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dbreunig

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投稿

Understanding the Dynamics of the AI Ecosystem with Pace Layers

dbreunig.com
3 ポイント·投稿者 dbreunig·7 日前·1 コメント

The Problem is Prompt Debt: You can't be model agnostic and hand-tune prompts

dbreunig.com
4 ポイント·投稿者 dbreunig·18 日前·0 コメント

When an agent can explain anything, what is the role of human-centric docs?

dbreunig.com
4 ポイント·投稿者 dbreunig·先月·0 コメント

Overfitting to First Party Harnesses

dbreunig.com
1 ポイント·投稿者 dbreunig·2 か月前·0 コメント

Cybersecurity looks like proof of work now

dbreunig.com
562 ポイント·投稿者 dbreunig·3 か月前·213 コメント

Claude Code builds a system prompt

dbreunig.com
4 ポイント·投稿者 dbreunig·3 か月前·0 コメント

The Cathedral, the Bazaar, and the Winchester Mystery House

oreilly.com
2 ポイント·投稿者 dbreunig·3 か月前·0 コメント

The 2nd phase of OSS in the agentic era: From clones to reimaginings

dbreunig.com
2 ポイント·投稿者 dbreunig·3 か月前·0 コメント

The Cathedral, the Bazaar, and the Winchester Mystery House

dbreunig.com
190 ポイント·投稿者 dbreunig·3 か月前·66 コメント

Build a deep researcher and learn DSPy Signatures and Modules

cmpnd.ai
2 ポイント·投稿者 dbreunig·4 か月前·0 コメント

[untitled]

1 ポイント·投稿者 dbreunig·4 か月前·0 コメント

Can chat bots accommodate advertising?

dbreunig.com
2 ポイント·投稿者 dbreunig·4 か月前·0 コメント

Learnings from a No-Code Lib: Keep the Spec Driven Development Triangle in Sync

dbreunig.com
4 ポイント·投稿者 dbreunig·4 か月前·3 コメント

Claude and the Dow: AI is unlike other tech because AI has embedded judgment

dbreunig.com
1 ポイント·投稿者 dbreunig·4 か月前·1 コメント

Two Beliefs About Coding Agents: Devs Don't Realize What They Bring

dbreunig.com
2 ポイント·投稿者 dbreunig·5 か月前·0 コメント

Why is Claude an Electron app?

dbreunig.com
428 ポイント·投稿者 dbreunig·5 か月前·458 コメント

Analyzing How System Prompts Define Agent Behavior

dbreunig.com
3 ポイント·投稿者 dbreunig·5 か月前·0 コメント

The Potential of RLMs

dbreunig.com
3 ポイント·投稿者 dbreunig·5 か月前·1 コメント

The Rise (and Limits) of Spec Driven Development

dbreunig.com
3 ポイント·投稿者 dbreunig·5 か月前·0 コメント

A OSS Library with No Code, Only Specs

dbreunig.com
3 ポイント·投稿者 dbreunig·6 か月前·2 コメント

コメント

dbreunig
·2 か月前·議論
Elizabeth Lopatto at The Verge makes a strong case we _do_ have proof that Musk is actively gathering and throwing fuel on the fire: https://www.theverge.com/ai-artificial-intelligence/929129/s...

> 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.
dbreunig
·2 か月前·議論
Among benchmarkers its a frequent topic. Qwen BURNS reasoning to get its scores.
dbreunig
·4 か月前·議論
Model testing and swapping is one of the surprises people really appreciate DSPy for.

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...
dbreunig
·4 か月前·議論
No reason it can't. I know people currently generating specs from existing code; just gotta write the pipeline.
dbreunig
·4 か月前·議論
"Think step by step," was just a sentence you appended to your prompt.

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."
dbreunig
·4 か月前·議論
Last year they pushed out an update stating if any “Meta AI” is left on, they can access image data for training,

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.
dbreunig
·4 か月前·議論
Check out “Recursive Language Models”, or RLMs.

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.
dbreunig
·5 か月前·議論
Author of the post here.

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.
dbreunig
·5 か月前·議論
I keep saying this, it’s my new favorite metaphor.
dbreunig
·6 か月前·議論
That's cute.
dbreunig
·8 か月前·議論
Agree. I bucket things into three piles:

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.
dbreunig
·9 か月前·議論
There was a good study on this a few years ago that ran the numbers on this and landed on white paint for residential homes as the best option, for a few reasons, if I remember correctly:

- 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
dbreunig
·10 か月前·議論
Yes, if you put unrelated stuff in the prompt you can get different results.

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...
dbreunig
·10 か月前·議論
Yeah, I agree. Almost mentioned in the post how I imagine an ad PM at OpenAI is jealous of an ad PM at Perplexity.
dbreunig
·昨年·議論
What is Windsurf's (or for that matter: Cursor, Cline, or CoPilot) moat? This seems like a great deal and timing for them.
dbreunig
·昨年·議論
I was struck by this as people suggest alternatives that refute the headline (QGIS, PostGIS, GDAL, etc): nearly every one emerged in the early 2000s.

Strongly agree with your sentiment around maps: most people can’t read them, they color the entire workflow and make it more complex, and (imo) lead to a general undervaluing of the geospatial field. Getting the data into columns means it’s usable by every department.
dbreunig
·昨年·議論
Not disjointed at all. That last topic is the big challenge to solve.
dbreunig
·昨年·議論
That’s great! The difference is you’re familiar and know how to do that

Getting started from 0 with geo can be difficult for those unfamiliar. DuckDB packages everything into one line with one dependency.
dbreunig
·昨年·議論
Most of those tools came out circa ~2000.

Yeah, I feel old.
dbreunig
·昨年·議論
Author here.

QGIS is amazing. It's really great. It also came out in 2002, so I think the headline is safe.