Claude estimates that tool use / input tokens might add 10-15% on top of that depending on exactly how the model went about the task.
Edit: better tok/s estimate buckets based on GPT 5.5 actual speeds since I couldn't find real benchmarks on 5.6 published anywhere. Also account for Sol Fast pricing.
The default answer is Qwen 3.6-27B in llama.cpp - tell a frontier LLM your goals and hardware and have it suggest settings. llama.cpp has built in benchmarking, you can ask the frontier LLM to tell you what benchmarks to run and what settings to tweak based on the results. Ask it why the setting changes affect the benchmarks in certain ways if you want to start learning some details.
At $0.07/kWh, that costs $70,000 every hour in just electricity. $1.7 million /day. $613 million /year.
I had claude estimate the GPU cost of such a deployment:
> To get racks per GW: a full NVL72 rack draws roughly 130-132 kW under full load. If a 1 GW facility runs ~715 MW of IT power (after a ~1.4 PUE for cooling), that's on the order of 4,000–4,500 racks. At $3.4M of compute hardware each, the GPU-system cost lands around $14–15 billion.
15 billion / 613 million / year = ~24.5 years til electricity costs catch up to the GPUs. Obviously electricity isn't 100% of OpEx, but I'd expect it to be the majority for AI deployments.
Regardless, if you can cut the $613 million/yr in half that's still massive savings.
> All these points are valid, and OpenAI did a great job identifying potential risks, especially misuse and biases, at an early stage.
Many of the OpenAI employees who were focused on these risks in GPT-2 later founded Anthropic, notably Dario [1]. Since the beginning and continuing through today Anthropic describes itself as an "AI safety and research company" [2]
I'm not sure if the OpenAI of today has the same focus on safety, or if they do the minimum to not look irresponsible given Anthropic's effort.
Unfortunately in the current market 32GB of ddr5 seems to run about $400 as 2x16gb DIMMS, and even more for 1x32GB DIMM (higher density chips are more expensive). So $600 really isn't much over market price, especially considering strix halo uses 8000MHz ram instead of the typical 6000 found in consumer dimms.
The premise is correct, but this particular source is definitely an ad for a blood testing program.
> The [Vitamin D Receptor] VDR is part of the nuclear receptor superfamily of steroid hormone receptors, which are hormone-dependent regulators of gene expression. These receptors are expressed in cells across most organs. [1]
I started using similar approaches in the sonnet 3.5 era and found them incredibly useful at the time. The frontier lab models have gotten significantly better about their guesses over time, but I still sometimes turn to the technique if my own ideation is only about 80% of the way there, as the LLM's questioning can help me identify the blind spots that need more consideration.
No simulation is perfect, so ideally you have a feedback look constantly looking at new real-world data as it comes in and finding where the simulation has errors, and updating the simulation to improve the correlation between the simulation and the real world over time.
My guess is they did have flooded street sims but the correlation was much lower than expected, or the details of the situation being simulated (lighting, building locations, how dirty the water is, ...) were sufficiently different from the situation that was encountered that the sim based training didn't generalize to the new context.
There's significant chip development and manufacturing talent there, so it's not really all that surprising if Apple has a team there for those purposes.
Also Apple bought out Intel's modem division in 2019 [2], so chances are that group was already Israel based under Intel.
Age gating the VPN age gates (pseudo-anonymous) access to 100% of the content on the internet. Regardless of whether or not you agree with it, age gating only the porn subset of the internet is a much smaller restriction.
A more accurate phrasing is: It's significantly less likely that one learns the portion of the work they offload to an LLM.
A random anecdote is that most of the people I know who went very far in theoretical math are relatively poor at basic mental arithmetic, because they always think in the abstract and offload addition and multiplication to the calculator. It doesn't mean they can't do it, they just aren't as practiced or as fast at it.
Amazon has far more roles than just software. PMs, FC area managers, managers - if your job involves writing anything you're expected to be using AI in some capacity.
Even if the ultimate measure is dollars most employers will attempt to predict which metrics of employment best correllate with dollars so they can predict how many people to hire
Any claim about "productivity is due to X" that doesn't define a timescale is either flawed or misleading. In fact all measures of anything need to be done across some meaningfully defined time scale to have any relevance.
> github has a huge warning saying to never use pull_request_target to run user code
This is an area where documentation is necessary but not sufficient. Github needs to add some form of automated screening mechanism to either prevent this usage, or at the very least quickly flag usages that might be dangerous.
Claude estimates that tool use / input tokens might add 10-15% on top of that depending on exactly how the model went about the task.
Edit: better tok/s estimate buckets based on GPT 5.5 actual speeds since I couldn't find real benchmarks on 5.6 published anywhere. Also account for Sol Fast pricing.