, but it’s not my daily driver anymore.

Models have also gotten so much better in the last year. I wrote this app with GPT-4o. We’ll do our redesign with Claude Opus 4.7, which isn’t even the latest model!

I’ll also use newer image generation models to get a better logo that matches our new design.

Prompting Claude Code for design

Read more

Here’s everything you need to get productive with Claude Code FAST.

Installing Claude Code

As you can probably gather from the screenshot above, installing Claude code is simple. Open your terminal and install it with NPM:

npm install -g @anthropic-ai/claude-code

Make sure you get a successful install.

Advanced Claude Code usage

Comparing Claude Code to other tools

Building apps with Claude Code

If AI is such an incredible tool, why aren’t we seeing an incredible explosion of useful software?

The answer isn’t too complicated. AI is just a force multiplier.

If you’re a great developer and use AI effectively, you’ll get increased productivity. If you have poor technical skills and lean on AI, you’ll ship more, but the outcomes will not be good. You ship slop, and lots of it, so much in fact, that you would rightfully be called an AI slop cannon.

What is an AI slop cannon

An AI slop cannon is a very high-output software engineer that doesn’t emphasize quality enough.

The weird part here is that in the short term, slop cannons seem productive. But code that is easy to produce can be hard to maintain and extend, so the productivity gains from the AI tool are eaten by technical debt.

If you’ve been a software engineer long enough, you’ve undoubtedly been bitten by a complexity dragon once or twice. Great software developers write code that is easy to understand and maintain, in addition to accomplishing the task. If you’re just feeding Claude Code Jira tickets to complete, you’re probably going to ship some AI slop.

So how do you avoid shipping slop? I’ll show you 5 guiding principles.

Today’s newsletter is generously sponsored by Friday, a powerful agent harness that connects to the software you already use.

Friday helps you get more done with AI agents, without having to glue tools together. You can bring your own API key for your preferred models, bring your existing skills, and connect to the tools you need with MCPs.

One of my favorite things in Friday is the incredible support for scheduled jobs. Friday lets you describe recurring agentic workflows in plaintext but gives you the option to edit and extend them as code.

Check it out today!

Try Friday

How to become a turbo brain instead of an AI slop cannon

If you’re already using AI, then your focus should be on becoming a great software engineer. If you’re a great software engineer, then put some time into becoming effective at using AI tools for your work.

One of the best things you can do for your career and output is to develop great engineering skills, isolated from AI.

1. Developing great engineering skills without AI

This section is primarily about developing good judgment. Unfortunately, this takes time. There are things you can do to accelerate developing the technical skill and judgment of an effective senior+ contributor, but you’ll also have to be patient.

The first and most obvious thing to focus on is coding skill. Some folks are pushing the idea that you don’t need to know your programming syntax when AI is an effective means of producing code. I think this is a bit short-sighted.

It’s important that you become competent in at least 1 programming language (ideally 2) so that you can judge AI-produced code. I’ve been writing Ruby for years, and it gives me a keen eye for reading Ruby code that is overly complex or hard to change.

Speaking of complexity, it’s important to learn how to avoid unnecessary complexity. Learning this is increasingly important the more you use AI, and I recommend you read “A Philosophy of Software Design” by John Ousterhout to speedrun this.

If you’re shipping enough code, you’ll eventually need to develop an eye for system design. AI lacks in this area, mostly because coding agents often focus on an individual app instead of a whole system. Getting really good at system design is a huge life hack in the era of coding agents. I recommend Alex Xu’s system design book for this.

2. Understanding all the code you write

Good software engineering requires that you understand the code you write. That extends to the tools you use. In the AI era, that means you can’t ship code agents write without wrapping your head around it.

Skipping this part is generally what people call vibe coding. Vibe coding has it’s place, but it’s not software engineering.

3. Owning all the code you ship

If you ever find yourself saying “Claude wrote a PR that did x” or “Codex fixed this bug”, STOP.

I call this agentic passive voice.

When you use Claude Code or another AI tool to write code, even with an agent, you are simply using a tool. It’s an incredibly effective tool, but it’s still a tool.

It’s a bad look to abdicate all your responsibility to an LLM. You have a real human brain capable of unique insight, and it’s your responsibility to use it to add value to your work. Extend your expertise with AI. Don’t replace it.

4. Using AI effectively

Using AI effectively is an important part of becoming more useful in the AI era. It’s the whole thesis for this newsletter.

This is where learning to use the tools we have comes into play. Plan mode, goals, context engineering, skills, and more are all important concepts to wrap your head around if you want to get maximum value out of your tools.

If you’re not already, consider becoming a member of the newsletter to multiply your efforts here. Members get access to detailed technical guides in the Member Vault, which are the fastest way to level up your AI-assisted programming techniques.

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5. Seeking mentorship

The last piece of advice I’ll leave you with is to seek mentorship for all of the above. Find folks that are incredible at system design and learn from them. Identify the people in your org that are using AI tools effectively and ask to pair program with them.

The fastest way to level up is often 1:1 conversations with folks just a few steps ahead of you at a particular thing, so don’t miss out!

I’m tinkering with a new product idea, and I wanted to show you exactly how I used Claude Code to build the landing page for the product waitlist. Here’s a sneak peek of what we’ll be building in today’s deep dive.

Building small projects like this these days is so much easier than even a few years ago. You can design, build, ship, and iterate faster than ever before. Without AI, I probably wouldn’t have time to test small ideas like this.

With AI, I can experiment and tinker while actually getting real feedback from users and customers. The product will be a thoughtful, human-written report, but there’s no reason to not use AI to help me build a great landing page.

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Why use Claude Code over no-code?

If you’re just building a landing page, it isn’t always worth the hassle to code up a custom website. Even though AI has made that work much easier/faster/cheaper, there’s some phantom costs to running your own website:

• Security maintence

• Framework and language upgrades

• Platform costs

• Complexity of change

So plenty of no-code tools like Wix, Webflow, and Carrd exist to make shipping landing pages easier.

If all you need is a landing page, then these are good options - maybe even better than what we’re about to do.

But I want more than a landing page.

I want full control over the site.

What we build today will just let users add their email to a waitlist and display some copy, but it probably won’t stay that way.

If the product gets enough interest, I’d use this initial site build out as the actual product experience too. What starts as a landing page would eventually turn into the platform people access the info product on.

Doing a custom build also gives me the ability to have full control over A/B testing, email capture experiments, and so on.

Writing the copy first

Since this is a landing page, I first wanted to nail the copy. The product idea is for a high-ticket monthly analysis report for engineering leaders, so I spent my first few prompts going back and forth in ChatGPT to refine the idea.

Once the idea was well-described and defined, here was my initial prompt:

Interview me and ask me all the questions you need to know in order to write an incredible copywriting page in the style of Sam Parr

“Interview me” prompts are underrated. The model returned FOURTY questions about my idea to further refine it, then it gave me a long copywritten sales page. This of course got a careful eye and plenty of tweaks from me, but it was an incredible time-saver.

I used this sales copy for the next piece, actually building the page with Claude Code.

Getting from copy to plan in Claude Code

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I’m not sure how to feel about this. I’m usually a big advocate of different tools for different things. I don’t like everything apps because they eventually become nothing apps.

But OpenAI Codex might be challenging that assumption with their recent releases and upcoming roadmap. They’ve built integrations for Gmail, Slack, Google Drive, Linear, even Stripe! The open MCP standard has given you a lot of options to work with Codex.

In today’s newsletter, I’ll show you:

• What’s changing in Codex

• Details on the merge with ChatGPT

• Why I think this is a mistake

• Some cool non-code things you can do with Codex now

Today’s newsletter is generously sponsored by Friday, an agent harness platform for building AI workflows that actually connects to the software you already use.

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The scheduled jobs are especially useful. You can ask Friday to generate a report, check a system, summarize activity, or email you an update every Monday at 9am, then inspect and extend the workflow as code when you want more control.

It is a practical way to make AI agents useful beyond the chat window.

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What’s changing in Codex

Codex took OpenAI from just answering questions (ChatGPT) to actually doing meaningful work. It followed quickly from the success of Claude Code, and moved from web to CLI and then again to app.

In case you missed it, here are some Codex guides to get you up to speed:

From these articles, you can see pretty clearly that Codex is a tool for developers. It’s a direct competitor to Claude Code, but soon it will take on Claude Cowork more directly.

Is Codex merging with ChatGPT?

I don’t know how this isn’t bigger news. At OpenAI’s recent Intelligence at Work event, their product leadership shared that they’re “very excited to share that in the next few weeks we are going to put codex into ChatGPT".

For a developer, the unit of work is a commit or a PR. For other white-collar workers, the unit of work is something else! Presentations, documents, excel tables, emails, etc. OpenAI wants to move beyond just code.

ChatGPT is where Open AI models help you do work.

Codex is where Open AI models work for you. It’s agentic, and they’re moving past just software engineering.

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So why bring it into ChatGPT? The everything app, that’s why. Claude did this recently with Claude Cowork. Now’s there’s a Claude Desktop app that contains a GUI for Claude (chats), Claude Code (software engineering), and Claude Cowork (other office-type work).

I expect ChatGPT to look a lot like this soon. They’ve neglected their desktop app to crush it on Codex, and it seems like they’re going to roll them together soon.

Becoming an everything app is a mistake

They recently highlighted that knowledge workers are adopting Codex 3x faster than developers, so it’s no surprise that they want to tap that market. Developers love Codex, so accountants should love Codex too, right?

I don’t think so.

I think Codex is so incredible because it’s for one thing. Taking focus away feels like a great way to lose the lead in the race for great developer tooling. But time will tell. Fortunately there’s a lot of choice in this market!

Some cool things you can do in Codex now

Now that I’ve complained about Codex becoming a tool for everyone, let me show you some genuinely cool use-cases that aren’t software engineering 😎

To use most of these, you’ll need to go to the “plugins” section of the Codex app.

Google calendar in Codex

Once you install the calendar plugin, you’ll get a prompt to sign in to Google calendar and grant permissions.

Once that’s setup, you can trigger calendar with @Google Calendar

Here’s an example of me asking Codex if my calendar is free on Saturday and then adding an event to it.

Automations in Codex

The next cool new(ish) Codex thing is automatons, or recurring tasks.

I don’t think Codex does this well, which is a symptom of being an everything app. See today’s sponsor, Friday, for a better recurring task experience! Regardless, here’s an example of a simple but useful automation I just set up.

Codex computer use

Computer use is the “Claude Cowork” feature. It let’s Codex actually use other apps on your computer directly, which is the fastest way to get it to be generally useful beyond just writing code.

Here’s a prompt OpenAI has in one of their docs:

@Computer

- Play some music to help me focus.
- Help me add my interview notes from Notes to Ashby.
- Look through my Messages app for the trip ideas Brooke sent me this week, add the best options to a new note called "Yosemite ideas", and draft a reply back to her.

It’s a killer feature if you’re willing to give it the access it needs in order to be useful. I haven’t experimented with it enough to give a full tutorial here, but perhaps in another newsletter!

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If you’re using an agentic tool, every little optimization to your workflow pays dividends. For CLI tools like Claude Code, it can be a little hard to get all the info you might need in one screen.

Fortuantely, Claude Code lets you configure the status bar to contain info that’s important to you, specifically.

Some good options for the Claude Code status bar

What kind of information might you want to display in the status line? Here are some popular ideas

• Context window usage

• Current session cost (this is becoming increasingly important)

• Git branch

• Current model

• Effort level

• Rate limit usage %

There’s more options shown in the Claude Code documentation, but these are the most common. They also happen to be the ones that I use!

I’ll show you how to use Claude to configure the status line in natural language with a prompt you can copy and paste.

How to customize the Claude Code status line with natural language

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Goals help you go from back and forth prompts to repeatable outcomes.

If you want a refresher on goals, check out this earlier post on Codex goals.

How are goals different than prompts?

One highlight from the post explaining goals in detail is how they differ from prompts.

Prompts can be a tedious chat-based workflow. Sometimes when you’re working on a big project, you’ll find yourself snapping at your agent, telling it to “keep going”, “finish the implementation”, or “run all the tests now”.

If you’re doing something that involves evaulating output from the previous response, goals can help you iterate faster by pulling yourself out of the loop.

This lets agentic software tools like Codex and Claude Code operate more independently, which helps you get more out of your token spend.

Working from a PRD

Goals require specific outcome definition, which is a great reason to reach for a PRD (Product Requirements Document).

In our post explaining how to use AI to plan a new project, we explained how to use a chat-based tool like ChatGPT to get to a PRD.

You don’t have to use AI to get a PRD, or even have a PRD at all, but this is a great way to get moving on your project faster (and with more confidence that it will actually be okay).

Once you have a PRD or a document describing the detailed requirements of your project, you can get started using /goal in Claude Code or Codex to implement it.

I’ll walk you through it now, showing how I use this exact process to make a tool that helps me distribute my newsletter with AI.

Implementing a PRD with /goal

You can use goals in Claude Code or Codex, including the Codex CLI. I’ve been daily-driving the Codex app most of the time, so that’s what I’ll use for this walkthrough.

First, I’ll put my PRD in codex and ask it to make a PLAN.md from it. Then, I’ll use /goal to have it autonomously implement that plan. Take a look at the first piece:

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Goals are a built-in way to move your conversation away from a sequence of isolated prompts and into a work loop that iterates until it hits a measured outcome.

You can use /goal in the Codex app or in the CLI, so you can just stick to your preference.

If this is the first you’re hearing of goals in Codex, you’re probably a bit curious how they differ from ordinary prompts. We’ll start by answering that question, then show you the helpful goal commands, then we’ll wrap up with a real example.

Ready to dig in?

What is the difference between a prompt and Codex goal?

Prompts work for this workflow

ask -> work -> result -> wait

Goals work like this

work -> self-eval -> loop if not complete

In short, Goals are outcome-driven.

If Codex can learn something from what it just done, a goal can help it keep going without you.

But do use goals, you need a verification surface (like tests or browser use) and a measurable outcome.

When to use a goal instead of a prompt

Have you ever been using an agent in Codex or some other tool and found yourself typing the same thing over and over again as responses to agent completions?

Nice, do the next test now.

Or

Awesome, clean up the next controller to use this new method in the parent class

Or

Don’t just show me how to do it, keep going and finish it

If you’re ever in a situation like this, the /goal command is probably going to be very helpful.

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Some helpful Codex goal commands

The /goal command is really simple, and there’s a few variations that can help you in both the Codex app and CLI.

How to set a goal in Codex

Setting a goal in codex is simple with the slash command:

/goal Your goal goes here

How to view the current Codex goal

If you have a long-running goal, you might need reminded of what you initially asked for:

/goal

How to pause the current Goal in Codex

If you want to pause a goal without losing progress or context, you can do so:

/goal pause 

How to resume a goal

Resuming a paused goal is easy with:

/goal resume

How to delete a goal

Deleting a goal will remove the context and allow you to set a new one. Just run:

/goal clear

How to write a Codex goal

The OpenAI docs are a great place to go for instructions on writing goals, but I’ll give you the most important stuff here.

Goals need outcomes. This is how Codex knows when it’s done.

Goals need a way to verify the outcome. Tests, benchmarks, browser use, etc.

Goals should have constraints. You’ll get better results if you tell Codex what not to change.

Goals should know when to report as blocked. If you tell Codex what would indicate that it’s blocked, you’ll save yourself from wasting tokens.

Template for a good goal

Here’s a template for a good goal:

/goal <desired end state> verified by <specific evidence> while preserving <constraints>. Use <allowed inputs, tools, or boundaries>. Between iterations, <how Codex should choose the next best action>. If blocked or no valid paths remain, <what Codex should report and what would unlock progress>.

You could simplify this as:

/goal Complete this thing without stopping until you’ve passed this success criteria

Here’s a goal I used to add some test coverage to a project that doesn’t have any tests.

Goals and token usage warnings

One word of warning for you is to pay attention to your token usage while using goals. The /goal command lets Codex iterate over and over, which will consume truly absurd amounts of tokens. On a sufficiently large project, you’ll probably start hitting your five hour limits long before five hours (on the Plus plan).

Fortunately your limits will reset, so you won’t be stuck for the month, but it’s something to keep in mind.

This is one reason I keep a Codex, Claude Code, and Cursor subscription all active at the same time. Having all three is cheaper than any top-tier plan, gets me ton more usage, and lets me daily-drive whatever tool as the best edge at a given time.

Goals actually work in Codex too, and I suspect Cursor will follow-suit soon.

Getting really good at using agents

I firmly believe the future of software development is agent-first. That doesn’t mean I think software development is going away as a profession. But it is changing. It’s getting harder and harder to deny that it’s changing.

We’ve gone from fancy-autocomplete to agents, and the /goal feature in Codex is making agents even more autonomous, while increasing their economic utility.

If you want to learn how to use agents to ship more software, earn more money, or even get promoted, you’re in the right place. This substack exists to help you use AI tools like Codex better, so I’m happy to have you here.

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Until now, I’ve had the newsletter’s sponsorship details in a Notion page. It describes some stats on The AI-Augmented Engineer for companies who may be interested in sponsoring the newsletter.

Here’s what I used the Claude Code desktop app to make:

Frankly, I’m bad at frontend development, so AI is doing more than making this project go faster. It’s making it possible at all. The reason it’s in a Notion page now is because that was the fastest way for me to get the info publicly accessible.

Before we dig into the Claude desktop app, it’s important to draw a line between vibe coding and AI-augmented engineering.

Separating vibe coding from AI-augmented engineering

It’s really important to draw a distinction in your mind between vibe coding and AI-augmented engineering.

I’ve written at length about this, but it can be summarized as this:

• Vibe coding has a focus on shipping, implementation details aren’t checked very thoroughly

• AI-augmented engineering has a focus on amplifying the output of incredible software engineers

If you’re employed full-time as a software engineer, you should try to avoid vibe coding at work (for the most part). You likely have a lot of responsibility for what you ship, and a keen eye on the work your agents are doing will go a long way. That doesn’t mean you can’t trust agents, you just need to learn how to set up agents you can trust.

That being said, vibe coding is still useful! This project is pretty low-stakes. It won’t store data, handle auth, handle payments, or have a lot of total visitors. So it’s a safe playground to use AI tools without too much oversight. The consequence of screwing something up is low, and the likelihood of screwing something up is similarly low.

Using the Claude Code desktop app

I’ve used Claude Code plenty over the last year, but always in the CLI. But every since I got hooked on the Codex mac app, I’d been meaning to try Anthropic’s take on Claude Code in GUI (as opposed to TUI).

I gave my initial prompt and was sure to select plan mode, as well as provide a reference image. Here’s the prompt I used:

Read more

That’s right - the makers of Codex are showing you cool things they built with Codex AND how they used Codex to do it.

In today’s newsletter, I’ll show you a few of these cool projects and highlight some cool prompt tricks they published alongside them.

Let’s start with “Swifty Dungeon”.

Using the imagegen skill in Codex to make a game

One of the cool project in this showcase is Swifty Dungeon, a native SwiftUI first-person dungeon crawler with generated textures, sprites, and telemetry.

Here’s the initial prompt they gave Codex:

Use $imagegen and @Build macOS apps to build a native macOS first-person dungeon crawler.

First, use $imagegen to generate a screenshot/interface of the ideal app: a native SwiftUI Liquid Glass app with the playable dungeon view in the center, the character and on-screen arrow keys in the bottom area, and player status plus inventory in the right sidebar.

Notice the use of the $imagegen skill.

In the Codex app, you can explicitly invoke a skill with the $ prefix. It’s easy to remember, and it will trigger a dropdown/autofill for available skills.

You can manually invoke a plugin with the @ prefix. Here, the @Build plugin allows computer use, which is super helpful (and also new).

These are two HUGE pieces of alpha that are hidden in this inital prompt.

Today’s newsletter is sponsored by my friends at Augment Code.

Augment was kind enough to give me early access to Intent, their next-generation tool for developing software.

All signs are pointing to the fact that the IDE is no longer the ideal place to create software.

Augment Code has jumped on this opportunity, and my first impressions are great.

Try Intent

Building an incredible landing page

Another project in the showcase that really caught my attention was the “Watchmaker Landing Page”, which is a simple but elegantly designed marketing site.

Here’s their inital prompt:

Create a new landing page for a complicated watch design with parallax effects. First generate the concepts using imagegen, then create the visual assets: each one a different piece of the watch mechanical design, and when scrolling, they should assemble into the whole design.

For each piece, there should be an accompanying paragraph on the left or right (alternate), and scrolling brings up the next paragraph and the next piece with a fading animation for the previous paragraph. I want a really smooth, traditional yet modern design - think high end luxury Swiss watchmaking.

It’s not complicated, but don’t discount its economic utility.

Building landing pages like this used to require really skilled frontend developers and designers, which is of course quite expensive. Now, sufficiently technical people with Codex can pull it off in single digit minutes.

I actually used a similar prompt to build my own landing page for interested sponsors of the newsletter, and I was really happy I could move it off of Notion with such little effort.

Building a procedural city generator

The last project I’ll highlight is a procedural city generator that lets you adjust layout, density, skyline, and visual parameters to see the city update in real time.

Here’s the initial prompt:

Generate a React app to do procedural 3D city generation. Start by generating a mockup of what a beautiful editor for procedural generation might look like, then implement it staying as close as possible to the mockup.

There should be a full screen view of the generation, then a floating control panel on the right with controls for city size, city density, block size, street pattern, commercial vs residential vs industrial balance, and presets like industrial belt or residential area.

Add skyline controls such as average height and height variance. Add city style controls for modern glass, European, Tokyo dense, cyberpunk, and other styles that change the overall color palette and building style.

Add world controls for river probability, parks percentage, terrain roughness, and terrain style switchers like coastline or mountains. Finally, include view presets such as overhead and orbit, and make the editor pane feel modern and like a 3D editor or game engine.

The big takeaway here is the specificity. They specify that it should be React, that it should match a given mockup, and the exact features they want. If you’re starting from scratch, consider using a chat tool to make a PRD and some wireframes and then feeding that into your coding tool with plan mode. More on that here:

If you want to see more exact prompts to hone your AI skills, join the paid membership for the newsletter, where you’ll find deep technical guides that take you further than initial prompting.

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I’ll include real examples from a project I’m just kicking off, as well as a prompt template you can use to kickstart your new project planning.

Our example project today is an AI-powered web app that helps writers turn their newsletters into short posts.

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New project planning prompt

We can use a chatbot first to get a solid set of requirements for our project. If you don’t even know what your requirements are, it’s way too early to write code.

The goal of the planning step is to produce three things:

• A Project Requirements Document (PRD)

• A high-level architecture overview

• A set of design wireframes

To start, use this prompt to have your LLM of choice interview you until it has what it needs to produce drafts of this document.

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They’ve gone CRAZY on capabilities and features for Codex, their fast-growing Claude Code competitor.

In the last couple of weeks, Codex got in-app browser use, image generation, and computer use.

But today’s newsletter isn’t about that. Today’s newsletter is about adding some fun to Codex.

Today’s newsletter is sponsored by my friends at Augment Code.

Augment was kind enough to give me early access to Intent, their next-generation tool for developing software.

All signs are pointing to the fact that the IDE is no longer the ideal place to create software.

Augment Code has jumped on this opportunity, and my first impressions are great.

Try Intent

What are Codex pets?

Codex pets are optional companions for the Codex app.

They are not for productivity. They’re for fun!

One thing to note is that Codex pets are only available in the app, so if you’re using the CLI you’re out of luck.

How to install the Codex hatch-pet skill

First, you have to install the hatch-pet skill.

$skill-installer hatch-pet

Note that you should run this inside a Codex chat, not just in your terminal.

Once it’s done, restart Codex.

I’m glad OpenAI adopted the Skill standard, it makes it easier to switch back and forth between Claude Code.

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How to make a Codex pet

You can make your first pet by invoking the hatch-pet skill, which you can do with a slash command.

You’ll need to be on a recent Codex version for this to work. It uses the $image-gen tool, which requires that your Codex app supports plugins (a relatively recent addition).

Codex will generate an image and do a LOT of work to get variation sprites ready so your “pet” can be animated.

If you click into one of the subagents, you can get a bit more visibility into what it’s doing.

Codex took quite a long time for me, but eventually “Byte”, my first pet was ready!

Codex offered to save the pet, and it was packaged at .codex/pets/byte/pet.json with its spritesheet at .codex/pets/byte/spritesheet.webp.

How to wake your pet

Running /pet will wake the default pet.

To select the pet you’re interested in, go to Settings → Appearance → Pets

Then, you can select your custom pet.

Using the overlay

You’ll notice that Codex puts your pet in an overlaay in the bottom right coner of your screen. The overlay will persist throughout other screens on your computer, which is part of the point!

The overlay is meant to show active Codex work while you use other apps. It shows the current task, plus whether Codex is running, waiting for input, or ready for review. It combines that state with a short progress prompt so you can glance at what changed without reopening the thread.

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For example, look at this Codex pet while a task runs as I write this newseltter!

And when it finished:

This makes using Codex marginally more productive and way more fun.

Finding other pets online

If you want to add a Codex pet but you aren’t feeling creative, you’re not alone! Someone already set up Petdex, a marketplace of installable Codex pets.

As you click into one of these, you’ll see a curl command for installing the pet.

Like this newsletter? Share it with a friend! If 3 of your friends join from your referral, you’ll get a free month membership!

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But you can’t just blindly trust AI tools to write code for you and ship it without issues. You might win sometimes, but you’ll eventually have a serious problem. It’s not responsible.

If you don’t want to end up doing something like deleting your production database in 9 seconds, this article (and newsletter!) is for you.

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Guard production against agents

Your first job is containment.

Agents should not have direct access to production systems. No direct database credentials. No ability to run destructive commands.

If you don’t already follow these patterns, you should start:

• Read-only replicas for exploration

• Feature flags for risky changes

• Strict environment separation

• Scoped API keys with minimal permissions

If an agent can cause irreversible damage in one step, that’s not an AI problem. That’s a systems design problem.

Design your environment so the worst-case agent mistake is annoying, not catastrophic.

Give agents a way to validate their work

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I know it’s just a test, but it feels like a canary in the coal mine. We've been operating on VC-subsidized tokens for years. That's ending. Someone has to pay for the compute.

If the cost of intelligence (especially with reasoning models) keeps rising, the economics of developer tooling get a lot harder to figure out.

In today’s newsletter, I’ll talk a bit about why this is happening, whether it’s isolated to Anthropic (it’s not), and some tips on saving tokens.

What’s happening to the cost of intelligence?

Read more

So to incentivize AI usage, they started measuring token consumption and including it in performance reviews.

More tokens = better performance

I’ll give you one guess as to what started happening.

Employees started competing on who could use the most tokens. If it sounds reasonable to you, take it to the extreme. Some folks are literally just burning tokens for the sake of their numbers, not even for productive purposes.

This Meta analyst reports that they’re calling it “tokenmaxxing” and they even have an internal leader board for it:

Today’s newsletter is sponsored by my friends at Augment Code.

Augment was kind enough to give me early access to Intent, their next-generation tool for developing software.

All signs are pointing to the fact that the IDE is no longer the ideal place to create software.

Augment Code has jumped on this opportunity, and my first impressions are great.

Try Intent

Meta isn’t the only one counting tokens for performance

I like to look at big tech companies like Meta as a nice leading indicator, but they’re not the only one doing this already. If you’ve been reading the newsletter for a while, you’ll already know that other companies are incentivizing and measuring AI usage.

Of course, measuring tokens for performance with a “more is better” attitude is out there. Still, it seems others are following suit per :

Goodhart’s law

It does surprise me that Meta would go with something this easily gameable. Goodhart’s law says:

When a measure becomes a target, it ceases to be a good measure

Do you think token usage is a good metric to measure performance?

Expect to see more weird metrics like this

Measuring engineering performance is hard. Like, really hard. Measuring AI proficiency is harder.

On one hand, should it matter how much an employee is using AI if their overall performance is great? No, it shouldn’t matter. The only reason a company wants to push AI usage would be because they believe AI makes people more valuable, even if those people aren’t bought-in.

So they seek to measure things like total token counts and incentivize higher usage. Which creates a perverse incentive, and you end up with tokenmaxxing.

My hope is that they see the flaw with this metric pretty quickly, but honestly it wouldn’t surprise me if we saw more like it pop up soon.

I want to live in a world where engineers are measured on output rather than process, but we’ll see where the next year takes us.

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