user43928
This would check out with the fact that there aren't many software products or platforms made in Germany, compared to say the US or China.
This raises questions about how sustainable this situation is and if it might further increase the gap in income of software engineers in the US compared to Germany.
Even the best benefits do arguably not make up for receiving $2500 net at the end of the month compared to $8k in the US.
And I get what you are saying about work life balance and so on, but if one cannot even be bothered to try a $20 AI subscription at home, there must be about zero passion for the craft.
To a lesser degree also Sonnet and Luna.
And when I say extensively I mean 500h+ on a side project in addition to my full-time job.
Even the difference between 5.6 Sol, which is miles ahead of local Qwen 3.8 27B, and Opus 5.5 is immediately obvious in the quality of the results.
Having been stuck on 5.6 Sol at my job while Opus 5.5 was available at home, I can say with full confidence that the difference is significant, even if I weren't looking at the output at all.
The way it understands my requests, forms a hypothesis, verifies the result and finishes the change is night and day different. This happens while 5.6 Sol is still flailing around somewhere in the monorepo struggling with even locating the relevant code.
If your employer is so cheap as to spend a $100 or less per month per engineer, while being large enough not to have access to the subscription plans, a move to self hosted or cheap providers does make sense in my view.
It created a 130 KB file with the prompt for the task of identifying the game + the weights.
I pasted that into Claude on the web and asked it to perform the task without using tools.
The other day I was thinking about the definition of AGI.
I was asking myself the question that when there is no form of data that I can think of throwing into Claude Code and have it not come up with some sort of intelligent answer about it, is that already general intelligence?
That's also matching what I see when I work with Opus 5.5 to optimize models. It seems to understand what individual layers are for, and can thus sometimes suggest optimizations, where I as a layman would be completely clueless.
The latest models are performing amazingly. Because of this, I disagree with popular comments saying that eg. DeepSeek is good enough, AI-assisted development was where the real value was at, etc.
We see huge progress at the frontier, while the open competition is around GPT-5.5's level, and that model was released in April. Let's see how things develop in the future.
In my view their interest, which aligns with that of all other major owners of crypto, is to acquire as much real currency as possible.
Acquiring more crypto by decreasing its valuation is not furthering that.
Maintaining a high valuation and positive sentiment in order to convert buyer's real currency into profit to cash out is what I believe their strategy is.
Crypto at it's current price has a market cap around $2.6T. Obviously the liquidity is questionable, and since crypto isn't connected to any mention worthy real economic activity, a change in sentiment can quickly crash the price and liquidate leveraged positions.
While that's of course true for other assets also, crypto not being connected to production of real value, combined with the comparative lack of regulation, makes it much easier to manipulate.
Binance obviously has influence on crypto prices, potentially affecting a liability denominated in crypto.
I understand Binance alone accounts for a third of the market, and their ability to coordinate with other exchanges is hardly restricted to the participants we know were in that specific group chat.
I think the point stands that a liability in eg. USD is much different from one denominated in crypto.
Anyway, that and your question are hardly relevant to the fact that Binance and their cabal own the crypto exchanges and consequently could manipulate the price to reduce the size of their obligation denominated in crypto rather than currency or stable coins.
I speculate that the AI didn't find this and that human effort and intuition for where to look were needed to find this vulnerability.
Comparing crypto to actual currency typically connected to large, real economies is not relevant here.
$3.5B stolen was "stable coins", which you could equate to a piece of paper with a dollar amount written on it.
Those are largely freezable by the issuer of the paper.
Then we have $2.4B in Bitcoin and ETH stolen. Those are not directly freezable.
However, if that does create a liquidity challenge for these exchanges, what if the price just so happens to drop by 60% after trading is restricted? That liability might shrink in actual value.
Supposedly Apple holds the encryption keys on their own infra, but I still found it interesting how they are just reselling Google Cloud storage here.
I wouldn't say it's obviously unsuccessful.
Clearly there are few limits to what you can implement with AI agents today.
Depending on the complexity, you will spend a lot of time QA testing, giving feedback, and iterating.
One can take shortcuts such as porting existing software, as you mentioned. Many are just content publishing crappy novel software that they barely tested.
You don't need to do that. No one is forcing you not to QA test and iterate until you have a good product.
There were a few common sources of large runtime dependencies in the past. Like the browserify Node polyfills, babel-polyfill and runtime with core-js, React -> fbjs, Material UI -> JSS and plugins, styled-components.
It's also not trivial to tell whether a dependency you listed under devDependencies is actually imported and consequently bundled into your production build.
Since today we no longer need polyfills and the modern stack with Vite etc. doesn't have a sprawling dependency tree, I think things are now looking much better.
I don't know what I'd need a channel for, creating bots/personas seems more natural.
Adding credentials is essential, contrary to what the author of the article said. I gave it my library login and it can fetch newspapers for me instead of me clicking through the app every day.
When I had to provide a GitLab token, it nicely spawned a box for me to save the credential.
- What did not work well -
Permission settings. There is a form where you can automatically allow actions you describe in natural language. I tried to allow it to run Claude on my computer or Send certain emails automatically, but the settings achieved nothing.
Coding. I wasn't able to make it use Claude, and its own work is worse than me using Opus 5.5 in Claude Code.
- What worked well -
Until spec files can be worked with so efficiently that we can just regenerate the binaries from them for every change, what you said makes little sense.
You're going to need an intermediate portable form of storing the program. It so happens that all the tools and AI are optimized to work with source code.
The Jest test runner alone has 250 dependencies in its latest version, and v25 had over 500.
We'd first need to establish that this side channel leaks usable information during regular use.