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[TensorTonic @TensorTonic]

ML engineer interview in 2016:
- Explain linear regression
- What's overfitting
- Train a model in sklearn
- Get the offer

ML engineer interview in 2026:

[@levelsio @levelsio]

I asked Claude Code to make a new GTA: San Andreas mission where CJ has to vibe code a startup and then raise money from investors in SF

[Niels Rogge @NielsRogge]

MiniCPM-V 4.7 running locally at 100 tokens/second!

New multimodal models from @OpenBMB using only ~3 GB of memory.

Below, I showcase how to run it with @llama_cpp's web UI 🧵

[Hon. Takudzwa Titus Nyanhanga @SirTitusVeTech]

Introducing TapMac by @fariraijames

Tap your MacBook to control it. No extra hardware needed.

Double-tap left of the trackpad to mute your speakers, or right of it to mute your mic.

You get six tap gestures in total, and each one can trigger a shortcut, open an app, run a Shortcut, control media, or sleep the display.

https://t.co/8Qp9BBZAH1

[Jerry Liu @jerryjliu0]

Benchmarks are becoming the core competency of any company.

Our company mission is to build the world’s best models for document processing. This also means that we have to build the world’s best benchmarks for doc parsing and extraction.

It’s been incredible to see the adoption of ParseBench the past few months. We have 26k+ monthly downloads on Hugging Face, 147+ models benchmarked, and has been cited by other labs in the space (Cohere, Mistral, etc.).

We use this as a benchmark to push our own internal capabilities within LlamaParse, and also to track the advancement of recent frontier/open-weight models (most recently, Gemini 4 Argon and Muse Spark/Wonder).

We’re happy to help apply this to any company looking at evaluating document processing solutions. Even if you don’t use our docs, you may find our metrics for evaluating various dimensions (e.g. tables, reading order, layout) useful.

Check out ParseBench: https://t.co/PWczfhp0OX

Hugging Face: https://t.co/meFLEzzgD8

[Sahil Lavingia @shl]

There is no moat

[Andrej Karpathy @karpathy]

Dusting off this tweet from April because this gap in shared understanding of LLM capability is *widening*. It's now less "two groups of people speaking past each other" and more a sharp funnel.

- Napkin math somewhere around 6B people (~75% of the population) have barely come in contact with LLMs at all.
- Around 1-2B (~20%) are casual and infrequent users of free-tier ChatGPT-like products. This group sees derpy chatbots and treats them a bit like a better Google search, a writing aid, or etc. Maybe an agent tries to book you a flight. I have non-tech friends who (reasonably, imo) say they have not much use for it at all. Even many professionals outside of math&code are in this tier. For example, execs and many other functions spend a lot of their time talking to other people, so while they understand what is happening intellectually, it is still second-hand and a bit abstract.
- Now we get to professional use of frontier-grade LLMs in math&code. Somewhere around 20M people (0.2%) see first-hand that large, complex projects that used to take them weeks/months can now be completed by agents with a prompt. Building apps, copying apps, translating apps, decompiling apps from binaries... This has all happened very quickly and recently - less than 1 year ago, I was writing code manually by hand, typing memorized computer code commands into a code editor character by character, occasionally pressing Tab to autocomplete a little chunk of code.
- And finally we get to the ~5,000 people (~0.00006%) with access to frontier-grade systems internally. The external world has seen the preview. It looks like swarms of thousands of agents collaborating over weeks on software mega projects: minting zero days, running cyber attacks and defenses at machine speeds, discovering new science, advancing the frontier of mathematics. Things that would have taken top professionals in the industry years of work. Meanwhile, human review and comprehension are starting to fall behind. For example, people are still involved in the "archeology" of the OpenAI-HF incident from many months ago. Mathematicians may be poring over the 722 manuscripts on frontier mathematics for a while.

The funnel is driven by a combination of factors:
1. The impact scales with ambition, problem size, and horizon. A question with a paragraph answer barely stresses the system. You need a reservoir of big, difficult problems that you really care about. This aspect drives the consumer / professional dimension of the funnel.
2. The jaggedness of the system (which I have written about a lot separately). Capability peaks in domains that are digital, verifiable and economically valuable. This is because LLM capability emerges from reinforcement learning on verifiable rewards on a curated environment mixture driven by revenue potential. This aspect primarily drives the area (e.g. math&code) dimension of the funnel.
3. Access. Free-tier, paid-tier, internal.

So this is the weirdness of the moment. The general public has mostly not interacted with these systems. When they have, it looks like a derpy chatbot. The majority of professionals still see only a modest uplift. And a small sliver of professionals are experiencing the vertigo of the curve going vertical. And it is all happening at the same time.

[staysaasy @staysaasy]

The degree to which you are awed by AI is perfectly correlated with how much you use AI to code.

[OpenAI Developers @OpenAIDevs]

The attention to detail is a little concerning.

[Tibo @thsottiaux]

Blinked and it’s already Saturday

[Niels Rogge @NielsRogge]

It’s the most Google thing to announce Gemini when they already have Gemini

Too many product teams result in this mess

[Lincoln 🇿🇦 @Presidentlin]

Claude is good all the time, and all the time, Claude is good

Claude is good all the time, and all the time, Claude is good

Everyone

Claude is good all the time, and all the time, Claude is good

One more time now

Claude is good all the time, and all the time, Claude is good

[kache @yacineMTB]

There is an optimal amount of courage x intelligence. You can't be so intelligent that it is worth it for you to spend time thinking. You need to be just a little retarded. And have unrealistic expectations of your ability. You also need to know that you're retarded, so you use simple stupid tricks to meet the expectations of your abilities

[dax @thdxr]

ai has massively accelerated how obvious this is

all these different roles are compressing into just one

be useful, that's it

[Matthew Berman @MatthewBerman]

setting up a new computer:

> install 1Password
> install ChatGPT...it installs everything else

[float @notesbyfloat]

A man cannot prompt the same agent twice, for it is has compacted and is not the same agent, and he is not the same man

[Mitchell Hashimoto @mitchellh]

I just called Chungus (my OpenAI Dot) on the phone while picking up mexican food and asked it to transfer some Rex benchmarks from last night on my Mac Studio to my MacBook Pro via Tailscale and I just got to my desk and there they are. What a fucking time to be alive.

[OpenAI @OpenAI]

GPT-6 and Intelligent UI, now rolling out in ChatGPT for everyone.

Intelligent UI in ChatGPT delivers fast, interactive answers that make everyday questions more visual, complex topics easier to grasp, and interactive tools for your task available on the spot.

[dax @thdxr]

how can she sleep with you when she's seen you beg for a reset

[Yuchen Jin @Yuchenj_UW]

I haven’t touched Claude Code or Codex CLI in a while.

The terminal era is over imo. It's the wrong interface for coding agents. Tabs are ephemeral, but context is persistent, and managing 30 tabs is pure cognitive overhead.

I don’t really need an IDE like Cursor either. I rarely navigate the whole codebase anymore.

The new primitive is the agent, not the file.

(Codex desktop app is the best agentic UI for now. But we’re still early.)