> “It’s not about the architecture per se,” Evans says. “It’s about the incentives.”
It would have been useful to check whether less original work was already getting more citations before AI adoption. That could reflect broader trends and network effects: heavily cited research areas attract more authors optimizing for citations, so high-productivity researchers end up clustering on the same topics.
I built a similar system for php and I can tell you what is the smart thing here: accessing data using tools.
Of course tool calling and MCP are not new. But the smart thing is that by defining the tools in the context of an authenticated request, one can easily enforce the security policy of the monolith.
In my case (we will maybe write a blog post one day), it's even neater as the agent is coded in Python so the php app talks with Python through local HTTP (we are thinking about building a central micro service) and the tool calls are encoded as JSON RPC, and yet it works.
I think the hardest for this kind of projects is to keep it active. Environments with a "global state" like this (everyone shares the same website) are ultimately limited to surges (seasonal events like Magnus vs The World or a single game of Twitch plays Pokemon) or to a recurring flow of new people.
Maybe having multiple "realms", so that there are not too many people in a single realm in case of virality, and the ability for people to spawn their own realms would be nice (think skribbl or Among Us) but then it would kind of be a Lovable and cost a lot to host the LLM. But since the html code is open source, local LLMs (like Gemini Nano embedded in Chrome) could theoretically do the editing. In that case, the web page should definitely be marked as even unsafer! I wonder how one could avoid the red flag of Chrome for pages that are deliberately made to host collaborative crap.
I don't get why one wouldn't just use webworkers to run the simulation instead, thus making the game fully executable in a web browser.
If deno has some perks during development, there must be a way to replace websocket with some other transport that works with webworkers for "production" builds.
Very interesting in the context where major porn websites blocked access in France (now reverted) and in some US states as a response to age verification regulations that were too difficult to implement without compromising user experience and privacy.
Interesting. My process is similar, although based on the GTD method (for example with an Inbox list) and using Trello for implementation (I get syncing, task level notes, multimedia, item drag and drop, etc)
Nice! Recently I had the need for pdf censoring, aka not adding black rectangles but actually removing the content underneath and I still haven't found a suitable tool. Any recommendation? Or even python library to do it?
The current visualization is by far not perfect but it was hard for me to put more information there. Please give your ideas to improve this visualization or make others!
I mostly aimed at making an aesthetically pleasant image that would represent what cells were controlled and moves were used the most.
As for usage examples, it's very easy to see the difference between European and Indian openings (the former advancing in the center and the latter on the sides) and it's quite easy to guess who won by looking at who controlled the most cells last.
On the tech side, this is a single-file, local first, vanilla JS app querying the (non official) chessgames.com API through corsproxy.io (because CORS). Then I draw using svg elements. Finally, I use canvg [^1] to produce png images. The js code is embedded in the HTML so you can read the code just by viewing the source (or look on github [^2]). I also have a Python version that I also maintain to produce the same outputs as the browser version.
I actually quit a quant trading job after 2 weeks because they used kdb+. I could use it but the experience was so bad...
People could complain about abysmal language design or debugging but what I found the most frustration in the coding conventions that they had (or had not), and I think the language and the community play a big role there. But also the company culture: I asked why the code was so poorly documented (no comments, single letter parameters, arcane function names). "We understand it after some time and this way other teams cannot use our ideas."
Overall, their whole stack was outdated and ofc they could not do very interesting things with a tool such as Q. For example, they plotted graphs by copying data from qStudio to Excel...
The only good thing was they did not buy the docker / k8s bs and were deploying directly on servers. It makes sense that quants should be able to fix things in production very quickly but I think it would also make sense for web app developers not to wait 10 minutes (and that's when you have good infra) to see a fix in production.
I have a theory on why quants actually like kdb: it's a good *weapon*. It serves some purpose but I would not call it a *tool* as building with it is tedious. People like that it just works out of the box. But although you can use a sword to drive nails, it is not its purpose.
Continuing on that theory, LISP (especially Racket) would be the best *tool* available as it is not the most powerful language out of the box but allows to build a lot of abstractions with features to modify the language itself. C++ and Python are just great programming languages as you can build good software on them, Python being also a fairly good weapon.
Q might give the illusion of being the best language to explore quant data, but that's just because quants do not invest enough time into building good software and using good tools. When you actually master a Python IDE, you are definitely more productive than any Q programmer.
And don't get me started on performance (the link covers it anyway even though the prose is bad).
Head of LLM and Gen AI @ https://www.tiime.fr/