That’s part of the reason for the name! “Rust” also has negative interpretations as well. A “rue” is also a kind of flower, and a “rust” is a kind of fungus.
Fair enough! I do like how others are framing this is as "write less code" -- if Rue makes one think more and more about the code that finally makes it to the production, that can be a real win.
"Growth" of those being led is a key concept it seems, which I would think is really only possible when the leader doesn't do everything by themselves as a die-hard servant, but utilizes the "leadership" part to help subordinates learn to lead themselves.
Granted this realm of ideas can be a gray-area, but it seems like servant leadership as presented by the author here does not incorporate the concept of growing those that they lead -- as indicated by the fact they have self-invented a new "buzzword" which actually seems to be involve the behaviors as laid out by servant leadership -- am I missing something?
Essentially, what information are they privy to that public is not? What asymmetry exists (timing, un-public information)? Is there any way for the public to be nearly as informed? What are they trading on? Upcoming funding changes (more money here -> buy, less money there --> sell)? COVID impact stands out.
Most "big" projects (huge chip foundries, etc.) require various forms of government approval (if not outright funding). They get asymmetry from knowing:
1. Sometimes that the project is happening before everyone else
2. If the project will or will not be approved or stopped e.g. in committee
3. Various other classified things like Dept of Defense briefings (if the Army says it needs XYZ component and plans to buy 10 billion worth of them, then buy the company that makes XYZ component).
It isn't necessarily that they have information the public doesn't (although it could be that, they would know about policy changes before the general public does). It could also be that they use their leadership position to push forward policies that benefit stocks that they own.
This could even happen for non-selfish reasons. If you genuinely believe that, e.g, "The future of America is BigTech," you'll both favor tech stocks in your portfolio and be more sympathetic to their lobbyists and favored bills.
There are 3 things always overlooked in this conversation.
- It’s not just representatives but their staff, family and friends. See the Covid scandals as an example.
- Often the information they’re privy to can come from their networks outside of government. The current transparency laws give us insights into their family members’ investments which is incredibly beneficial public knowledge.
- The current laws have no teeth and are not enforced well. Immediate disclosure should be required & automated for elected representatives, judges, family & staff within 24 hours. Late reporting should be immediate sale and loss of any profits.
If you compared Pelosi to other investors in San Francisco, you wouldn’t see much of a difference. Anyone who has gone heavy into tech in the last couple of decades has outperformed the market at considerable risk of going broke if there was a tech bust. Compare Facebook or Google to an index fund, especially before index funds decides to go heavy into FAANGs.
People who make/have more money also have more appetite for risk and also in general make more returns. Even without insider trading, being able to take a lopsided position on tech with the expectation that if it loses you still have a comfortable life, that is how the rich become richer.
That doesn't explain why persons in leadership positions outperform other members of congress. Presumably they all talk to each other and could share trading strategies. There's no reason not to unless your strategy involves inside information that might get you in trouble if spread around.
It actually does, if you believe that the people in leadership positions have been earning money for longer and have more experience in investing. You could also easily argue that they are more successful in general than congress people of similar tenure who aren't in leadership positions.
You are basically comparing the CEO to middle layer management, and then what do you expect? You need to do a more balanced comparison than that to show an actual discrepancy. Or maybe get congress to dole leadership positions out at random and then compare?
I’m not defending congressional trading, but there are potentially other confounding variables (emphasis on potential). Leaders may tend to be older, have more appetite for risk, or leadership may correlate with wealth/status because “the connected” can also raise more money etc etc. Unless those types of variables are controlled for, it should temper how strongly we draw conclusions.
Where is AI actually selling and doing well? What's a good resource for these numbers? What are the smaller scale use-cases where AI is selling well?
I am generally curious, because LLMs, VLMs, generative AI, advances are proving useful, but the societal impact scale and at this the desired rate is not revealing itself.
I am running a container on an old 7700k with a 1080ti that gives me vscode completions with rag with similar latency and enough accuracy to be useful for boilerplate etc…
That is something I would possibly pay for but as the failures on complex tasks are so expensive, this seems to be a major use case and will just be a commodity.
Creating the scaffolding for a jwt token or other similar tasks will be a race to the bottom IMHO although valuable and tractable.
IMHO they are going to have to find ways to build a mote, and what these tools are really bad at is the problem domains that make your code valuable.
Basically anything that can be vibe coded can be trivially duplicated and the big companies will just kill off the small guys who are required to pay the bills.
Something like surveillance capitalism will need to be found to generate revenue needed for the scale of Microsoft etc…
Given how every CPU vendor seems to push for some kind of NPU, local running models will probably be far more common in next 5 years. And convincing everyone to pay subscription for very minimal improvements in functionality gonna be hard.
Have you documented your VSCode setup somewhere? I've been looking to implement something like that. Does your setup provide next edit suggestions too?
I keep idly wondering what would be the market for a plug and play LLM runner. Some toaster sized box with the capability to run exclusively offline/local. Plug it into your network, give your primary machine the IP, and away you go.
Of course, the market segment who would be most interested, probably has the expertise and funds to setup something with better horsepower than could be offered in a one size fits all solution.
Professional legal services seem to be picking up steam. Which sort of makes sense as a natural follow on to programming, given that 'the law' is basically codified natural language.
I don't know how it is in other countries, but in the UK using LLMs for any form of paid legal services is hugely forbidden, and would also be insanely embarrassing. Like, 'turns out nobody had any qualifications and they were sending all the work to mechanical Turks in third world countries, who they refused to pay' levels of embarrassing.
I say this as someone who once had the bright idea of sending deadline reminders, complete with full names of cases, to my smart watch. It worked great and made me much more organised until my managers had to have a little chat about data protection and confidentiality and 'sorry, what the hell were you thinking?'.
I am no stranger to embarrassing attempts to jump the technological gun, or the wonders of automation in time saving.
But absolutely nobody in any professional legal context in the UK, that I can imagine, would use LLMs with any more gusto and pride than an industrial pack of diarrhoea relief pills or something - if you ever saw it in an office, you'd just hope it was for personal use and still feel a bit funny about shaking their hands.
Most of my family uses ChatGPT instead of Google to answer questions, despite my warnings that it’ll just make stuff up. I definitely Google much less now than I used to, directing a fair amount of that into ChatGPT instead.
that's frankly mostly because google search got so massively worse... I'd still use google more if not for the fact the stuff I asked it 5 years ago and got answer no longer provides useful answers
you can check on trustmrr.com (mostly indie/solo businesses) that a large chunk of those smaller companies make money by selling AI video generation and other genAI services.
It feels like making a UI with skins is a lot easier. Just draw the bitmap onto the window, and set some areas to be clickable (and change the bitmap of that area to "button is being pushed" when the user is holding the mouse down)...
At this point, you may as well get a powerpack for a mini and put it in one of these slings, you could have a crazy powerful machine in your "sock-et" sling thing here...
When the iPhone Air was just another huge phone...but thinner...smh. Apple should put up some page to check interest level in a smaller phone, and with enough interest, go manufacture it. If it is more expensive because economies of scale don't work out, but they create one that is small yet powerful, that's what I would buy at premium, because apparently compactness is a luxury.
It's not the "modern expert system", unless you're throwing away the existing definition of "expert system" entirely, and re-using the term-of-art to mean "system that has something to do with experts".
I don't know what the parent was referring to, but IMO "expert system" is one of the more accurate and insightful ways of describing LLMs.
An expert system is generically a system of declarative rules, capturing an expert's knowledge, that can be used to solve problems.
Traditionally expert systems are symbolic systems, representing the rules in a language such as Prolog, with these rules having been laboriously hand derived, but none of this seems core to the definition.
A pre-trained LLM can be considered as an expert system that captures the rules of auto-regressive language generation needed to predict the training data. These rules are represented by the weights of a transformer, and were learnt by SGD rather than hand coded, but so what?
Well, OK, perhaps not a declarative rule, more a procedural one (induction heads copying data around, and all that) given the mechanics of transformer layers, but does it really make a conceptual difference?
Would you quibble if an expert system was procedurally coded in C++ rather than in Prolog? "You see this pattern, do this".
Yes, it makes a conceptual difference. Expert systems make decisions according to an explicit, explicable world model consisting of a database of facts, which can be cleanly separated from the I/O subsystems. This does not describe a transformer-based generative language model. The mathematical approaches for bounding the behaviour of a language model are completely different to those involved in bounding the behaviour of an expert system. (And I do mean completely different: computer programs and formal logic are unified in fields like descriptive complexity theory, but I'm not aware of any way to sensibly unify mathematical models of expert systems and LLMs under the same umbrella – unless you cheat and say something like cybernetics.)
You could compile an expert system into C++, and I'd still call it an expert system (even if the declarative version was never written down), but most C++ programs are not expert systems. Heck, a lot of Prolog programs aren't! To the extent a C++ program representing GPT inference is an expert system, it's the trivial expert system with one fact.
> what does the rag for uploaded files do in discourse?
You can upload files that will act as RAG files for an AI bot. The bot can also have access to forum content, plus the ability to run tools in our sandboxed JS environment, making it possible for Discourse to host AI bots.
> also, when i run a discourse search does it really do both a regular keyword search and a vector search? how do you combine results?
Yes, it does both. In the full page search it does keyword first, then vector asynchronously, which can be toggled by the user in the UI. It's auto toggled when keyword has zero results now. Results are combined using reciprocal rank fusion.
In the quick header search we simply append vector search to keyword search results when keyword returns less than 4 results.
> does all discourse instances have those features? for example, internals.rust-lang.org, do they use pgvector?
Thanks for the details. Also, always appreciated Discord's engineering blog posts. Lots of interesting stories, and nice to see a company discuss using Elixir at scale.
Claude Code with Opus 4.5 is a watershed moment, moving software creation from an artisanal, craftsman activity to a true industrial process.
It’s the Gutenberg press. The sewing machine. The photo camera.
"""
- Sergey Karayev
> https://x.com/sergeykarayev/status/2007899893483045321
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