Paul Meller

Building Applied AI. Formerly Global Head of Engineering at EY, CTO at Dow Jones & The Wall Street Journal, and ex-Googler. Cloud, AI and other buzzwords.

[Malte Ubl @cramforce]

Brex is using just-bash deep into their production-critical financial agents. Really strong post here about impact on cost, latency, and quality.

[OpenRouter @OpenRouter]

Introducing the Fusion API, the smartest compound model in the market.

Fusion achieves Fable-level intelligence at half the price.

How it works 👇

[Peter Steinberger 🦞 @steipete]

@MatthewBerman /goal refactor until you are happy with the architecture. ensure you live test after each significant step and autoreview/commit. track progress in /tmp/refactor-{projectname}.md

[Paul Meller @paulmeller]

@johnyeo_ Try https://t.co/NVSs30V1Y1

[Paul Meller @paulmeller]

Congratulations on this! Already seeing HUGE speed improvement.

[Jerry Liu @jerryjliu0]

We've created the world's fastest PDF parser ⚡️

And it's more accurate than any other open-source, model-free PDF parser out there (pymupdf, pypdf, markitdown, pdftotext, opendataloader, pymupdf4llm)

Introducing LiteParse v2 - we rewrote the entire library into Rust and adapted it as native packages for Python and Node.

It supports 50+ different document types, can be triggered directly or installable directly within your favorite AI agent.

Blog: https://t.co/ckb0G73ESs

Repo: https://t.co/JNER0mVcB8

[Cloudflare @Cloudflare]

Cloudflare's security team spent the last few weeks testing Anthropic's Mythos against fifty of our own repositories. What we learned about offensive AI, why faster patching is the wrong reaction, and what the architecture around vulnerabilities has to look like next. https://t.co/RSrRtIhgaV

[Paul Meller @paulmeller]

@gabepereyra @loubohan Check out https://t.co/FOb7gAOC15 - addresses every challenge listed. CMA for everyone!

[Kimberly Tan @kimberlywtan]

We @a16z decided to compile hard data on what’s actually working in enterprise AI.

* Nearly 30% of the Fortune 500 and ~20% of the Global 2000 are live, paying customers of the leading AI startups. This goes counter to the MIT statistic that 95% of AI pilots are failing in the enterprise

* Coding, customer support, and search are the use cases with clearest enterprise demand, and adoption isn’t just concentrated in traditionally tech-forward sectors.

* Models are improving very quickly at economically valuable tasks, based on @OpenAI's GDPval. We’re tracking GDPval closely to determine where model capabilities will enable the next set of breakout enterprise AI companies.

Read more from our enterprise AI report, linked in the comments

[ollama @ollama]

Ollama is now updated to run the fastest on Apple silicon, powered by MLX, Apple's machine learning framework.

This change unlocks much faster performance to accelerate demanding work on macOS:

- Personal assistants like OpenClaw
- Coding agents like Claude Code, OpenCode, or Codex

[Paul Meller @paulmeller]

Maxing out a $200 Claude Max account in a given week is a true badge of honour

[Julien Bek @JulienBek]

https://t.co/hsKrbn1gEq

[Matan Grinberg @matanSF]

one of the largest enterprise deployments of fully autonomous agents

"adoption took off like wildfire [...] EY had to throttle traffic to Factory and Droids and restrict which repos could connect"

the way we build software is changing before our eyes

[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.”

[Himanshu @himanshutwtxs]

https://t.co/0qcaPF7bGi

[Paul Meller @paulmeller]

These aren't user-facing labels. They're the raw signals Gmail's machine learning uses under the hood and not publicly documented.

[Paul Meller @paulmeller]

Gemini just casually surfaced a hidden layer of internal labels that Google's ML models use to categorise emails in Gmail.

[Paul Meller @paulmeller]

Paste this into Gmail search: label: (^cob_sm_order OR ^cob_sm_cl_jc_order OR ^cob_sm_cl_llm_order)

Every order confirmation you've ever received. Instantly.

[Heinrich @arscontexta]

https://t.co/XmYSO3mC8f

[Boris Cherny @bcherny]

@big_duca Someone has to prompt the Claudes, talk to customers, coordinate with other teams, decide what to build next. Engineering is changing and great engineers are more important than ever.

[Naval @naval]

Vibe coding is the new product management.

Training and tuning models is the new coding.