I am not being sarcastic or flippant in this, it limits me to not be willing to take advantage of the risk/reward system, but it's somehow deeply tied into my ethics in ways I didn't even really notice until my second startup. And I'm okay with that limitation, but I'll work through it sooner or later and regain my appetite for risk.

Aside from that, many people despise thinking this way about their work. Like me, even though I'm willing to do it. I've met founders who loved negotiation. Like I swear they must have been nightmare children. I hate negotiation, I want people to treat each other fairly and will stick with a good human that does so forever. I suspect those people don't mind ripping off family and friends, much less strangers. It is harsh, and I've seen things, but anecdotes aren't statistics, and that's a huge obvious red flag to me. If the leaders like to do internal negotiation games I just want out. It's just painful to have to deal with being around when you just want to make stuff work.

It's important to talk about ahead of time, and get in writing, but that's also something easily overlooked among family and friends who do not do this kind of thing much. And the downsides need to be honest and clear to avoid mistrust.

Now, after working at two startups and starting three, my third is a consulting company that exclusively does things hourly except in very special circumstances. It's never going to grow fast because it scales as my hours of labor. But I am way more able to pick a price for my labor than decide how to price something that I haven't done yet. I could figure it out, I just kinda don't want to. For now?

A much more fair retort is that an extra device to output video costs more, though I might argue that if you don't use the TV's built in system the manufacturer is losing ad revenue. So if you only use it as a normal TV you kinda are buying it subsidized by everyone else watching ads on theirs.

If you insert AI into your thinking process, it has a bias, for sure. It will helpfully reinforce whatever you tell it you think makes sense, or at least on average it will be interpreted that way because of a wide variety of human cognitive biases even if it hedges. At the least it will respond with ideas that are very... median.

So at each one of these tiny branches you introduce a bias towards the "typical" instead of discovering where your own mind would go. It's fine and conversational but it clearly influences your thought process to, well, mitigate your edges. Maybe it's more "correct", it's certainly less unique.

And then at some point they start charging for the service. That's the part I'm concerned about, if it's on-device and free to use I still think it makes your thought process less interesting and likely to have original ideas, but having to subscribe to a service to trust your decision making is deeply concerning.

I mean, I agree that such companies are over-represented in thinking about small businesses if that's what you mean. Normal companies have to be profitable quickly for sure.

It feels like tons of companies get valued based on userbase or revenue or theoretical breakthrough rather than ever having to really think about breaking even, but I know that's just because those folks get all the press.

https://github.com/gorhill/uMatrix

I don't know the implications of that, it's the only tool I've ever found that lets me feel in control of what programs my browser is executing.

Yes, comments of this nature are bad, annoying, and should be downvoted as they have minimal original thought, take minimal effort, and are often directly inaccurate. I'd still rather they have a disclaimer to make it easier to identify them!

Further, entire articles submitted to HN are clearly written by a LLM yet get over a hundred upvotes before people notice whether there's a disclaimer or not. These do not get caught quickly, and someone clicking on the link will likely generate ad revenue that incentives people to continue doing it.

LLM comments without a disclaimer should be avoided, and submitted articles written by a LLM should be flagged ASAP to avoid abuse since by the time someone clicks the link it's too late.

I would not concede that speed is not as important as doing it correctly in the context of evaluating learning. There are homework, projects, and papers where there is a lot of time available to probe whether they can think it through and do it correctly with no time limit. It's ideal if everyone can finish an exam, but there needs to be some kind of pressure for people to learn to quickly identify a kind of problem, identify the correct solution approach, and actually carry out the solution.

But they shouldn't be getting penalized for not doing a page of handwritten linear algebra correctly, I totally agree that you need to make sure you're testing what you think you're testing.

Arduino (the original AVR boards anyway) have always relied on GCC, and not just that but the entire open source chain that already existed for AVR-GCC. I'm sure they contribute back (I guess "sure" is an exaggeration), but it worked pretty darn well already.

Arduino, for me, replaced emacs for an IDE. The main reasons I use it are because I don't need to write a makefile, and the integrated serial port. Those are good enough features that I still use the IDE even though I haven't touched a real Arduino in a decade or more. But I work alone and don't usually have more than a few thousand lines of code so it's not too complex to manage.

Arduino was a nice beginner friendly IDE for sure that eliminated the need for make files or reading documentation for GCC, but the existing ecosystem was definitely not closed source.

In older homes there isn't necessarily HVAC at all and instead there are actual radiators. I've lived in two like that, there is just no forced air to rooms.

But these things trickle down to renters last. And if the landlord installs it, you bet your ass the rent is going up more than your savings on electricity.

Lose lose lose, if it gets installed then the current residents probably get priced out anyway. It eventually trickles down but we could do so much better.

A third of the country rents. Renters pay the utility bills. Landlords pay for appliance upgrades.

Why would the landlord put any effort into upgrading appliances when the cost of not upgrading them is borne by the renters?

I've never rented at a place where they didn't want to fix broken equipment with the cheapest possible replacement. And no renter would ever consider purchasing a major appliance like this since they'll end up priced out before they recover the cost in utility bills.

They're a nice technology, but our incentives are all wrong for a lot of housing stock.

I've been using Teensy devices for over a decade and have always had it just recognize the device as if it were a USB to serial adapter and I can talk to it as what I'd call "serial over the USB port". But that obviously doesn't involve what I think software people usually mean when they're talking about firmware debug -- which usually entails stepping through execution, right?

I'm used to just printing debug statements to the Serial.println() function, I learned on the 8051 where the best bet was to toggle different pins when code lines are passed, so even Serial.println() was a huge step up.

I think they do quite well considering the disparate resource levels, but some servers are effectively unmoderated while others are very comfortable; plenty are racist or other types of bigot friendly, but the infrastructure for server-level blocks is ad-hoc. Yet it still seems to work better than you'd guess.

Decentralization means whomever runs the server could be great, could just not be good at running a server, could be a religious fundamentalist, a literal cop, a literal communist, a literal nazi, etc etc. And all have different ideas of what needs moderating. There is no mechanism to enforce that "fediverse wide" other than ad-hoc efforts on top of the system.

I have encountered side effects that probably no one has seen before, simply because of rarity and peculiarity of behavior. You don't run into a ton of people using both interferon and doing karate, so if bruising more easily happens 10% of the time... would anyone notice?

Personally I would be more worried about persistent inflammation causing inflammatory disorders, of which there are many. If there are like 10,000 individuals with this trait then there just aren't enough to detect. But that seems direct... wouldn't you expect something like this to potentially even destroy viral reservoirs over time?

The fact that this is short term in the treatment made me 1000x more comfortable with the idea in any case.

Of course now no one needs to visit Wikipedia because Google has already scraped them with AI so you can just see the maybe accurate summary. Seems risky, as if you should have to log in to use Google since the AI might have forbidden information.

"artificial" maybe I should have said "synthetic"? I mean the computer can teach itself.

"constrained" the game has rules that can be evaluated

and as to the other -- I don't know what to tell you, I don't think anything I said is inconsistent with the below quotes.

It's clearly not just a generic LLM, and it's only possible to generate a billion training examples for it to play against itself because synthetic data is valid. And synthetic data contains training examples no human has ever done, which is why it's not at all surprising it did stuff humans never would try. A LLM would just try patterns that, at best, are published in human-generated go game histories or synthesized from them. I think this inherently limits the amount of exploration it can do of the game space, and similarly would be much less likely to generate novel moves.

https://en.wikipedia.org/wiki/AlphaGo

> As of 2016, AlphaGo's algorithm uses a combination of machine learning and tree search techniques, combined with extensive training, both from human and computer play. It uses Monte Carlo tree search, guided by a "value network" and a "policy network", both implemented using deep neural network technology.[5][4] A limited amount of game-specific feature detection pre-processing (for example, to highlight whether a move matches a nakade pattern) is applied to the input before it is sent to the neural networks.[4] The networks are convolutional neural networks with 12 layers, trained by reinforcement learning.[4]

> The system's neural networks were initially bootstrapped from human gameplay expertise. AlphaGo was initially trained to mimic human play by attempting to match the moves of expert players from recorded historical games, using a database of around 30 million moves.[21] Once it had reached a certain degree of proficiency, it was trained further by being set to play large numbers of games against other instances of itself, using reinforcement learning to improve its play.[5] To avoid "disrespectfully" wasting its opponent's time, the program is specifically programmed to resign if its assessment of win probability falls beneath a certain threshold; for the match against Lee, the resignation threshold was set to 20%.[64]

Not really a big deal, though I'd have... you know... linked to the actual paper or maybe mentioned the professor's name more prominently.

I think most stuff I read emphasizes institute and researchers more heavily, but I can see why anyone doing public research might want to expand the scope of credit.