adam

Senior Software Developer and too tall. bc1p2g7sfrp0u2r69vue5kdyvydqur5z8sjllqldh8ss3mzexmknkynstt7yrl

[adam @TheRealAdamRobb]

@popwavesx @grok find me this product

[adam @TheRealAdamRobb]

I seek none of your approval but all of your love.

[adam @TheRealAdamRobb]

A butt fugly Turkey Vulture

[Samay @Samaytwt]

Unpopular opinion: "AI makes everyone a developer" is true the same way "cameras makes everyone a photographer"

[adam @TheRealAdamRobb]

Brutal

[Benjamin De Kraker @BenjaminDEKR]

Former Codex engineer: "we believe that software is solved"

[adam @TheRealAdamRobb]

The Ai just teaches you the right thing at scale.

[adam @TheRealAdamRobb]

https://t.co/3L9ROE3vW6

[adam @TheRealAdamRobb]

•A language agents write natively (not transpiled from Python)
•ZK proofs for trust
•Confidence as a first-class type
•Idempotency as a language primitive
•Memory scopes with fuzzy recall

Working on it

[Chris McCord @chris_mccord]

The Erlang VM has all these incredible things. Did you know you can boot a `:peer` node as a VM within the VM, fully meshed? Enter `FlyDeploy.BlueGreen` - "hot bluegreen" deploys where we spin up the new incoming VM, then cutover seamlessly:
before/after code changes:

[Hugo Baraúna @hugobarauna]

OpenAI built a cool agentic project using Elixir! 🔥

#ElixirLang

[Naval @naval]

Is Traditional Software Engineering Dead?

“Does this mean that traditional software engineering is dead? Absolutely not. Software engineers—even the ones who are not necessarily tuning or training AI models—these are now among the most leveraged people on earth. Sure, the guys who are training and tuning models are even more leveraged because they’re building the tool set that software engineers are using.

But software engineers still have two massive advantages on you. First, they think in code, so they actually know what’s going on underneath. And all abstractions are leaky. So when you have a computer programming for you—when you have Claude Code or equivalent programming for you—it’s going to make mistakes.

It’s going to have bugs. It’s going to have suboptimal architecture. So it’s not going to be quite right. And someone who understands what’s going on underneath will be able to plug the leaks as they occur.

So if you want to build a well-architected application, if you want to be able to even specify a well-architected application, if you want to be able to make it run at high performance, if you want it to do its best, if you want to catch the bugs early, then you’re going to want to have a software engineering background.

The traditional software engineer is going to be able to use these tools much better. And there are still many kinds of problems in software engineering that are out of scope for these AI programs today. The easiest way to think about those is problems that are outside of their data distribution.

For example, if they need to do a binary sort or reverse a linked list, they’ve seen countless examples of that, so they’re extremely good at it. But when you start getting out of their domain—where you have to write very high-performance code, when you’re running on architectures that are novel or brand new, when you’re actually creating new things or solving new problems, then you still need to get in there and hand code it.

At least until either there are so many of those examples that new models can be trained on them, or until these models can sufficiently reason at even higher levels of abstraction and crack it on their own…

And remember: there is no demand for average. The average app—nobody wants it, at least as long as it’s not filling some niche that is filled by a superior app. The app that is better will win essentially a hundred percent of the market. Maybe there’s some small percentage that will bleed off to the second-best app because it does some little niche feature better than the main app, or it’s cheaper, or something of the sort.

But generally speaking, people only want the best of anything. So the bad news is there’s no point in being number two or number three—like in the famous Glengarry Glen Ross scene where Alec Baldwin says, “First place gets a Cadillac Eldorado, second place gets a set of steak knives, and third place you’re fired.”

That’s absolutely true in these winner-take-all markets. That’s the bad news: You have to be the best at something if you want to win.

However, the set of things you can be best at is infinite. You can always find some niche that is perfect for you, and you can be the best at that thing. This goes back to an old tweet of mine where I said, “Become the best in the world at what you do. Keep redefining what you do until this is true.”

And I think that still applies in this age of AI.”

[Peer Richelsen @peer_rich]

10,000 employees 6,000 employees

40% smaller

[adam @TheRealAdamRobb]

@SoFi when crypto withdrawals?

[Aakash Gupta @aakashgupta]

John Collison told a London audience last year that Stripe averaged 8,015 pull requests per week across ~3,400 engineers. That’s 2.3 PRs per engineer per week, actually below the industry average of 3.5.

Now 1,300 of those weekly PRs are fully AI-generated. Zero human-written code. That’s the equivalent output of ~565 engineers, running 24/7, triggered by a Slack message, spinning up isolated dev environments in 10 seconds, and producing review-ready code that passes CI.

Stripe’s median engineer total comp sits around $270K. Those 565 “phantom engineers” would cost ~$150M per year in compensation alone. Instead, they run on compute that costs a fraction of that.

And this went from 1,000 to 1,300 in a single week. A 30% increase in AI engineering output with no hiring pipeline, no onboarding, no equity grants.

The companies that figure out how to build this internal tooling layer, the MCP servers and pre-warmed sandboxes and 400+ tool integrations, are creating a compounding advantage that gets wider every quarter. The companies waiting for off-the-shelf solutions will be buying what Stripe already built three generations ago.

Every engineering leader should be reading the blog post, then asking their team one question: what percentage of our PRs could look like this in 12 months?

[adam @TheRealAdamRobb]

@AviFelman @jvb_xyz If you guys need help with this. I'm a very good dev.

[Mitchell Hashimoto @mitchellh]

We need corporate PR speak like this to stop. Execs need to talk to people like people. The dev community is filling in the blanks anyways, whether they're right or wrong. Just own your direction and move forward, don't hide behind corporate cowardice. https://t.co/hFQtJD7ZHH

[Poaster Fluffhead @MinnieFluff]

People got stuck on the "French Canadian Buckethead" aesthetics, but Angine de Poitrine is absolutely crushing prog akin to early Phish or Zappa.

Listen to the last minute of the performance you keep seeing, it's fantastic!

Can't wait to see them when they get back from Europe!

[Mark Ericksen @brainlid]

Introducing Sagents -- an open-source #ElixirLang framework for building #AI agents with human oversight, composable middleware, and real-time @ElixirPhoenix LiveView integration.

Built on OTP. Each agent is a supervised GenServer.

Watch the full demo: https://t.co/TLXPMHixY4

[Elixir Tip @ElixirTip]

Why Elixir is the best language for AI by @josevalim https://t.co/lyUx7MdPrl