French musical genius FKJ’s latest touring show, the “Typer Tour,” is coming to Bangkok! Come see this one-man band wizard work his magic on the guitar, saxophone, and synthesizer. 🎶 Tour Dates 📅 📍 Bangkok | November 27, 2026 | UOB LIVE 🎫 Presale 📍 July 22, 13:00 (Beijing Time) ✨ Let the rhythms of French romance intertwine with soul and jazz, taking you on an escape from reality. 🔗 Click the link to explore the details: #FKJ#

New Google paper: A forecast needs context, not just history. Some patterns are caused by events, not time. Nexus reframes forecasting as a reasoning problem, where events and numbers have to explain each other. Nexus argues that forecasting improves when models read the world around the numbers, not just the numbers themselves. In the Zillow tests, one Claude-based version cut average MAPE by 86.6% versus direct chain-of-thought prompting. That matters because most time series models are fluent in pattern, but mute about cause. A housing inventory curve can reflect seasonality, mortgage pressure, migration, layoffs, and local supply, while a stock price can be bent by earnings, regulation, hype, and fear. Nexus separates those jobs instead of asking one prompt to do everything. One agent turns messy historical text into a clean event timeline, one reads the broad regime, another tracks local shocks, and a synthesizer reconciles them with calibration from past errors. The interesting result is not merely that context helps, but that structure helps the language model use context without losing the time series. The evidence is still narrow: Zillow counts, seven equities, post-cutoff data, and single-run evaluations, so this is not a universal law of forecasting. But the direction is clear: future forecasters will not only extrapolate curves; they will argue about what made the curve move. ---- Paper Link – arxiv. org/abs/2605.14389 Paper Title: "Nexus : An Agentic Framework for Time Series Forecasting"

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🌍 GLOBE HUB We're allocating Genesis Hub access to the strongest signals inside the network. Study GLOBE. Understand the model. Synthesize the signal. How to participate: 1. Publish a Twitter thread explaining GLOBE and its network architecture. 2. Join Telegram: ➔ 3. Submit your thread together with your ETH wallet inside Telegram. ⏳ Submissions close in 48 hours. Selection is based on: • Depth of analysis • Original insight • Community engagement Write in any language. The network remembers signal.

🤝 A team of models beats the single strongest one. And a bundle of cheap models can outscore a solo frontier model. OpenRouter just backed it up with data. Title: Surpassing Frontier Performance with Fusion URL: 💡 Overview Fusion is an OpenRouter tool that synthesizes the outputs of multiple AI models in a single API call. You pick a panel of participant models plus a judge model that fuses their results, and you call it just like one model. ⚠️ The problem Standard benchmarks measure factual recall or reasoning puzzles, but not real research ability: synthesizing multiple sources into comprehensive, well-cited analysis. And in practice, getting past a single model's ceiling has been hard. 🛠 How it works ・Dispatch the prompt to every panel model in parallel (web search and fetch enabled) ・A judge analyzes all answers into structured output: consensus, contradictions, partial coverage, unique insights, blind spots ・The calling model writes the final answer grounded in that synthesis ・Benchmark contamination is blocked by excluding the rubric's host domains from search 📊 Results (100 DRACO tasks) ・Fable 5 + GPT-5.5 (fused by Opus 4.8) scored 69.0%, beating every individual model ・Opus 4.8 self-fused hit 65.5%, a 6.7-point jump over solo Opus 4.8 (58.8%) ・A cheap 3-model budget panel reached 64.7%, beating solo GPT-5.5 and Opus 4.8 at about 50% lower cost It shows that synthesis itself adds value, and that diverse cheap models can rival a solo frontier model. #LLM# #AIAgents#

BMW just released their new all-electric M3 Concept at Le Mans today. • Projected 700-900 hp • Quad-motor powertrain • 800V Architecture • Simulated gear shifts and "synthesized M soundscape" • Neue Klasse platform (shared with the upcoming i3 sedan) BMW says it will deliver "unrivalled high-performance standards while preserving M DNA as a true driver’s car."

📢New Updates✨ 《Improvements》 ・Fixed an issue where, after adjusting accent settings for subtitles/TTS, changing the text and re-synthesizing the voice would cause the previous audio to play back instead. ・To prevent subtitles text and synthesized audio from becoming out of sync, the subtitle text is now locked from editing while voice synthesis is in progress. ▼Update History and More Details ▼Try it for Free #Live2D# #nizimaACTION#

Today we’re releasing Qwen-Scope 🔭, an open suite of sparse autoencoders for the Qwen model family. It turns SAE features into practical tools： 🎯 Inference — Steer model outputs by directly manipulating internal features, no prompt engineering needed 📂 Data — Classify & synthesize targeted data with minimal seed examples, boosting long-tail capabilities 🏋️ Training — Trace code-switching & repetitive generation back to their source, fix them at the root 📊 Evaluation — Analyze feature activation patterns to select smarter benchmarks and cut redundancy We hope the community uses Qwen-Scope to uncover new mechanisms inside Qwen models and build applications beyond what we explored.Excited to see what you build! 🚀 🔗🔗 Blog: HuggingFace: ModelScope: Technical Report:

We are entering an extremely exciting era for open-weight models. Kimi K2.6 now feels like a top agentic model. I took it for a spin via @FireworksAI_HQ fast inference APIs. Kimi K2.6 has impressive agentic capabilities, design skills, and the ability to synthesize large amounts of information. I built a little Skill that produces survey papers on any AI research topic you want. (see example in the clip) You can use the skill to tell your agent to generate a survey on whatever topic and watch it go to work. The artifact was fully generated by @Kimi_Moonshot's Kimi K2.6. It's cheap and fast. Next step for me is to explore ways to continue integrating the capabilities of these models on use cases like automating my LLM knowledge bases and augmenting my agent memory capabilities. Stay tuned for more.