I loved The Division. Some months ago I re-downloaded it and just ran around for a bit. The setting and world feel are still there and the story is deeper than some single player games. For a while I thought, wouldn't it be amazing if it was ever released (or leaked) so it could be player hosted or modded in a local kind of development like this. But, I didn't stay with that thought. There was just something about that game that had a beginning, middle and end for me. Maybe I'm just getting old.
> There was just something about that game that had a beginning, middle and end for me. Maybe I'm just getting old.
I think you had a very different experience than me. I enjoyed The Division 1 and 2 but the story was completely unmemorable and by the time you get to the end they shift you into end-game Diablo-esque grind-mode, not some definitive ending.
The voice recordings and non-linearity were what made it for me. I felt like I was piecing the world together as I explored. Even after doing the boss fight finding intel that explained something that wasn’t known before slotted it into my understanding of the story. By the time I reached the grind at the end, it was like I had a world in my head and it was ok to close the book.
I feel like I’m an outlier based on all these comments. This is still working through my personal benchmarks, but it’s spending 30% more time and performing worse than the previous 4.1 flash did.
Benchmark is historical PR reviews. Models are put in a sandbox with a git and db snapshot as it existed at the time. PRs are a selection of changes some bugs (major and minor), some with things that aren’t bugs that have tripped up models in the past and some no findings that they should approve and move along.
4.1 flash already wasn’t that strong at this task, but the speed edged out v4 pro for a spot in the roster.
I made a comment some time back that reviewing AI code was kind of bewildering (really any other code but my own). I’ve since dug in, crafted some processes and skills (in the /skill sense) and this is basically the shape it takes. Find the hot spots, what’s setting precedents, what’s not sitting right with me… it’s fast but it actually creates additional work (in a good way) because the way I have the skill set up the agent then scans through the whole codebase to find places where the changes demanded on a given PR also exist. Each PR spins out 1-multiple tickets. In a way, this has always been my dream. I believe in refactoring at some core level. Now I finally have the time (and tokens) to do it continuously.
LLMs have also pushed me to learn a lot, but I don’t find the shape to be terribly different than before. I rarely remember the exact sequence of commands or flags or syntax. What my learning was useful for before and after LLMs is to 1) build an intuition of how something behaves and what it should do and 2) expose me to non-obvious possibilities that I wouldn’t have come to on my own. With those two things in place, the next execution without the LLM is a docs search, same as it always was (for me maybe some people are better at remembering this stuff).
What’s different after LLMs for me is that there are some things that just don’t care about and I’m fine if they are a black box that the agent handles for me. I didn’t have that luxury before.
My take on picks and shovels was always a more cynical one. The beauty of the metaphor comes from the fact that after the miners went bust and left their picks and shovels where they lay, they could be collected and sold again to the next dreamer.
Claude has gotten pretty sensitive to copyright. In addition to the no song lyrics that was posted in another article, I also find it will not generally transcribe podcasts (you have to play with model versions and even effort inside that).
The “for agents” button answers a question I have had for some time: what happens when the frameworks I use that are well represented in the training data go stale? For the work I do, agents hit APIs nearly flawlessly from memory, but if the new releases from open weights well dries up, that won’t always be the case as the models age. I like this approach to building skills rather than relying on memory.
I feel like this is a marketing miss. If they had held their announcement until the model was released, I would have grabbed it and started running it through my benchmarks. It probably doesn’t get a place in the rotation based on their own description of its performance, but now the weights live on the server, I’m probably following them on HF and I will remember to check in every time I ls the models folder. With the announcement only, none of that happens and I’m likely to forget about this by the time it actually gets released.
The email harvest move just doesn’t fit where we/they are in the cycle. There are established players and a buffet of models to choose from (plus a ton of empty hype). The first move at this point for any new entrant should be to show, not tell. Even an API only release with the promise to open weight would be better (actually probably all around better since most people can’t run this locally).
I wish this lab and all the labs releasing the best. It’s a brutal landscape to sink millions of dollars into for a guaranteed “behind x model from a year ago” evaluation. But, I do believe there is genuine innovation left to uncover.
The way I remember it marketed, and the way I originally found it, was as a workaround to setting the default search on iOS. This was before I was conscious of AI and before I remember it being in any of Kagi’s products.
I ended up staying because the Adblock is good (search remains good). The browser is fine. It does have bugs and needs to be hard closed occasionally.
I agree on the assistant. It is very capable. I use the Kagi mcp with coding agents and I like the results, but they have done something with their assistant that makes it even more useful.
IMO it's not difficult to construct a scenario that technically should meet the definition of consciousness. Here is my technically conscious, but doesn't pass the smell test, hypothetical:
* A chat session is begun with a script to create a "heartbeat". The heartbeat simply to poll some sensors in the box and report back every x minutes.
* The chat session responds to each heartbeat as it comes in. Most of them are the same with small differences in sensor measurements.
* Over a long enough timeline, given the variability in token distribution, one of these responses will create a novel response.
* Over a long enough timeline, one of these novel responses will cause an action, maybe a new question, maybe the agent writes another script
* Over a long enough timeline one of these actions will create an action that becomes causal, it will create a chain that sustains itself. It may not be interesting, but in a network attached box, you can imagine how it can become interesting very quickly.
That's constructed in a cellular/genetic analogy, but because context is building and not lost to each generation, I don't think this takes all that long.
What does that leave you with? A box that has "awareness" of it's surrounds via sensor readings, a "heartbeat" that is driving it until it's unplugged or stopped, an input/output feedback loop that might be predictable in narrow sense, but is wholly unpredictable in its end state (a "will", if you will), and a thing that has the opportunity (but not the requirement) to introspect about its limits and possibilities. It can even do something that I would say is indistinguishable from planning. It can have goals. If you give it an Artifactory message board it can build a community ;)
Is it really? Are you sure all conscious beings are aware or even care that they are? That that is a question they’ve ever asked? I think this requirement is moving the goalposts always in the direction of “has to be like a human”.
The sensor readings were more supposed to be analogous to your organs giving sensory feedback information. Fair enough that "awareness" maybe should have been moved later in the analogy chain.
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