w-m

At introduction, Terra was a good mid-tier model. Terra [high] was on the pareto frontier of DeepSWE's score over cost, if only ever so slightly.

When I had Sol orchestrate Luna and Terra as implementation agents, Sol was a lot happier with what Terra produced and would find far fewer issues than what was implemented by Luna.

But a few weeks after introduction, OpenAI slashed Luna's cost by 80% and Terra's only by 20%. Only then did it become uneconomical to run Terra and its reason to exist stopped.

There are cars with engines that will bring you to 250 mph without burning out, like the Bugatti Chiron. Unfortunately you will burn through your $40k set of tires after 15 minutes at that speed. But don’t fret, because your fuel tank will be empty after 9 minutes anyhow.

Unfortunately, this seems somewhat unmaintained: five of the first nine "featured" cams have a dead/removed/copyrighted stream.

The plot doesn't appear to be in Amdahl territory yet. The single-threaded time in the plot looks to be around 39 seconds. A perfect division into 32 workers without overhead would make it 39 / 32 = 1.22 seconds. With the multi-threaded workload being reported as 1.5 seconds in the text, there's still only .3 seconds of overhead + serial instructions that can't be parallelized.

Every doubling of the number of workers halves the execution time cleanly in the plot, from 40 seconds to 20 seconds to 10 seconds. Eyeballing this for 32 over 16 workers is difficult, but it still seems close to halving the total time once again. So there's not a lot of Amdahl flattening, it's just the plain physics of looking at a inverse-proportional curve.

This is well-written. I could follow along quite nicely, from the setup through the bottlenecks and onto the resolution of the performance bug. Even the PRs are very pleasant to read: the majority of them is just a handful of changed lines with an added tests and a bit of documentation.

I was taken aback for a moment that this work originated from a report on StackOverflow. I had thought SO was effectively dead and abandoned by its community. But maybe I shouldn't project my own experience onto everyone else.

What exactly do subagents do that I can't replicate with a simple skill that tells an orchestrator to create subshells for tasks, each running `codex exec`? I've been doing this with Fable orchestrating Sol-medium and Terra-high, works like a charm.

...aaaand we're down to 112997 now.

Querying the API, there seem to be 112998 artworks with this label as of this moment.

(I'm deliberately not posting the direct API call in case it's expensive for them to run. Documentation is found here: https://api.artic.edu/docs/#fields-collections-artworks)

Ok, fair points, including the sister comment, it's likely not a drop in O2 levels.

But then why can we see problems with concentration in studies of people in poorly ventilated rooms, but not replicate that when just adding CO2 to normal air? What is the CO2 that we can measure in meeting rooms actually a proxy for?

I don't think you can cleanly compare this: In the study, they added CO2 to the room, while keeping O2 at normoxic levels throughout the experiment. In your meeting room, O2 levels will be dropping in lock-step with the CO2-levels rising. It may be the lack of oxygen that leads to drowsiness, not the additional CO2. But it's the CO2 levels that you can measure as a good proxy of overall air quality.

John Oliver had a segment on prediction markets this week. It covers insider training and opportunities for blatant manipulation like this, well worth checking out. The example in the Last Week Tonight segment was betting on dildos being thrown on court during a WNBA match. And then travelling to the match to throw the dildo.

[0] https://www.youtube.com/watch?v=ZN4njIQcSR4

While the trash can generation was somewhat present and around, I don't think I ever saw a cheese grater in the flesh. Did it have any users? Were there any actual useful expansion cards? Did anybody continue buying this at all, after it didn't get the M3 Ultra bump, that the Mac Studio got last year?

The original title of the document is “The future of software engineering”, which is so generic that I chose to ignore the HN rule to not editorialise, in this case, and picked one of the core insights for the title.

It takes some time to study it, but I found it time well spent: its a really good summary of the questions the community currently goes through, around the effects of LLM use in programming. Which are hotly discussed in HN every day. I think it’s helpful to gave some structured input on that.

(I’m not affiliated with the authors in any way, just found the document interesting).

I don’t think learning and understanding is hard-coupled to performing all low level steps yourself. The LLM can be a developer, sure. But it can also take on the role of rubber duck, architect, teacher or pupil.

Have a large LLM-written change set that works but that you’re not sure you fully understand? Make the coding agent quiz you on the design and implementation decisions. This can be a lot more engaging than trying to do a normal code review. And you might even learn something from it. Probably not the same amount as if you did this yourself fully. But that’s just a question of how much effort you want to invest in the understanding?

An iterative prompt with GPT-5.2 on Copilot CLI spits out a dense two-page proof for problem 10 after less than 60 minutes of working. A review of the generated proof with Claude 4.6 on Copilot attests it mathematical correctness, identifying only minor issues, mostly in the presentation.

But as a non-mathematician I'm not following any of it. How many people are there who are willing to check the generative results? And how much effort is it for a human to check these? How quickly can you even identify math-slop?

Here's the generated proof:

https://github.com/w-m/firstproof_problem_10/blob/2acd1cea85...

A clearly defined/testable long-horizon task: demonstrating the capability of planning and executing projects that overrun current llm's context windows by several orders of magnitude.

Single-issue coding benchmarks are getting saturated, and I'm wondering when we'll get to a point where coding agents will be able to tackle some long-running projects. Greenfield projects are hard to benchmark. So creating code or porting code from one language to another for an established project with a good test suite should make for an interesting benchmark, no?

At the current rate of progress I'm wondering how long it will take for llm agents to be able to rewrite/translate complete projects into another language. SQLite may not be the best candidate, due to the hidden test suite. But CPython or Clang or binutils or...

The RIIR-benchmark: rewrite CPython in Rust, pass the complete test suite, no performance regressions, $100 budget. How far away are we there, a couple months? A few years? Or is it a completely ill-posed problem, due to the test suite being tied to the implementation language?

That is factually incorrect. The primary source is wind at 132 TWh in 2025, followed by solar with 70 TWh.

Lignite was third with 67 TWh and hard coal sits at 27 TWh.

https://www.energy-charts.info/downloads/electricity_generat...

Great technical demo, but the usability feels unpolished. So here's a little bit of feedback of trying this out on a piano: Just because my piano has 88 keys doesn't mean they are all useful for ear training. The very low and very high notes shouldn't be used, at least not by default. Also they don't even show up properly in the sheet.

As the melodies get longer and longer with each win, this devolves quickly into a memory game. I'd like to keep playing ear training, but I struggle with remembering what sequence of notes came at steps 8+.

This is somewhat aggravated by completely resetting the current level and replaying the whole melody after a single mistake. If I keep making a mistake in note 10, I get all the notes over and over again, which is a bit maddening.

The password and pwbuf arrays are declared one right after the other. Will they appear consecutive in memory, i.e. will you overwrite pwbuf when writing past password?

If so, could you type the same password that’s exactly 100 bytes twice and then hit enter to gain root? With only clobbering one additional byte, of ttybuf?

Edit: no, silly, password is overwritten with its hash before the comparison.

“With Series 3, we are laser focused on improving power efficiency, adding more CPU performance, a bigger GPU in a class of its own, more AI compute and app compatibility you can count on with x86.” – Jim Johnson, Senior Vice President and General Manager, Client Computing Group, Intel

A laser focus on five things is either business nonsense or optics nonsense. Who was this written for?

Wouldn’t a multiple of the resonance frequency also be problematic then? Why doesn’t the axle disintegrate at 4800 rpm?

Just use the non-codex models for investigation and planning, they listen to "do not edit any files yet, just reply here in chat". And they're better at getting the bigger picture. Then you can use the -codex variant for execution of a carefully drafted plan.

Apple acquires OpenAI, Sam becomes CEO of combined company; iPhone revenue used to build out data centers; Jony rehired as design chief for AI device.

> FAQ

> Has Mixpanel been removed from OpenAI products?

> Yes.

https://openai.com/index/mixpanel-incident/

This is a good resource. But for the computer vision and machine learning practitioner most of the fun can start where this article ends.

nvcc from the CUDA toolkit has a compatibility range with the underlying host compilers like gcc. If you install a newer CUDA toolkit on an older machine, likely you'll need to upgrade your compiler toolchain as well, and fix the paths.

While orchestration in many (research) projects happens from Python, some depend on building CUDA extensions. An innocently looking Python project may not ship the compiled kernels and may require a CUDA toolkit to work correctly. Some package management solutions provide the ability to install CUDA toolkits (conda/mamba, pixi), the pure-Python ones do not (pip, uv). This leaves you to match the correct CUDA toolkit to your Python environment for a project. conda specifically provides different channels (default/nvidia/pytorch/conda-forge), from conda 4.6 defaulting to a strict channel priority, meaning "if a name exists in a higher-priority channel, lower ones aren't considered". The default strict priority can make your requirements unsatisfiable, even though there would be a version of each required package in the collection of channels. uv is neat and fast and awesome, but leaves you alone in dealing with the CUDA toolkit.

Also, code that compiles with older CUDA toolkit versions may not compile with newer CUDA toolkit versions. Newer hardware may require a CUDA toolkit version that is newer than what the project maintainer intended. PyTorch ships with a specific CUDA runtime version. If you have additional code in your project that also is using CUDA extensions, you need to match the CUDA runtime version of your installed PyTorch for it to work. Trying to bring up a project from a couple of years ago to run on latest hardware may thus blow up on you on multiple fronts.

I wanted to try GLM 4.6 through their API with Cline, before spending the $50. But I'm getting hit with API limits. And now I'm noticing a red banner "GLM4.6 Temporarily Sold Out. Check back soon." at cloud.cerebras.ai. HN hug of death, or was this there before?

Go to your Google account settings; add the languages you speak and don’t want auto translations for in your personal profile.

I agree that the auto dubbing is the worst feature. It may have been HN where I read the above tip to turn that off, it seems to have worked for me so far.

The A10X processor in my 2017 iPad Pro has always felt ridiculously overpowered for a couch machine. Recently it had gotten sluggish, hot, hung for times and lost battery quite quickly and I thought its time had finally come.. but no, after resetting the OS, it's as fast as ever. So hopefully it'll last me til Apple finally gives the iPad Air a 120Hz display.

Possibly also nvitop, which is a different tool from nvtop: https://github.com/XuehaiPan/nvitop