This isn’t the case at all, the most technical and best engineers are all using Codex now and have been for roughly six months.
It’s a known “secret” for a while now how much better Codex is than Claude. I’ve used both since they were released and I often implement in both to compare and 95% of the time Codex writes better code and also less code!
You're being silly. The actual technical people are using Claude for implementation and relying on MCP servers to use Codex 5.5 and Gemini 3.1 pro to build teams, councils, and long running senior engineer conversations within Claude to handle the technical bits that're too complicated for Claude.
This seems like one of the most obtuse or bad faith comments I’ve ever seen.
Practically every country has pathways to permanent residence or citizenship via non immigrant visas, including the US.
Why? Because it makes practical sense. You can be living in the US on a H1 visa for 6 years, and at this point you could have a wife, kids etc, so it makes sense to have a pathway to residency where you don’t have to leave the country at that point.
The reason is, that using hashcat is not complicated for people who have linux experience and the amount of people wanting to crack a password is probably not that high.
Otherwise you do find plenty of people on YT walking you through hashcat. The first YT Video alone has 7 Million views: "how to HACK a password // password cracking with Kali Linux and HashCat"
I wish him luck, great drive to do this, i hope it works out well enough, books are just in general not easy to sell.
Tons of people in it service occasionally would like to crack local passwords for clients. It’s a big world. That’s thousands of people needing to do this every month. More than enough to make a self published book worth publishing. I’ve sold a few books that even though they maybe only sell a few copies a month have made me more than 250k over the years. Slow returns, but it’s the gift that keeps on giving.
When I lived in Adelaide, Australia 2006 or 2007, flexible-neck LED lamps that you plugged into an USB port to have light on your keyboard (backlit keyboards were not the norm on laptops) were a novelty item.
People simply didn't /know/ about them/that they existed at all.
I went to a computer/electronics shop in town and asked for them.
The guy told me: "We don't stock them because people don't ask for them."
I'd say that this is a bit relevant to the entire field of cyber security and a good chunk of development roles. If you're not concerned about how password hashing (which is a key component of understanding cracking) works as developer-- I'm not sure what to say. While not all of the in-depth research is probably needed. It's definitely relevant to many technical fields. I work in offensive security and we use tools like this daily in our industry. And no we are not cyber criminals.
This. Neural Nets have existed conceptually since the 1950s. They weren't realized materially and practically until later, but it's astonishing how ignorant people are of the history of AI.
That's where you're wrong, the election was very, very close. In fact, if roughly 40k voters (across three states) had switched from Trump to Hillary, she would have won, that's how close it was.
40k voters, that's really not very many. So it's hard to say whether Trump had a 30% chance of winning or 40% or whatever, but the election at most was a toss-up.
Many random events could have resulted in a different outcome.
You misunderstand my point. I am talking about the actual election that happened where these many random events that could have resulted in a different outcome did not happen. I was being a bit facetious maybe in my point. But the point is that the thing that is to be predicted is the actual real event that occurs in this universe. Silver made a prediction, and it was wrong.
"Oh but it was only a 70% prediction"
You can't 70% win an election. Silver's prediction was that Clinton would win, but he was not super confident about it. The prediction was wrong. He was right to not be super confident about it, but the prediction of who would win was still wrong.
Statistical likelihood is a measurement of the known data at the time. If you engage with the content otherwise then it's on you if you have the wrong takeaway. No one who makes a prediction based on a statistical model is going to be right every time. That doesn't mean it's not right to make a prediction. The statistical modeling can help you to be correct more often than not. And if you were going to be truly fair you would note that Nate in fact repeatedly said that it was still very much possible for Trump to win but that the current known polling data and other factors in his model pointed to a loss.
538's own post-mortem's on the event highlight that Trump was a very unusual candidate running in a very unusual election and as such the model was missing a lot of important information. They learned from the experience and adjusted the model going forward. Anyone complaining about that event is really just highlighting that they don't understand how statistical modeling works and are upset about how the model misled them or others which isn't Nate or 538's fault and is entirely on the consumer of their reporting. It's not like they didn't try to educate their consumers in their reporting.
I know what statistical likelihood is. I don't have a problem with them using a model or models and doing some statistics on it to develop these predictions, or even necessarily with the way they report their predictions as a % chance to win. I have a problem with the insinuation that "70% Clinton" is somehow a prediction of a singular real event or that Trump winning is consistent with said prediction "because if we held another 99 of those 2016 elections then Clinton probably would have won about 70 of them therefore I was right".
The prediction is for one single outcome at one point in time. The prediction can not be that Clinton 70% wins it, or wins it 70 out of every 100 times because there is no 100 2016 elections. Those things may apply to his mathematical models, but obviously the models are attempting to predict the real world. Try to weasel out of it as much as you like, but the prediction was that Clinton would win, and the prediction was wrong.
"Oh he was only giving the odds for his model, you don't understand it's your fault he mislead you" -- no. Every analyst and pundit has a model or a system, obviously nobody thinks any of them can see the future. Nate Silver was very explicitly predicting the outcome of the election. As you can see from all his commentary articles that came out along with the numbers.
And yes, 538's vaunted models and data science fell over when encountering situations that had not been seen or anticipated or built on before, obviously. We didn't need Einstein or even Nate Silver to tell us that. That's the problem isn't it. All this hamming up of "data science" and "mathematical models" is meaningless. Your data and math can be perfect and correct, but if they fail to provide an understanding of the world, then they are perfectly useless.
Just want to say, I appreciate your pragmatic perspective on this. Nate Silver had one job: Predict who would win. And he failed at that. With lots of hand waving he can excuse himself but at the end of the day his visitors wanted an answer and he gave them the wrong answer.
That's not really the point. I was wondering at what point of complexity the SynthID watermark is added.
I.e. it doesn't make sense for a purely white or black image, but as you gradually add colors or features, at some point they would want to add a watermark, but based on what? It's an interesting question.
Those projects are a complete joke. Neither of them were even original, people have been playing around with those ideas for well over a year.
They just became "famous" because Karpathy is effectively an AI celebrity, so he could throw shit at a wall and post it on X and it would get 10k Github stars.
But seriously, people have been using the models to tweak hyperparemeters, or using LLMs to help create a second brain using markdown or json files or 100X other combinations of files, for a long time already.
Hallucination is also much better controlled in the context of agentic coding because outputs can be validated by running the code (or linters/LSP). I almost never notice hallucinations when I’m coding with AI, but when using AI for legal work (my real job) it hallucinates constantly and perniciously because the hallucinations are subtle—e.g., making up a crucial fact about a real case.
Yes, you can catch many mistakes that LLMs make whike coding, but I wouldn't necessarily call it "controlled." Every now and then the LLM will run into dead ends where it makes a certain mistake, the compiler or unit tests find the mistake, so it tries a different approach that also fails, and then it goes back to the first approach, then tries the second approach again, and gets stuck in an endless loop trying small variations on those two approaches over and over.
If you aren't paying attention it can spend a long time (and a lot of tokens) spinning in that loop. Sometimes there might be more than two approaches in the loop, which makes it even harder to see that it's repeating itself in a loop. It's pretty frustrating to see it working away productively (so you think) for 20 minutes or so only to finally notice what's going on
For coding the worst I've seen recently is gemini using or suggesting library methods that dont exist in c# which it catches when it builds the project (something I've told it to do to catch these.)
but for research it makes shit up all the time, I asked GPT5.5 to make me a build for Rogue Trader and not only did it use out of date info, it made up a bunch of skills that were NEVER in the game.
I attribute that to there not being enough online information in the wikis or whatever but I wish it would just say "I dont know" instead of hallucinating but I know that's not how the tech works.
Why would you write a formal, historical article intended for a long read and then add jump scare animated locusts to scare people?
The two things aren’t compatible.
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