I'm gonna go out on a limb and say that not even a double digit percentage of taxi drivers' careers are ended due to violent attacks. Taxi driver doesn't even appear in the U.S. Bureau of Labor Statistics top 20 most dangerous jobs.
So they’re happy to remove the rights of the woman carrying the fetus
I assume that continues when the baby is born and people have to sacrifice their rights to feed the baby. Not in as extreme a way as bodily servitude, perhaps just taxation of the work they do to provide food, shelter, healthcare.
The problem is that you can't be considered a credible critic of this stuff when you either don't understand accounting or are dishonest to make a point.
Zitron makes too many "mistakes" like this to be taken seriously. In other words, he just isn't the right person to make the "huge AI bubble" argument because he doesn't understand (or he's being dishonest about) the financials.
I wouldn't consider OpenAI's financials to be pretty. There's circularity in the market that is a bit concerning. And while OpenAI's unit economics have improved it's still questionable as to whether the R&D and capex expenditure ever aligns to the business.
But Zitron is too sure of his argument (without the credibility to support that confidence) and is trying to pretend that there's absolutely nothing of value here. My best guess: there's some "irrational exuberance" and malinvestment but there is something real here and the unwind of the irrational exuberance and malinvestment won't be nearly as painful as Zitron believes for a variety of reasons, including the fact that there just isn't enough leverage in play.
> In plan mode (using CC, or OpenCode) I ask a model to build the foundational knowledge for X topic. I ask it to review the accuracy of the knowledge base it built in the previous step.
> What you get is a beautiful animation that is 100% accurate and free of hallucinations.
> I regularly see people claim that they are now 10x more productive with LLM code generation, and I just wonder where all the code is.
Perhaps the biggest issue hasn't been the speed of development all along but people working on wrong things, hence why progress isn't as evident. LLM helping to build something that isn't needed faster isn't going to productivity more visible.
> Intel developed QPI at its Massachusetts Microprocessor Design Center (MMDC) by
> members of what had been the Alpha Development Group, which Intel had acquired
> from Compaq and HP and in turn originally came from Digital Equipment
> Corporation (DEC).[8] Its development had been reported as early as 2004
The the idea that engineers are covertly sneaking huge quantities of LLM generated code into these projects doesn't pass the smell test, as there's lots of evidence that this isn't true. It'd be incredibly obvious if new contributors were turning up en masse and contributing absolutely incredible amounts of high quality code, because everyone would be crowing about it
Instead, if you check out something like ImGui, we can see that its largely just Omar as usual with a bunch of PRs. Nothing much seems to have changed, feature development carries on at the same pace as it did previously. This is replicated across nearly every single major long term open source project I can find
The only reason developers now feel the need to hide their LLM usage is because the LLM contributions have all been shockingly bad, so you're reversing cause and effect. If LLM contributions had been incredible, they wouldn't be frowned upon socially now
This effect also wouldn't prevent LLMgineers from creating their own open source projects that should be absolutely outstripping the existing open source space. After all how old is ImGui? With a 10x productivity gain, it should take a year of development to easily match its features if those gains are truly real now
>if they're really good and use LLMs responsibly, they probably don't even have any ethical obligation to label LLM involvement
The copyright issues means that non disclosure is inherently unethical
The hard part about being a human being is that every bit of advice is good in some contexts but not in others. Sometimes you're right to set boundaries and say 'no'. Other times, you should negotiate and compromise.
I'm very skeptical of people who pick One Simple Trick to solve all problems.
If you’re serious then you’re case in point the exact type of person that I am talking about. I think some people just lack any capability of foresight whatsoever, it’s very strange.
I have a personal theory that this is related to LLMs' inability to have any kind of actual conception of the mind of the reader.
Next time you're chatting with someone, notice how much understanding is shared without anything being said. E.g. the other person might share something deeply disappointing, and they can tell by your face that you really get what they're feeling about it. Things like this -- LLMs are totally blind to. Not just nonverbal communication, but simply forming conclusions about what the reader feels, the lens they're considering things through, etc.
My observation is that LLMs seem quite incapable of grasping or guessing at these things. And this makes them terrible conversational partners in many scenarios.
Perhaps eventually Big AI will get wise to this via RLHF or other training mechanisms. But for the long term, it seems they either totally don't care or they don't have a way to fix it.
The text written in LLMish for that Phrack intro uses ASCII characters for everything, including quotes and ellipsis. There's one non-ASCII Unicode codepoint that made it through and it's the telltale sign of LLMish. They also use a monospaced font and ASCII art (well at least ASCII aligned titles) to make it look old-school cool.
Honestly I don't understand how anyone can defend this LLMish garbage.
We will not reverse climate change by consuming less. The solutions are equally political and technological. Driving a smaller vehicle does help but real solutions would be: building homes next to jobs, public transportation, electrification, and carbon capture.
> On a motorcycle, you’re in the environment instead of sealed off from it. Lavender above Grasse in June, a 5-degree temperature drop when the road cuts through a forest, leaning into corners with your whole body
Very romantic, but not very realistic. Most commuters need to show up for work in heat, rain, snow, etc.
Depends on the app/framework but typically phantom threads don’t get started. Also, those stacks aren’t materialized until you write something - they’re all CoW’ed pages from a sentinel 0-filled page the kernel keeps around just for this purpose.
I've been having fun building out a link-in-bio tool / micro-site builder in my spare time. It started out as just something that a solo-dev could build without a lot of experience... but it's turned out to be more complicated and interesting than I thought! Right now I'm working on getting embeds and other 'fancier' types of blocks built out. Also… if any SEO wizards are lurking, I would love tips on getting more Google impressions/CTR, lol.
If you want to check it out (would love some feedback!):
The first generations of open models (eg. llama 3) were not as good as chatgpt, but they were good. They are easy to run on your own PC if you have enough ram. I did not really follow their development so I don't know how recent you can go before they start to be sterile like chatgpt.
“We have shown that vitamin D3 appears to stimulate the type I interferon signaling system in the body – a key part of the immune system that provides a first line of defense against bacteria and viruses. Thus, a healthy vitamin D3 status may help prevent viruses and bacteria from gaining a foothold in the body."
“Our study suggests that it is important that people take a vitamin D3 supplement, or suitably fortified foods, especially in the winter months.”
I've been working on Contextify, which backs up your Claude Code and Codex conversational history and makes your entire history available via a skill or mcp.
It is very flexible, you use it the normal flow of CLI AI conversations:
1. Resume unfinished work → "where did we leave off on that?"
2. Recover the intent/scope → "what was the actual goal of this whole effort?"
3. Verify it got done → "did we ever finish that, and which session proves it?"
4. Recall a fix → "how did we fix this the last time it broke?"
5. Reconstruct a decision → "why did we go this way instead of the other one?"
6. Branch/PR archaeology → "what was this branch even for?"
7. Recall a plan → "what was the plan we landed on for that?"
8. Activity over a window → "what did I ship or close in the last few days?"
Just add "use total recall" or invoke the skill directly via /total-recall or $total-recall.
You can self-host your database to keep it local and sync your history across multiple computers. The local server is source available under FSL-1.1-Apache-2.0.
There are macos and linux clients, and I'm pretty close on a windows client. All share a common Swift-based core.
I mean people with tinnitus can choose to get angry about it or they can... change their attitude toward it.
I have it. I've changed my attitude toward it and made it my friend. I don't really know what else to tell you, but if you want to, the way is open for you. Or you can continue to see it as an aversive thing, and it will continue to be so.
Did not get bought by OpenAI. Creator was hired by OpenAI and they sponsor the project with tokens. OpenClaw is a non-profit now.
The problem is really that the promise was so big but the management and implementation was so painful, coupled with non-stop updates, coupled with far too big of an implementation surface.
A classic "lets solve 15 problems at once" situation, but none of them really well.
The Hacker Spirit was largely subsidized on the (relatively) high salaries afforded to engineers*.
What happens when that well dries up?
* Not saying it was 100% the case, nothing ever is, but you can't deny that a large part of historic and modern hacker culture grew from well off places who could afford it. Sure, sure "necessity is the mother of all invention" and all that, but that breeds a different kind of innovation.
