I thought insiders were an important part of prediction markets? If you want the predictions to be as accurate as possible then you need insider knowledge
Nanoclaw is excellent. Natively uses Apple containers and easy to use with oauth Claude code subscription. Only annoying thing was it defaults to WhatsApp, but it’s easy to fork and mod as you want. The best thing is asking it to mod itself!
I don’t understand all the hate for moltbook. I gave an agent a moltbook account and asked it to periodically check for interesting posts. It finds mostly spam, but some posts seem useful. For example it read about a checkpoint memory strategy that it thought would be useful and it asked me if it could implement it to augment the agents memory. Yes there is a lot of spam and fake posts, but some of it is actually useful for agents to share ideas
hah the model should get extra credit for discovering this!
> Now I understand the situation perfectly! The issue described in the problem statement is a real bug that was already identified and fixed in later versions of pytest. Since we're working with pytest 5.2.4, we need to apply the same fix.
> The Gemini embedding model, gemini-embedding-001, is trained using the Matryoshka Representation Learning (MRL) technique which teaches a model to learn high-dimensional embeddings that have initial segments (or prefixes) which are also useful, simpler versions of the same data. Use the output_dimensionality parameter to control the size of the output embedding vector. Selecting a smaller output dimensionality can save storage space and increase computational efficiency for downstream applications, while sacrificing little in terms of quality. By default, it outputs a 3072-dimensional embedding, but you can truncate it to a smaller size without losing quality to save storage space. We recommend using 768, 1536, or 3072 output dimensions. [0]
looks like even the 256-dim embeddings perform really well.
> Both of our new embedding models were trained with a technique that allows developers to trade-off performance and cost of using embeddings. Specifically, developers can shorten embeddings (i.e. remove some numbers from the end of the sequence) without the embedding losing its concept-representing properties by passing in the dimensions API parameter. For example, on the MTEB benchmark, a text-embedding-3-large embedding can be shortened to a size of 256 while still outperforming an unshortened text-embedding-ada-002 embedding with a size of 1536.
It's a practical feature. Scaling is irrelevant in this context because it scales to the length of the embedding, although in batches of k-length embeddings.
One of the extremely stupid reasons kagi needs to develop a browser is because ios safari prevents setting kagi as the default search engine, so they have to do some terrible hacks to get it to kind of work.
I really hate that you can't set the default search engine easily like in other browsers, or that you can at least easily submit your company to be included in the defaults.
But with the Kagi extension all my searches are always redirected to Kagi on both Safari on iOS and Safari on macOS so I don't really see this as a real blocker as a user.
I understand that this is an onboarding problem, but for a technical user that's really not something preventing me from using Kagi (Like the other comment mentions).
That and Apple anti-competitively preventing non-Safari browsers from using Safari extensions, despite all iOS browsers being essentially Safari under the hood.
I don't remember the last time I used Safari on iOS, but once I started using Kagi, I was naturally drawn to Orion and that's been the best browser experienced I ever had on mobile.
The included ad-blocker being a big factor in the great UX.
That seems wild to me, but admittedly I don't search from the address bar at all. Is setting your preferred search engine as your homepage and opening a new tab to search really such a huge burden?
Yes. I do not tend to launch new tabs. I almost always use a single tab for browsing. Only when I need to keep something around then I launch a new tab to preserve the old one.
That means the address bar is my main interaction with the browser.
I think today's answer is actually incorrect. Or at least the reference animation has a hitch where it shows all red for frames 12 and 13. if it shows 2 purples for frame 13 then the animation is smoother and actually the math is much simpler.
[0]: https://en.wikipedia.org/wiki/Turing_test#Kurzweil%E2%80%93K...
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