Worth separating “the algorithm” from “the trained model.” Humans write the architecture + training loop (the recipe), but most of the actual capability ends up in the learned weights after training on a ton of data.
Inference is mostly matrix math + a few standard ops, and the behavior isn’t hand-coded rule-by-rule. The “algorithm” part is more like instincts in animals: it sets up the learning dynamics and some biases, but it doesn’t get you very far without what’s learned from experience/data.
Also, most “knowledge” comes from pretraining; RL-style fine-tuning mostly nudges behavior (helpfulness/safety/preferences) rather than creating the base capabilities.
I have a script called catfiles that I store in ~/.local/bin that recursively dumps every source file with an associated file header so I can paste the resulting blob in to Gemini and ChatGPT in order to have a conversation about the changes I would like to make before I send off the resulting prompt to Gemini Code Assist.
Heres my script if anyone is interested in as I find it to be incredibly useful.
It’s not even just about that. The real advantage is being able to dock a phone and edit photos or videos shot earlier in the day within a desktop-style environment directly on the device. This removes the need to transfer files to a separate editing system. The phone itself becomes a complete creative workstation that can sync to the cloud whenever needed.
Nobody is saying that it is caused by one thing, only that one of the causes are directly attributed by acetaminophen.
This is coming from studies by Johns Hopkins University, Harvard University, and Mount Sinai to name a few.
You're joking right? Google is integrating photo editing features that can exist at the point of inception. It doesn't get more integrated or fit better than that.
The Chinese state operates the country much like a vast conglomerate, inheriting many of the drawbacks of a massive corporation. When top leadership imposes changes in tools or systems without giving engineers the opportunity to run proof-of-concept trials and integration tests to validate feasibility, problems like these inevitably arise.
Developers should revisit using indexed color formats so they only map the colors that they are using within the texture rather than an entire 32bit color space. This coupled with compression would greatly reduce the amount of ram and disk space that each texture consumes.
I’m not entirely sure, since it seems that a very slimmed-down version of Gemini has been attached to search. It’s definitely not the full Gemini 2.5-Pro that engineers use to carefully reason through answers. Instead, it relies mostly on tool calling to stitch together a response.
I did a bit of research on the environmental impact with regards to the United States. Recent numbers suggest that ChatGPT handles about 2.5 billion prompts per day worldwide, with roughly 330 million of those coming from the United States. Since the U.S. population is about 335 million, that works out to about one prompt per person per day on average, though actual users issue several times more.
On the energy side, Google recently estimated that an average Gemini inference consumes around 0.24 Wh, which is roughly the same as running a microwave for a single second. Older rule-of-thumb comparisons put the figure closer to 3–6 seconds of microwave use, or about 0.8–1.7 Wh per prompt. If you apply those numbers to U.S. usage, you get somewhere between 79 MWh and 550 MWh per day nationally, which translates to only a few to a few dozen megawatts of continuous load. Spread across the population, that works out to between 0.09 and 0.6 kWh per person per year — just pennies worth of electricity, comparable to a few minutes of running a clothes dryer. The bigger concern for the grid is not individual prompts but the growth of AI data centers and the energy cost of training ever-larger models.
I generally agree, but I think there is a real disconnect. Middle and upper management often do not understand how developers and engineers are actually supposed to use these tools.
For example, I work in operations, so most of what I touch is bash, Ansible, Terraform, GitHub workflows and actions, and some Python. Recently, our development team demonstrated a proposed strategy to use GitHub Copilot: assign it a JIRA ticket, let it generate code within our repos, and then have it automatically come back with a pull request.
That approach makes sense if you are building web or client-side applications. My team, however, focuses on infrastructure configuration code. It is software in the sense that we are managing discrete components that interact, but not in a way where you can simply hand off a task, run tests, and expect a PR to appear.
Large language models are more like asking a genie. Even if you give perfectly clear instructions, the result is often not exactly what you wanted. That is why I use Copilot and Gemini Code Assist in VS Code as assistive tools. I guide them step by step, and when they go off track, I can nudge them back in the right direction.
To me, that highlights the gap between management’s expectations and the reality of how these tools actually work in practice.
For Google's Gemini LLM, the energy impact is negligible, with the average prompt consuming the equivalent energy of just three seconds of a microwave's operation.