Arnon

building in AI

[Arnon @ArnonCavaeiro]

we are in the bundling stage of the "bundling and unbundling" of tech roles

[Cory House @housecor]

Roles are collapsing.

If you're a "Software Engineer" today, you may be a "Product Engineer" soon - with many more responsibilities.

So:
- Learn design/UX/QA/PM skills.
- Focus more on the user, and less on the syntax.

Image via Gartner.

[eric zakariasson @ericzakariasson]

agents like to read a lot to gather context before and after working, so optimizing for reading makes a big difference!

if you're building agents, i highly recommend reading this (or giving it to your agent and ask it to implement the findings)

[Arnon @ArnonCavaeiro]

most agents reasoning with .md files generates results that are "well enough", which that is not sufficient in many business cases that can't afford guesswork at all.

[David K 🎹 @DavidKPiano]

Calling it now: the trend of putting control flow in markdown for agents (skills, prompts, etc) is going to look *really* silly in a couple months

[Obie Fernandez @obie]

Your job may be surviving on inertia. Your resentment won’t extend the runway. What do you actually want to do with the intelligence now at your disposal? https://t.co/vikawjCmZD

[Arnon @ArnonCavaeiro]

the death of software engineer (that is opening doors to product engineer)

[voxium @v0xium]

I am done with this shit. It is over. The state of engineering right now is horrible. It has been half a month since I started a new role at a big company. Nobody knows anything here. The specs, code, tests, PRDs, tickets, resolution of those tickets, reports, etc., everything is made by Claude Code. Nobody on my team likes this. They are being forced to ship as much as they can. I have heard multiple times from higher management that pushing code is not a bottleneck, so why are we slow? People are working 12 to 13 hours a day just to press enter. Nobody is reading anything. Humans in corporate are doing nothing on their own. Everyone, literally everyone, from an L1 to an L7 engineer here is doing the same thing. Talk to Claude. There is no sense of victory. Nobody is resolving bugs. In reality, nobody is thinking anymore. Everything is done by LLMs. It is so soul-sucking. I would not mind it, to be honest, if we were at least given the time to check out the code and see what is going where. But no, the goal is to just ship. No matter what happens.

[luis @microchipgnu]

true agi means inference at the speed of the trenches attention span

[Trevin Chow @trevin]

“Goal: optimize our website for agents. Score my site <url> using https://t.co/r32rnCGdg0. Identify the issues that apply directly to my site and don’t target issues that don’t matter for the type of site/product that it is. Open PR, babysit it and merge when ready and deploy. Repeat until we can get 100/100 score”

[Arnon @ArnonCavaeiro]

this 🫡

[Gergely Orosz @GergelyOrosz]

So many vendors are NOT getting this

I have one or two agents I use and like. For anyone else: give me an MCP interface to connect these agents to so I can use your service

Unless your a frontier AI lab, I prob don't want to use your agent, sorry

[Arnon @ArnonCavaeiro]

yep

[Justin Schroeder @jpschroeder]

Do you agree?

"Agents are deterministic software that harnesses the non-deterministic results produced by LLMs in pursuit of a business objective."

I think its spot on 🎯

[Arnon @ArnonCavaeiro]

we are meat proxy 😌

[Josh tried coding @joshtriedcoding]

alright whoever came up with the term MEAT PROXY is insane

[Naval @naval]

When software was expensive - thin, horizontal, best-of-breed software stacks extracted rents across every business.

Now that software is cheap - value moves to vertically integrated businesses that deliver opinionated end-to-end experiences.

[Arnon @ArnonCavaeiro]

@trevin do you use /ce-compound for sessions that are non-technical related?

asking because the skill itself says it is to be used for coding tasks.

[Arnon @ArnonCavaeiro]

for the builders out there, this is a great way to prompt better UI.

[🥔🥔🥔 @argofowl]

introducing https://t.co/20es64pAqZ

a dictionary for ui things you can see but can't name

made it because i'm primarily a designer, and my biggest resistance was always knowing what things are called when prompting my agents

it learns as people use it: every search teaches the site new words, and the built-in pocket dictionary grows with it

give it a try and let me know what you think

can't find something? dm me and i'll add it

i want this to be the lowest resistance resource you have

you can just build things

[Arnon @ArnonCavaeiro]

@BraceSproul good job 👏 how do you compare it with gbrain from @garrytan ?

[Arnon @ArnonCavaeiro]

@milstan loved this initiative! would you come to Berlin if there are people interested btw?

[Arnon @ArnonCavaeiro]

do not outsource understading.

[Addy Osmani @addyosmani]

https://t.co/YOd4ptcTeD

[Arvind Jain @jainarvind]

The approach here is exactly right. Pairing builders with domain experts, and grounding the work in the company’s real knowledge, systems, and workflow context.

Most AI initiatives don't stall for a lack of ambition or model capability, but because the people building the tools or workflows are too far removed from the friction of the actual work.

The breakthrough happens when you combine technical builders, domain experts, and the scattered knowledge, workarounds, context behind the workflow itself.

You can’t redesign the future if you’re too far removed from the friction of the present.

[Andrew Ng @AndrewYNg]

“Loop engineering” is a hot buzzphrase after mentions of it by Boris Cherny (Claude Code’s creator) and Peter Steinberger (OpenClaw's creator) went viral on social media. Loops are now a key part of how we get AI agents to iterate at length to build software. In this letter, I’d like to share my 3 key loops, shown in the image below, for building 0-to-1 products. These loops guide not just how I build software, but also how I decide what software to build.

Agentic coding loop: Given a product specification and optionally a set of evals (that is, a dataset against which to measure performance), we can have an AI agent write code, test its work, and keep iterating until the code is bug-free and meets its specification. This idea of closing the loop took off around the end of last year, and it has been a game changer in enabling coding agents to work longer productively without human intervention. For example, over the weekend, I was building an app for my daughter to practice typing, and my coding agent could easily work for around an hour, using a web browser to check what it had built multiple times before getting back to me, without needing my intervention.

The engineering loop executes quickly. Every few minutes, the coding agent might build and test a new version of the software. I hear frequently from developers who are finding new ways to engineer more effective engineering loops. This is an active area of invention!

Developer feedback loop: In this loop, a developer examines the current product and steers the coding agent to improve it. Last year, a lot of developers (including me) were acting as the QA (quality assurance) function for our coding agents, manually finding bugs and then asking the agent to fix them. But with coding agents much more able to test their own code, the amount of time we need to spend on this function has decreased significantly. This allows us to make higher-level product decisions, such as what key features to offer, where the UI needs improvement, and so on.

The developer-feedback loop operates over time intervals between tens of minutes and hours — that's how frequently a developer might review a product and give feedback. In the case of the typing app, I changed my mind a few times about the visual design, what cat costumes she can unlock as she learns (she loves cats), and the user flow for a grown-up to log in and steer the child's learning experience.

When a developer has a clear vision for what to build, it is still a lot of work to translate that vision into a specification for a coding agent to implement. Further, after the developer has seen an implementation, they might update (or perhaps clarify) the spec to steer it toward what they want. If you find that the system repeatedly runs into certain problems, building a set of evals for the agent becomes useful.

AI-native teams are increasingly using AI to help shape product direction, for example, automating the gathering and analysis of usage data, summarizing written and verbal customer feedback, or carrying out competitive analysis. However, for pretty much all the products I’m involved in, I see humans as having a significant context advantage over current AI systems — we know a lot more than the AI system about the users and the context the product has to operate in — and thus humans play a critical role. Many people describe this human contribution as “taste,” but I prefer to think of it as humans having a context advantage, since that gives us a clearer path to helping AI systems get better. This also speaks to why this step can’t be automated: So long as the human knows something the AI does not, human-in-the-loop is needed to to inject that knowledge into the system.

External feedback loop: This includes a wide range of tactics like asking a few friends for feedback, launching to alpha testers, or putting the code into production with A/B testing. These tactics are usually slow, rarely taking less than hours and sometimes taking days or even weeks. This data informs the developer vision, which in turn continues to drive the detailed product spec, which in turn drives the coding agent.

With coding agents speeding up software development, more engineers are starting to play a partial product management role. For many engineers who are growing into this role, the hardest part is shaping the product vision and striking a balance between building (bridging the gap between vision and spec) and getting user feedback to evolve the vision. It is important to do both!

I will write more about how to do this in future posts, but for now, I find it encouraging that engineers are playing an expanded role (just as product managers and designers now do more engineering).

[Original text: The Batch]

[Boris Cherny @bcherny]

As engineering, product, design, DS, etc. melt into a new kind of role, I was reflecting on what roles might look like in the future. For example, when I look at the Claude Code team I see what I think is five archetypes:

1. Prototyper: comes up with brand new ideas; churns out many ideas, most of which don't ship
2. Builder: quickly turns a prototype/idea into production-grade product/infra
3. Sweeper: cleans up the UI, simplifies the code and system, unships, optimizes performance
4. Grower: takes a product that has been built and iterates on it to improve Product-Market Fit
5. Maintainer: owns a mature system to make it secure, reliable, fast, and efficient as it scales

Many people span across 2 roles, and sometimes 3 roles. I also notice that these roles are not really tied to job function -- eg. across Anthropic, some designers match category 1, some 2, some 3; same for engineers, PM, DS.

A healthy team needs a mix of these, depending on the product:

- A product that is new and pre-PMF needs people that are strong at 1+2+3
- A product that is growing and has found PMF needs 2+3+4 and some 5
- A product that has strong PMF needs 3+4+5 and some 2

Maybe product roles of the future will look more like this, and less like the domain-specific roles of today?

[Arnon @ArnonCavaeiro]

"The mid-game is selling AI labor at human-labor prices.

The end-game is using that labor to build the network, context, and trust that become the real company."

[Justin @jrwoodbridge]

https://t.co/OXMV3L5rrh