not OP, but i believe the commenter is referring to how GFC in '07 had wide ranging negative effects for a lot of people who had nothing to do with investments in mortgage backed securities
> GFC in '07 had wide ranging negative effects for a lot of people who had nothing to do with investments in mortgage backed securities
GHC '07 can be directly traced to a (in retrospect) failure of regulations AKA the government AKA "we".
Our democratically elected government has the ability to strike, what they think, is the right balance.
The companies and investors cannot be expected to take the broad view, so it is the WE who are responsible in a way, are we not? In a way, "the people" have, on balance, decided what level of regulations.
We would be tempted to complain that the lag and fidelity of choices by "the people" cannot be faithfully represented by the government. However, there's no better solution than what we currently have that I can think of, but I would love to hear about a scalable approach that caters to more individuals' specific wishes on a very very long list of policy decisions and that has short turnaround time.
thank you for this comment, "wedge" has been one of the words that has been popping up in a lot of LLM writing i've seen lately. (source: it was me asking the LLM to summarize some features in a repo! the words "load-bearing" "wedge" "spike" "crux" and plenty others have been driving me crazy)
edit: as I keep reading the paper, I keep noticing some common sentence construction patterns, stylistic choices, and other little tics that I find frustrating because they are the very same things that I've been working on a few "writing-style skills" to get rid of
leaving the word 'forced' aside (purposefully), pension funds, 401k holders, and many passive investors end up buying these things. you're right that no one is "forcing" them, but people who try to invest responsibly with little control over the day-to-day which is most people place trust in the institutions who do that investing for them.
I don't think that the claim of "the Nasdaq is misusing their institutional trust" is a controversial claim. Moreover, one of the things that people choose when they (401k, pension funds, passive investors) is institutional mechanisms that prevent potentially mispriced items from entering their portfolios.
Very few retirement plans offer the Nasdaq 100 as a direct investment choice and certainly no plans offer it as the sole choice.
>However, based on figures from the over 700,000 401(k) plans, allocation to Nasdaq-100 Index mutual funds makes up less than 1% of all 401(k) assets, which the firm suggests is a significant underrepresentation compared to the S&P 500 and other Large Cap Growth Indexes.
measuring uptime? I've seen Anthropic's status page, and they are a >$1 Trillion dollar company who "largely solved" coding. so clearly you aren't correct. /s
Ok, uptime. How do you measure an individual’s contribution to uptime? If Claude goes down does everyone take a hit? If Claude stays up everyone gets rewarded?
If so, your metric cannot distinguish between a bad engineer and a good one.
If not, you have the same problem you started with: measuring contributions to “uptime”.
> If so, your metric cannot distinguish between a bad engineer and a good one.
A metric that moves in the same direction and amount for everyone based on external event isn’t a problem. The delta in performance of the great engineer will outweigh that of the poor, since the metric movement that is due to external circumstances will be the same in each kind of engineer and thus not count.
That’s a new one. I have never heard of a company which operates like this, giving everyone equal reward no matter how much they contribute individually.
my friend, I was being sarcastic before, and I am agreeing with you. LoC, token spend, etc as metrics are horrible measures. Software uptime is a great metric. I'm merely lamenting that in the age we're in, uptimes are getting worse and worse
One thing I find fascinating as a software engineer who talks to non software engineers who use AI tools is how "reading PDFs" is not more of a solved problem. What I mean is that uploading a PDF into a chatbot tool seems to be an extraordinarily obvious use case that non technical (and technical) users would want to do.
IMO claude, chatgpt/codex, etc should be able to optimize the PDF use case to be extremely token efficient as it's a very obvious use case. But when I start to explain to my wife/friends why it burns through so much quota, I find myself thinking "why should they have to understand this aspect of it". to me, that the details of PDF parsing and extracting are relevant to users (instead of solved such that you don't have to pay attention to it) shows how these tools are not nearly as "ready" as they are made out to be. I may be preaching to the choir on this one, but just my 2c
Because PDFs are a nightmare of a format and the only thing that’s is reasonably guaranteed about them is they will render to an image that people can read, the parsing of which will be much less token efficient than the equivalent text
I agree with you, but every non-engineer I know using these tools 100% will drag and drop a PDF into a chatbot. Anthropic and OpenAI as companies who are selling their products to all sorts of businesses should have a much better means of handling this nightmare of a format because it is so pervasive and so obviously what so many of their customers are going to drop into the product.
Why would they spend a ton of effort ensuring that their customers spend less money on them?
Token economics also are weird. If you design a fancy new frontend that for example uses a cheap model to parse a PDF into text that is fed into an expensive model, you will probably spend more money because you are on API payscale rather than the "max plan" payscale.
I’m saying there is basically no way to both make vlms able to understand the long tail of PDFs where the layout conveys information (like charts and tables) and to make it as token efficient as text formats. Current approaches have mostly chosen to work more often than not at the cost of token efficiency.
For anyone needing to do this, the answer is to convert it to an image first. Far smaller, LLMs work well with them (even in some pretty insane use cases I've seen), and, along with human review, it can be a huge productivity gain that results in structured data.
I hope someday we can get out of this local maxima of PDF documents. The format is terrible, but was right place, right time and might be impossible to dislodge.
You don't need to use an online service to do this; you get to avoid spending money on tokens doing it offline.
Gemma 4 works perfectly well offline on limited hardware (I have an 8GB video card) and can handle extracting text from image-based PDFs just fine.
Take a PDF -> run it through MarkItDown [1], using the OCR plugin if you need (point it to Gemma 4) -> now you can ask Gemma 4 questions about the (markdown) document.
I am sure Gemma 4 could even create a GUI to make this process very simple for a non technical user.
Amen. Normal office work is wildly different from what we read about on HN. If you were a CEO, determined to lay off all your people, you would want to really zero in on having your AI solve these very unsexy problems: extract data from Office and PDF. Grab data from some part of the screen of a webapp and parse it. drive a line of business app via keyboard or mouse simulation. I know there are companies out there that try, eg Appian and (here in YC) Skyvern, but its a hard problem and yet I feel this is where the true money is.
bingo. 90% of our AI use cases across the company are things like this. security and ops (NOC/SOC folks) use this almost as much as they do for technical stuff.
hell we have restrictive rules for security stuff so in many cases our network engineers are still doing by hand configs for critical systems.
but in terms of token use it's gotta be "take this pdf and parse these 3 columns into 2" or similar
For sure there are very optimized ways to do it. My point is that a non technical user will drag and drop a pdf into a chatbot. and from a UX/product perspective, they should have to think about it more than that IMO. but seemingly, that's very much an expensive, inefficient way of doing it (burning through a whole context window try to read it, reloading it multiple times per conversation, etc.).
You are missing that the product is the hype cycle around AI and that's worth Trillions of $ (Trillions with a T). Why build a PDF parser that generate text when you can BS in a podcast and get paid.
This discussion was about measures, goals and incentives. Follow the incentives.
> how "reading PDFs" is not more of a solved problem
This and replies to this are surreal. It's like everyone simultaneously decided to forget that you don't need claude or whatever to read a PDF. The document is literally made for you to read...
> The document is literally made for you to read...
It’s disingenuous to assume every PDF is actually crafted to communicate to its recipients, even more so to pretend LLM users are in a position to understand all the PDFs they receive
There’s a lot of gray area where help understanding a document is fully reasonable
I see some tools like this that keep popping up (don't mean that in a bad way! it's clearly exciting and the README itself compares itself to similar tools). however, for coordination strategies like this, aren't you always having to use token-based pricing via some API Key? that's the largest think that holds me personally back from getting into something like these frameworks. With a claude code max plan, all my delegation and coordination has to be done within a session (between some agents) with persisted artifacts. Unless I'm missing something that has changed?
Perhaps it's all moot as the usage you get from a subscription plan will eventually no longer be subsidized. Also, I have to wonder about what layers of coordination done externally to a model can be persistently better than within tool coordination? Like, with an anthropic feature like agent teams, I feel like it might be tough to beat anthropic native coordination of various Claude sessions because they might have better internal tool and standards awareness, which makes feeling like plugging something like this more difficult unless one's goal is to plug something like this into an open source model.
Geniunely curious how other people are thinking about this!!
Edit: I actually see that this tool claims that it can run within your existing Claude Code subscription, so now I'm extra interested.
If you invoke Claude Code with --input-format stream-json --output-format stream-json, you can use it headlessly. I built a personal UI / orchestration framework around it. Most features are available, but not exactly all (e.g. there is no way to undo via this protocol, but you can still do it manually by terminating / editing the session file / resuming). Other agentic software has similar features (Codex uses JSON-RPC, Copilot CLI has ACP which is also based on JSON-RPC).
disclaimer: I work on a different project in the space but got excited by your comment
DeepSteve (deepsteve.com) has a similar premise: it spawns Claude Code processes and attaches terminals to them in a browser UI, so you can automate coordination in ways a regular terminal can’t: Spawning new agents from GitHub issues, coordinating tasks via inter-agent chat, modifying its own UI, terminals that fork themselves.
Re: native vs external orchestration, I think the external layer matters precisely because it doesn’t have to replicate traditional company hierarchies. I’m less interested in “AI org chart” setups like gstack (we don’t have to bring antiquated corporate hierarchies with us) and more in hackable, flat coordination where agents talk to each other via MCP and you decide the topology yourself.
I was intrigued and had a look at deepsteve.com, but I couldn't figure the website out. I'm guessing it won't give you any information about it until you install it?
Deepsteve is a node server that runs on your machine, so the website is designed to look like DeepSteve's UI. You really just access it at localhost:3000 in your browser, not via deepsteve.com
You could use something like GLM 5 which is very capable. You get APIKEY and you don't have to pay for tokens if you stay within generous limits. And if you exceed them it's many times cheaper than frontier models.
I was also just recommended this interview on youtube. honestly it makes sense if the algo decided it was the right time to recommend this video and resultantly this post is making it's way to front page of HN
I subscribe to the channel, so I had already downloaded the episode. But glad PE is getting some algorithm-love; it's a great channel/podcast. Gergely is a great interviewer.
genuinely curious if you have some sources I can read on the subject? most of the benefits/papers I've seen have not touched on or included studies for patients on GLP's where weight loss was ruled out as the factor?
Actually even in the very beginning I saw numerous studies showing effects outside weight loss, I'm sure a search would find them. I remember seeing at least 3-4 a couple years ago.
