nichochar

i open-sourced the app, although you can make your own quite trivially https://github.com/nichochar/ari

i borrowed the SOUL.md concept from openclaw, but repurposed as TUTOR.md

it uses codex locally so you can use your subscription credits rather than an API key

it's very fun!

my read is that PG is one of the lucky people who was very successful before kids, so he could just put YC in self-driving mode by handing off to the next generation and his life's work still feels meaningful.

I think it's very different if you have not achieved your life's work yet.

Healthy things. For me its riding a bike, which is magical because i) its solo activity so my brain meditates ii) its healthy but no shocks on my knees and body iii) its technology free, no notifications on a bike

these people have ai too. AI increases competition amongst the ambitious, so it doesn't solve the problem

i really like your framing, this encapsulates the problem very consisely:

> Time is finite. Desire / drive are not. Solve for X

(original author)

this is true for me, wouldn't be remotely possible without my wife.

yep all of this resonates

Surprisingly pragmatic and info packed article..

Kudos to databricks, I also find it interesting that such different companies (Stripe, Ramp, Databricks) are all building the exact same internal tools.

I think building companies is going to look more generic in the future because intelligence is an API now.

yep, that's what i qualify as "not an advantage".

It's totally fine if thats what your company is like, but the labs have an unfair advantage.

This was often true when writing code manually to be fair.

You could get to "something that works" rather fast but it took a long time to 1) evaluate other options (maybe before, maybe after), 2) refine it, 3) test it and build confidence around it.

I think your point stands but no one really knows where. The next year or so is going to be everyone trying to figure that out (this is also why we hear a lot of "we need to reinvent github")

We ran some tests at mocha (we have a coding agent with our own harness to build web apps, with a lot of tools and medium length tasks (3min to 10min).

Our notes:

Sonnet 4.6 feels like a fundamentally different model than Sonnet 4.5, it is much closer to the Opus series in terms of agentic behavior and autonomy.

Autonomy - In our zero-shot app building experiments, Sonnet 4.6 ran up to 3-4x longer than Sonnet 4.5 without intervention, producing functional apps on par in terms of quality to the Opus series. Note that subjectively we found Opus 4.5 and 4.6 are better "designers" than Sonnet 4.6; producing more visually appealing apps from the same prompts.

Planning / Task Decomposition - We found Sonnet 4.6 is very good at decomposing tasks and staying on track during long-running trajectories. It's quite good at ensuring all of the requirements of an input prompt are accounted for, whereas we were often forced to goad sonnet 4.5 into decomposing tasks, Sonnet 4.6 does this naturally.

Exploration - In some of our complex "exploration" tasks (e.g. cloning/remixing an existing website), Sonnet 4.6 often performs on par or better than Opus 4.5 and 4.6. It generally takes longer, and takes more tokens, though we believe this is likely a consequence of our tool-calling setup.

Tool-use - Sonnet 4.6 seems eager to use tools; however, we did find that it struggles with our XML-based custom tool use format (perhaps exclusive to the format we use). We did not have a chance to assess with native tool use

Self-verification - Similar to Opus 4.5/4.6, Sonnet 4.6 has a proclivity for verifying it's work.

Prompting - We found Sonnet 4.6 is very sensitive to prompting around thinking, planning, and task decomposition. Our prompt built for sonnet 4.5 has a tendency to push sonnet 4.6 into incredibly long thinking and planning loops. Though we also found it requires significantly less careful and specific instructions for how to approach problems.

How are we thinking about this:

We can't launch this model day 0, it requires more changes to our harness, and we're working on them right now.

But it reminds me a bit of 3.5 to 3.7 --> It's a pretty different model that behaves and responds to instructions in new ways. So it requires more tuning before we can extract its full potential.

I think Karpathy[1] summarized why he thinks this is the case quite well (as described he was himself hyping it up a bit much, but there are some foundational reasons why it's a very interesting experiment).

[1] https://x.com/karpathy/status/2017442712388309406

We're building an AI powered app builder. We use elixir, phoenix and of course OBAN.

It feels like such a super power. What you're describing is particularly important in the era of long running AI processes. Something as simple as running a deploy creates pressure on your agent orchestration. But if everything is powered by OBAN you have wonderful ways to build durability.

By the way, it's all "free" and built-in.

In other language ecosystems, people literally pay for durability that has this shape (like temporal)

Little to no evidence was presented.

This is vibe argumenting.

The effort described in the article is maybe a couple hours of work.

I understand the "enjoy doing anyway" part and it resonates, but not using AI is simply less productive.

i knew simon would be top comment. it's not an empirical law

I'm building an app builder (getmocha.com) and one of my favorite use cases I have seen is "small private social network":

some people are building custom, tailored social networks only available to their family, church, community, sports team, school, etc...

This was previously impossible but now AI changes that. I don't know if it will materialize, but a more federally distributed web with tons of small private social networks could be a future of healthy social

Congrats on the launch, spreadsheets are getting lots of AI upgrades these days, exciting!

If i were to try this out with some somewhat sensitive company data, what is the security profile of this? Would it potentially leak the data to MCP servers? Do I have control?

Elixir and Phoenix are very underrated.

It combines the "opinionated" aspects of ruby and rails and the power of erlang. The BEAM is like no other runtime and is incredibly fun to work with and powerful once you get how to use genservers and supervision trees.

We use Elixir for Mocha, and my one issue with it (I disagree with OP on this) is that live-view is not better than React for writing consumer grade frontends. I wish Phoenix took a much stronger integration with React approach, that would finalize it as the top choice for a web stack.

I literally cannot read this article because of a paywall.

The title is deeply ironic.

yes same category