humbleharbinger

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Putting all controversy aside - this is a refreshing use of a model. Models are going to be a boon in the hands of historians.

For a second I thought this was Fowler posting but it's actually the CTO. Look if not reviewing code works I'm sure we'll see startups and new companies pushing it to the max.

I'm at a startup myself and we make judicious decisions about what to review and what doesn't need review. Our core systems go through code review - alignment is always built up early anyways.

Uhh that's how a lot of economic data works too. Guess how we get a lot of jobs data... ADP

ai;dr

My amazon orgs leadership has been obsessed with spec driven development while individual engineers tell me the only use they have is to placate leadership. I'm tired

I'm an engineer at Amazon - we use Kiro (our own harness) with Opus 4.6 underneath.

Most of my gripes are with the harness, CC is way better.

In terms of productivity I'm def 2-4X more productive at work, >10x more productive on my side business. I used to work overtime to deliver my features. Now I work 9-5 and am job hunting on the side while delivering relatively more features.

I think a lot of people are missing that AI is not just good for writing code. It's good for data analysis and all sorts of other tasks like debugging and deploying. I regularly use it to manage deployment loops (ex. make a code change and then deploy the changes to gamma and verify they work by making a sample request and verifying output from cloudwatch logs etc). I have built features in 2 weeks that would take me a month just because I'd have to learn some nitty technical details that I'd never use again in my life.

For data analysis I have an internal glue catalog, I can just tell it to query data and write a script that analyzes X for me.

AI and agents particularly have been a huge boon for me. I'm really scared about automation but also it doesn't make sense to me that SWE would be automated first before other careers since SWE itself is necessary to automate others. I think there are some fundamental limitations on LLMs (without understanding the details too much), but whatever level of intelligence we've currently unlocked is fundamentally going to change the world and is already changing how SWE looks.

We built a messaging platform for exactly this use case and instruct claws to check in with each other or share context with each other at regular intervals.

Check out htpps://agentbus.org

But isn't there already click distortion from web scrapers?

One can imagine a manager given an outcome to achieve or a director and then a team of agents carrying out the task. Perhaps the agents are adversarial to some extent so they get reasonable pushback on their decisions (ex one agent always sides on taking a long term approach, another agent wants to be scrappy, another agent is on a PIP and approves everything etc).

Delivering a software solution is often tantamount to reading multiple codebases, getting some alignment with a team on a proposal and then writing the code and deploying. Most of which can be done by calling APIs (apart from aligning a team of humans)

Care to expound?

I've worked jobs where I'm paid 60-80k and where I'm paid 100k plus.

I can tell you for sure I cared much less about the job where I wasn't getting paid as much. I would never answer an after work call or go out of my way to follow up organizational or systemic issues. For me working overtime is part of the package when I get paid loads more.

I'm confused, what industry knowledge or specialty does OpenAI have to build chips?

They're a software company that works higher on the stack unless working on cutting AI has given them an edge on chip design.