agentcoops

“ 39. Re graphics: A picture is worth 10K words - but only those to describe the picture. Hardly any sets of 10K words can be adequately described with pictures.”

Perlis has an aphorism for this, as he does every important problem [0].

[0] https://www.cs.yale.edu/homes/perlis-alan/quotes.html

I’ve been doing really heavy text analysis work with LLMs where false negatives/misses are important to minimize and my god did I hit cost thresholds quickly with 5.6 Luna — it was the first time I felt motivated to seriously work with local open models, even if inference was degraded for the task. Cheaper and much better inference now brings me back to the closed models for better or worse.

Yes, I originally only meant to note that in the extended statement on a/I the PKK isn’t mentioned and the focus is rather on a network of “antifa” groups [0]. With respect to your legal point, I reread the document and we’re in agreement: the state department goes to lengths to demonstrate that a/I “manually vets all potential users.”

[0] https://www.state.gov/releases/office-of-the-spokesperson/20...

It’s more than that, I think. I read through the various government statements as I was curious about rationale and precedent: the other two groups impacted by the order are rather unambiguous extremists, so understandable. In the extended justification for this a/I group, the US gov lists a large number of US and EU “Antifa” groups that variously leverage their services, including one that the individual recently arrested for wiping his phone is meant to be connected to. It suggests to me this was the result of some considerable network analysis and that the intent is to now have stronger legal tools to go after those groups.

I think that second (speculative, of course) step would be the more worrying precedent: if the use of technical infrastructure also used by designated groups was formally made to be incriminating/right-waiving. Why wouldn’t that apply to Signal, for example? The OP suggests the dividing line should be between true infrastructure open to all, including unsavory actors, versus tools that advertise services to a particular faction. It’s not an entirely disagreeable distinction, but I think in practice it’s rather fuzzy (Moxie of signal is/was rather far left himself, if I recall).

EDIT: Re-reading the document, the state department emphasizes that a/I “manually vets all potential users.” OP’s distinction stands for now.

I don’t think the effects on the hardware market of open weight models triumphing has been reflected upon enough. It’s not clear that it will be less impactful if every enterprise decides to build for predominantly on-premise inference. In fact, before the question is ultimately decided, we’re probably heading towards a few years where hobbyists, enterprises and ‘hyper scalers’ are all competing for certain parts in common. Ram booked through 2027 sounds about right.

Professionally, Scala was always my favorite language to work in and I was lucky to get to use it most of my career. It is, however, probably the worst language I’ve experienced using with LLMs. Next worst is any dynamic language: it’s just so hard to not introduce strange bugs after iterating on a large-ish project across multiple agent sessions.

I’ve had good enough experiences with Rust, but actually OCaml has been hands down the language I’ve seen best results with. The quality (and performance) of code is just phenomenal — and the main issue when working as a solo human with the language, namely smaller pool of community libraries, just isn’t an issue any more. Jane Street has really done tremendous work modernizing the language and tooling.

This is fascinating. I haven't really kept up with SEO for years, but was recently helping an academic publisher setup their web presence and ran into exactly this issue: Google was/is refusing to index the actual journal articles on the new site and they were moved into this liminal "Crawled, not indexed" state before vanishing entirely. Comically, it's now easier to get your content indexed and served with a visible link by ChatGPT than it is Google...

If I recall correctly, the film actually had Emmanuel Goldstein of 2600 and Kevin Mitnick as consultants, so despite the hollywood treatment you have moments of accuracy like https://www.youtube.com/watch?v=4U9MI0u2VIE. Probably the only time Compilers: Principles, Techniques and Tools made it to the big screen. At the yearly 2600 conference, they used to always do a big group rollerblade through NYC.

In finance and so the world we live in, the value of an equity is less related to the merit of the product than the firm's capacity to generate future free cash flow. Software companies, B2B SaaS in particular, have been basically unbeatable in this regard, hence the state of the market (and our salaries) for the past few decades. Industrial firms have to use metrics like "EBITDA" to show how much cash they'd have to potentially pay to investors if they didn't have to pay so much in interest on their debt, taxes, and fixing up decaying equipment...

I used to work in this field myself long ago (academically and practically). The difficulty that OP didn't suggest much appreciation for is that the interesting spaces for automated verification -- especially of distributed systems, say -- require exploring the space of formalisms between the trivial proofs of propositional logic (SAT solvers) and the undecidable proofs of first or second-order logic (interactive theorem proving environments such as Isabelle and Lean). This is the incredibly large space occupied most importantly by the various modal logics, which allow for quantification over graph traversals. There are remarkably expressive logics where the decision problem remains tractable, such as the modal mu-calculus.

That is, precisely if one wants automated tools for verification the seemingly obtuse work on novel formal systems is necessary.

Yeah, it's been a relatively rational decision for a while now, but one I personally hadn't really seen taken until recently. Again, all anecdata, but I am curious to track how much of a trend it really is.

Yes, it's never a trivial process, so a lot of work is certainly being performed by that "relatively": I have extensive personal experience with the UK immigration process and know of the US equivalent through the experiences of former colleagues back home. France, for example, is five years to a passport/naturalization. Germany is three years of skilled work to indefinite leave to remain. The Netherlands is five years to indefinite leave to remain. None of those examples require European ancestors, EU spouse etc, but generally it's easier if you have a university degree and work in the various fields most readers of hacker news do.

Generally continental Europe -- except the Scandinavian countries -- makes it relatively easy to get long-term residency and even a passport. The UK is considerably more difficult, but very easy to work in for an extended period of time (intra-company transfer visas etc).

If you're living and working in California or New York, as I suspect a large number of hacker news readers are, EU taxes on income are generally not prohibitively more expensive, especially relative to increase in quality of life. 'Native' salaries are considerably lower, however, and tax treatment of equity-based compensation is very much not in favor of employees...

In my niece's (relatively rural) US high school class several students decided to attend university in Europe with no family ties to the countries in question. It was pretty common in my generation to see, as you note, kids moving to Berlin etc after their studies, but this strikes me as relatively new. Anecdata that seems supported in some of the public numbers [0].

[0] https://www.forbes.com/sites/michaeltnietzel/2025/07/21/reco...

I agree with your fundamental point. However, I don't think steady erosion of mastery is the only way that these next years have to go, even if it looks the most likely at present. Supposing LLMs or whatever future architecture surpass even the greatest human minds in intelligence, why is that situation fundamentally different to living in a world with Einstein, i.e. a level of mastery I'll never reach before the end of my life? As one interested in the depths, I prefer to live in a world with peaks ever greater than myself---it doesn't prevent me from going as deep as I can, inspired by where they've reached, and doing the things that matter to me.

Turing's view, in fact, is similar: "There would be great opposition [to AI] from the intellectuals [read programmers in the context of this thread] who were afraid of being put out of a job. It is probable though that the intellectuals would be mistaken about this. There would be plenty to do, i.e. in trying to keep one’s intelligence up to the standard set by the machines, for it seems probable that once the machine thinking method had started, it would not take long to outstrip our feeble powers. There would be no question of the machines dying, and they would be able to converse with each other to sharpen their wits."

[0] Thomas Bernhard's The Loser is a fantastic account of the opposite standpoint---of the second best piano student, who cannot stand existing in a world with Glenn Gould.

Amazon's free cash flow rises year over year (apart from the post-COVID period) [0] while Walmart's doesn't [1] and price multiples are largely determined by expected FCF over time not directly revenue/EBITDA. FCF rain or shine maps roughly onto possibility of paying out dividends/buybacks, which determines the value of an equity in capital markets ("discounted present value of a company's future cash flows").

[0] https://www.macrotrends.net/stocks/charts/AMZN/amazon/free-c... [1] https://www.macrotrends.net/stocks/charts/WMT/walmart/free-c...

Yeah, this is certainly the correct take. There's an alternate timeline where the Scala community focused during the peak on making it a better language for numeric computing/ML rather than the Nth category theoretic framework, but here we are. At a job almost a decade ago, we made some progress on an open source dataframes (and unfortunately proprietary data visualization) library for Scala, but we didn't get far enough before the company closed and the project died [1].

Still my favorite language I had the privilege to work with professionally for over a decade. However, in this post-JVM world, I'm actually excited to see a lot more OCaml discussion on here lately. The Jane Street work on OxCaml is terrific after a long period of stagnation with the language. I'm using it for most of my projects these days.

[1] https://github.com/tixxit/framian

Honestly, I don't think it's irrational: the car industry is just horrible from a business perspective, which is why Tesla had to be financed for so long by crypto scams and most investors wouldn't touch it. Historically (if of course briefly/crudely), it was always a debt-backed gamble on overproduction hoping you could expand forever globally without competition (Ford) or into new market segments through financing (GM).

It's paywalled unfortunately, but [1] is an illustrative Financial Times article discussing car manufacturer behavior in relation to Covid shutdowns and strikes. Many firms found the manufacturing shutdowns to be a boon: the winning strategy to accept it as a cost cut and just raise prices on existing inventory for above average financial performance.

My sense is that Tesla is now just taking that a step further by getting rid of their Fordist aspirations and applying the unarguably successful Apple model to the automotive industry. They don't want to mass produce cars and hope for X% conversion rate to software and services over time: they literally don't want customers who are not able or not going to pay for recurring software services. Software is where free cash flow comes from and free cash flow is where dividends/buybacks come from, which determines the value of an equity. That, of course, is why we get paid well.

I end with the disclaimer that obviously I don't believe the world should be meticulously and exclusively organized for the production of free cash flow, but I do think it's important to understand the logic.

[1] https://www.ft.com/content/4da6406a-c888-49c1-b07f-daa6b9797...

I don't know what it looks like on the ground now, but Scala was the defacto language of data infrastructure across the post-Twitter world of SV late stage/growth startups. In large part, this was because these companies were populated by former members of the Twitter data team so it was familiar, but also because there was so much open source tooling at that point. ML teams largely wrote/write Python, product teams in JS/whatever backend language, but data teams -- outside of Google and the pre-Twitter firms -- usually wrote Scala for Spark, Scalding etc in the 2012-2022ish era.

I worked in Scala for most of my career and it was never hard to get a job on a growth stage data team or, indeed, in finance/data-intensive industries. I'm still shocked at how the language/community managed to throw away the position they had achieved. At first I was equally shocked to see the Saudi Sovereign Wealth fund investing in the language, but then realized it was just 300k from the EU and everything made sense.

It's still my favorite language for getting things done so I wouldn't be upset with a comeback for the language, but I certainly don't expect it at this point.

Yeah, I've lived in London, New York and San Francisco for work and the first had the lowest cost of living in absolute terms. Local developer salaries are, however, shockingly low outside of finance and consulting to US eyes.

The public transportation point is definitely key: London is just so unspeakably large spatially and it's all more or less well-connected that there isn't the same scarcity of commutable apartments as in NYC/SF. It wasn't uncommon for older colleagues to even commute in from Kent or elsewhere in the English countryside -- and often their morning train wasn't much longer than my own.

Yeah, that's the bull case for sure. Chinese firms might not accept training setbacks even given CCP regulations that they dogfood X homegrown chip.

Most of my use cases these days are for hobby projects, which I would bucket into the "data science"/"data journalism" category. I think this is the easiest audience to develop for, since people usually don't have any strict disciplinary norms apart from clean and sensible design. I mention double y-axes because in my own past library I stupidly assumed no sensible person would want such a chart -- only to have to rearchitect my rendering engine once I learned it was one of the most popular charts in finance.

That is, you're definitely developing the tool in a direction that I and I think most Hacker News readers will appreciate and it sounds like you're already thinking about some of the most common "extravagances" (annotations, reference lines, double y-axis etc). As OP mentioned, I think there's a big need for more performant client-side graph visualization libraries, but that's really a different project. Last I looked, you're still essentially stuck with graphviz prerendering for large enough graphs...

That's so funny: I also first started to realize I had aphantasia during a period when I was taking chess very seriously during university. Unlike even lesser skilled peers, it was so difficult for me to understand games written out in chess books without playing them out on the board and I couldn't understand why...

Experiences like that are how I understand the question of 'shame' relating to aphantasia and the importance of 'diagnosis'/understanding how your mind actually works. 'Diagnosis' just helps you understand how to adapt and prevents you from slamming your head against approaches that won't work no matter how hard you try.

Similarly on sleep, I can sleep anywhere anytime with little effort and always tell my wife, who often has insomnia, "just close your eyes until you sleep" to her frustration.

What's really remarkable is how similar the life experiences are of most who have aphantasia...

It seems to all be debt financed, i.e. just a private equity model slightly specialized for tech. The "innovation" is that Bending Spoons has an in-house engineering team it seems they try to keep constant yet scale out to all the acquisitions. I hadn't looked into them much before, but https://www.colinkeeley.com/blog/bending-spoons-operating-ma... is an interesting report -- though not focused on the finance side.

Really fantastic work! Can't wait to play around with your library. I did a lot of work on this at a past job long ago and the state of JS tooling was so inadequate at the time we ended up building an in-house Scala visualization library to pre-render charts...

More directly relevant, I haven't looked at the D3 internals for a decade, but I wonder if it might be tractable to use your library as a GPU rendering engine. I guess the big question for the future of your project is whether you want to focus on the performance side of certain primitives or expand the library to encompass all the various types of charts/customization that users might want. Probably that would just be a different project entirely/a nightmare, but if feasible even for a subset of D3 you would get infinitely customizable charts "for free." https://github.com/d3/d3-shape might be a place to look.

In my past life, the most tedious aspect of building such a tool was how different graph standards and expectations are across different communities (data science, finance, economics, natural sciences, etc). Don't get me started about finance's love for double y-axis charts... You're probably familiar with it, but https://www.amazon.com/Grammar-Graphics-Statistics-Computing... is fantastic if you continue on your own path chart-wise and you're looking for inspiration.

I agree with you as an adult with aphantasia, but I think it's a relatively common experience as an undiagnosed kid in grade school etc.

I hear your argument, but short of major algorithmic breakthroughs I am not convinced the global demand for GPUs will drop any time soon. Of course I could easily be wrong, but regardless I think the most predictable cause for a drop in the NVIDIA price would be that the CHIPS act/recent decisions by the CCP leads a Chinese firm to bring to market a CUDA compatible and reliable GPU at a fraction of the cost. It should be remembered that NVIDIA's /current/ value is based on their being locked out of their second largest market (China) with no investor expectation of that changing in the future. Given the current geopolitical landscape, in the hypothetical case where a Chinese firm markets such a chip we should expect that US firms would be prohibited from purchasing them, while it's less clear that Europeans or Saudis would be. Even so, if NVIDIA were not to lower their prices at all, US firms would be at a tremendous cost disadvantage while their competitors would no longer have one with respect to compute.

All hypothetical, of course, but to me that's the most convincing bear case I've heard for NVIDIA.

I definitely agree on the importance of personalized benchmarks for really feeling when, where and how much progress is occurring. The standard benchmarks are important, but it’s hard to really feel what a 5% improvement in X exam means beyond hype. I have a few projects across domains that I’ve been working on since ChatGPT 3 launched and I quickly give them a try on each new model release. Despite popular opinion, I could really tell a huge difference between GPT 4 and 5 , but nothing compared to the current delta between 5.1 and Gemini 3 Pro…

TLDR; I don’t think personal benchmarks should replace the official ones of course, but I think the former are invaluable for building your intuition about the rate of AI progress beyond hype.

Yes, it’s a very narrow-minded perspective that cannot understand the second-order implications of this development beyond their own experience as an experienced developer. For argument, let’s imagine that the quality of software at the top valley firms is just phenomenal (a stretch, as we all know, even as a hypothetical). That is obviously not the case for the quality of software at 99% of firms. One could argue that the dominance of SaaS this past decade is an artifact of the software labor market: any vaguely talented engineer could easily get a ridiculously well-paid position in the valley for a firm that sold software at great margins to all the other firms that were effectively priced out of the market for engineers. I think the most interesting case study of this is actually the gaming industry, since it’s a highly technical engineering domain where margins are quickly eroded by paying the actual market wage for enough engineers to ship a good product, leading to the decline of AAA studios. Carmack’s career trajectory from gaming industry to Meta is paradigmatic of the generational shift, here.

TLDR; in my opinion, the interesting question is less what happens at the top firms or to top engineers than what happens as the rest of the world gains access to engineering skills well above the previous floor at a reasonable price point.