HN user
And a whole universe of random tests got baked into the AI’s training data. Websites of antelopes driving trains and hammerhead sharks swinging in a tyre swing were created. It was a short while until AI became so focused on animals that it gave up competing with developers. Life became sane again.
The AI labs have been subsidising. When they try turn a profit, people will move to the fast followers. The only people that won’t are those that compete on leveraging the very latest models and even then, once spend and scale goes to the cheaper providers, we’ll see deeper research from those providers too. Think “PC compatibles beat IBM, Sun, SGI eventually”.
If the US companies need trillions to barely beat Chinese companies spending billions, despite a multi year head start...
If I had to summarise Google’s effort it would be: stay close but let the others burn themselves out. Position for the long game until you see something worth betting the company on.
Apple similar, without the “stay close” bit.
I made something to ping an AWS service to tell me the uptime of my internet connection. The idea was to sprinkle them around our area, connected to various home WiFi’s, and get a better triangulation of outages. Eg is whole pipe out, just one ISP etc.
I made and tested it but didn’t care enough to continue.
Sean Carroll’s Mindscape for me
How much do you think it’s holding them back? Market share seems to be surviving the lack of quality vs the other players.
Or possibly: they’re focusing where it matters?
I think Google just needs to keep its foot in the door and let the other two spend their way into oblivion, channeling “AGI or die trying”.
The benefits were in communication and relationship arbitrage? Surely both of those can be automated over time.
This is just one aspect but it’s still useful. Many people want to see a house and say “please help me make that”. In Australia there’s a set of house patterns that reduce the overhead of just landing a design and pushing it through the admin hurdles.
There’s probably a massive Chinese bot net scraping models from within the US already. If not there soon will be.
Anthropic: your next ad writes itself. Nobody else is worth restricting.
I generally accuse LLM’s of having no sense of value. The machine will make a complicated plan but entirely lose sight of eg the fact that response time matters to humans.
Not always, but enough that I consider it a thing to fire in a direction, not a thing that aims.
I assume the bet is that as you swap humans for machines, this pays for itself. Swap entire devs and teams and frankly, managers, and you make up a lot of 5%’s fast.
If it works. And I’m not sure who is going to buy the stuff the machines produce, but shrug. Presumably some bots click ads for NFT’s that other bots generate.
Hard problem. I find myself adding in filler to stop the thing from jabbering.
I also think it spends most of its iq on sounding good rather than thinking about the problem. “Yeah absolutely I can see why you’d like to…” etc. This is likely because it’s on a timer and maybe voice is more expensive to process? Text responses spend more time on the task.
Not sure if you remember this chess game. 1 man vs 50000. Quite an amazing outcome.
The easiest way would be a straight tax on AI usage, and using that tax to pay a universal basic income
Problem for jobs is that there are 200 countries and all the earnings will go to a few. Universal basic income for everyone? Or just the US?
Who gets to keep their house locations in a new fair world? The person whose parents bought in the right place 50 years ago? Who pays the money these models earn, if nobody clicks ads or does a job? What is income for if we don’t work and can just ask the AI for everything we want?
What happens when the super smart AI comes up with “better” (more fair, consistent, etc) answers than you think you have to questions like the above? What if they end up socialist? Do we force it (and invite risk it escapes and fights us for the greater good) or give in to the presumably more thorough reasoning?
That’s how I do side projects.
The cost between an A500 and a VGA-enabled PC in 1987 ($699 vs $3500-ish?) would have put them in such different categories and customer segments that they would rarely interact.
I remember seeing a PC one of the rich kids brought to boarding school in 1990 and realising it was just crisper than my A500. The PC’s in the school lab were all green and orange screens with one colour CGA, so this was quite a surprise. Still took me some time to accept reality :)
I’m moving away from Claude for anything complicated. It’s got such nice DX but I can’t take the confident flaky results. Finding Codex on the high plan more thorough, and for any complicated project that’s what I need.
Still using Claude for UX (playgrounds) and language. OpenAI has always been a little more cerebral and stern, which doesn’t suit those areas. When it tries to be friendly it comes off as someone my age trying to be a 20-something.
I did ~this in the 200X’s for a C# todo list, not nearly as polished but it was still So Much Work. Filed under things that should just work already.
Daniel is a very impressive guy. Well within the realm of “fund the people not the idea” that YC seems to do. Got a few bucks from them and probably earning from collaborations etc. Odds of them not figuring out a business model seem slim.
I have this as a skill Claude created to run the rest. It mentions each skill in turn, see below. It’s not deterministic but it definitely runs each skill and it’s raised a bunch of issues, which I then selectively deal with. Where I can, once an issue is identified, I make deterministic tests.
Text includes:
Invoke each review/audit skill in sequence. Each skill runs its own comprehensive checks and returns findings. Capture the findings from each and incorporate them into the final report.
IMPORTANT: Invoke each skill using the Skill tool. Each skill is independently runnable and will produce its own detailed output. Summarize findings per skill into the unified report format.
4. Architecture Health
Invoke: Skill(architecture-review)
Covers: module boundaries, cross-module communication, dependency direction, infrastructure layer rules, hexagonal architecture compliance.
5. Security Health
Invoke: Skill(security-review)
Covers: hardcoded secrets, SQL injection, authorization, HTTPS, CORS, input validation, authentication patterns.
$200, over December it was doubled. I tried my best in between family time and friends to burn a hole in it. Never got near doing so.
I found that when I have “infinite” tokens my behaviour changed. 3-5 tabs so I’m not waiting, free side quests, huge review skills over whole codebase, skills that wrap 10 other skills. It’s like going from expensive data to uncapped.
I think these token doubles are there to kick you into a abundance mindset (for want of a better term) so going back feels painful. Stop counting tokens, focus on your project and the cost of your own time.
“In December, Gemini traffic increased by 28.4% month-over-month, while ChatGPT traffic decreased by 5.6%”
https://www.businessinsider.com/openai-chatgpt-vs-gemini-web...
"What's you number one piece of hiring advice?"
"Hire for slope, not Y-intercept. This is actually my number one piece of life advice."
-@sama, who I’m generally a big fan of. But the job is now harder
And China is likely to do to Tesla robots what they’ve done to the cars. I assume the bans will be incoming, because the US can’t have millions of Chinese kung fu robots sitting about pouring tea, waiting for critical mass.
Every so often my YouTube logs out and I’m exposed to the view a “random visitor” would see. Instantly visible because it’s filled with stupid content and sexual provocation.
I manage the shit out of FB and YouTube. You need to block a few things so it stops testing a few segment ideas.
Got a friend who is in the high frequency trading industry and uses both Java and C#. I asked about GC. Turns out you just write code that doesn’t need to GC. Object pools, off-heap memory etc.
It won’t do the absolute fastest tasks in the stack quite as well but supposedly the coding speed and memory management benefits are more important, and there’s no GC so it’s reliable.
I view LLMs akin to a dictionary
…If every time you looked at the dictionary it gave you a slightly different definition, and sometimes it gave you the wrong definition!
Totally. Surely the IDE’s like antigravity are meant to give the LLM more tools to use for eg refactoring or dependency management? I haven’t used it but seems a quick win to move from token generation to deterministic tool use.
It’s a $90k engineer that sometimes acts like a vandal, who never has thoughts like “this seems to be a bad way to go. Let me ask the boss” or “you know, I was thinking. Shouldn’t we try to extract this code into a reusable component?” The worst developers I’ve worked with have better instincts for what’s valuable. I wish it would stop with “the simplest way to resolve this is X little shortcut” -> boom.
It basically stumbles around generating tokens within the bounds (usually) of your prompt, and rarely stops to think. Goal is token generation, baby. Not careful evaluation. I have to keep forcing it to stop creating magic inline strings and rather use constants or config, even though those instructions are all over my Claude.md and I’m using the top model. It loves to take shortcuts that save GPU but cost me time and money to wrestle back to rational. “These issues weren’t created by me in this chat right now so I’ll ignore them and ship it.” No, fix all the bugs. That’s the job.
Still, I love it. I can hand code the bits I want to, let it fly with the bits I don’t. I can try something new in a separate CLI tab while others are spinning. Cost to experiment drops massively.