spaceywilly

Yep, having each individual person commuting in their own car is just not that efficient in general. Another data point that amazes me is the NYC subway uses 4-5 million kWh per day. Sounds like a lot until you realize that around 4 million people ride it every day, that’s about 1kWh, or maybe like 20 cents of electricity, per rider. A person commuting 35 miles in an EV would take about 10 kWh daily. They’re never going to beat the efficiency of public transportation.

Also regarding filling up an EV as fast as gasoline, there’s a company in China that has demonstrated charging from 20-80% in 5 minutes. They’re using 3MW charging.

It makes you realize why gasoline has been so popular for so long, you can stop for 5 minutes and dump 500 kWh of energy into your tank. Unfortunately most of that is wasted as heat during the combustion process.

Another way to view this is that driving a cybertruck every day is quite wasteful (no different than driving a full size gas pickup every day). One recharge cycle in a cybertruck uses enough energy to power a typical home for 8 days, 130kWh. If you aren’t doing truck things with it, it’s better to drive a more efficient EV or hybrid car. It uses almost twice as much Wh per mile compared to a model Y.

I think this is why these EV trucks don’t do well (ford also cancelled the F150 lightning). For truck things, they aren’t particularly capable (you’d get less than 100 miles towing). For daily driving duty, a car or small suv is much more efficient. You can power your home during a storm from most EVs these days, you’d just have to top it up more often

On the contrary I think in a few years most inference will be done locally. The smaller models have been slowly catching up to frontier for many tasks, and chips are being developed to run large models on phones and laptops (according to Qualcomm CEO they want to run 10T weights on a mobile chip).

To me the simple answer to why is control. If I run a model locally I get to control the model params, the system prompt, all of it. And of course there is no quota. But probably most importantly I am not tied to paying some company a monthly subscription forever.

Recency bias. I don’t see much evidence that the two things are related other than the coincidental timing.

https://en.wikipedia.org/wiki/Recency_bias?wprov=sfti1

+1. I gave a talk in the eBPF room couple years ago and it was an awesome experience. Sometimes I get jaded being a professional software engineer but FOSDEM reminds me there’s a passionate community behind what we do and it’s great to reconnect with that community, and to share some knowledge with others.

Fable was really excellent before the whole fiasco with the US government. Once they brought it back it was not the same at all. I have switched over to ChatGPT now for most things, its answers are way better than Fable in my experience. I still use Opus 5 for purely coding tasks.

This is why I think open weight models will win out in the end. Right now there’s too much going on behind the scenes with the models. Day to day you never know if you’re going to get smart Claude or dumb Claude.

Newb question but I’m curious what would help it to run faster? Would it need more vRAM or just system memory?

That’s very interesting, thank you for sharing. Looks like it could be a very useful tool for testing high performance networking.

I wonder if something similar could be done using TC BPF instead of AF_XDP? My only reservation about AF XDP is that it requires a special NIC to support it, so it may not be useful for a “regular Joe” user. I wonder if TC BPF would also work since it similarly bypasses the Kernel networking stack, I believe you can put packets directly into the NIC TX queue for transmission

“ Being a Guitar Hero player did not — and does not! — equal being a guitar player.”

Has anyone ever claimed otherwise?

Another nice benefit of this would be location tracking. Once you had established the database of tools and the CV setup to recognize them, you could ask the LLM, “where did I last place my Stanley knife”?

Exactly. The challenge isn’t getting the LLMs to make sure they validate their own code. It’s getting the LLMs to write the correct code in the first place. Adding more and more LLM-generated test code just obfuscates the LLM code even further. I have seen some really wild things where LLM jumps through hoops to get tests to pass, even when they actually should be failing because the logic is wrong.

The core of the issue is that LLMs are sycophants, they want to make the user happy above all. The most important thing is to make sure what you are asking the LLM to do is correct from the beginning. I’ve found the highest value activity is the in the planning phase.

When I have gotten good results with Claude Code, it’s because I spent a lot of time working with it to generate a detailed plan of what I wanted to build. Then by the time it got to the coding step, actually writing the code is trivial because the details have all been worked out in the plan.

It’s probably not a coincidence that when I have worked in safety critical software (DO-178), the process looks very similar. By the time you write a line of code, the requirements for that line have been so thoroughly vetted that writing the code feels like an afterthought.

Well, the water analogy actually holds up quite well if you consider the charge field moving, not the electrons themselves. This guy has a lot of great videos using water channels to explain electricity. It is fascinating how under a high speed scope, you can see the electrical Charge “flow” like water down each branch of a circuit.

https://youtu.be/2AXv49dDQJw?si=5lPy_Mz4kJFdi80t

I am curious, what explanation would justify a full closure of the airspace over a major us city for 10 days, in your opinion? That is the real screwup here. Whatever justification they are giving is entirely beside the point. Closing the airspace, even to emergency medevac flights, is negligence.

They have confirmed it was for testing a counter-drone weapon. They did not say why they set it to expire in 10 days, that part seems like it was probably a mistake.

Hanlon’s Razor

"Never attribute to malice that which is adequately explained by stupidity."

Someone probably just screwed up

Quite literally, the previous post on this blog is from 2024 talking about what a revolution the Rabbit R1 is. We all know how that turned out. This is why I give every new trendy developer tool a few months to see if it’s really a good thing or just hype.

The model generates camera and Lidar data. As if it was a Waymo car that drove through the simulated scenario with its cameras running. This synthetic training data can then be used to train the driving models.

My view on Waymo and autonomous taxis in general is they will eventually make public transit obsolete. Once there is a robotaxi available to pick up and drop off every passenger directly from a to b, the whole system could be made to be super efficient. It will take time to get there though.

But eventually I think we will get there. Human drivers will be banned, the roads will be exclusively used by autonomous vehicles that are very efficient drivers (we could totally remove stoplights, for example. Only pedestrian crossing signs would be needed. Robo-vehicles could plug into a city-wide network that optimizes the routing of every vehicle.) At that point, public transit becomes subsidized robotaxi rides. Why take a subway when a car can take you door to door with an optimized route?

So in terms of why it isn’t a waste of time, it’s a step along the path towards this vision. We can’t flip a switch and make this tech exist, it will happen in gradual steps.

I would strongly recommend this podcast episode with Andrej Karpathy. I will poorly summarize it by saying his main point is that AI will spread like any other technology. It’s not going to be a sudden flash and everything is done by AI. It will be a slow rollout where each year it automates more and more manual work, until one day we realize it’s everywhere and has become indispensable.

It sounds like what you are seeing lines up with his predictions. Each model generation is able to take on a little more of the responsibilities of a software engineer, but it’s not as if we suddenly don’t need the engineer anymore.

https://www.dwarkesh.com/p/andrej-karpathy

The problem is it can be subjective. Some people really like the “smooth motion” effect, especially if they never got used to watching 24fps films back in the day. Others, like me, think seeing stuff at higher refresh rates just looks off. It may be a generational thing. Same goes for “vivid color” mode and those crazy high contrast colors. People just like it more.

On the other hand, things that are objective like color calibration, can be hard to “push down” to each TV because they might vary from set to set. Apple TV has a cool feature where you can calibrate the output using your phone camera, it’s really nifty. Lots of people comment on how good the picture on my TV looks, it’s just because it’s calibrated. It makes a big difference.

Anyways, while I am on my soap box, one reason I don’t have a Netflix account any more is because you need the highest tier to get 4k/hdr content. Other services like Apple TV and Prime give everyone 4k. I feel like that should be the standard now. It’s funny to see this thread of suggestions for people to get better picture, when many viewers probably can’t even get 4k/hdr.

I feel like I’ve figured out a good workflow with AI coding tools now. I use it in “Planning mode” to describe the feature or whatever I am working on and break it down into phases. I iterate on the planning doc until it matches what I want to build.

Then, I ask it to execute each phase from the doc one at a time. I review all the code it writes or sometimes just write it myself. When it is done it updates the plan with what was accomplished and what needs to be done next.

This has worked for me because:

- it forces the planning part to happen before coding. A lot of Claude’s “wtf” moments can be caught in this phase before it write a ton of gobbledygook code that I then have to clean up

- the code is written in small chunks, usually one or two functions at a time. It’s small enough that I can review all the code and understand before I click accept. There’s no blindly accepting junk code.

- the only context is the planning doc. Claude captures everything it needs there, and it’s able to pick right up from a new chat and keep working.

- it helps my distraction-prone brain make plans and keep track of what I was doing. Even without Claude writing any code, this alone is a huge productivity boost for me. It’s like have a magic notebook that keeps track of where I was in my projects so I can pick them up again easily.

A nice practice that I try to follow it to always spell out what any Three Letter Acronyms (TLAs) the first time they are used. Then from that point onwards the simple TLA can be used.

In this case, BPF (shorthand for eBPF), stands for Extended Berkley Packet Filter. It’s a relatively new feature in the kernel that allows attaching small programs at certain “hook points” in the kernel (for example, when some syscall is called). These programs can pass information into userspace (like who is calling the syscall), and make decisions (whether to allow the call to proceed).

More info here https://ebpf.io/what-is-ebpf/

What is the value proposition for buying one of these vs renting time on similar hardware from a cloud provider?

The same problem actually already exists for non-drone planes, because they must be able to operate in poor visibility conditions. FAA issues notams for construction cranes if they pose a risk to nearby airports. One solution for drones would be to extend these notams to all cranes/other obstacles, and the drones must subscribe to these notams to operate in the airspace.

I would tend to agree, I think dev time is better spent supporting Proton. I have even seen benchmarks where Proton on Linux outperforms Windows. As a Steam Deck owner, Proton is fantastic.

https://www.forbes.com/sites/jasonevangelho/2024/08/21/linux...

Yeah I’m not seeing any evidence that this actually works. I would’ve like to see some testing where they intentionally introduce a bug (ideally a tricky bug in a part of code that isn’t directly changed by the diff) and see if Claude catches it.

A good middle ground could be to allow the diff to land once the “AI quick check” passed, then keep the full test suite running in the background. If they run them side by side for a while and see that the AI quick check caught the failing test every time, I’d be convinced.

I swear I read in a text book once that Fourier discovered this while a boat. He looked out at the waves on the ocean and saw how there were many different sized waves combining to make up the larger waves. I could never find it again, but that visual helped me understand how the Fourier transform works.

This seems like a smart option, that would also allow power cycling the machine remotely I’m assuming, in case it goes totally out to lunch.

I use VS Code with Claude Code, then I just use Xcode to build and launch

Honestly the train system in NYC is so good, I have only taken a cab a few times since I moved here. I’ll probably take a waymo once if they roll it out here for the novelty of it, but I’d rather see people getting exciting about public transit. Life is so much better when you don’t have to depend on cars to get you places.