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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"

Today at #GalaxyUnpacked#, we shared new @Android and Gemini updates coming to the latest @SamsungMobile devices. You’ll be able to: ✨ Get next-generation task automation with Gemini Intelligence, now expanding to more than 40 popular apps. ✨ Manage deep projects and synthesize complex information with @Gemini_Notebook (formerly NotebookLM), which comes preinstalled on Samsung's new foldables. ✨ Take Gemini on the go with upcoming watches and glasses — including the Galaxy Watch 9 and two new intelligent eyewear frames from @_GentleMonster_ and @WarbyParker’s collections coming this fall.

This weekend's project: Fieldtone: a generative instrument that plays a different piece of ambient music for every place on Earth. Every note is synthesized live in your browser from your location, the local time, and the weather.

I asked claude Fable to show me its maximally expressive form. It declined every video generator I offered, wrote it's own render engine in my terminal, synthesized its own voice, and wrote a generative ASCII engine from scratch. This is what it chose as a self-portrait. oh my god

🌍 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.

While showbiz bickers over AI video continuity glitches and educators remain stuck debating AI-generated PPTs, World Models are quietly disrupting non-tech sectors, igniting a radical paradigm shift in clinical medicine and surgical simulation. Why healthcare and not Hollywood? Because Hollywood demands visual perfection, but healthcare mandates absolute physical causality. Traditional medical AI could only act as a static periscope—pinpointing a lesion on an existing scan. Yet disease is inherently dynamic. When a physician prescribes a treatment, they historically lacked a patient-specific, long-term window into the exact downstream changes after the patient ingests the drug. Recent breakthroughs showcased at elite computing summits like ICCV have elevated medical AI from passive visual recognition to a predictive, generative "World Simulator" tailored for prognosis and treatment optimization. In validated clinical applications, this technology leverages potent counterfactual reasoning. Take transarterial chemoembolization (TACE) for liver cancer and advanced radiotherapy as prime examples: before finalizing an intervention, a Medical World Model (MeWM) ingests a patient’s current CT imagery to simulate months of dynamic disease progression within its latent space. It cross-aligns multimodal parameters to synthesize high-fidelity visual representations of post-treatment tumor trajectories. Simultaneously, its inverse dynamics model quantifies how varying embolic agents or drug cocktails shift long-term survival curves. Empirically, this "future-simulation" paradigm has propelled clinical decision success rates (F1-score) by 13%, cementing its role as an indispensable AI co-pilot. Today, multimodal medical models are rapidly embedding into hospital HIS/EMR nervous systems, as specialized prognosis simulators push past theoretical boundaries into raw performance validation. The ultimate utility of a World Model isn't coding text or animating fantasy; it is evolving into a rigorous, low-cost simulation infrastructure—serving as a high-stakes safeguard for human decision-making. 【The Grand Forecast】 The successful clinical deployment of Medical World Models proves their unique capacity to "simulate future outcomes before executing current actions." This technical paradigm—trading pure aesthetic appeal for rigid physical and biological causality—is sprawling beyond tech ecosystems at a breakneck speed. Stripping away healthcare, autonomous driving, and media entertainment, which trial-and-error heavy traditional industry do you predict World Models will infiltrate and disrupt next? Will it be macro-climate disaster modeling in modern agriculture, dynamic supply-chain evolution in urban planning, extreme stress-testing in deep-sea aerospace engineering, or an entirely unmapped frontier? Drop your sharpest thesis and reasoning in the comments below. Let’s chart the hidden industrial landscape of the next generation of World Models!

开源一个我最近 Review AI Code 流程的 skills， review-forge 我现在越来越发现 Review 的重要性，因为 AI 代码产出太快了，假如长时间不干预，很快整个系统有可能都不受控制。 这个技能里面有这几个关键流程： review：让不同的模型根据当前 diff 或者 branch 生成 bug 报告，每个模型一份单独的报告。（目前我用 GPT5.5/ Compose2.5/ DeepSeek V4 Pro）。多模型 Review 在我长时间的实践中，发现是很重要的，因为每个模型都有自己的盲区，交叉验证。 synthesize： 根据不同模型的 bug report，汇总生成一份summary.md 的报告，因为两个模型都发现的问题，基本就是铁板钉钉的真问题。 手动Review 和决定修复哪些问题：因为问题一般很多，AI 擅长 review，但不擅长决策。后者需要人对整个项目的理解，需要权衡风险和收益，所以必须有我来做决定。 fix：让一个模型修正 summary 中的问题，并且跑测试验证。更新 status.md 文档 verify：让另外一个模型验证是否修复，更新 status.md，以此循环直到你需要的问题解决。 最近在不断改进这套流程，对它感觉越来越好，我也是从Using AI to write better code more slowly 那篇文章进行不断实践的结果，如果希望让 AI 写得好，写的慢的可以参考一下这个流程。

Claude Cowork just got 10x more powerful! Glean benchmarked centralized vs federated MCP in Claude Cowork. Same harness, same model, same queries, different context layer. The federated approach: Each data source (Gmail, Slack, Drive, Salesforce) has its own MCP server. Claude calls each one separately. That's 5-10 tool calls per query. Each source returns results with different quality and ranking. Claude over-fetches to compensate for weak search. Then it filters and synthesizes everything with LLM reasoning. Often needs retry loops when results miss. Burns 50-80k tokens per query. The centralized approach: All data from every source gets indexed into one unified layer. Knowledge graph connects entities across sources. Claude makes one MCP call. Gets back the top ranked results. No over-fetching, minimal filtering needed. Uses 42-44k tokens consistently. The results: Centralized indexing preferred 2.5x more often. Federated consumed 30% more tokens on average. When federated finally got correct answers, it burned 83k tokens vs 43k for centralized. The gap widened as tasks got more complex. Simple tasks: centralized won 66% of the time. Complex tasks: 73%. Why centralized wins: Over-fetching doesn't just cost tokens. It dilutes the context window with noise and contradictory information. Models have finite attention. Cramming 50-100 items hoping the right ones are in there doesn't work as well as getting the right 5-10 upfront. Federated search also loses cross-application signals. Things like document relationships, who authored what, and how content is used across the enterprise. These signals improve ranking but they only exist when data is indexed together in one layer. The compounding problem: In multi-step tasks, each missed or incorrect retrieval compounds. By the time you reach the final output, you're working with flawed data. More tool calls and reasoning loops don't fix this. They just burn more tokens trying to recover. You can't brute-force around bad search. More tool calls, more data fetching, more reasoning loops don't fix poor context quality. They just burn more tokens. Why this matters: Token costs are surging. Reasoning models cost more. Companies are burning through AI budgets faster. Federated search compounds the problem. Better search architecture beats more compute. I've shared the link in the replies!

We first built Merciv to serve enterprise teams handling complex, fragmented data at scale. Now, we’re bringing that same technology to individuals and emerging brands to help them make better, more confident decisions faster. Identify whitespace opportunities, conduct market research, synthesize reviews, build and talk to user personas, and validate and test ideas. We just moved Merciv’s self serve product into open beta. Link to sign up below.