Raissa Correia @raideveloper

Não entendo como o movimento maker n faz mais o barulho que fazia antigamente. Hoje em dia é possível comprar componentes da China, recebê-los em 7 dias, jogar td no Claude, escrever seu firmware, modelar um case 3d, subir um servidor e personalizar td para as suas necessidades

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For anyone curious how Jev works, I made a visual explanation using @claudeai :) This is based on the Qwen2.5-RLCD model which @harshagundal released on @huggingface The idea is to replace autoregressive LLM generation by a single Transformer decoder (of a pre-trained LLM), which processes the context + JSON schema only once. The keys and values of those tokens are cached. Next, for each field of the JSON schema, we: 1. pass its field suffix tokens through the Transformer decoder again (reusing the KV-cache) 2. obtain a final hidden state, which we pass through the language modeling head 3. we obtain scores, also called logits, for all tokens in the vocab of the LLM 4. we only look at the scores of the tokens we care about for the given field, and pass those through a softmax to obtain probabilities which sum to 1 5. we take the token with the highest probability. The benefits of this are that: 1. it's fast (we don't need to generate the JSON schema token by token) 2. it's 100% valid JSON (we don't need to rely on the model to generate a valid schema)

Niels Rogge @NielsRogge

2 weeks ago

12 million views for a JSON classifier? Yeah, we're in a bubble

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Terence Tao makes a terribly important point: "One notable feature of the current AI era is the absence of any definitive such boundaries. While AI tools have flattened the difficulty landscape now in many areas of the subject, thus destroying the ability to locate promising new problems in that area, there are no clear frontiers that are separating the "AI-feasible" problems from the "AI-hard" problems (which certainly still exist, given that the difficulty level of problems are unbounded, and can even be undecidable). This is in part due to the rapidly changing nature of the technology, but also compounded by the refusal of AI companies to disclose their negative results, or reveal the process towards obtaining their solutions. In fact, it is now the identification of a promising problem which is the scarce and precious resource. We have now seen that even the rumor of someone working on a problem can trigger a massive amount of AI-powered effort to flatten it before the original research project has time to reach its full potential. The incentives may now be pointing in the direction of no longer sharing any promising research directions with the broader community, which would reverse centuries of traditions of open science and do serious long-term damage to the future of the field. In short, the indiscriminate use of powerful solution-extraction tools can achieve the immediate short-term goal of solving problems at hand, but at the cost of sustaining the ecosystem for the next wave of progress, or in understanding the progress already obtained." mathstodon.xyz/@tao/117237320…

Terence Tao made a very powerful point: “In most cases in pure mathematics, the problems are posed not because we desperately want the solution to these problems in and of themselves, but because we have seen from past experience that human-directed efforts to solve these problems tend to spur further development of the field through the efforts to solve such problems, and then to digest any partial or complete solutions that emerge for further insights. Prematurely solving the problem by purely AI-powered methods—particularly without full transparency into the solution process—can contaminate this process to the point where it actually becomes a net negative for the progress of mathematics as a whole.” I’ve been following Terence Tao for like 20 years, and I have always been impressed by the clarity of his thinking. This clarity stands out even more in this difficult moment, as AI labs turn mathematics into a battleground in their race for supremacy. In this race, they risk damaging one of humanity’s highest intellectual endeavours.

This works really well btw, at the end of your query ask your LLM to "structure your response as HTML", then view the generated file in your browser. I've also had some success asking the LLM to present its output as slideshows, etc. More generally, imo audio is the human-preferred input to AIs but vision (images/animations/video) is the preferred output from them. Around a ~third of our brains are a massively parallel processor dedicated to vision, it is the 10-lane superhighway of information into brain. As AI improves, I think we'll see a progression that takes advantage: 1) raw text (hard/effortful to read) 2) markdown (bold, italic, headings, tables, a bit easier on the eyes) <-- current default 3) HTML (still procedural with underlying code, but a lot more flexibility on the graphics, layout, even interactivity) <-- early but forming new good default ...4,5,6,... n) interactive neural videos/simulations Imo the extrapolation (though the technology doesn't exist just yet) ends in some kind of interactive videos generated directly by a diffusion neural net. Many open questions as to how exact/procedural "Software 1.0" artifacts (e.g. interactive simulations) may be woven together with neural artifacts (diffusion grids), but generally something in the direction of the recently viral x.com/zan2434/status… There are also improvements necessary and pending at the input. Audio nor text nor video alone are not enough, e.g. I feel a need to point/gesture to things on the screen, similar to all the things you would do with a person physically next to you and your computer screen. TLDR The input/output mind meld between humans and AIs is ongoing and there is a lot of work to do and significant progress to be made, way before jumping all the way into neuralink-esque BCIs and all that. For what's worth exploring at the current stage, hot tip try ask for HTML.

Thariq @trq212

5 months ago

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@giovannibassi Pra derrubar as empresas ocidentais acho que não, seria muita idolatria do ocidente presumir que eles ligam tanto assim. Acho que é para trazer soft-power através da tecnologia, porque eles ainda estão só começando com a parte midiática

Gemini 3.0 just refactored my entire codebase in one call. 25 tool invocations. 3,000+ new lines. 12 brand new files. It modularized everything. Broke up monoliths. Cleaned up spaghetti. None of it worked. But boy was it beautiful.

O que os startupeiros, criadores de SaaS acham da stack do theo t3? Next.js + Clerk + Convex

Vi um colega usando esse terminal e eu fiquei em CHOQUE muito bom! Mesmo se não usar as features de IA. Se alguém puder fazer signup com o meu link eu agradeço porque ganha acesso ao tema exclusivo e eu to curiosa pra saber como é app.warp.dev/referral/L6QK94

@akinncar Reddit + Discord >> todas as redes sociais atuais Na categoria networking, e obter conhecimentos, debates, memes específicos de uma bolha

@Marcelolynx problema é que o firebase é uma db no-sql, o que vou precisar é de um hibrido de sql e no-sql. será que firebase-supabase é uma boa ou nesse caso não? ou sera que um object as string no sql é melhor pro mvp?

Imagine a situação: Vc está fazendo um SaaS um E-commerce, tem que ser um MVP sério! E to fazendo tudo sozinha sem investidor por enquanto. Uso database como o supabase? Porque eu sempre usei o postgres no docker e aws rds tradicionalzão, custo e trabalho é a preocupação

@Marcelolynx não compensa pq? tive muita experiencia ruins com drives no ubuntu em dual boot, e tem VM tradicional mas não curti tanto quanto wsl

Eu fico PERPLEXA como o Ubuntu no WSL incha com o tempo! VM de 1 ano e meio tem 75GB! A nova tem 5GB! E eu só tenho 26 repositórios clonados na antiga. Não é possível que seja tudo só o node_modules de cada projeto, o WSL fica enchendo de pastas, tipo essas pq?

Antes de questionar como um e-com é uma ideia séria ao invés de usar shopify, não vai ser um e-com tradicional em termos de estrutura, a ideia é revolucionar justamente isso, por isso tenho que fazer boa parte do zero. Além disso, alguém aqui já usou algo como o Clerk para auth?

@arnonco Informalidade e ilegalidade altíssima explica tudo isso, e muita muita gente vive além dos próprios meios com divida

@andrenit tem isso também!

Fiz um script aqui, e to vendo que boa parte é cache, especialmente do Yarn, a quantidade de instâncias da pasta node_modules no cache do yarn era inacreditável. Fica aqui a linha de CLI para ver e resolver find ~ -mindepth 2 -type d -name "node_modules" -prune yarn cache clean

Contra-argumentos possíveis que eu acho razoáveis: 1) Não procurei o suficiente 2) Empresas BR não contratam que já trabalhou para o exterior 3) Não abaixei a proposta salarial o suficiente 4) Os sistemas de contratação estão ruidosos demais e com dificuldades

6) Quem tem conhecimento de web e mobile deveria estar criando um negócio próprio agora, pois quando for demitido vai ter o tempo das suas reservas de emergência para dar certo.

Uma coisa que eu tenho refletido aqui: tem uma chance de que IA acabou com a área de dev mas isso será um processo lento e difícil de provar que realmente está acontecendo, vamos a minha análise.