I don't think so. LLMs tend to over-index on providing results in general whether it's a conclusion or not. When you ask it "What are typical things that go wrong with this type of vehicle?" you're forcing it to make a conclusion about which results to include and it will almost certainly provide results even if those issues aren't as much of a concern compared to typical issues with other vehicles.
For example, I just prompted Kimi-K2.6 with:
> I'm considering buying a used base model 2010 Honda Civic with 80k miles that's been garage kept. What are typical things that go wrong with this type of vehicle?
It listed 10 issues including the engine block cracking (which wasn't even an issue with 2010 Civics). Started a new chat and asked about a 2010 Toyota Camry, another unbelievably reliable car, and it listed 9 similar issues. Started a new chat and asked about a 2011 Jeep Grand Cherokee, a notoriously unreliable vehicle, and it listed the same number of issues.
Sure it's data to make decisions on either way, but it really all comes down to how good your prompts are and whether or not you can think critically about the output, whether or not that output is a conclusion or just data collection.
Heh, I hear stories like that everyday from my partner who is an ICU nurse. Not as dire, but there are constant inter-department arguments about moving patients because of resource constraints and the ICU could end up completely understaffed/resource constrained if the wrong NP or charge nurse is working. I'm amazed our healthcare system works at all to be honest.
The whole point of the article is that families found spaces in their house to fit in a computer and it became the computer room even if it served other purposes.
> At my grandparents’ house, it was their office in the corner of the house. Their desktop PC was far from the kitchen, bedrooms, and living room, sandwiched between the coat rack and the washing machine.
In my house, the computer room was just our spare bedroom which ended up being the bedroom that my cousin lived in when we moved in with us to finish high school. I remember bugging my mom while she was playing solitaire, free cell, or minesweeper to see if I could use the computer to play Civilization 3 or Roller Coaster Tycoon.
At my friend's house it was a little inset desk on the middle floor of their split level that doubled as their dad's office. At my aunt's house it was a deck in the back of their living room next to their CD collection.
It was never a room completely devoted to the computer, but to the person who used the computer the most it became the computer room.
Scammers who do the lowest effort diagnostic and "fix" to get you to pay a smaller amount of money to fix the problem in the short term even though it'll re-present itself a week/month/year later.
Upsellers who will find other things "wrong" with your car and pressure you into paying to fix them because they sound a lot worse than they are.
Good mechanics that will explain what they did to diagnose the issue and recommend different options depending on what the issue is.
Funnily enough, I've found that doctors tend to also fit into these 3 archetypes.
Not the person you're responding to but I bought the Steam Deck to use mainly as a handheld and docking it is a bonus. The Steam Deck's controller support is the best of the other cheaper options that you could also use to stream.
There's already a lack of information online for simple things like torque specs. I can't imagine that a skilled professional could design the engine mounts even with if they had all of the relevant context online.
As far as I know, the way that these reproduction hardware companies operate is that they have physical cars that they can design around.
I have a 1993 Subaru WRX and I needed to replace the coolant header tank because mine had a bunch of leaks. I ended buying one from a specialty fab shop in the UK and I had to make a few measurements for them because there was varying bolt spacing for GC8 Impreza models.
Low cost carriers are almost an entirely different industry than traditional airlines. For example, Frontier has a loyalty program as well as their "Go Wild" pass which is essentially "Movie Pass" for flying domestically in the US, but that operates as a loss leader for ancillaries, where they make most of their money (around $70 per passenger). As others have mentioned, RyanAir also has a loyalty program that they lose money on.
Traditional airlines are very much like Starbucks nowadays in that they are essentially banks, but low cost carriers are closer to movie theaters where they essentially make nothing selling the actual seat so the more people they get in the door, the more they can make on ancillaries.
At the end of the day it's all different levels of abstractions and whether or not you're using the abstraction correctly. With k8s, the best practices are mostly set in a lot of use cases. For LLMs, we still have no idea what the best practices are.
I usually have a tube of anchovy paste in the fridge for whenever I make Caesar salad, but in the rare times that I don't I just use fish sauce in the dressing instead and it works surprisingly well.
You will want to search for IPTV services. It's a bit of a wild west out there but I'd recommend finding one that has a Discord. Most will offer a free trial for 24 hours or a week for you to try them out.
The NBA is similarly annoying. It's $110 for the season to watch all non-blacked out games. As a Knicks fan, it was great when I was living in Philly because I could watch all Knicks games unless they were nationally broadcast or if they were playing the Sixers. Now that I live in NJ I'm technically in the NY broadcast region. The only way for me to watch local games legally is to have MSG through a cable provider or pay for Gotham Sports Plus which is $35/month.
I don't know if we're all 10x'ing but our entire org is shipping PRs using an in-house framework akin to Stripe's Minions [1] and many of those PRs are generated from Slack. We definitely have work to do on the latter part of the SDLC to have more confidence in these changes but we can still rely on the existing observability layer to make sure things are working as expected.
Another commenter mentioned that Docker, git, etc. were all tools that greatly enhanced productivity and coding agents are just another tool that does that. I would agree, but argue that it's more impactful than all of those tools combined.
Same here. I do well in existing codebases because I can follow patterns and adapt to existing limitations but starting a new project is always so daunting to me. Writing a spec and iterating on it is so much more natural than writing code in a new project for me.
It doesn't prove anything. It provides context for why someone would make that statement. Can you prove that things that do not need to eat and drink to survive can suffer?
This is essentially just setting up an MCP connection to your kanban provider and instructing the agent to plan out an epic. I did this this morning for some data modeling our team needed to do. For the most part it generated a good set of tickets, but there were some hallucinations due to ambiguity. Reviewing the already written out tickets was much better than writing them out myself.
But the standard that will hopefully take over in most mature shops is spec driven development where instead of a team reviewing code, they review a spec which is used to generate tasks and subsequently code to satisfy the spec. Then 2 kanban boards exist. One for writing and submitting specs and another for the agents themselves to implement the approved specs.
These chips are large by fab standards and even with state of the art processes we likely won't see any kind of integration on consumer tech any time soon, but I imagine they will absolutely see instant demand if they can deliver on what they laid out in the post.
> From my own experience, models are at the tipping point for being useful at prototypes in software
You must not have much experience using the new frontier models then. A lot of large tech companies are replacing their SDLC with agentic workflows. The tooling and frameworks are still ramping up, but the models have no problem producing production ready software given proper specifications.
But at one point the model is sufficiently large enough to accomplish any task a human could specify. For software development, I think we're pretty much at that point with the latest Anthropic/Google/OpenAI models. We have no idea where the direction of token pricing is going to go in the future, but the consensus seems to be that it will only get more expensive. If Taalas can offer the same functionality that we have with frontier models today at a 1/10 of the cost and 10x the speed then they're going to take over a large part of the market.
Except they're still not accepting any feedback around AGENTS.md as a standard. You need to explicitly symlink CLAUDE.md to AGENTS.md in a workspace in order to Claude to work like every other agent when it comes to loading context.
For example, I just prompted Kimi-K2.6 with:
> I'm considering buying a used base model 2010 Honda Civic with 80k miles that's been garage kept. What are typical things that go wrong with this type of vehicle?
It listed 10 issues including the engine block cracking (which wasn't even an issue with 2010 Civics). Started a new chat and asked about a 2010 Toyota Camry, another unbelievably reliable car, and it listed 9 similar issues. Started a new chat and asked about a 2011 Jeep Grand Cherokee, a notoriously unreliable vehicle, and it listed the same number of issues.
Sure it's data to make decisions on either way, but it really all comes down to how good your prompts are and whether or not you can think critically about the output, whether or not that output is a conclusion or just data collection.