codewithcheese

sol-6 is terra-6. They figure that no one was using terra and they could bring the speed and cost saving of terra distilled on astra, but rebranded as the more popular sol.

Back fired because of opus 5.5.

So now we get the real sol-6 as sol-6.1, and OpenAI will eat the cost to stay competitive.

This could be invalidated if sol-6.1 is the same speed as sol-6.

you can config codex to compact at a higher context limit

> You have merged the 2 things the post keeps apart - capacity and attention over it.

Maybe you just write like an LLM.

> So in this scenario you could input something like: { NodeType: Person, EdgeTypes: [IS_PARENT_OF, IS_CHILD_OF] }

RDF, OWL are existing formats for defining a schema

Domain driven design is well aware that is not feasible to have a single schema for everything, they use bounded contexts. Is there something similar for the semantic web?

Yes, looks like they could be adapted to create Story Maps

Looking forward to their durable execution Workflows. Writing Temporal workflows has great DX but their pricing and hosting requirements put it out of reach for many projects.

That's openrouter, they are listed

Think chromium compile is widely used

> - Monorepo with FE, BE and shared types - React + Next.js (frontend) - pnpm - Nest.js (server) - Tailwind - Material UI - Apollo (GraphQL) - Jest (Testing) - Typescript + ESLint + Prettier + Husky - Turbo - TypeORM - Segment - Database migrations - Docker - Logging (Pino.js)

How will non-tech founders develop their product on this stack? That's a stack for developers to build the product.

This is a very hard problem, when you see a fragmented ecosystem its because players in the ecosystem have wide ranging and demanding requirements.

You will have to acquire your target customer when they are ready to setup this stack but before they have done so themselves. This would require very high brand awareness. Like "Oh I was going to setup on AWS but XYZ makes it so easy". Only a few companies have achieved this type of awareness in the devops space. Heroku comes to mind. None of them are indie-hacker projects.

How are you using Generative UI?

It can be faster and more effective to fallback to a smaller model (gpt3.5 or haiku), the weakness of the prompt will be more obvious on a smaller model and your iteration time will be faster

Yeah also prompts should not be developed in abstract. Goal of a prompt is to activate the models internal respentations for it to best achieve the task. Without automated methods, this requires iteratively testing the models reaction to different input and trying to understand how it's interpreting the request and where it's falling down and then patching up those holes.

Need to verify if it even knows what you mean by nothing.

Leetcode correlating with IQ tests makes sense they are very similar. Correlating either with general intelligence is less obvious.

Vervel charges $400/TB for excess bandwidth, it's not even DDoS you should worry about, just moderate success.

Strange loop, one of the few conferences where videos from 10 years ago are still relevant today, will be missed.

Retrospective https://www.youtube.com/watch?v=suv76aL0NrA

Pasted your comment in ChatGPT Plus, it did a search and found the link

https://www.remote.org/frederik/projects/railserver/history....

I think it got relatively lucky with the query, since tried a few variations, you.com and Perplexity and they all failed to find it.

Hi, nice idea. I recently learnt about framer, haven't tried it yet. They have a publishing features. Have you used framer? How would you compare your work with theirs?

https://www.framer.com/

As a non academic this is an example in how to express a simple opinion with structure and references. My first reaction was to think this is much ado about nothing, but then I saw it provides a small map one can use to start to navigate this topic.

Cool, sent you an email to the address on your profile

Yes within a timezone is a good idea. I am based in Australia, teaching myself deep learning, and happy to connect with anyone in Asia. Of course happy to connect with anyone globally, but being in a similar timezone can lead to more serendipity

If the context size is unbounded, how does the time complexity scale with the size of the context, and what are the limiting factors that affect performance as the context size grows larger?

What makes it "clean" vs animal grown meat?

Interested to see a GPT version of this and compare.

How about a browser extension that scrapss Google results and asks GPT to summarise?

There are many prompts beyond summarisation that would be interesting, for example ask GPT to present conflicting point of views in the results.