The splat of the studio has the perfect amount of detail. It looks like you're streaming from the camera direct to the computer usually? How do you check the progress / quality while capturing? Seeing the (great) results makes me more curious about the process of creating now.
The main point raised in the article is that these bots may void attorney client privileges.
But the real danger with these IMO is that they're turning casual conversations into a permanent record, and one that will be completely discoverable in court, should the company get into trouble later.
> In just 11 months since the company arrived in Memphis, xAI has become one of Shelby County’s largest emitters of smog-producing nitrogen oxides, according to calculations by environmental groups whose data has been reviewed by POLITICO’s E&E News. The plant is in an area whose air is already considered unhealthy due to smog.
Had this set the precedent of working with the community, and _not_ breaking the law, I think we'd be in a better place all around.
Similarly, Amazon tried to take the excess nuclear power, without paying back into the electrical grid infrastructure, and got denied in 2024:
> In just 11 months since the company arrived in Memphis, xAI has become one of Shelby County's largest emitters of smog-producing nitrogen oxides, according to calculations by environmental groups whose data has been reviewed by POLITICO's E&E News. The plant is in an area whose air is already considered unhealthy due to smog.
> The turbines spew nitrogen oxides, also known as NOx, at an estimated rate of 1,200 to 2,000 tons a year — far more than the gas-fired power plant across the street or the oil refinery down the road.
The details are in the specifics here. People are _already_ feeling the effects of the AI race, the consequences just aren't evenly distributed.
And if we look at the "clean" nuclear deals to power these data centers:
> The Talen agreement, however, would divert large amounts of power currently supplying the regional grid, which FERC said raised concerns about how that loss of supply would affect power bills and reliability. It was also unclear how transmission and distribution upgrades would be paid for.
The scale of environmental / social impacts comes down to how aggressive the AI race gets.
Tailscale is a great. I think of it as a swiss army knife for easier routing and connectivity.
I use it in projects to stream internet / connectivity from my phone to the NVIDIA Jetson line, making my robotics projects easily accessible / debuggable:
So I think there's an assumption you've made here, that the models are currently "60-80% as good as human programmers".
If you look at code being generated by non-programmers (where you would expect to see these results!), you don't see output that is 60-80% of the output of domain experts (programmers) steering the models.
I think we're extremely imprecise when we communicate in natural language, and this is part of the discrepancy between belief systems.
Will an LLM model read a person's mind about what they want to build better than they can communicate?
That's already what recommender systems (like the TikTok algorithm) do.
But will LLMs be able to orchestrate and fill in the blanks of imprecision in our requests on their own, or will they need human steering?
I think that's where there's a gap in (basically) belief systems of the future.
If we truly get post human-level intelligence everywhere, there is no amount of "preparing" or "working with" the LLMs ahead of time that will save you from being rendered economically useless.
This is mostly a question about how long the moat of human judgement lasts. I think there's an opportunity to work together to make things better than before, using these LLMs as tools that work _with_ us.
When you work for most public corporations, you aren't allowed to bring personal devices linked to company servers to specific countries. You need to bring a burner device instead, because you are perceived as a target for corporate espionage.
This is like that, except the government and the type of people on the list are even better targets for their personal devices. The government has strict rules about secrecy and communication for military operations, and strong punishments for not following these protocols, because they can lead to a loss of life.
This is a different sort of "unsecure". The platform itself may be "secure", but the device, being in public where someone could take a picture of military secrets, etc. isn't.
That example code on DeepSeek doesn't actually include the logic to call a weather API? It just puts a fake answer back in, and you've got to handle the process manually.
The pyproject.toml in the Model Context Protocol example is just showing the new, "best" way to distribute and install Python projects and dependencies. If you haven't used uv before, it makes working with Python projects substantially better.
The Model Context Protocol server lets the model autonomously use the tool and incorporate its result. It's a much cleaner (imo obviously) separation of tool definition and execution.
I think another category of error that Simon skips over that breaks this argument entirely: the hallucination where the model forgets a feature.
Rather than the positive (code compiles), the negative (forgets about a core feature), can be extremely difficult to tell. Worse still, the feature can slightly drift, based upon code that's expected to be outside of the dialogue / context window.
I've had multiple times where the model completely forgot about features in my original piece of code, after it makes a modification. I didn't notice these missing / subtle changes until much later.
I've been using Mongo while developing some analysis / retrieval systems around video, and this is the correct answer. Aggregation pipelines allow me to do really powerful search around amorphous / changing data. Adding a way to automatically update / recalculate embeddings to your database makes even more sense.