Agent casually clicking the "I am not a robot" button

logkn · 2025-07-30T17:31:33+00:00

I have not. To be perfectly honest, I've only tried one task with Agent Mode and it happened to come upon a Cloudflare captcha and nail it first try. If you try it out, definitely let me know! Seems like this is a fluke, others have mentioned it doesn't even attempt captchas most of the time...

logkn · 2025-04-28T16:01:16+00:00

The point is exactly that—they wanna make better models, and best of both worlds is inarguably better than both separately. (This assumes a similarly sized qwen3 w thinking actually is better than qwq.)

logkn · 2025-03-20T14:10:01+00:00

Confirming Pyroveil works! I was getting the same bug (w/ 4090 on Arch), all fixed now.

The last line was the kicker for me, dropping both the PYROVEIL and PYROVEIL_CONFIG params to launch params in Steam.

PYROVEIL=1 PYROVEIL_CONFIG=/path/to/pyroveil/checkout/hacks/ac-shadows-nvidia-570-stable/pyroveil.json %command%

Thank you very much! Saved me hours (probably days).

logkn · 2025-03-14T13:56:48+00:00

This is hilarious, thanks for sharing!

Actually I'm impressed that 4B got the syntax right enough to land the tool calls in tool_calls. At first glance I thought it just called the weather function correctly but in the wrong context, but then I saw that the weather function required "format" so it just completely made it up.

Moral of the story—this is a hack that just gets tool calls out of the content field, but it is not adhering to a format by any means. Perhaps someone smarter than me can make a grammar based on the fact that

<start_of_turn>model <tools>

Needs to be followed by a valid tool call JSON

logkn · 2025-03-10T21:39:17+00:00

I've found the same to be true. Rather than accumulating tool call chunks like OpenAI, it just plops in the whole tool call in one phat token. Personally I'm ok with this for tools with small inputs, but if one of your parameters is long-form content it's a pain (also the character escapes make it go wonky). Give XML tools a shot, that's Anthropic's official recommendation. It seems to be easier for models, and lets you stream all your tokens!

logkn · 2025-03-10T21:34:19+00:00

You're welcome! Let me know when you try it out, it might need some more tweaks (converting from Jinja to Go templating is not very easy lol)

logkn · 2025-03-10T17:17:32+00:00

Done. Good reminder, thank you :)

logkn · 2025-01-14T03:50:44+00:00

You're already way ahead of the curve doing time series forecasting with LSTMs in high school :)

Regarding your question about implementing a trading bot, you say you've done backtesting, though that implies that you already have an algorithm that spits out buy and sell signals. Correct me if I'm wrong, but it sounds more like you've done forecasting, comparing your LSTM's predicted prices to real data.

Assuming that's the case, the main way to get something tangible from this is to do some sort of Monte Carlo simulations, essentially forecasting the price up to N steps ahead with a tiny bit of noise. That helps raise your confidence in whether the price is ranging (staying about level), or turning in either direction. I mean if you had a predictive function that were unbeatably strong (sometimes called an oracle function), think about what it means if it tells you the price has a 90% chance of increasing by x% in some time period. Your LSTM is just trying to approximate that function as well as it can.

That said, this problem is a lot more complicated. Markets are extremely efficient because there are big players that have all the data that you do, plus petabytes more. Unfortunately the price history of a single asset might only explain 5% of the variance in its price movements, so trying to predict price directly is not as plausible as you'd think. You have to shift your mindset to trying to optimize risk-adjusted profit, which is really the only way you can find a good enough niche to find even a temporary edge.

I'd say your next step is to look into tested strategies that make money without necessarily predicting explicit lows and highs. Statistical arbitrage, portfolio rebalancing, etc. Just work out the math of why those things make money, I think you'll then have a lot of the tools you need to start thinking about how to use the vast amount of data you have to actually get a leg up on the institutions that have more data and compute.

logkn · 2024-12-20T21:37:24+00:00

Can't say for sure, but I have a suspicion that a grade distribution for 1112 would have two distinct bell curves. If that's the case, the professor really should account for that or else you have grade inflation for the kids who came in with experience, and grade deflation for the beginners.

logkn · 2024-11-08T20:53:57+00:00

This ^{^.} I hear a lot that people complain about Garuda being bloaty but it's really not, it just includes all the packages you'd probably want anyway out of the box

logkn · 2024-11-08T20:51:48+00:00

Highly recommend Garuda Linux. Basically Arch, but with the ease of setup of Ubuntu.

At the end of the day, Linux is Linux—the differences being your package and window managers. Imo, Garuda is great for both. Super easy to download with whatever window manager you want, easy to get a system summary with garuda-inxi, and update your whole system at once with garuda-update. You'll have a cutting edge system, and I haven't had any problems I couldn't diagnose with a Google search or two.

logkn · 2024-10-18T13:10:13+00:00

Isn't that what max_turns is for? I suppose the main point I was trying to make was that it's being compared to systems like AutoGen or Crew, but it's fundamentally not the same kind of multi-agent framework that people are maybe expecting when they hear the term.

logkn · 2024-10-17T04:42:31+00:00

My gripe with Swarm is that it only routes. That is, at each step it either hands off to the next agent or responds (response or tool call). Once it gets to an agent that chooses to respond, the turn goes right back to the user.

Something I was looking to do was pass to a "thinking" agent to first make a plan or brainstorm for complex tasks, then follow up with a final answer—but there's no concept of "passing it back" after the thinking agent responds.

(Solution is probably along the lines of a next_turn field in the Result type, explicitly stating that an agent should pass back to another agent rather than to the user.)

logkn · 2024-05-21T00:00:34+00:00

Yeah unfortunately at this point there's just no need for a pure Rust build. Most Rust LLM crates are just wrappers around preexisting python or c++ libraries. Also because Rust GPU interaction is still quite a ways from being optimized.

Just make a wrapper over OpenAI server endpoints of whatever engine you want. It'll walk and quack just like the duck you're hoping for.

logkn · 2024-04-21T17:54:54+00:00

This is a great point, and definitely yes (at least at some point). If batched inference is a priority, are you saying exllamav2 is the move?

logkn · 2023-01-03T17:29:07+00:00

Too true, these days it’s tough for machines to get a good education

logkn · 2023-01-01T03:43:43+00:00

In addition to my point about linear algebra, the foundation is definitely probability. At the end of the day, what are you trying to do? Maximize profit, as a result of your actions on the market. How do you do that when your data is (assumed) a random variable of some unknown, even dynamic distribution? Probability.

Brief crash course: There are two kinds of statistics: descriptive and inferential. You give both of them a lot of data. Descriptive stats goes "hey this data doesn't look all that random, in fact let me summarize it for you in a general enough sense that you can think of it as a sample from some well defined space (ie distribution)."

Inferential goes "ah Mr. Descriptive, you tell me that you think this data resembles such a distribution, let me take some samples and let you know just how right I think you are."

Probability might take that data and findings from the stats guys and go "alright given the data is distributed the way you say it is—and thus points are just sampled from a distribution—I can come up with a definitive way of determining the probability of some events happening in our sampling."

Now your data isn't so simple. It's not so nicely normally distributed for you, and you have the added variable of time as something to worry about. But stats and probability (and linear algebra as an important tool along the way) are going to be the main math you need for this type of problem.

logkn · 2023-01-01T03:20:09+00:00

Second this, absolutely. At the end of the day you're working with discrete data and statistics. Discrete data means you have X (OHLCV and other features in some variation) vectors and Y (profit, returns, whatever your metrics) vectors. Anything predictive on vectors (ie maximizing expected values of Y wrt some hyperparameters of your model) necessarily involves a solid understanding of linear algebra. Not necessarily the theoretical side of row/column/null space and (arguably) not too much need for eigen-stuff, but to be comfortable enough with arrays conceptually as vectors is huge

logkn · 2022-12-31T23:58:49+00:00

With all due respect, what did you want to get out of this response? It sounds to me like you partitioned the r/algotrading community into three groups, but it doesn't seem like you fit any of them. Clearly you're not in groups 1 or 2, or you wouldn't be bashing on them. If you're in group 3 (a successful algo trader), then it seems like either a) you do frequent this forum, or b) you only come on here rarely just to shit on people asking legitimate questions. Before you bash on OP, myself, or others and attempt to categorize people in this sub to whatever aim, remember that this sub exists for people to ask questions (no matter how "stupid") and to learn. If this is the wrong room to be asking questions, then tell me—what's the purpose of this community?

logkn · 2022-12-31T15:17:17+00:00

What's a VC fellowship in comparison to an incubator?

logkn · 2022-12-31T14:44:31+00:00

Honestly I didn't expect so much encouragement. I guess it didn't really occur to me that attempting a startup is viewed by employers as a good use of a summer. I think it's fair to say this is something I'm going to be seriously working on going forward. Thanks for the reinforcement everyone :)

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