Exclusive | The Sudden Fall of OpenAI’s Most Hyped Product Since ChatGPT - WSJ
Exclusive | The Sudden Fall of OpenAI’s Most Hyped Product Since ChatGPT WSJ
Could not retrieve the full article text.
Read on Google News: ChatGPT →Sign in to highlight and annotate this article
Conversation starters
Daily AI Digest
Get the top 5 AI stories delivered to your inbox every morning.
More about
productchatgpt
Fine-tuning Whisper-large-v3 for child reading assessment with numerals and proper names
Hi everyone, I’m working on a reading assessment product for children. Current setup: a child reads a known passage for about 1 minute our system then counts how many words were read correctly right now we use whisper-1 as a baseline we now want to move to an open model and fine-tune Whisper-large-v3 on our own infrastructure This is not a generic ASR task: we always know the reference text in advance our main metric is correct-word-count accuracy against the reference passage The main cases we want to improve through fine-tuning are: numerals / spoken-written forms, for example “three” vs “3” proper names and other rare words child reading speech in general I’d like advice specifically on the fine-tuning strategy for this type of task. My questions: For this use case, what training target
Seedance 2.0 API: Integration Guide with Three Access Paths and Full Mode Reference
This post covers the Seedance 2.0 API — ByteDance’s multimodal AI video generation model, now accessible through EvoLink. The focus is on practical integration: three access methods, all three generation modes with code examples, the async task workflow, pricing model, and optimization techniques. Model Capabilities Overview Seedance 2.0 introduces several capabilities that distinguish it from previous-generation video models: Multimodal @-reference system : Up to 9 images + 3 video clips + 3 audio tracks as simultaneous input references per request Video-to-video editing : Modify specific elements in existing video while preserving overall structure and timing Frame-accurate audio synchronization : Auto-generated dialogue, sound effects, and background music aligned to individual frames M
Knowledge Map
Connected Articles — Knowledge Graph
This article is connected to other articles through shared AI topics and tags.
More in Models
![2MM: AI Roundup – Roche and NVIDIA’s AI drug discovery factory and surgical robotics foundation model, Amazon’s nationwide health AI expansion, and Brown’s AI therapy ethics warning [March 2026] - 2 Minute Medicine](https://d2xsxph8kpxj0f.cloudfront.net/310419663032563854/konzwo8nGf8Z4uZsMefwMr/default-img-wave-pattern-4YWNKzoeu65vYpqRKWMiWf.webp)
2MM: AI Roundup – Roche and NVIDIA’s AI drug discovery factory and surgical robotics foundation model, Amazon’s nationwide health AI expansion, and Brown’s AI therapy ethics warning [March 2026] - 2 Minute Medicine
2MM: AI Roundup – Roche and NVIDIA’s AI drug discovery factory and surgical robotics foundation model, Amazon’s nationwide health AI expansion, and Brown’s AI therapy ethics warning [March 2026] 2 Minute Medicine
QSBench: Synthetic quantum circuit datasets for QML benchmarking
QSBench: Synthetic Quantum Circuit Datasets for QML Benchmarking Hi everyone, I’m sharing QSBench — a collection of synthetic quantum circuit datasets designed for machine learning benchmarking, especially for graph-based models and noise-aware learning. Resources Datasets collection (HF) Generator (GitHub) What is QSBench? QSBench is an ecosystem of datasets and tools for generating quantum circuits enriched with structural and physical metadata. The goal is to move beyond: purely random circuits classical datasets embedded into quantum states and instead provide structured, ML-ready quantum data . Key Features Structural Metadata (Graph-Ready) Each circuit includes: Adjacency matrices Gate-level statistics Entanglement metrics This makes the datasets directly usable with Graph Neural Net
We aren’t even close to AGI
Supposedly we’ve reached AGI according to Jensen Huang and Marc Andreessen. What a load of shit. I tried to get Claude code with Opus 4.6 max plan to play Elden Ring. Couldn’t even get past the first room. It made it past the character creator, but couldn’t leave the original chapel. If it can’t play a game that millions have beat, if it can’t even get past the first room, how are we even close to Artificial GENERAL Intelligence? I understand that this isn’t in its training data but that’s the entire point. Artificial general intelligence is supposed to be able to reason and think outside of its training data. submitted by /u/CrimsonShikabane [link] [comments]
7 Settings That Turned My Claude AI from 35 to 92 Quality Score
7 Settings That Turned My Claude AI from 35 to 92 Quality Score 直接看數據： 同一句提示詞：「幫我寫一個登入頁面」 ┌─ 裸用 ────────────────┐ ┌─ 有設定 ──────────────────┐ │ 基礎 HTML form │ │ React + TypeScript + Tailwind │ │ 50 行 │ │ 200 行 │ │ 無驗證、無響應式 │ │ Zod 驗證 + 響應式 + 無障礙 │ │ 無安全考量 │ │ CSRF + Rate Limit │ │ 品質：35/100 │ │ 品質：92/100 │ └────────────────────────┘ └──────────────────────────────┘ 差距不在 Claude 的智力——兩邊用的是同一個 Sonnet 4.6。差距在 Claude 知不知道你的情境 。設定就是告訴 Claude 你的情境。 4.1 為什麼前置設定如此重要？ 本章教的每一個設定，都在回答 Claude 心中的一個問題： Projects → 「這個專案是什麼？」 Custom Instructions → 「你是什麼角色？要什麼品質標準？」 Styles → 「你喜歡什麼風格的回覆？」 Memory → 「你之前跟我說過什麼？」 MCP → 「我能不能直接幫你存取外部資料？」 Extended Thinking → 「這個問題我要想多久？」 完整的對比實驗數據 → Ch27 4.2 Projects（專案）設定 建立 Project 步驟： 步驟 1：登入 claude.ai 步驟 2：左側欄 → 點「+ Create project」 步驟 3：輸入專案名稱（例如「電商平台前端」） 步驟 4：上傳 Project Kno
Discussion
Sign in to join the discussion
No comments yet — be the first to share your thoughts!