It would be cool if the bottoms of MacBooks weren’t flat and instead wavy or rippling to increase surface area. There are probably a lot of cool designs (ayyyyy) you could machine in.
Is there a value to increasing surface area on the top or bottom case of a MacBook? I'd imagine most of the thermal management is achieved by fan-directed airflow through the internal heatsinks and convection through the keyboard.
Well the MacBook Air has no fans so it’s a different beast from a design perspective. If I recall, at least with an earlier m series MacBook, notably improved performance could be gained by inserting a thermal pad between the chassis’ bottom panel and the compute module. Apple probably didn’t do this in an effort to avoid uncomfortably hot temperatures contacting people’s thighs.
It's really cool that performance cores are the same between base, Pro, Max (, Ultra) chips of a generation. That really feels like Apple did it right.
This is always the case. Negotiate equity, but assume it’s worth zero. It’s not liquid and highly speculative. It’s a nice to have.
edit: which doesn’t mean join companies you don’t believe in! Please do. But don’t expect it to be there, don’t include it in life plans, don’t pay attention to valuations, etc.
Not really a perverse incentive. The government isn’t making any money here. They’re paying someone from their own pocket only to take it away again?
At that point it really is just slavery, which they can already do as protected in the US Constitution.
(I’m not arguing for this. I agree with restitution and believe that sentences longer than a certain point are also pointless and a net negative to society.)
Hypothetically let's say govt is allowed to use unpaid labour outside menial tasks and the prison system is setup in a way to efficiently utilize the skills of their labour pool and is allowed to outsource their skills to private entities at attractive rate for covering prison costs (i.e. more money left for govt spending)
E.g. tradesmen employed on their related jobs. A programmer employed in software jobs or a technician "loaned" to a nearby lab etc.
Don't you think the local/state governments will then have incentive to fill their pool with "missing" talent according to the job requirements.
I remember when they were seeking approval to provide blow jobs on flights (free in business class iirc.) The only thing that they won’t up charge. They even tried to get approval to charge for bathroom access.
Wild company, but they are entirely on brand.
To be fair, consumers have driven airlines this way. They’ve shown that they’ll buy based almost entirely on price and suffer any amount of agony in exchange.
I just don’t find basic economy or early flights or shitty airlines worth the bad stress.
The advantage of Ryanair and a lot of the other low cost carriers is that they do a lot of point to point flights between regional hubs - for example we flew Edinburgh to Marrakesh with them a few years back which was fine and I think they were the only airline offering direct flights. Going via Heathrow, Gatwick or CDG would have been a nightmare and we were only going for a few days.
It's not really an apples-to-apples comparison - I enjoy playing around with LLMs, running different models, etc, and I place a relatively high premium on privacy. The computer itself was $2k about two years ago (and my employer reimbursed me for it), and 99% of my usage is for research questions which have relatively high output per input token. Using one for a coding assistant seems like it can run through a very high number of tokens with relatively few of them actually being used for anything. If I wanted a real-time coding assistant, I would probably be using something that fit in the 24GB of VRAM and would have very different cost/performance tradeoffs.
For what it is worth, I do the same thing you do with local models: I have a few scripts that build prompts from my directions and the contents of one or more local source files. I start a local run and get some exercise, then return later for the results.
I own my computer, it is energy efficient Apple Silicon, and it is fun and feels good to do practical work in a local environment and be able to switch to commercial APIs for more capable models and much faster inference when I am in a hurry or need better models.
Off topic, but: I cringe when I see social media posts of people running many simultaneous agentic coding systems and spending a fortune in money and environmental energy costs. Maybe I just have ancient memories from using assembler language 50 years ago to get maximum value from hardware but I still believe in getting maximum utilization from hardware and wanting to be at least the ‘majority partner’ in AI agentic enhanced coding sessions: save tokens by thinking more on my own and being more precise in what I ask for.
- For polishing Whisper speech to text output, so I can dictate things to my computer and get coherent sentences, or for shaping the dictation to specific format eg. "generate ffmpeg to convert mp4 video to flac with fade in and out, input file is myvideo.mp4 output is myaudio flac with pascal case" -> Whisper -> "generate ff mpeg to convert mp4 video to flak with fade in and out input file is my video mp4 output is my audio flak with pascal case" -> Local LLM -> "ffmpeg ..."
- Doing classification / selection type of work eg. classifying business leads based on the profile
Basically the win for local llm is that the running cost (in my case, second hand M1 Ultra) is so low, I can run large quantity of calls that don't need frontier models.
My comment was not very clear. I specifically meant Claude Code/Codex like workflows where the agent generates/run code interactively with user feedback. My impression is that consumer grade hardware is still too slow for these things to work.
You are right, consumer grade hardware is mostly too slow... although it's a relative thing right. For instance you can get Mac Studio Mx Ultra with 512GB RAM, run GLM-4.5-Air and have a bit of patience. It could work
I was able to run a batch job that lasted ~2 weeks of inference time on my m4 max by running it over night against a large dataset I wanted to mine. It cost me pennies in electricity and writing a simple python script as a scheduler.
This generally isn't true. Cloud vendors have to make back the cost of electricity and the cost of the GPUs. If you already bought the Mac for other purposes, also using it for LLM generation means your marginal cost is just the electricity.
Also, vendors need to make a profit! So tack a little extra on as well.
However, you're right that it will be much slower. Even just an 8xH100 can do 100+ tps for GLM-4.7 at FP8; no Mac can get anywhere close to that decode speed. And for long prompts (which are compute constrained) the difference will be even more stark.
A question on the 100+ tps - is this for short prompts? For large contexts that generate a chunk of tokens at context sizes at 120k+, I was seeing 30-50 - and that's with 95% KV cache hit rate. Am wondering if I'm simply doing something wrong here...
Depends on how well the speculator predicts your prompts, assuming you're using speculative decoding — weird prompts are slower, but e.g. TypeScript code diffs should be very fast. For SGLang, you also want to use a larger chunked prefill size and larger max batch sizes for CUDA graphs than the defaults IME.
Ruby isn't necessarily for web devs. Ruby is popular for all sorts of business line applications. In Japan is popular for lower level programming. You can do game programming via something like Dragon Ruby. Sure its very popular for Rails, but you don't necessarily need to do web dev.
Not sure if you're single, but go on some dates. Getting excited about another human being can be a huge boost. You don't need to replace work with other intellectualism (though you certainly can!)
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