What was your expectation? That your prompt would trigger a web search, first, before the introspection of past conversations and a training set recall?
How did Sonnet 4.6 respond that was objectively better for your use case?
That ship has long sailed. You’re correct, but the author isn’t the one who “named the thing” in this case, they are just using the name commonly used to describe it.
Multi-rotor drones have been called tricopters, quadcopters, hexacopters, octocopters based on their propeller counts conversationally for as long as I can remember.
There are plenty of commercial vendors who use the exact term for their expensive industrial drones.
Update: I see that in the four minutes it took for me to validate my initial inclination and post that plenty of others also had the same thought :) No need to me to belabor the point!
In this case, I’d expect it should make a web search tool call to find the Python library best suited for SVG generation and manipulation, and then use what it learns there to execute the task you’ve asked it to do (either asking if you’d like to incorporate the library as a dependency or to roll its own implementation of a subset of the features if that was your preference),
Assuming tool calling hasn’t been entirely stripped out of this model.
In some cases, I’m sure you are right. That said, the online communities which currently employ shadowban systems continue to use them. That tells me there’s a value greater than their implementation and operation costs.
There is no perfect tech solution to a human problem. But in my opinion, having access to a partial mitigation is better than no mitigation.
A slur is rarely the problem. Trolls can (sometimes) add flavor to a community and keep things interesting. Mostly they can be an annoyance.
I’m specifically referring to people who have seemingly made it their sole purpose to create as much indiscriminate damage as possible.
You can ban them, block routes for them to attempt to Sybil themselves back to having accounts, etc. but even with great moderation tools and systems, it’s extremely difficult to set up a strong enough set of controls which don’t adversely impact everyone else who you want to have participate in the community.
Yes, a shadow environment is dystopian. It’s not my nature to want to even consider using one.
But we’re talking about privately run communities which also deserve to exist to serve their purposes.
So given the choice between anarchy which drives away people who contribute to make the community what it is and a shadow option for those actively working against its interests, I’ll consider the community first.
You may have misinterpreted my comment. I’m not suggesting you use LLMs as moderators. I’m talking about using LLMs as participant “members” of this shadow board to interact with someone whose account was flagged by a human moderator.
After having been a moderator for a number of communities over the years—BBCs in the 90s, niche forums during the 00s & 10s, Reddit boards in the 20s—I’ve come to believe that running shadow services where disruptive individuals can comingle may be the best way to handle this.
The advent of LLMs really opens the door to shunting off these “community members “ who’d rather contribute in misanthropic ways for the lulz than either leave or not contribute at all. They can take part in an interactive echo chamber that gives just as well as they can. You don’t even need a powerful model so the overall costs to the community are probably lower than the alternatives of trying to coexist with community-arsonists.
I spent years trying to find ways to bring people productively “into the fold” but eventually realized that it is futile in some cases because there’s zero value to the individual or the community to find a middle ground. They want to see things burn, and the community simply wants them out.
“Loudoun County currently has 199 data centers, with another 117 in development, according to Michael Turner, vice chair of the board of supervisors transportation and land use committee and Ashburn’s district supervisor.”
It would be helpful if you could define “useful” in this context.
I’ve built a number of team-specific tools with LLM agents over the past year that save each of us tens of hours a month.
They don’t scale beyond me and my six coworkers, and were never designed to, but they solve challenges we’d previously worked through manually and allow us to focus on more important tasks.
The code may be non-optimal and won’t become the base of a new startup. I’m fine with that.
It’s also worth noting that your evidence list (increased CVEs, outages, degraded quality) is exclusively about what happens when LLMs are dropped into existing development workflows. That’s a real concern, but it’s a different conversation from whether LLMs create useful software.
My tools weren’t degraded versions of something an engineer would have built better. They’re net-new capability that was never going to get engineering resources in the first place. The counterfactual in my case isn’t “worse software”—it’s “no software.“
GitHub, Bitbucket, GCE, AWS…all have licensing agreements for user contributions which the user flagged as “public” so I’m not exactly clear of your point if you are holding SO up as a bastion of intellectual property rights different from the other places LLM training sets were scraped from.
I generally agree with your underlying point concerning attribution and intellectual property ownership but your follow-up comment reframes your initial statement: LLMs generate recombinations of code from code created by humans, without giving credit.
Stack Overflow offers access to other peoples’ work, and developers combined those snippets and patterns into their own projects. I suspect attribution is low.
How did Sonnet 4.6 respond that was objectively better for your use case?