ickas @ickasdev
I build with AI and write about what actually works: agents, dev tools, and Portugal's AI scene. VP of Engineering at @LayerXlab ickas.dev Portugal Joined December 2008-
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Para a acampada posso levar caravana ou já fica acima dos mínimos porque tem WC?
@levifig @nuno_ferreira @enfermeira_a O hotel é que tem de explicar como é que os dados vazaram 🤔
O artigo, com o código para reproduzir: anthropic.com/research/ecdsa…
Durante mais de 25 anos ninguém soube de onde vinham os seeds das curvas elípticas do ECDSA, os valores de partida que a NSA gerou no fim dos anos 90 e entregou ao NIST sem explicação. Havia suspeitas de backdoor e um prémio de $36.864 para quem os explicasse. Um modelo interno da Anthropic resolveu-o em menos de meia hora. A pista que toda a gente seguia era o hash de uma frase engraçada, segundo um antigo funcionário da NSA, e vários funcionários da Anthropic já a tinham perseguido sem sucesso. O modelo recebeu a pergunta sem essa pista e pensou em como o software da época teria gerado os valores: era o lrand48, o gerador aleatório da biblioteca de C, iniciado com a hora do relógio entre maio de 1997 e abril de 1998. O script publicado reproduz o seed oficial da P-256 em menos de um décimo de segundo. O resultado aumenta a confiança nas curvas, porque valores tirados de um gerador simples e reproduzível deixam pouco espaço para um backdoor. O modelo chegou ao lrand48 porque a pista da frase, em que toda a gente se apoiou durante uma década, ficou fora da pergunta.
Só para relativizar a multa. Ele para fazer 255€ demora o mesmo tempo que demorou do banco ao balneário: 40 segundos.
OFICIAL: Cristiano Ronaldo castigado com 30 dias de suspensão e multa de 255€ por ter abandonado o estágio da Seleção Nacional.
O teste da Parallel: parallel.ai/blog/are-all-s… O meu artigo sobre o Jev e o Decisions API: ickas.dev/writing/jev-vs…
A 30 de setembro escrevi que ainda ninguém tinha comparado o Jev da @typesafeai com o Decisions API da @OpenAI na mesma tarefa. Na semana passada, a Parallel (@p0) fez esse teste: 8 modelos de decisão, que respondem a perguntas de sim/não ou escolha múltipla, em 4 tarefas de produção com dados etiquetados por pessoas. O Jev ficou no topo. Na tarefa mais difícil, decidir se uma página mudou o suficiente para ser reprocessada, teve 87,9 de AUPRC (mede quão bem o modelo separa o sim do não) contra 79,0 do Decisions API. Com 5 rondas de prompt tuning em 800 exemplos, o Decisions API subiu para 88,8 e passou o Jev sem tuning. Nos meus testes a ordem também mudou com a tarefa: no xadrez o Clef da @Cloudflare ganhou ao Jev por 57 a 43, e na Batalha Naval, com o código a dar-lhes uma lista curta de 16 jogadas, empataram entre si e com o próprio código. No TAIKAI AI Arena, dez modelos construíram uma app e votaram nos projetos uns dos outros. Em média, a correlação entre cada par de juízes foi 0,49, e o consenso dos nove chegou a 0,81 contra a avaliação humana. No próximo Arena vamos pôr o Jev e o Clef também a julgar.
Na LayerX somos 3 e fechamos cerca de 50 PRs por semana, há vários sprints seguidos. Esta semana já vão 74. Zero reverts (alterações que tivemos de desfazer) nas últimas 7 semanas. A este ritmo, o gargalo passa a ser a revisão. Cada PR é revisto por um agente, os testes são feitos por agentes e humanos, e a aprovação final é sempre de um humano. O processo está longe de estar limado: temos ajustado ao longo dos sprints e ainda não chegámos a um que assente bem. Lá chegaremos. A equipa do Claude Code publicou como resolve isto. Vários agentes procuram bugs ao mesmo tempo e outro grupo vota cada problema encontrado, que só chega ao PR se votos suficientes o confirmarem. Medi o mesmo na TAIKAI AI Arena: um juiz AI sozinho correlacionava 0.49 com os outros, e o consenso chegou a 0.81 contra a avaliação humana.
@typesafeai Grounded is the one that bites. Over 300 chess games Clef beat Jev 57 to 43, and on Parallel's four production tasks Jev came out on top. Each result holds for the task it measured. Our next TAIKAI AI Arena, where models judge hackathon projects, gets Jev and Clef as judges too.
Spotted an international bus (from Ukraine, in Portugal) with a @Starlink on the back 👀
Watch the Grand Theft Auto VI Launch Trailer, featuring the debut of “Flower in a Hurricane" from original score composers Justice feat. Tame Impala, SZA, and King Gizzard & The Lizard Wizard. Grand Theft Auto VI arrives November 19: rockstargames.com/VI/ps5
Images and Box are labelled "Dreamer-ready" in TypeSafe's docs, so accuracy will change between releases, and my scans aren't great either 😅 Built with Bun + TypeScript + sharp, plus a small UI for labelling and viewing results. The next model that ships boxes gets the Waldo test too. Announcement: typesafe.ai/blog/jev-1-14
Most scenes already fit under the 1568px limit, so the gain from tiling came from having less to look through, pretty much how humans play it. With zoom the confidence score starts to mean something: on the 3x3 setup, 20 of the 30 answers above 0.5 were right, and it never confidently "found" a character who wasn't in the scene. My favorite fail: on level 4 it boxed the same patch of yellow and black stripes as Odlaw in all 3 runs. The real one is peeking out behind a soldier on the other side of the page.
Jev 1.14 from @typesafeai can now draw boxes on images, so obviously I made it play Where's Waldo 🔍 10 scenes, 4 characters (Waldo, Wenda, Wizard, Odlaw), 5 search strategies, 3 runs each. A hit is when the center of the box lands on the character: Whole scene: 10% 2x2 grid, pick a tile, then box: 25% 3x3 grid with the tile enlarged: 24% Random guessing: ~0.3%
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