rafaelmn

Copyright is artificial scarcity rationalized by arguing that producing novel intellectual work is valuable, but requires substantial effort that can't be recouped, so we have to incentivize it somehow.

LLMs and AI are changing that proposition substantially - human effort involved in producing copyrightable content is getting reduced constantly to the point that if we abolish copyright entirely we'll still have more content than we could ever hope for.

AI/robotics eliminating scarcity of physical goods sounds very far fetched but in the intellectual space it looks very very plausible in the near future - so it could be time to abolish IP laws soon, especially if AI manages to advance enough in R&D and research space.

You don't have to have a JIT to have a JS VM ?

There's also a recency bias, where you ignore how valuable some decision was in the past and ignore the fact that you maybe wouldn't even get to the place you are if "you made a different decision in the past".

Debugger MCP better

Meh I just replaced a battery in my 14 pro (and the charging port) for ~200 EUR. I didn't want to upgrade yet and I would not even be considering upgrading if the device had USB-C

How exactly ?

We heard this since Uber days and all it did was break the old taxi systems and brought taxi service to a wider audience. As a consumer I benefited immensely from Uber.

Likewise for AirBnB and hotels.

Not saying that these don't have secondary effects on the society - but as a consumer they were and arguably still are pretty amazing.

You can easily setup codex rules to auto approve local stuff but gate external effects like push/jira write/curl - works better for me than full yolo mode - depends if it's a solo project or working with a team.

I'm sorry but no - output quality matters a lot more for me.

Just yesterday I tried to use Google antigravity to do a side project I've had on the back burner for 10 years now. Gemini flash is insanely fast - at first I was amazed at how quickly I was getting responses, and it seemed to hold it's own in technical discussion, although sycophancy is next level. But then when I actually let it do the coding part it was just drivel. I wouldn't even bother improving that code - like cleaning up after a lazy unskilled coworker - throw everything away and start over because the foundation is just leading in bad direction.

I spun up Astra on the same problem and although it was sluggish in comparison, and much more pedantic about irrelevant details - the feedback/pushback was actually meaningful. The implementation PoC also took tweaking but we got on the same page really fast.

Gemini Flash 3.8 was just producing garbage ultra fast, Astra could actually be steered into a direction I want and it provides valuable/insightful feedback.

I don't have infinite reading capacity/mental stamina - I would rather the model take it's time and let me see something high quality rather than get bombarded with garbage. If it can be faster that's great - but I'll always default to smarter model. The only exception is stupid trivial tasks like log analysis and similar.

You can run any open model on a shared API host via OpenRouter and pin to which host you want to go for the quant/privacy/etc. mix you care about. You can pay them directly if you don't want the OpenRouter overhead - but the convenience of switching, having one invoice, etc. is worth it IMO

It's not closed hosted models vs open local models, it's hosted open models vs local open models where the math doesn't work for local LLMs.

The only local inference use-case I can think of is porn generation (because most providers don't want to deal with it) and illegal shit like hacking to minimize the tracing.

And if you're super paranoid - but honestly giving sensitive info to LLMs in any scenario is a gamble.

If you game and can use your GPU I guess then it works as well but models that fit into a gaming GPU suck too much to bother IMO.

Except there's a huge gulf of self-hosting and using API hosts - no way you can reach the economics of a shared host. Privacy is a problem but you can chose who you host with and where it's hosted (which jurisdiction).

When privacy/compliance really starts to matter it's up to the client/business to provide you with tooling - you're not running that on your own hardware anyway.

So the local AI for individuals is just a hobby/gimmick at this point not a rational decision. Self-hosting for business is a different story.

First time this benchmark impressed me - 10/10 I would believe a human spent hours making this. There are details a human would have done better (beak attaching to the head) but still impressive.

Agreed but I haven't found a nice cross platform way to share VM setups. Like I can setup a sandboxed compose dev environment for all my coworkers and tell them use this to recreate our app environment locally and it's already configured with skills/config for agents to find their way around it.

Ideally I would just compose up the infra and let agents run in the host VM but there's nothing as idiot proof as docker compose that works on windows/mac/linux/whatever the new guy prefers to run.

So I incus my devboxes and just run agents inside, but I compose work stuff

You should be containerizing your dev environments these days even if you're not using LLMs - supply-chaining is getting too insane to follow. No dev tools installed outside of VM/containers on my machines. I'm even paranoid about VSCode because of plugins.

One greenfield project I worked on our AWS rep specifically told us to avoid Cognito and go towards auth0 or whatever else.

On another project people didn't get this advice and we spent a two weeks working around the limitations to scrap it eventually.

I have an identical gen mac mini I bought for testing out the apple silicon for my workloads a year and a half ago, except it's 16GB min spec version - I got ram throttled on that thing constantly as soon as I start using it for development. If it was just productivity I could get away with it - but as soon as I start something like JetBrains Rider on it or VSCode with C# language memory pressure is in the yellow and if I start a next server for frontend the machine just grinds to a halt. Even when I'm using remote workstation my memory usage is hovering above 20gb with active swap on my laptop. Soo many electron productivity apps eating up ram like crazy.

If it was more readily available on retailers such as Amazon I would even recommend 32gb ram air for local workflows - if you're doing development these days you're just going to need it - especially with LLM workflows and parallelizing work. The price difference between 16 and 24 is not even close to not justify picking it for a work machine.

Thing is having more cores/faster cores might make your workflows 1-2x faster but hitting swap is going to be like >10x degradation

I'm kind of lost on what to do with it. I have better workstation PCs, and I couldn't find a good way to turn it into a media center - it would be a great living room PC - but couldn't find a BT remote friendly solution last time I checked. Anyone has a solution for this ? I don't want to be using mouse/keyboard every time I sit on the couch - something that would let me stream something like jellyfin fullscreen with a remote ?

If I was going to run LLMs locally I would definitely go for a pro.

I meant the only downside of not using a pro for my use-case is that I don't have a HDMI port on device with high refresh rate. I have a decent dock that can give me 4k/60hz HDMI but not 120hz refresh for macs (it works on windows). It's a minor thing but having 120hz is nice when I'm docked at home.

I would not use Neo, Air with 24 GB ram should be your minimum because you will want to do some work locally even when you're running a VPN/SSH thin client setup.

I'm currently SSH-ing into my workstation from my M4 Air with 24 gb ram and it's ideal for this flow. Slack/editors/clients/browsers/etc. easily gobble up over 16 GB. I have no more dev tools/compilers/source on my client machines, everything is dockered up on remote workstation in isolated VMs (too much supply-chaining).

My only downside to using a pro is not having 120/4k HDMI port on air and my dock won't support it with Apple (it does with windows)

This is about comparing different exercise intensities ? Sprinting > jogging basically ?

Literally all OEMs do this, if you go to Apple site you won't see any sales, but Amazon and other retailers will have stuff at 20% off regularly

You don't have to spread your payment info to 10 different places ? You pay a fee for centralized API credits

Payment processing for tokens - sounds like the thing Stripe does.

I recently saw a blog post [1] about a famous Haskel shop moving away from Haskell to Python because the iteration speed with LLMs was just that much better. There is so much React in training data, TS compile times are minimal compared to Rust and similar.

I suspect user facing/fast moving code (UX layer) will move to dynamic systems with fast iteration times. Infra layer will move towards safe systems level environments like Rust. I'm not sure where Java/C# lands in all of this - it's kind of the middle ground between these two worlds but the tradeoffs change drastically with LLMs - my gut feeling says that TS/Python is good enough for UX work and Rust is better for systems work so it gets less popular going forward.

[1] https://avi.press/posts/2026-07-10-after-7-years-in-producti...

404 link ?

Same here - it's probably that OpenAI needs to buy goodwill with developers to infiltrate corps and Anthropic is trying to squeeze the lead into revenue. The only question is - how much longer can OpenAI burn money before it needs to start showing signs of profitability

> at least I know their motives are purely profit driven.

What profit ? They are blatantly focusing on investment narratives, politics, control, stifling competition. Profit is like a footnote at this point.

> Some of the issues mentioned above like dead code removal, code duplication, unreachable code are already solved using deterministic linters for quite a while now for most language ecosystems.

I have legacy endpoints that are no longer used in practice, there for historical reasons, intertwined with existing code etc. They might be marked obsolete, services implementing it are not - agent greps those, builds off of them - produces half legacy garbage.

Linters only handle trivial cases most of us already solved.

If we want reliability - we come up with processes to make it reliable and not rely on individuals getting it right. Code is a way to create a reliable process in the digital world.

> The strange thing is this is happening in multiple companies at the same time. It's like all the CEOs and HR reps met at some golf retreat and decided to follow a script.

Industry trends spread in all roles ?

>You can build application outside of container, but run it in container. I think that it is simpler workflow, than everything in container (when you actually need to develop it with IDE).

At this point I will not do any dev outside of a container - so many things can be supply chained in the OSS dev stack it's just not worth it, and once you get used to developing in containers it's actually a lot cleaner to move between hosts - you're essentially treating your client as a remote terminal.

If you're doing web dev work in this day an age SSH with tmux or some editor with SSH server support should be your dev setup.

Good for you ?