Pin📍Pinternet Works

Pinternet Works で気になるプロダクトや研究活動等やってる人にインタビューしていこう思い、第一号として個人開発で素敵なプロダクト出し続けてる @isshukan_1 さんにお話聞かせていただきました！！🎉 自分にもこのセンスがほしい...📍 pinternet.works/articles/mui…

オンラインであまり情報出してなくて最近何やってるのか良くわからない人になっていたのですが、AppBrew の取締役 CTO を退任し、Pinternet Works という自分の会社を始めていたので、改めて会社と事業に関する記事を書きました🔥📍 pinternet.works/articles/fou…

Slay the SpireをAIに解かせるベンチマーク、A20心臓だとGPTは勝てるのにClaude全敗なの面白い epoch.ai/publications/ai-sys…

RAG vs. retriever-free RAG, clearly explained! (powered by decision models like Jev) In a standard RAG setup, documents are split into chunks, converted into embeddings, and stored in a vector index. When a query arrives, the system embeds it and pulls the top-k chunks with the closest vectors. A reranker then reorders those candidates against the query, and the best few go to the LLM as context. The problem is that the similarity search decides what the LLM can ever see. If the right passage never made the top-k, nothing later in the pipeline can bring it back, not even a strong reranker. Retriever-free RAG, from a new Cornell and ASAPP paper, keeps the documents and the LLM but replaces similarity search with navigation. The documents are organized as a document tree, like a table of contents, and a decision model (Jev 1.13) walks down it. A decision model doesn't generate text. It reads one shared context and returns a probability for every yes/no question about it, in one forward pass per stage. Here's how a query moves through the tree: ↳ Top documents: the model reads the query beside every candidate document, asks "Is this relevant?" for each, and keeps the best few. ↳ Top passages: it opens only those documents and asks the same question for every passage inside them. ↳ Most relevant? (optional): a single multiple-choice question reorders the top 20 passages (top 5 on Natural Questions). Dropped documents are never opened, so their passages are never scored. On Natural Questions (2.68M Wikipedia paragraphs), its top pick contained the answer 65.4% of the time. Embedding search plus an 8B reranker got 48.0%, and GPT-5 reranking got 60.8%. The gain comes from ranking, not coverage. Embedding search's top 100 holds the answer more often than the navigated shortlist (98.4% vs 94.0%), but navigation is better at putting it first. Median retrieval takes under 2 seconds per query, but each method was timed in a different setup, so it isn't a controlled speed comparison. The main limit is that it needs a sensible hierarchy, and a document pruned early is gone for good. It also skips embedding search only at query time, since the Wikipedia tree was built offline using embeddings. The diagram below depicts the complete flow. - RAG retrieves by similarity, then reranks. - Retriever-free RAG navigates a document tree with a decision model. - The LLM answers from the top passages. If you're building a RAG system, this is worth benchmarking against your current setup. Here's the paper: arxiv.org/abs/2610.35868 If you're new to decision models, I've quoted my full breakdown of how Jev works below.

Here's a direct link to OpenAI's PDF claiming a refutation of ETH. In particular, it's claimed that 3SAT on n variables is in about exp(n/(log n)^{1/2+o(1)}) time github.com/openai/math/blob/…

looking up at the stars with extra awe tonight. "thy sea is so great and my boat is so small."

【新規上場承認】 estieが東証グロースに新規上場を承認されました。 想定時価総額：165.784億円 仮条件決定日：2026年10月20日 公募価格決定日：2026年10月28日 上場日：2026年11月06日 会社情報詳細は以下より。 ipo-navi.jp/company/estie?ip…