I lasted about a week before giving up on 4.7 and reverting to 4.6 myself. It introduced so many regressions it was nuts, then failed to troubleshoot the very regressions it introduced, leading to a vicious cycle that tended to compound itself.
It occurred to me recently that AI's degradation of the human factor via way of increased pressure on the remaining ranks of humans might actually be far more damaging than the AI's output itself.
>Dennis is the best, but the book did him a disservice by painting an unrealistically sunny picture of him as some kind of visionary figure.
Wait, 'unrealistically sunny'? You better not be talking about Dennis from It's Always Sunny in Philadelphia, because we're all screwed if so.
Then again, the western AI landscape has become somewhat stale recently. Claude and Gemini may have cute names, but they all pale in comparison to The Golden God.
>I cannot see any reason, over than oversight and a lack of imagination, why something useful in Ukraine in 2022 was not feasible or useful in 2017 by the USA.
Perhaps it had to do with optics? It's not like there was a lack of capability in 2017. [0]
The war in Ukraine provided a way for the US to assist in rapid iteration of the technology without having to shoulder the negative sentiment or grapple with the morality of it.
Also worth noting that the two conflicts were wildly different: Afghanistan was more of an occupation across a much larger area with air superiority. There's not really much impetus to field killer drone swarms when you already have the 24/7 ability to instantly delete most enemy combatants off the map to begin with.
Whereas Ukraine with neither side having air superiority and it resembling something closer to modern trench warfare. In most cases with literal trenches.
>We already used drones quite handily well before that time frame but in a much more limited manner in a different form factor.
The picture below is from 1995. [1]
By approximately 2001 it received the MQ-1A designation indicating it was capable of employing AGM-114 (hellfire) payloads. Kind of crazy to think about.
Having written more sed invocations by hand than I care to remember, please bin me in the mediocre camp.
Aside: The speed at which AI can spit out complex diagnostics is nuts. Par is usually half a second for a dozen complex shell commands tailored to the exact problem at hand.
>... but as there's no complete feedback loop, it still would require a lot of human effort.
Not for long. Picture this: a robot receives instructions on what to physically solder in order to complete the desired modification task.
However, before it can send an image back to the vision-aware LLM guiding it, the PCB lights on fire along with the robot because said LLM confidently gave the wrong instructions.
Then, the robotic fire brigade shows up and mostly walks into walls unable to navigate anywhere useful.
I wasn't judging nor disputing that, just think it's a sad commentary on the current state of the world when outsourcing is considered as dirty as scam and cam girl operations.
I'm putting my money on miniature RC Incom T-47s complete with tow cables.
It should be required that as soon as firmware detects that its bipedal host has, in fact—had its legs tied up by a snow speeder—that it must proceed with a claymation-style display of panic before falling onto its face.
>I'm a pre-launch sole proprietor who put all my personal savings into this startup. This bill would financially destroy me for usage that generated zero business value.
That's terrifying.
If I'm not mistaken even AWS tends to forgive instances like this, so here's hoping Cloudflare has a similar disposition.
Pomodoro and todo list apps are so yesterday. Now I can have my graphics card observe me as an ever-vigilant guardian of productivity.
That might sound sarcastic, but moving context between prompts and just keeping the gears turning often isn't really that cognitively engaging these days. Thus, attention suffers.
Using Claude 4.6 [Extended thinking] exclusively via the web interface: No, not really.
My use case is having Claude tear through an extremely complex Kubernetes setup, reviewing code and drafting plans.
Despite the near-instant answers, it still manages to do this effectively at a speed I can't even hope to keep up with as a human. It's reconciling concerns that easily span dozens of dimensions with each problem I give it.
The trade-off here is that you sometimes see the model make subtle errors in thinking, but they're easily recognized and corrected for when called out. I've also noticed the model will make a statement and then correct itself mid-stream, which sometimes muddles my job of reviewing its output.
Compared to competing top-tier models taking anywhere from 5-40 minutes for a sometimes impeccably-reasoned answer, there's no comparison velocity-wise. The real win is the speed at which Claude troubleshoots, though. Near-instant turns really wins here.
It's tempting to directly assume speed is proportional to quality, but we really don't know what's going on at any given provider's back-end serving configuration, nor the internal model routing configuration.
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