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Amazon pilots ad services in ChatGPT

https://www.marketingdive.com/news/amazon-pilots-ad-services-in-chatgpt-what-marketers-need-to-kn...
1•thm•36s ago•0 comments

Glq a port trellis quantization of large language models as a vLLM plugin

https://github.com/cnygaard/glq
1•acd•53s ago•1 comments

Show HN: Calmscroll – a reader that shows the current paragraph in the library

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1•arkhan89•1m ago•0 comments

The UK's Standout Success at Schooling Immigrant Kids Baffles Experts

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Shopify moves back to Native from React Native

https://shopify.engineering/back-to-native
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iPhone Duo will have a screen crease over time

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An Overview of Large Language Models for Statisticians

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1986–1988 radioactive milk distribution in Mexico

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Ryanair will dispute passenger's claim he was sucked out of window

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Magic 8ball

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Japan seeks change to new UN world map over Russia 'misconception'

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1•prmph•6m ago•0 comments

What we learnt from OpenAI's hack of Hugging Face

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SayScroll – A Teleprompter built for people who were never meant to be on camera

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AI agents helped me win a Kaggle silver medal – and how they failed me

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Economics of Open Weight Inference [pdf]

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Nastystereo.com

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Arc Blanc: a real time ocean simulation framework

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The case of the progress callback that never got called when progress happened

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China just missed the income cutoff to become a high-income country this year

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1•alphabetatango•14m ago•0 comments

On-Demand Compute: Instant compute for your most intensive workloads

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2•kvruizrogelj•15m ago•0 comments
Open in hackernews

I Replaced My Devs with Agents – Part 2: What Happened to the Team

https://easylab.ai
2•buzzbyjool•1y ago

Comments

buzzbyjool•1y ago
Follow-up to Part 1, where I explained how we rebuilt our dev process around LLM agents at Easylab AI and stopped writing most code by hand.

The original post sparked a lot of questions — the most common being:

“Okay, but how did your developers react?”

Here’s a breakdown of what actually happened inside the team — who stayed, who didn’t, and what new skills emerged.

⸻

Some embraced it. Some left. That’s okay.

When we committed to building with agents — not just using LLMs for autocompletion, but making them first-class executors of logic — not everyone was thrilled.

Some engineers were fascinated. They saw the shift coming and wanted to be ahead of it. They became architects of multi-agent workflows, prompt designers, QA strategists, validators.

Others didn’t want to work that way. They liked writing every line, owning every detail, and were (understandably) uncomfortable giving up control to a system that feels less deterministic.

They moved on. We didn’t push them.

Like every evolution in software tooling, this one came with a natural selection effect. Not better or worse. Just different skillsets, different energy.

⸻

This isn’t no-code. It’s new-code.

Some assumed we were just automating CRUD. That’s not what happened.

The tools we use today — Claude 3.7, DeepSeek, bolt.new, role-based agents, memory stacks — aren’t trivial macros. They’re a new level of abstraction. They reason. They refactor. They test. They fail with style.

You don’t “ask the AI to do it.” You engineer constraints, context, fallbacks, tooling, and create robust systems through language.

At Easylab AI, we use context protocols, Redis-based memory layers, and model routing logic based on latency and task weight. It’s not less technical — it’s just built differently.

⸻

Did their skills atrophy?

Actually, the opposite.

Sure, they’re not practicing DSA interview puzzles every day. But they’re building systems that can write tests, simulate failure, and self-correct.

They’re learning new skills you can’t yet Google: • Prompt minimalism • Agent composability • Multi-agent state consistency • Prompt-based debugging

They think more like staff engineers than syntax solvers.

⸻

This is abstraction, not disappearance

The fear that “AI replaces engineering” misses the nuance.

This isn’t magic. It’s not cheating. It’s just abstraction — like every wave before: • Assembly to C • C to Python • Python to Terraform • Terraform to prompt-based execution

As Jensen Huang (NVIDIA CEO) said earlier this year:

“English is now the world’s most popular programming language.”

He’s not wrong. We’re just learning to write instructions that build systems — without the middle step of syntax.

⸻

One more thing

Some developers left. Most who stayed leveled up. And today, no one wants to go back.

That tells me something: It’s not easier work. It’s better work.

Happy to answer more if folks are curious.

falcor84•1y ago
I'm not clear - is this comment the actual post, while the link that you shared is irrelevant? If so, it would have probably been more appropriate to submit this as an "AMA:" without a url.
buzzbyjool•1y ago
Hi thanks for your comment, honestly I don't know how to do it. Thanks
falcor84•1y ago
Oh, it's just that you can make a submission without anything in the 'url' input. Here are a couple of examples:

https://news.ycombinator.com/item?id=15853374

https://news.ycombinator.com/item?id=43363056

buzzbyjool•1y ago
thanks
JohnFen•1y ago
> Some developers left. Most who stayed leveled up.

"Leveled up" is a subjective, loaded term. I assume what you mean here is "adapted to your way of doing things."

> And today, no one wants to go back.

Well, of course, because those who would have wanted to go back already left. This appears to be selection bias more than evidence that your approach is a good one.

To be clear, I'm not trying to imply that your approach isn't a good one. I'm just saying that the devs who remained not wanting to go back isn't evidence that it is.

buzzbyjool•1y ago
You’re absolutely right to call that out — and I appreciate the thoughtful framing.

“Leveled up” is subjective, yes. What I meant more precisely is this: the devs who stayed stopped spending time on tasks like writing boilerplate logic or tweaking form validation, and started focusing on higher-order thinking — designing agent workflows, debugging reasoning paths, writing specs that are machine-parsable, and thinking in systems rather than syntax. That shift, in terms of skill depth and adaptability, is something I genuinely view as a level-up. But I agree, it’s through the lens of our environment.

And yes — absolutely fair on the selection bias. When I say “no one wants to go back,” I don’t mean it as proof the approach is universally better. It’s just true for our current team, within the culture and processes we’ve chosen to embrace. Those who didn’t align with this way of working left early — and I don’t hold that against them.

So your comment is a valuable nuance: internal satisfaction is a necessary condition for success, but not a sufficient one. Our team’s enthusiasm is a sign that the model can work — not that it will for everyone.

Thanks for calling it out clearly.