Mixing metaphors, there is signal and noise. You can keep asking for noise, but the suggestion is to not train your neural networks with it as it will impair your inferencing. That said, we all have our own cost and reward functions...
Assuming brains work like computers, maybe yeah, that'd make sense :) You also won't know what's a signal vs noise until you've read and tried to understand it, and at that point you've already read it. Besides, something could be "noise" at the point you read it, but be a "signal" in a completely different context and/or time.
Just yesterday I was looking for a free-as-in-freedom image to embed in my repo, and this gem popped up again!
I'm adding a BubbleTea Picture widget to ntcharts. So the example is a (retro art of (retro art redone on retro tech)) done on (a redo of retro tech) ...
I've added it, but it's still on a feature branch :
The repo doesn't say it, but the Author noted on the Gophers Slack #showandtell that the style was inspired by SwiftUI. That VStack example shows it quite well.
> MCP tools don't really work for financial data at scale. One tool call for five years of daily prices dumps tens of thousands of tokens into the context window.
I maintain an OSS SDK for Databento market data. A year ago, I naively wrapped the API and certainly felt this pain. Having an API call drop a firehose of structured data into the context window was not very helpful. The tool there was get_range and the data was lost to the context.
Recently I updated the MCP server [1] to download the Databento market data into Parquet files onto the local filesystem and track those with DuckDB. So the MCP tool calls are fetch_range to fill the cache along with list_cache and query_cache to run SQL queries on it.
I haven't promoted it at all, but it would probably pair well with a platform like this. I'd be interested in how people might use this and I'm trying to understand how this approach might generally work with LLMs and DuckLake.
> A year ago, I naively wrapped the API and certainly felt this pain.
Most people, before being confronted to it, have no idea how big market data feeds really are: I certainly had no idea what I was getting into. There's a reason all these subscriptions are that pricey.
Here's an example of the pricing for the OPRA feed for Databento you mentioned:
We're talking about feeds that sustain 25+ Gb/s and can have spikes at twice or even three times that. And that's only for options market data.
I mean: even people with 25 GB/s fiber (which we can all agree ain't the most common and that's an understatement) at home still can't dream of getting the entire feed.
Having a bandwith big enough, storing, analyzing such amount of data: everything becomes problematic as such scales.
As to me I'm reusing my brokers' feeds (as I already pay for them): it's not a panacea but I get the info I need (plus balances/orders/etc. tied to my accounts).
I used the word firehose, which is typically reserved for streaming data and is a whole other very interesting problem space! Big Data vs Fast Data.
I’m just noting for interest that firms are applying transformers and other networks at this streaming microstructure level, but specially trained for feature extraction. HRT + Nvidia have some nice videos about it
I will also note that it is insane how much better all the LLMs are at calling MCP tools after just a year, especially the local ones.
One of the reasons I like DuckDB is the scale flexibility. I started with grabbing data and playing on my laptop, then I jumped to a server with high cores and a NAS.
While I’ve had tremendous success with Golang projects and Typescript Web Apps, when I tried to use Metal Mesh Shaders in January, both Codex and Claude both had issues getting it right.
That sort of GPU code has a lot of concepts and machinery, it’s not just a syntax to express, and everything has to be just right or you will get a blank screen. I also use them differently than most examples; I use it for data viz (turning data into meshes) and most samples are about level of detail. So a double whammy.
But once I pointed either LLM at my own previous work — the code from months of my prior personal exploration and battles for understanding, then they both worked much better. Not great, but we could make progress.
I also needed to make more mini-harnesses / scaffolds for it to work through; in other words isolating its focus, kind of like test-driven development.
Huh?? You are replying to a comment about a Free Speaker who was murdered point blank on a US street for protecting another Free Speaker who was being pepper sprayed for exercising their Free Speech!
Wikipedia: "The Last of Us is an action-adventure video game series and media franchise created by Naughty Dog and published by Sony Interactive Entertainment.[a] The series is set in a post-apocalyptic United States ravaged by cannibalistic humans infected by a mutated fungus in the genus Cordyceps."
We've got these sessions stored in ~/.claude ~/.codex ~/.kimi ~/.gemini ...
When you resume a session, it's reading from those folders... restoring the context.
Change something in the session, you change the agent's behavior without the user really realizing it. This is exacerbated by the YOLO and VIBE attitudes.
I don't think we are protecting those folders enough.
I think the main technological limitation is that other browsers cannot just-in-time compile (JIT) JavaScript or any other embedded language. Except in the EU.
ETA: your link includes JIT; I’m pointing out that that’s why they don’t exist outside of the EU. Non-JIT browsers would just not be very performant.
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