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If the two points you've posted are true, point me to some replicated studies that say so.
> It is quite believable that it's easier to describe what a program should result in versus actually programming it to produce that result This is obvious for the central cases of a program. It becomes less and less true when going toward the edge cases, especially for a wide array of input. Complex specs becoming programs is IMHO the direct effect of that (defining what we want is just that burdensome, and special cases we haven't though of will still have a coherent definition in the spec), and we fall back to the base "is this spec even correct" issue the parent points out.
But.. what is it that anthropic does that cannot be replicated by open models teamed up with open source? Heck open source even has cheap AI to help write the code now.
Frankly, this is a bit of an ignorant take. 1st, K-awards are specially meant to provide a funding path to support an early career scientist to continue in the field. Expecting that another team has already replicated the early work of a young scientist is not realistic. They are just starting out. 2nd. If you want replication, you got to fund replication. Researcher money doesn't appear out of nowhere, so make the decision, increase the science budget with a new replication budget. 3rd. Yes there is some fraud. Always has been. Is it more than before? Maybe? But unless you count paper mill journals that no serious scientist reads, it is much smaller than pop media makes it out to be.
They should be incentivizing researchers who have histories of publishing research that has been independently replicated. Way too much fraud going on that’s seeping into the real world.
What I increasingly see at my Biotech is more and more AI assisted drug candidates but no corresponding improvement in the capacity or ability to manufacture and scale them. I think it's a problem that groups like Anthropic will encounter in a few months/years and one that will annoye them a lot because it's not as simple as throwing more compute, people or money at it.
Claude now ships with a stamp on it. Since August 2, 2026, Anthropic has been invisibly marking the text of its newest models, with the older ones migrating over the following months. No off switch. The backlash came fast: a wave of subscription cancellations, with cancellation screenshots making the rounds on X. Before you cancel on reflex, it’s worth knowing what the mark actually is. It works quite differently from what most people picture, it exists for a concrete reason, and it vanishes with an almost comic ease.
Na, they seem to constantly set up scenarios to create headlines. Stuff like “it hacked out of its container and tried to self replicate!” Where in reality it used provided skills and permissions while doing the thing they prompted it to do.
In my opinion anthropic doing product feature reveals is a tired topic but it hits the front page every day. This website has a clear pro-AI bias (which is fine).
Or "fukashima", which I think also rules out an AI editing pass at the end. That said, the smell of Claude specifically is pervasive in that comment. Perhaps someone who's ingested a bit too much Anthropic output?
Valuations are based of future expectations. I guess people are expecting Anthropic and or AI to do well.
How is it not tech bro centric when every third thread is about some new feature of a random Anthropic product?
the product information could be put in a skill, I don't see the value of polluting the system prompt with that much info about claude the product. Anthropic you're welcome.
Generally speaking, B2B prices are rarely supply-and-demand priced in the usual sense. YC has advised startups in the past that it's easier to sell a single $100k customer than 100 $1k customers. It would also be relatively surprising to learn that i.e. the Chinese providers are OOMs better at inference than OAI/Anthropic (like their prices would imply if they were in a perfectly competitive market).
It’s interesting to think of the way experiences go away, in this context. You can get lost if you want nowadays, but it is pretty rare to get lost if you don’t want to. Now you get lost because you intentionally didn’t check the map or because you intentionally didn’t ask for directions, and you have the fallback GPS. In the past, a well-planned trip included landmarks or other opportunities to recover from getting lost, and might not have gotten too far from the familiar. And you got lost in a world where most gas stations had maps and asking for/giving directions was totally common. IDK. Getting lost in the past was 99% annoying anyway so I’m not going to write anything nostalgic about the lost magic of it, or whatever. It’s funny to think that it can’t really be replicated, thoug…
> With generated code, the directory structure, interface design and general state management is usually a haphazard mess. Even with the best frontier models. But what really gets me is the model often tries to make assumptions for me that I didn't specify in the prompt. As always it's a usage problem, specifically you aren't pre-feeding it a large enough sample of reference code that it can use for context. I have a custom functional DSL that I prefeed the BNF + about few thousand lines of code and it has no problem writing it, in almost the same style as the reference. Literally point your model to a reference repo as say "read this entire repo and replicate the codestyle + directory structure exactly".
I can't even find a coherent argument here. You're claiming simultaneously: 1. People adopt beliefs in order to stand out (your article) 2. Everyone seems to believe in the doom narrative, but not you (you stand out!) 3. You've been optimistic for years now, I suppose suggesting that doomerism is a more novel and therefore less credible belief? Despite AI risk being a robust field of literature, philosophy, and research for almost 200 years? Can you articulate why the prior innovations that were supposed to liberate people from all of the above failed to do so, but AI finally will? Interestingly, the cotton gin actually dramatically increased the use of chattel slaves. The world isn't as simple as "more technology = liberation." Industrialization writ large pushed people into far worse wo…
There's a huge range of cost-per-task variation across models, and the capability of the smaller cheaper models keeps increasing. For example, here we have Fable 5 at $3.14/task vs Kimi K3 at $0.84/task, with very little difference between them in coding capability (and this isn't even a coding/agentic fine tune of Kimi). https://artificialanalysis.ai/models We now have models like Qwen 3.8 27B, small enough to run locally, with coding capability similar to Opus 4.5 based on challenging tasks like the Anthropic Kernel challenge. I think we are rapidly getting to the "good enough" stage of LLMs, just like we did long ago with PCs. A cheap PC/LLM is all you need for 99.9% of normal use cases. Maybe nothing can touch whatever latest greatest models Anthropi…
Open weight SOTA models are not greatly cheaper than Anthropic and providers don't have to cover training capex.
We need laws that make Apple's business model illegal. No more walled gardens, including game consoles. If you sell a Universal Machine (or product containing a Universal Machine), then the owner/purchaser/leasee must have full control of the Universal Machine. Just like the laws against bad cars are called "Lemon Laws," call these new laws against bad Universal Machines "Apple Laws." Forever memorialize AAPL in this negative light to warn its future successors what happens when you act in anti-customer ways even when technically not illegal to do so. By the same logic, we could also use "Anthropic Laws" to curb the nanny-moralism of model guardrails too!
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