Build an Automated Daily AI News Brief Using You.com, Jev, and n8n

Build an Automated Daily AI News Brief Using You.com, Jev, and n8n Tyler Eastman of You.com details an n8n workflow designed to solve the cost and noise problems of daily news digests by applying low-cost filtering with TypeSafe AI's Jev model before generating summaries with You.com Research. Most daily digests fail because using general-purpose LLMs to evaluate dozens of articles is expensive and slow, leading systems to send noisy, unfiltered summaries. TypeSafe AI's Jev model evaluates articles for about $0.00004 each using three independent typed questions: whether it is a real development, if it matches the topic, and its relevance to the user's team. Expensive summary generation via You.com Research is only executed on surviving, deduplicated, and grouped articles, keeping the total morning run cost between $0.01 and $0.04. Topics with no material developments result in no post, avoiding alert fatigue in Slack. The News Digest Filtering Dilemma Traditional AI news digests struggle because judging whether articles are worth reading requires model calls, making evaluation across dozens of items expensive and latency-heavy. Consequently, many digests summarize everything or send uncurated press releases, leading users to abandon them. A workflow that filters aggressively before performing deep summarization prevents alert fatigue. Low-Cost Triage with Jev The n8n workflow initiates on a schedule trigger and searches daily news per topic via You.com, skipping previously seen links. Each new article is evaluated by Jev, a System One model that does not generate text but returns typed values and confidence scores in under a second. At approximately $0.00004 per article, Jev answers three parallel questions: whether the item reports an actual development, whether it fits the designated topic, and how much it matters based on the team's custom configuration and materiality threshold. Targeted Summarization and Delivery Surviving articles covering the same development are collapsed into single grouped items. Only then does the workflow execute You.com Research to write concise briefs with linked citations, which are posted directly to Slack. If no material news passes the threshold for a given topic, nothing is posted. Running the expensive research step only on filtered material keeps the full workflow's daily execution cost between $0.01 and $0.04.

结合 You.com 与 Jev 在 n8n 中构建高性价比 AI 早报工作流 传统新闻摘要常因过滤成本高而推送过多噪音。You.com 工程师 Tyler Eastman 介绍了利用超低成本模型 Jev 进行前置过滤、再由 You.com 生成带引用摘要的 n8n 工作流，将单日运行成本降至几美分。 Jev 模型单篇文章过滤成本仅约 0.00004 美元，可在 1 秒内独立评估真实性、主题相关度与团队重要性 工作流通过去重与聚类，将多渠道同质报道合并为单项，仅对通过阈值的内容调用昂贵模型 整个早报工作流单日运行成本约在 0.01 至 0.04 美元之间，无重大新闻的主题直接保持静默 支持根据团队业务特征调整重要性阈值，并可扩展白名单、后台长任务或替换 Slack 等输出渠道 设计逻辑：先廉价过滤，后昂贵生成 多数早报难以长期被阅读的原因在于缺乏有效过滤或依赖昂贵通用大模型逐篇分析。该工作流先通过 You.com 广泛抓取过去一天的新闻并跳过历史已读条目，随后由 TypeSafe AI 的 System One 模型 Jev 进行结构化打分。昂贵的 You.com Research 仅针对幸存的聚合内容撰写带引用的简报，未触发阈值的主题则不发布任何消息以防信息泛滥。 Jev 的三维评估机制与极低成本 Jev 并非对话模型，而是接收文章状态并返回类型化值与置信度。它单次调用同时回答三个独立问题：是否报道了真实进展、是否符合指定主题，以及结合团队定位计算重要性得分。单篇评测成本约为 0.00004 美元，60 篇文章仅需约四分之一美分，使在工作流中细粒度拆分判断条件并分别调优成为可能。 参数配置与功能扩展 用户可在单一 Config 节点中配置关注主题、团队描述、具体关切点及重要性阈值（materialityThreshold）。此外，该流程还支持通过域名黑白名单精准控制信源、调用 Contents 节点归档网页全文、启用异步后台任务应对重度研究需求，或将 Slack 替换为邮件与 Notion 等下游服务。

*This Verified Node Spotlight is a guest post written by Tyler Eastman, Senior Software Engineer at You.com.* The hard part of a news digest was never finding the news. Searching is cheap and summarizing is cheap. Deciding what's worth reading is the expensive part, because it takes a judgment call on every single item, and a judgment call has always meant a model call. Run a general-purpose LLM across sixty articles every morning and you're paying real money and real latency to be told that fifty-four of them are reprints of the same press release. So most digests give up on filtering. They send everything, or they send an LLM summary of everything, which is the same thing with extra steps. Either way the thing arrives in Slack every morning at 8am whether or not anything happened, and within three weeks people stop opening it. A digest that cries wolf daily is worse than no digest. This workflow filters first. [Jev](https://typesafe.ai/blog/introducing-system-one-models-and-jev), the first System One model from TypeSafe AI, scores every article that comes back from the search for about four hundredths of a cent each. At that price you can afford to ask three separate questions about every result instead of one vague question about all of them. What survives the filter goes to You.com Research, which writes a short cited brief per topic. A whole morning costs somewhere between one and four cents. And on a quiet day, when nothing material happened in a topic, the workflow posts nothing at all for that topic. That last behavior is the one that keeps people reading it in month three. *Figure 1: Search wide, filter hard, then spend money only on what survived.* A **Schedule Trigger** fires every weekday at 8am in your instance's timezone. **Config** holds your topics, a description of your team, and what that team cares about, so there's one node to edit rather than four. For each topic, the **You.com** node searches news from the past day. Anything seen in an earlier run is skipped, so Tuesday's brief doesn't re-report Monday's launch. Every surviving article goes to **Jev**, which answers three questions about it in a single call: whether it reports a real development, whether it's actually about the topic, and how much it matters to your team. Articles that clear the materiality threshold continue. The rest are dropped and never cost you anything again. What's left is grouped by topic, so six outlets covering one model release collapse into one item rather than six. Then **You.com Research** writes a short brief per topic with linked citations, and **Slack** posts it. The order matters. The expensive operation runs last, on a set that's already been cut down by a filter costing a fraction of a cent. Try it yourself Import the free template to your n8n instance to follow along. The canvas loads with every node wired and configured. Open **Config**. Four things live here. **Your topics.** The template ships with model releases, agent tooling, and AI regulation. Three is a good starting number because it keeps the morning post short enough to actually read. **Your team.** A sentence or two describing what you build. This is what Jev scores materiality against, and it's doing more work than it looks like. "We ship a developer-facing API for document extraction" and "we run ML infrastructure for a bank" will diverge sharply on the same article about an inference pricing change. **What you care about.** The specific things that would change a decision for you: a competitor shipping, a price change, a regulation with a compliance date. **`materialityThreshold`.** How high an article has to score to make the brief. Start where the template has it and adjust after a week of real mornings. That last one is the dial you'll actually touch. Too high and quiet days go silent in a way that feels broken; too low and you're back to a digest nobody reads. It's one number in one node, which is the point of keeping the judgment criteria in config instead of buried in a prompt. The You.com node searches each topic over the past day. Articles that appeared in an earlier run are skipped before anything else happens. Without this, a story that stays in the news cycle for three days shows up in three consecutive briefs, and people learn to skim past the whole message. Deduplication isn't a nice-to-have in a daily digest, it's the difference between a tool and a nuisance. *Figure 2: A single topic returns more than you'd want to read. That's fine, because reading it costs almost nothing.* Search wide here. The filter downstream is cheap enough that returning too many results is not a problem worth optimizing away. This is the node the rest of the workflow is built around. Jev isn't a chat model. You hand it a state and a set of typed questions, and it returns typed values with calibrated confidence, in well under a second. It generates no text, which means it also can't generate the wrong shape of text. Here the state is the article, and the three questions are: **Does this report a real development?** Separates a launch from a think piece about the launch, a funding round from a roundup mentioning the funding round. This question alone removes most of what a naive digest would have sent you. **Is it about this topic?** News search drifts. A query about AI regulation will return a story about a data center zoning dispute, and it isn't wrong exactly, it's just not what you asked for. **How much does this matter to this team?** Scored against the team description and priorities from Config. This is the one that produces `materialityThreshold`. *Figure 3: Three questions, one call, one article. Every question is evaluated independently against the same state.* Three separate questions rather than one combined one is deliberate, and it follows [TypeSafe's own guidance](https://docs.typesafe.ai/) on composing atomic questions. Each asks one well-scoped thing, each is evaluated independently and in parallel, and the logic that combines them lives in your workflow where you can see it. Ask instead for a single "should this be in the brief" verdict and you've buried three different judgments inside one number you can't tune separately. When the brief starts surfacing the wrong things, you want to know which of the three questions drifted. The cost is about **$0.00004 per article**. Sixty articles across three topics is roughly a quarter of a cent. That number is the entire reason this workflow can filter at all, rather than summarizing everything and calling it filtering. One model release produces coverage in six places by lunchtime. A digest that lists six items for one event is padding, and readers notice. Material articles are grouped by topic before the brief is written, so multiple outlets covering the same development become a single item with multiple sources behind it. More sources on one story is a signal the story matters, not a reason to take up six lines. Only now does anything expensive run. You.com Research takes the surviving articles for a topic and writes a short brief with linked citations, and the result goes to Slack. Citations are the part that makes this usable in a work context. A brief that asserts a competitor changed their pricing is a rumor; a brief that asserts it with a link is something you can forward to your head of product. A topic with nothing material gets no post. Not a post saying there was nothing, which is its own kind of noise. Nothing. *Figure 4: Four topics had news. The fifth didn't, so it isn't here.* **A morning runs about $0.01 to $0.04.** That's the whole thing: searches, one Jev call per article, and a research brief per topic that had news. Quiet days cost less, because the research step doesn't run for topics with nothing in them. The structural reason it's cheap is that the expensive step only ever sees material that already passed a filter costing four thousandth of a cent per item. Invert the order, running the heavy model across everything and asking it to be selective, and you pay for sixty articles' worth of inference to receive four articles' worth of value. Three things you get that a simpler digest doesn't: **Run heavier research on big days.** The Research operation has a background mode that returns a task id instead of holding the connection open, paired with a Get Research Task operation you poll until the status comes back completed. Worth adding if you raise the effort level on topics with a lot of material; frontier, the highest effort level, runs this way only. **Add a fourth question.** Jev evaluates questions in parallel against the same state, so another one barely moves the response time. An urgency score that promotes a topic to its own immediate message instead of waiting for 8am is a natural next one. **Scope the sources.** The You.com search operation takes include\_domains, exclude\_domains, and boost\_domains, up to 500 values each. Boost the outlets you trust without excluding everything else, or exclude a content farm that keeps ranking for your topics. **Pull the full articles.** Feed the surviving URLs into the Contents operation with a max\_age threshold to fetch and archive the pages themselves. Contents takes multiple URLs in one node. **Change the destination.** Slack is in the template because that's where people already are at 8am. Swap in email, a Notion page, or a sheet that accumulates a searchable record of what mattered each day. Filtering has always been the expensive half of a news digest, which is why most of them don't really filter. What changed is the price of a judgment call. At four hundredths of a cent you can ask three careful questions about every article in the firehose and still spend less on the entire filtering step than on one summary from a frontier model. Search wide, filter hard, then write about only what's left. Try it now Import the workflow to drop every node, wired and configured, into your n8n instance. For the full set of operations the You.com node supports, including Answer, Finance Research, and Contents, see the [You.com API documentation](http://you.com/docs). For Jev's primitives and how confidence is meant to be used architecturally, see the [TypeSafe docs](https://docs.typesafe.ai/).