docheinestages

I wouldn't be surprised if Anthropic and OpenAI restrict these weight-related queries the same way they limiting cybersecurity queries.

Deskilling is not necessarily a bad thing, if what you refer to as skills are mundane things that should be automated. I do agree with the author in that people are becoming lazy when it comes to critical thinking and evaluating the decisions or work that AI presents to them. These are people who have little passion to do what they do, and for whom their role is primarily a chore.

In 2026, the wheel that keeps getting reinvented is the agent harness.

Artificial Analysis is ranking it as #1. Wait a couple days until they come up with a new index versions and Anthropic will lead again.

JetBrains simply can't accept that IDEs are dying.

Good idea, but the real value is in having high-quality built-in templates. Right now, they look just like what I could get by asking Claude.

> (8,000+ word, plus 2 companion pieces)

I appreciate the idea, but I doubt you wrote or actually read the whole thing. There's no point in trying to impress people with complexity. The art of writing is to convey your message with the least amount of text, while retaining maximum information, and making it easy to read.

I could've written what I wrote above with 8,000+ words too.

Why would I need to install your tool for that? It could be an instruction living in AGENTS.md or with some sort of hook to remind the agent.

I just can't fathom how a multi-billion dollar company like Cloudflare could forego their design standards. They seem to have updated their landing page [0], but it's still very sloppy.

[0] https://try.cloudflare.com

Codeberg is trying to solve the right problem with the wrong solution. But hey, it's democratic!

Jev has good use cases that require speed. But speed is not all. Tasks like code review need quality, which costs time. If you just want speed, you might as well go with a non-reasoning model like Haiku.

> Within two weeks of our April launch, most of Stripe was using Stripe's Knowledge AI Platform, also known as Kai.

By force or by choice?

It's not just OpenAI. It can be any frontier-level lab that has more funding than Jev.

> that it is not OpenAI

For now. Any company that grows to OpenAI/Anthropic's size and gets VC money is ought to become greedy.

JetBrains is on track to become another Nokia.

The premise of the article is that the Model Context Protocol (spelling it out to emphasize the core purpose) is inefficient in its current form. We can agree to that.

Now, does it mean agents don't need a protocol to connect to a server and discover its capabilities, tools, and distributed skills? I disagree.

> Agents with terminal access can replace most MCP servers

What should all other agents that don't have terminal access do?

If you don't truly enjoy what you do, you'll get crushed by the pressure. Don't try to impress people. If you feel pressure, take a step back, reflect, and think about what's important to you. Then communicate this clearly to people around you, including yourself.

It really depends on the use case, which the author didn't hint to (sure, they say procedurally generating text, but exactly how and for what reason?). But whatever it is, I assume it needs to be accurate to some degree, and having a deterministic ruleset is just too subjective and superficial to cover all cases.

It doesn't need to be a full-fledged LLM either. What I mean is that a probabilistic machine learning model trained on a sufficiently large dataset is probably a better choice here. Small language models are pretty efficient nowadays, even for on-device use cases with constrained resources.

My first impression is that it's not so good at following prompt directions. I asked it to place a 3D text made of glass in a particular city. It instead gave me a broken 3D text on a white background. Maybe with different seeds it gets better, but it's more of a trial and error process than reliable results.

This is exactly what language models are good at. It's not a problem that could be solved purely deterministically, because it requires knowledge of the actual pronunciation in that particular language or dialect. There can be so many exceptions to the rule, so it's just easier to train a small language model on a sufficiently large dataset.

I feel like ChatGPT Images 2.5 is another step up in terms of quality of designs. But it still needs to be told very specifically what to generate. A generic output is in turn due to a generic input.

Exactly. If humans were good at writing proofs, they'd just write the code.

Unless the proofs themselves are defined with natural language, I don't see them being adopted by humans. It takes a high cognitive load to read let alone write a proof.

Such a shame to see Mozilla cut corners like this

Why is this so cryptic to read?

It's hard to believe that a model can nowadays solve mathematical challenges and break ciphers, yet it fails to do trivial tasks involving critical thinking, having taste, and not just running around in circles.

Nice try, Dario.

Same here.

A dead giveaway that I think it's AI-made (not necessarily a con) is the complexity of the UI and redundant information. Things like the green dot paired with "interface is up". It looks polished though.

Now I'm starting to doubt the credibility of Artificial Analysis.