“Humanity isn’t ready for that much software” Very true. I’m not sure, where the new bottleneck is located. Currently trying to build nicer, beautiful things and make MVPs a bit less messy. But not really settled on the right way, yet

The public perception around this topic seems to shift. Slowly, but some hope exists that we will end up in a better position someday ssir.org/articles/ent...

🚀 Big news for TRL users with AI agents: We just released the trl-training SKILL in TRL v0.29.0 🤗 Agents can now use the TRL CLI as a structured capability:

I see AI-forward teams spending a ton of time building bespoke tools to help them develop more effectively using agents (see: proliferation of CLIs to work with git worktrees), trying out different models, testing our new workflows.

We’re excited to share a new preprint that may reshape how humans write - TreeWriter: AI-Assisted Hierarchical Planning and Writing for Long-Form Documents 🌳 📃 arxiv.org/abs/2601.12740 Demo: drive.google.com/file/d/1R-X6... Live demo: treer.ai [1/5]

This study presents "abstractive red-teaming," a method for pre-deployment audits of language models, identifying problematic query categories causing character violations. This innovation improves model reliability by exposing risks like illegal behavior and sexism. https://arxiv.org/abs/2602.12318

Quantitative measures are always a dangerous yardstick. You can describe severe diarrhoea as "an explosion in bowel productivity", but you wouldn't say it correlates positively with gut health.

Think of Alex and Palantir what ever you want; this is probably the best 3D-chess move you could make...

Started a collection of LLM use-case dataset on HF. Key entrances: the super cool ZenML LLMOps use-case database and Anthropic's new interview dataset. Details below, any other things to include? huggingface.co/collections/...

One of the few examples of how to use AI tools in a creative process that's fully curative instead of generative. Describing images, OCRing documents and make it searchable. www.youtube.com/watch?v=9ngb...

Classrooms as cluster-mixing machines because of poor air? Color me surprised. </s> This Nat. Comm. study found: In schools, it is not only direct contact, mainly the “shared classroom exposure.” Poor air quality increases the risk exponentially. www.nature.com/articles/s41...

Teile alle genannten Punkte, glaube aber, das viele der aktuellen Probleme eher auf “you are holding it wrong” fußen. Beispiel Bildungswesen: copy-pasta von KI-Antworten ist eine dumme Idee (und das ist so offensichtlich, das ich mich frage wieso man das diskutiert).

Beautiful typographic map

Creative work/parenting 2025 festive edition: ChatGPT creates the Mandala for K3 and you need Gemini nano banana to get the spelling right.

Really nice how you can get music notation for your kids to play along for the holidays:

Finetuning for coding tasks probably isn’t worth the effort. Your own model can be great for classification, dense vector search or other small tasks. For complex stuff like ‘coding’ in a broader sense, it’s not holding up to the promises.

Skimming the comments, acceptance is still far away

The water thing is mostly overblown andymasley.substack.com/p/the-ai-wat... The electricity and CO2 impact is real and deserves serious attention, which is why it's annoying that the water issue steals so much of that attention

Building datasets to train smaller, task-focused models used to be incredibly time-consuming. Very excited to see SAM3 massively lower that barrier. Describe the class you want to detect and get annotated datasets automatically! Try it yourself: huggingface.co/datasets/uv-...!

Yes, LLMs can automate parts of consumer research. But if you tell ChatGPT your SaaS idea and ask for feedback, don’t be surprised when it pats you on the back. The real question is: how do you get beyond the fluff?

Interesting point (still problematic tasks) but wrong conclusions. I’m again and again puzzled how intelligent people neglect the utility of LLMs/VLMs. They are far from flawless but can do so much already. If their abilities don’t overlap with your problem space, don’t use them there.

Have to dig deeper and maybe running a few experiments on this. But the abstract reads very promising: arxiv.org/abs/2509.24372

As a data guy who analyzes all sorts of effects on and from C19 since early 2020, I second this

It‘s unbelievable. Hunderts of “yeah, me too” replies and quotes. I mean, there are points to be concerned about the LLM/AI space. But this 🙈 tactic

Probably the most interesting podcast on AI I listened to in the last months www.youtube.com/watch?v=2Ngd...

This channel has some nuggets! Relatively new and only a few videos so far, but I read and watched the dbt and DuckDB related ones and they get much deeper than the usual 'hey look, cool new tools' fluff.

And in Germany you can directly map the medium of the back and forth to an industry: Fax: healthcare email: public Slack: startups Teams: DAX Whatever the google tool is called this week: SME Discord: 🙃

Today is the new semester for @db.cs.cmu.edu's Intro to Database Systems! We're going harder into material than before. More challenging projects but you can use LLMs to help. We also have 10min talks each Wed from leading DB companies: 15445.courses.cs.cmu.edu/fall2025

Happy Friday 🌍! We are giving away one copy of 'Spatial Linear Models for Environmental Data'. To enter, simply follow me and re-post this by the end of Sunday, August 17. #spatial #environment # #data #StatsSky routledge.com/9780367183349 not associated with bsky

You can’t park here

The Open Model release from OpenAI is a good reason to check out local AI models on your device. Is it the best model out there? Definitely not, even in it's parameter class, it may not be THE best, benchmark wise (aside: OpenAI build sizes that are uncommon, so direct comparison is harder), but

I honestly think, we need to adapt our notion of reality. The tools to reshape it are here already. We can try to regulate them, we can mourn for our loss or we can adapt. I would see it as an exercise in media literacy... bsky.app/profile/mart...

Dropping generated videos is a good test if you want to know what other platforms your followers use. Bluesky has a large user base of AI sceptics and seems generally isolated from the hot topics on the video platforms (not judging, but the latter is probably a good thing).

Need to read more carefully. Was watching Sabine's recent video and was expecting to learn about "Bohemian mechanics". You can't imagine my disappointment www.youtube.com/watch?v=DvpS...

DHH on the Lex Fridman Podcast (2h in): probably you can see a productivity drop this week from all the techies wading through it...

POV: EV charging in rural Germany

A 'desire path' represents the shortest or the most easily navigated route between an origin and destination. If you can preempt these in your UX/UI you'll lower support tickets and increase user retention. People are going to people. Let them.

A workflow with just and uv, close to what I had stitched together over the last months. I got a handful of new ideas (like putting PORT in .env); recommend watching it!

So, Simon says I should quitting this computer programming thing <taking notes>

Puzzled about the common approach for LLM and agent integration in native iOS (and likely Android) apps. Most LLM libraries and ecosystem tools are Python or JavaScript.

Power users drive LLM adoption: As we run our survey on work related LLM usage in a large, mid-sized German technology firm, we observed a divide into heavy users and non-user, but very few in between.

If your company implements stringent rules regarding the use of Generative AI tools, there's a high probability employees will find ways to circumvent them.

If you want to build an AI feature into an existing product, I really recommend to go through this book. It’s one of the most enlightening technical reads I had in the last few years.

If your company implements stringent rules regarding the use of Generative AI tools, there's a high probability employees will find ways to circumvent them.

Looking into a 'Hello, World!'-like super simple example for GenAI usage (via an API). Thinking about summarisation (has many nuances that you can build upon) or are there any other obvious ideas?

Very interesting read; puts my vague thoughts and ideas on MCP in better words. The parallels are really striking.

"23andMe was trading on ignorance of how the genome actually works, and perpetuating a deterministic view of genetics that is outmoded and wrong." So much this. As Adam says, some good things came from some of the data, but the harm done by this message about genes is probably huge.

MCP servers and integrations pioneered an easy way to plug knowledge and functions into AI systems. But I would be surprised if they are the final thing; a good first attempt but other concepts may arise down the road (so don't put all your efforts into the MCP hype).

AI agent pricing: Charge per successful task, not seats. Aligns incentives, rewards efficiency, & adapts to varying task complexity. Usage-based or time/compute models could work too, but tracking success ensures value for SaaS providers & users.

In-place Assistants > Chat windows! Hugging Face's integration of an "AI Query" overlay in their SQL console exemplifies this. Users input natural language, AI suggests SQL queries—streamlining data exploration seamlessly. Probably the best showcase of this pattern in a freely accessible product.