nfcampos

That commit happens to be a find replace because i usually write for my company’s blog (we)

No, they really are my thoughts, I've been doing this stuff pretty much every day for the past 6 months

My own thoughts on this, with examples https://github.com/nfcampos/loop-dev/blob/main/README.md

I think the same will be true of the ‘popular science’ category of nonfiction

For what it’s worth, I personally discovered something similar with another category of nonfiction books - economics and business. I read a lot of these circa 10-15 years ago and the rate of new signal id get from each new one eventually became low enough that I stopped reading in that category altogether.

He does mention audiobooks, see quoted below

But what about ebooks and audio? Looking at all formats (print + ebook + audio) for the catalog in 2025, the second half of the year was down ~45% versus the first half.

This compares to 46% on print only

The title of the submission is an almost comical example of hn navel-gazing - of the many interesting things in the article surely the job prospects of hn readers should not be near the top of the list

Anthropic marketing (and even supposedly technical write ups) sadly has become more hyperbole and less substance over time imo. This technology is so impressive on its own, really feels like shootings themselves in the foot in the long run, but what do I know

Case in point here where they conveniently fail to report the false positive rate, while also saying that if it wasn’t for Address Sanitizer discarding all the false positives this system would have been next to useless

But on the other hand the claude app is garbage… https://github.com/anthropics/claude-code/issues/22543 obviously native apps can be garbage too, but I must say electron apps have a surprisingly high incidence of terrible performance issues, unsure if it’s a correlation or causation issue

LangGraph implements a variant of the Pregel/BSP algorithm for orchestrating workflows with cycles (ie. not DAGs) and parallelism without data races. You can design your graph as a state machine if you so desire

Should be merged with https://news.ycombinator.com/item?id=42895466

Hi do you mind sharing more what you mean by "the api makes excessive calls"?

Hi, you don't have to use langchain tools or ToolNode with langgraph, you can absolutely write your custom tool handling logic. In langgraph nodes are just functions that can do whatever you want, seems to be some misconception somewhere?

FWIW we have first class support for subgraphs in the library, would love to know what issues you faced there. Support for subgraphs in the studio is coming soon.

A topological sort can't be used for graphs with cycles

Fwiw we didn't start with Pregel, but instead reached for it when we saw issues (described elsewhere in this thread) with the previous approach

https://github.com/elastic/kibana/tree/main/x-pack%2Fplugins...

one I saw recently, obvs not the only one

The value of using pregel is supporting cyclical graphs, with concurrent tasks, but completely deterministic behavior (eg. doesn't matter that task A this time happens to finish before task B, its results will be applied in a predetermined order, etc)

Pregel actually doesn't require the structure of the graph to be declared ahead of time, the next set of tasks (functions) to run is computed at the beginning of each step, so in theory the graph topology could be entirely dynamic. Langgraph supports dynamic topologies too, ie. don't declare any hard edges, add a conditional edge after every node

I'm one of the authors so my opinion would be biased ha

Langgraph is not a no-code tool for visual programming, it's an implementation of the Pregel algorithm for execution of cyclical computation graphs (ie not DAGs)

Not sure where I stand on this, but “do not judge a book by its cover” comes to mind

So what do people do with these things in summer? Won’t it then heat up their house excessively

A park you can get (a little) lost in is a wonderful thing

Revised follow-up paper https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3805927

This reminded me of https://www.weforum.org/agenda/2018/09/mining-for-cryptocurr...

(I know this isn’t storage, jk)

Interestingly both “halves” of the word as it split it have the same number of tokens

This post reads like Nokia/Microsoft reacting to the first iPhone — myopic

Even if I take the most charitable view, ie. that everything they did was above board etc, there’s still a lesson here about diversification -

a startup (ie. a private company which represents the majority of the founders assets) is already a massively concentrated, ie. not diversified, financial position -

no need to compound it by making a myriad bets (through Alameda or otherwise) in in other tokens/companies in the same market, which are ultimately incredibly correlated assets.

If there was no fraud, that will ultimately be the explanation for their downfall.

Abstract: Aside from some living tissues, few materials can autonomously learn to exhibit desired behaviors as a consequence of prolonged exposure to unanticipated ambient loading scenarios. Still fewer materials can continue to exhibit previously learned behaviors in the midst of changing conditions (e.g., rising levels of internal damage, varying fixturing scenarios, and fluctuating external loads) while also acquiring new behaviors best suited for the situation at hand. Here, we describe a class of architected materials, called mechanical neural networks (MNNs), that achieve such learning capabilities by tuning the stiffness of their constituent beams similar to how artificial neural networks (ANNs) tune their weights. An example lattice was fabricated to demonstrate its ability to learn multiple mechanical behaviors simultaneously, and a study was conducted to determine the effect of lattice size, packing configuration, algorithm type, behavior number, and linear-versus-nonlinear stiffness tunability on MNN learning as proposed. Thus, this work lays the foundation for artificial-intelligent (AI) materials that can learn behaviors and properties.