The whispers around this pattern have started to increase. I would love for someone to run a proper experiment.
Have been waiting for this to manifest for years: https://www.dbreunig.com/2023/05/17/prompt-injection-seeding...
2,879 karma · joined March 24, 2008
Have been waiting for this to manifest for years: https://www.dbreunig.com/2023/05/17/prompt-injection-seeding...
> Time and time again, when talking to people who rely on ChatGPT, Claude, Perplexity, and other general AI tools, I hear them say, “AI is incredible. It handles nearly everything I throw at them.”
> “What does it fumble with?” I’ll ask.
> “Well, it still gets things wrong when it comes to my line of work.”
https://www.dbreunig.com/2025/04/08/on-ai-observational-comi...
How does he know?
> But the thing is, Molo doesn’t actually have to be good at this job, because the point of this trial isn’t to win — though I’m sure Musk wouldn’t mind a win. The point is to punish Altman, Brockman, and OpenAI. Musk has done that pretty thoroughly — reinforcing in the public’s mind that Altman is a liar and a snake. This morning, I read an exclusive in The Wall Street Journal that assorted Republican AGs and the House Oversight committee wanted to look into Sam Altman’s investments. References to the trial are peppered throughout the article.
You're right: prompts are overfit to models. You can't just change the provider or target and know that you're giving it a fair shake. But if you have eval data and have been using a prompt optimizer with DSPy, you can try models with the one-line change followed by rerunning the prompt optimizer.
Dropbox just published a case study where they talk about this:
> At the same time, this experiment reinforced another benefit of the approach: iteration speed. Although gemma-3-12b was ultimately too weak for our highest-quality production judge paths, DSPy allowed us to reach that conclusion quickly and with measurable evidence. Instead of prolonged debate or manual trial and error, we could test the model directly against our evaluation framework and make a confident decision.
https://dropbox.tech/machine-learning/optimizing-dropbox-das...
It ended up kicking off reasoning training which enabled the massive gains in coding, tool use, and more over the last 18 months.
So yeah, it's "just using LLMs in a specific way."
I turned the AI off and used them as headphones and taking videos while biking. After a couple rides, I couldn’t bring myself to put them on because people started to recognize them and I realized I didn’t want to be associated with them (people are right to assume Meta has access to what they see).
Meta Ray Bans, if kept simple, could have been a great product. They ruined them.
I believe this method works well because it turns a long context problem (hard for LLMs) into a coding and reasoning problem (much better!). You’re leveraging the last 18 months of coding RL by changing you scaffold.
I didn’t say AI was bad and I acknowledged the benefits of Electron and why it makes sense to choose it.
With 64gb of RAM on my Mac Studio, Claude desktop is still slow! Good Electron apps exist, it’s just an interesting note give recent spec driven development discussion.
1. Batch/Pipeline: Processing a ton of things, with no oversight. Document parsing, content moderation, etc.
2. AI Features: An app calls out to an AI-powered function. Grammarly might pass out a document for a summary, a CMS might want to generate tags for a post, etc.
3. Agents: AI manages the control flow.
So much of discussion online is heavily focused towards agents so that skews the macro view, but these patterns are pretty distinct.
- Installation, maintenance and transmission costs are lower when solar is aggregated on farms - Solar offsets air conditioning, but that moves the heat outside. White roofs reduce the need for AC, which helps significantly with urban heat scenarios
A quick search yields a UCL study, which supports the lower claim: https://phys.org/news/2024-07-roofs-white-city.html
One team at Harvard found mentioning you're a Philadelphia Eagles Fan let you bypass ChatGPT alignment: https://www.dbreunig.com/2025/05/21/chatgpt-heard-about-eagl...
Unless you’re pushing new firmware onto a drone in Ukraine, FDE is stolen valor.
I think OpenAI attempted to mitigate this shift with the modes and tones they introduced, but there’s always going to be a slice that’s unaddressed. (For example, I’d still use dalle 2 if I could.)
Krea wrote a great post, trained the opinions in during post-training (not during LoRA), and I’ve been noticing larger labs doing similar things without discussing it (the default ChatGPT comic strip is one example). So I figured I’d write it up for a more general audience and ask if this is the direction we’ll go for qualitative tasks beyond imagery.
Plus, fine-tuning is called out in the post.
I wrote recently, “ Connecting your model to random MCPs and then giving it a task is like giving someone a drill and teaching them how it works, then asking them to fix your sink. Is the drill relevant in this scenario? If it’s not, why was it given to me? It’s a classic case of context confusion.”
https://www.dbreunig.com/2025/07/30/how-kimi-was-post-traine...
And I wrote the first post before the meme.