> His algorithm attempts to resolve that tension, giving high ratings to engineers who write many lines of code only so long as that code is maintainable, solves complex problems, and is easy to implement.
If current tools are incapable of generating code beyond a certain level of complexity with consistent accuracy, then how are they capable of accurately judging human output? This reads like a puff piece citing some person’s unreviewed, “Microsoft management in a black box” algorithm.
A fundamentally fraudulent company that destroyed the lives of thousands of its employees relaunches with a press release, no public information on its new leadership or concrete plans, opting instead to vaguely reference an interest in pursuing some sort of crypto play.
It says a lot that this brand is worth enough to someone who wanted to revive it, if for no other reason than to capitalize on its infamy.
An interesting glimpse into just how curated and monetized one’s life can be, played out in a lawsuit. As the article points out, it’s hard to have sympathy for them (although I do personally find the idea of mimicking another person’s life to this degree to be quite sad). Ultimately, the work these women do also endorses copying the work of others, something Amazon is notorious for, taking the irony to a whole new level:
> If the argument is that Sheil is duplicating Gifford’s existence, there’s something to be said about the fact that the items both of them promote are also imitations of someone else’s work.
> In my post, Netflix can be replaced with any other "vice" that takes your time: Other streaming services, News, Youtube, Social Media, etc. All of them are not bad when used moderately. But when they become a drug habit things need to change, or at least for me, they must.
Work can be abused and addictive just like any other drug. Optimize life to fit your needs, but if every moment spent not working makes you feel like you should be working, you might want to look into that.
Hype cycles come and go, AI will be no different. The core technology may remain and advance, but the fever will break once reality sets in/and or prices continue to climb.
Some people are being pressured by their management to use AI to increase efficiency in their jobs, with varying degrees of success.
I have a friend who works in logistics for GE and they’re getting training on the basics of GenAI and then they have to go out and find ways to integrate it into their workflow. The problem is that management isn’t doing the legwork to understand how to integrate the tooling, they’re just handing that responsibility off to the actual users. Those people wind up complaining that taking time to integrate these tools winds up slowing them down and they struggle to find meaningful applications for the LLMs.
It’s like management is saying “here’s a new hammer, we don’t know how to use it but the guy who sold it to us convinced us you can figure out how to use it. So go out and do it and be better and faster at your job, good luck”.
> “It’s a good thing from a lender’s perspective,” said one of the bankers on the debt deals. “They have control the whole way up the supply chain. That’s a good thing as Nvidia won’t let things get too bad.”
So Nvidia produces the chips, invests heavily in “preferred partners” that buy their top line chips in bulk, and promises that those preferred partners don’t get any special treatment, yeah right.
This whole thing reads like the first act of a Michael Lewis book.
Agreed. The problem is that many SaaS products want to integrate AI into their systems in ways that strike this balance, but in my experience, giving individuals an OpenAI account and letting them figure out how to automate the boring parts of their job is more effective. However, different people in the same job will likely use AI slightly differently, and it can be hard to find the right abstraction that suits a SaaS product.
And then, like it or not, companies are hopping on the agent train hoping to automate out a percentage of their headcount because that’s how they’re being pitched on it behind closed doors.
I really liked Zitron’s interview with Adam Conover a few months back, it was a much more conversational version of this same overall premise.
I wanted to like this, but it fell really flat for me. He led with emotion and came across as bitter and angry, meandering around several examples and calling Facebook execs perverts. Those accusations could very well be true, but it always makes me question the legitimacy of the speaker because that’s such a common cheap shot in politics nowadays and it distracts from his main point.
I think we need more folks like Ed using their platforms to call out what they see because I think he’s right overall.
I’m sure the silence is intentional, they’re a subsidiary of Panasonic. It seems like this story is just starting to hit the press, and Blue Yonder publicly commenting on it would surely validate concerns around the severity of the incident. It doesn’t really benefit them to proactively communicate at this point.
Mexico supplies well over half of all produce consumed in the US, and produce prices have so far been less impacted by inflation/price gouging/however you prefer to think about it.
If this winds up going through, people all over the country are going to see the sticker shock at the store very quickly.
And migrating from a no-code app setup to a homegrown application incurs its own set of costs, particularly if both need to be maintained in parallel. One problem with low/no code tools is that they tend not to encourage any sort of data validation, which becomes a problem when you want to migrate a no-code database into a “real” database.
Not necessarily. There’s huge demand to simplify the integration process between frontier models and consumers. If specs like this wind up saving companies weeks or months of developer time, then the MCP-compatible models are going to win over the more complex alternatives. This unlocks value for the community, and therefore the AI companies
There’s clearly a need for this type of abstraction, hooking up these models to various tooling is a significant burden for most companies.
Putting this out there puts OpenAI on the clock to release their own alternative or adopt this, because otherwise they run the risk of engineering leaders telling their C-suite that Anthropic is making headway towards better frontier model integration and OpenAI is the costlier integration to maintain.
I recommend building an intermediary data access layer like a graphQL API that the LLM consumes, otherwise you run the risk of someone successfully tricking the LLM to drop all or part of the DB via a raw input-to-SQL step.
Access to the latest foundation models, which can’t be run locally. AI feels like it’s in this really weird place where the latest Claude model sets expectations that can’t be matched by an on-device model.
Even Apple Intelligence is getting a lot of negative feedback by the review crowd due to its limitations like being unable to summarize a very large document (which is pretty much the point of such a feature).
The problem is that AI has few well-defined use cases and a mountain of expectations, and this really shows in the execution by these companies. It’s hard to build good products when the requirements are “we don’t really know”
I doubt this, there are dozens of hosting options for WP out there.
I think he was threatened by WPEngine’s approach as a more popular offering than Wordpress.com and decided to go scorched-earth on them instead of trying compete on the merits of their product. He’s threatening anyone that tries to build bespoke hosting with extra tools like ACF.
> The shift has other business model implications too. Take the observability sector. The industry still grapples with data overload, skyrocketing costs, and a critical shortage of skilled personnel. Incumbent company pricing models (think Datadog or Splunk) are wildly mismatched to customer needs, charging per GB for log management, for example, or ratcheting up costs with infrastructure size and data volume.
We have no reason to believe that AI costs won’t skyrocket long term either. It feels like the early days of streaming right now, but how long will it last?
Foundation models are pretty reasonably priced depending on the workload, but having a plurality or majority of companies all hooking up to the same two or three model APIs is gonna get really interesting when prices start to increase, because your exposure to the price increase scales with the number of times that same provider is used by your vendors, your vendor’s vendors, etc.
If Altman wakes up one day and decides to double the token price, it could be Armageddon in the SaaS space.
If current tools are incapable of generating code beyond a certain level of complexity with consistent accuracy, then how are they capable of accurately judging human output? This reads like a puff piece citing some person’s unreviewed, “Microsoft management in a black box” algorithm.