Agentic AI. Hard tech. Build things! Reindustrialise 🇦🇺

Unpopular opinion: this will prove to be a dumb and uneconomic idea.
BREAKING: Google launching TPUs in space NEXT WEEK on SpaceX falcon 9 to test AI data centers orbit ITS HAPPENING
1
4
80
David Peterson retweeted
Replying to @GergelyOrosz
In my career, compilers have always generated better code that I ever could have. I find this to be increasingly so with contemporary AIs. But you know what neither can do better than me? Knowing what software to write, what shape it should take, and what I want it to do. The entire history of software engineering has been one of rising levels of abstraction. This is how it was, how it is now, and how it shall be in the fullness of time.
48
269
1,763
138,485
David Peterson retweeted
24
698
5,488
56,042
David Peterson retweeted
Unless you are actually fab-ing your PCB / CNC-ing and manufacturing your parts, your benchmarks are bullshit, your slop posts are bullshit, it’s all bullshit. Take it the last mile.
54
32
636
143,635
David Peterson retweeted
It's so humbling watching the thinking traces of a model like GLM 5.3 Flash or MiniMax M3 debug a complex problem involving math and code. They're so damned smart. And yet they can run on fairly cheap hardware. Somehow it was less humiliating when it required millions of dollars.
12
7
130
5,838
David Peterson retweeted
Omarchy is now all grown up! It's moved out from its incubation under basecamp/ on GitHub. The Omacom Foundation is the new home. #TheyGrowUpSoFast github.com/omacom/omarchy
57
160
3,436
100,156
David Peterson retweeted
It's Oligarchy, fyi. oligarchy.fyi
Gotta admit, Oligarchy Linux kinda slaps 😂
235
207
4,792
384,376
David Peterson retweeted
Ok, how fucking cool is this. stuartgarage.com/about This small Porsche service and restoration shop in Žilina, Slovakia uses Basecamp to run the shop. And they even highlight it on their site! This is why I absolutely love building software for small businesses. It's such a thrill to bring outstanding, affordable, best-of-the-best software to businesses like these. It's a joy to work for them. Basecamp and Porsches. I mean... What a match! cc: @dhh
36
15
576
111,503
David Peterson retweeted
We're running at almost 5,000 ISO downloads of Omarchy Quattro a day now. Absolute insanity. Never, ever have we seen anything like this before 🤯
148
84
4,138
168,594
David Peterson retweeted
Aaaaand there goes the switch from high electricity rates to high electricity daily supply changes. The rate is lowering, but the daily supply charge is almost doubling. Why are they doing this? Because they encentivised millions of people to get solar and battery systems, and now that they are self-sufficient and don't use as much or any power, you'll now pay through the nose to connect to the system instead.
91
42
397
45,588
David Peterson retweeted
A founding member of OpenAI just told a room full of founders that the entire way they think about building software is about to flip upside down, and most of them are still working in a paradigm that is quietly going extinct. I watched the talk at 1am and finally understood why the people I know who are best at AI all started saying the same thing. His name is Andrej Karpathy. The talk is called From Vibe Coding to Agentic Engineering. Here is the framework he laid out, and why almost nobody outside the frontier labs has fully internalized what it means yet. He started with a confession. He said as recently as last year, he was using agentic coding tools the same way most people still use them. The model would write a chunk of code. Sometimes it worked. Sometimes he had to fix it. It was helpful but inconsistent and you had to babysit it. Then something happened in December 2024 that he says fundamentally broke the old paradigm, and most people experienced AI last year as a ChatGPT-adjacent thing and never went back to look again. What changed was that the chunks just started coming out fine. He kept asking for more and the code kept working. He could not remember the last time he had to correct it. He started trusting the system. And then he was vibe coding for real, and his side projects folder ballooned because suddenly every weird idea he had was something he could actually ship in an afternoon. The reason he kept stressing this point in front of the room was simple. The transition was not gradual. It was a phase change. And if your last serious encounter with AI coding was anywhere before December, your mental model of what is possible is already a year out of date. The core idea he then introduced is the one that should sit with every founder. He calls it software 3.0, and the framing is precise. Software 1.0 is humans writing explicit code. Software 2.0 is humans creating datasets and training neural networks where the weights become the program. Software 3.0 is something nobody has fully wrapped their head around yet. The neural network itself becomes the computer. Your prompt is the program. The context window is the lever you pull to control what the interpreter does. He gave two examples that landed harder than anything else in the talk. The first was the install instructions for OpenClaw. Normally you would expect a shell script. A command you run. Some configuration. Instead, the install instructions are a paragraph of text you copy and paste into your agent. The agent reads it, looks at your machine, figures out the environment, debugs in real time, and installs everything. There is no script. There is no code. There is a piece of text written for an intelligent reader who happens to be made of weights. The second example was the one that made him stop and rebuild his entire mental model. He had built a small app called MenuGen. You photograph a restaurant menu, the app OCRs the items, generates images of each dish, and shows you what the food looks like. He shipped it. People used it. Then someone showed him the software 3.0 version of the same thing. Take a photo of the menu. Hand it to Gemini. Ask Nano Banana to overlay images of each dish directly onto the menu in the photo. The model does it in a single pass. The image comes back exactly like the menu he photographed, except now every dish has a picture rendered into the pixels. He paused and said the line that should haunt every founder in the room. The entire MenuGen app should not exist. He had built something in the old paradigm that the new paradigm just does, in one model call, with no app at all. The deeper insight underneath both examples is the part most people miss. The software 3.0 paradigm does not just make existing apps faster. It dissolves entire categories of apps. The neural network does so much of the work that the scaffolding around it becomes unnecessary. You are not speeding up the old workflow. You are noticing that the old workflow does not need to exist. He extended this further. Most products today are written for humans. Documentation is written for humans. Setup flows are written for humans. He said his pet peeve has become going to a docs page and being told to do something. He does not want to do anything. He wants to know what to copy and paste into his agent. The companies that figure out how to be agent-native first, where every interface, every doc, every setup flow, every API is built for the agent reading on your behalf rather than for you reading directly, are going to make the human-first versions feel as outdated as a website that does not work on a phone. The final part of the talk was about taste, and this is the part that separates the people who will compound from the people who will plateau. He said the agents are still interns. They have remarkable recall. They have superhuman speed. They can fill in any blank you point them at. But they have no aesthetic judgment, no sense of what matters, no understanding of why the system is being built in the first place. He gave an example from MenuGen where his agent tried to match Stripe purchases to Google accounts using email addresses, even though users could obviously sign up with one email and pay with another. The agent had no model of what a user actually is. It just pattern matched fields together. So the human stays in charge of the spec. The architecture. The taste. The thing that has to be true for the system to be worth building at all. The agent fills in everything underneath that. Then he said the line he had read on the internet that he keeps coming back to every other day. You can outsource your thinking. You cannot outsource your understanding. That is the whole talk in one sentence. The agents will write your code. They will draft your emails. They will research your topics. They will execute your plans. But something still has to direct them, and that something has to actually understand what is being built and why. The bottleneck is no longer typing speed. The bottleneck is comprehension. And the people who keep training their own ability to understand things deeply are the ones who will keep getting more leverage out of every model release. The people who outsource the understanding too will quietly become passengers. He ended on the part most founders will skip and the few who do not skip it will quietly compound on for the next decade. The agents are getting cheaper, faster, and more capable every quarter. None of that matters if you have stopped doing the hard work of understanding what is actually worth building. The ceiling on agentic engineering is not the model. It is the human standing at the top of the system, deciding what to point it at. Most founders are still working in software 1.0 with a 3.0 tool sitting on their desk. The ones who flip first are the ones who win the next decade.
15
35
155
16,576
David Peterson retweeted
Replying to @cremieuxrecueil
I'm a software engineer with 50 years of experience. If you know how to steer an LLM properly, the frontier models are extremely good at generating code. They're weak at architecture, which is one of several reasons you want a human in the loop, but they can have a very low error rate compared to most humans. When they don't - when they generate slop - it's because you didn't know how to use the tool correctly.
70
125
2,152
130,817
David Peterson retweeted
The green line is not the “ideal case” by any stretch of the imagination. smh.com.au/property/news/why…
35
17
454
38,483
David Peterson retweeted
4月21日消息,Linus Torvalds已将全新的NTFS文件系统驱动程序并入Linux 7.1版本主线,Linux系统从此具备原生NTFS读写能力。 新驱动的开发者Namjae Jeon曾为Linux开发exFAT驱动,此次耗时四年完成了超过3.6万行代码的NTFS驱动取代旧方案。 新驱动在多线程写入性能上提升35%至110%,挂载4TB等大容量硬盘的速度提升4倍。 Linus Torvalds对这份贡献表示高度认可,称之为Linux系统的"NTFS复活",对于需要在Linux与Windows之间切换或共享数据的双系统用户而言,这是近年来最重要的内核更新之一。
42
239
2,156
171,646
About time!
Ternus as CEO, Srouji as Chief Hardware Officer. The people who turned Apple Silicon into the leading local AI platform are now the ones steering the company. These were the people doing heterogeneous on-device AI 10 years ago (w/ CoreML on CPU/GPU/ANE). The software sucked - but the hardware + architecture was way ahead of its time. This is Apple leaning into local AI. This will define the next 20 years of Apple. Great news for local AI.
4
242
David Peterson retweeted
This has at least 1000xed my productivity on weird side projects. I have a very very long backlog that I'm clearing with alarming speed. Unbelievable that we used to fill buildings with smart people and have them type algorithms into computers letter by letter.
Another day, another PhD project kicked off with Claude Code. This is crazy.
25
59
1,542
349,367
David Peterson retweeted
Delta Force in the 1980's, when Tier 1 guys were indistinguishable from suburban dads. No tattoo sleeves or PED enhanced muscles. They could literally blend in anywhere.
524
1,965
30,196
1,359,954
The @eff, bastion of free speech, announced it is leaving X while at the same time restricting who can comment on their tweet. Pretty pathetic. Won't be missed.
After almost twenty years on the platform, EFF is logging off of X. This isn’t a decision we made lightly, but it might be overdue. 🧵(1/5)
3
153
Took it a step further and built xtrc8 — an open-source toolkit for the ingest layer of this pattern. Extracts X bookmarks, web articles, arXiv papers, and PDFs into raw/ folders, ready for LLM compilation. With a TUI for browsing and selecting X bookmarks, auto-follows links to pull the actual papers / repos / gists, and auto-translates X bookmarks on extract. Repo here: github.com/peterson/xtrc8
LLM Knowledge Bases Something I'm finding very useful recently: using LLMs to build personal knowledge bases for various topics of research interest. In this way, a large fraction of my recent token throughput is going less into manipulating code, and more into manipulating knowledge (stored as markdown and images). The latest LLMs are quite good at it. So: Data ingest: I index source documents (articles, papers, repos, datasets, images, etc.) into a raw/ directory, then I use an LLM to incrementally "compile" a wiki, which is just a collection of .md files in a directory structure. The wiki includes summaries of all the data in raw/, backlinks, and then it categorizes data into concepts, writes articles for them, and links them all. To convert web articles into .md files I like to use the Obsidian Web Clipper extension, and then I also use a hotkey to download all the related images to local so that my LLM can easily reference them. IDE: I use Obsidian as the IDE "frontend" where I can view the raw data, the the compiled wiki, and the derived visualizations. Important to note that the LLM writes and maintains all of the data of the wiki, I rarely touch it directly. I've played with a few Obsidian plugins to render and view data in other ways (e.g. Marp for slides). Q&A: Where things get interesting is that once your wiki is big enough (e.g. mine on some recent research is ~100 articles and ~400K words), you can ask your LLM agent all kinds of complex questions against the wiki, and it will go off, research the answers, etc. I thought I had to reach for fancy RAG, but the LLM has been pretty good about auto-maintaining index files and brief summaries of all the documents and it reads all the important related data fairly easily at this ~small scale. Output: Instead of getting answers in text/terminal, I like to have it render markdown files for me, or slide shows (Marp format), or matplotlib images, all of which I then view again in Obsidian. You can imagine many other visual output formats depending on the query. Often, I end up "filing" the outputs back into the wiki to enhance it for further queries. So my own explorations and queries always "add up" in the knowledge base. Linting: I've run some LLM "health checks" over the wiki to e.g. find inconsistent data, impute missing data (with web searchers), find interesting connections for new article candidates, etc., to incrementally clean up the wiki and enhance its overall data integrity. The LLMs are quite good at suggesting further questions to ask and look into. Extra tools: I find myself developing additional tools to process the data, e.g. I vibe coded a small and naive search engine over the wiki, which I both use directly (in a web ui), but more often I want to hand it off to an LLM via CLI as a tool for larger queries. Further explorations: As the repo grows, the natural desire is to also think about synthetic data generation + finetuning to have your LLM "know" the data in its weights instead of just context windows. TLDR: raw data from a given number of sources is collected, then compiled by an LLM into a .md wiki, then operated on by various CLIs by the LLM to do Q&A and to incrementally enhance the wiki, and all of it viewable in Obsidian. You rarely ever write or edit the wiki manually, it's the domain of the LLM. I think there is room here for an incredible new product instead of a hacky collection of scripts.
2
123