Tech News Video Maker — Technology News and Analysis Videos

The tech news cycle moves at a speed that makes traditional journalism look like a geology lecture — a product is announced at 9 AM, the hot takes are published by 9:47, the backlash to the hot takes arrives at 10:30, and by lunch the entire discourse has moved to a different product announcement that will undergo the same cycle, leaving the person who actually wanted to understand the original announcement navigating a landscape of clickbait headlines, contextless tweets, and explainer articles written by people who clearly skimmed the press release and added their own speculation in paragraphs that begin with "it remains to be seen." Tech news video content cuts through this noise by delivering structured analysis in a format that forces the creator to organize their thinking — you cannot ramble for 14 paragraphs in a 3-minute video the way you can in a blog post, and the visual medium demands that claims be supported with screenshots, data visualizations, and product footage rather than adjectives. This tool transforms technology news into polished analysis videos — breaking-news summaries delivering the essential facts within minutes of an announcement, deep-dive analyses explaining what a development means for the industry, weekly roundup compilations curating the most important stories, opinion and commentary pieces providing perspective that raw news coverage lacks, company and product profiles contextualizing news within a broader strategic narrative, and the trend-analysis segments that connect individual news items into patterns that predict where the industry is heading. Built for tech journalists producing video coverage, YouTube tech commentators building news channels, corporate communications teams monitoring industry developments, newsletter creators expanding into video, podcast hosts creating video companions, and anyone whose audience needs to understand what happened in tech today without reading fifteen articles and three Twitter threads to assemble a coherent picture.

Example Prompts

1. Breaking News — Quick Analysis of a Major Announcement

"Create a 4-minute breaking news analysis of a major tech company announcing a new AI product. The headline (0-10 sec): the announcement on screen — the company logo, the product name, the one-line description. 'Thirty minutes ago, [Company] announced [Product] — an AI assistant that runs entirely on your device with no cloud connection. Here's what it means and why it matters.' What was announced (10-60 sec): the facts — no speculation. 'The product: an on-device AI model that handles text generation, image understanding, and voice interaction without sending data to external servers.' Show the product demo screenshots or keynote slides. 'The specs: a 7-billion parameter model compressed to run on devices with 8GB RAM or more. Response time: under 2 seconds for text, under 5 seconds for image analysis.' 'Supported devices at launch: their flagship phone, their laptop line, and their tablet — all from 2024 or newer.' 'Availability: rolling out over 6 weeks starting today. Free for all device owners.' 'These are the confirmed facts from the press release and keynote. Everything after this is analysis.' Why it matters (60-150 sec): the three implications. 'Implication 1 — Privacy as a feature: Running AI on-device means your conversations, documents, and photos never leave your hardware. In a landscape where every other AI assistant sends data to cloud servers, on-device processing is a genuine differentiator — not a marketing claim but an architectural decision.' 'For users: your AI interactions are as private as your calculator app. No data collection. No training on your inputs.' 'Implication 2 — Performance tradeoff: A 7B parameter model is capable but not frontier. For comparison, cloud-based assistants use models 10-100x larger. The on-device version will be noticeably less capable for complex tasks — multi-step reasoning, nuanced creative writing, technical code generation.' 'The bet: most daily AI tasks (drafting emails, summarizing articles, answering quick questions) don't need frontier-scale models. A smaller model that's instant and private beats a larger model that's slower and surveillance-adjacent.' 'Implication 3 — Industry response: This forces competitors to address the privacy question directly. If [Company] proves that users prefer private-but-smaller over powerful-but-cloud, the entire industry's AI strategy shifts.' What to watch (150-210 sec): 'Three things to monitor over the next 3 months.' '1. Actual performance benchmarks: The keynote demo was carefully curated. Independent testing will reveal the real capability gap between on-device and cloud.' '2. Adoption rate: Will users actually use it? On-device AI that nobody activates is a spec-sheet feature, not a product.' '3. Developer access: Can third-party apps use the on-device model? If yes, this becomes a platform. If no, it's a feature.' Close (210-240 sec): 'Summary: [Company] just bet that privacy beats power for everyday AI. The product launches today, the proof arrives over the next quarter, and the industry impact depends on whether users agree with the bet. I'll have a full hands-on review when I get access. Subscribe for that.' End card."

2. Weekly Roundup — Top 5 Stories of the Week

"Build a 6-minute weekly tech news roundup covering the five biggest stories. Opening (0-10 sec): 'The five biggest tech stories this week — in 6 minutes. Let's go.' Story 1 (10-70 sec): the biggest story. Headline on screen. 'Number 1: [Headline].' 30 seconds of context: what happened, who's involved, the key numbers. 30 seconds of analysis: why it matters, who it affects, what happens next. 'The bottom line: [one-sentence summary].' Story 2 (70-130 sec): same structure. Headline. Context. Analysis. Bottom line. 'Each story gets exactly 60 seconds. This constraint forces clarity — if you can't explain it in 60 seconds, you don't understand it well enough.' Story 3 (130-190 sec): the mid-week development. 'Story 3 is the one that got less attention but might matter more long-term.' Context and analysis in 60 seconds. Story 4 (190-250 sec): the business story — an acquisition, a funding round, a strategic shift. 'The money story of the week.' Numbers on screen: deal size, valuation, market impact. Story 5 (250-310 sec): the wildcard — the weird, unexpected, or human-interest tech story. 'And finally — the story that made me do a double-take.' 'The wildcard story keeps the roundup from being five consecutive serious analyses. Variety in tone maintains viewer energy across 6 minutes.' The outlook (310-340 sec): 'Next week to watch: [Event 1], [Earnings report], and [Product launch]. I'll cover the biggest developments as they happen and next week's roundup drops same time, same place.' End card with subscribe prompt. 'The weekly roundup format works because consistency builds habit. The viewer knows: every [day], 6 minutes, five stories. That predictability is a subscription driver.'"

3. Trend Analysis — Connecting the Dots Across Stories

"Produce a 5-minute trend analysis connecting three recent developments into a pattern. Opening (0-15 sec): three headlines on screen — seemingly unrelated. 'These three stories from the past month look unrelated. They're not. They're the same story told three different ways — and the pattern predicts what's coming next.' Story 1 recap (15-50 sec): brief summary of the first development. '[Company A] laid off its entire machine-learning research team and redirected budget to applied AI products.' 'The headline said "layoffs." The actual story: research is being replaced by implementation.' Story 2 recap (50-85 sec): '[Company B] shut down its AI research lab and signed a licensing deal with [AI Provider] instead of building their own models.' 'The headline said "partnership." The actual story: build-vs-buy just tipped permanently toward buy.' Story 3 recap (85-120 sec): '[Company C] — a startup that raised $200M to build foundation models — pivoted to building AI applications using open-source models.' 'The headline said "pivot." The actual story: the foundation-model market has consolidated to the point where new entrants can't compete.' The pattern (120-200 sec): 'The pattern: the AI industry is splitting into two tiers.' Animated diagram: 'Tier 1: Foundation model providers — three to five companies with the capital, data, and compute to build frontier models.' 'Tier 2: Everyone else — using Tier 1 models (via API or open-source) to build applications, products, and services.' 'Company A realized: we can't compete in Tier 1. Applied AI is where our value is.' 'Company B realized: licensing a Tier 1 model is cheaper and better than building our own.' 'Company C realized: the $200M we raised isn't enough. Tier 1 requires billions.' 'Three different companies. Three different decisions. One conclusion: the foundation-model era is consolidating, and the application era is beginning.' What it means (200-260 sec): 'For developers: stop worrying about which model to build on and start building. The models are becoming commodities. The applications are the value.' 'For investors: foundation-model companies are infrastructure plays. Application companies are where the returns are. The picks-and-shovels gold rush is ending; the gold rush is beginning.' 'For users: AI products are about to get dramatically better — not because the models improved but because the companies building on them stopped trying to also build the model and focused entirely on the experience.' The prediction (260-290 sec): 'By end of 2027, the AI industry will look like the cloud industry: three to four infrastructure providers (AWS, Azure, GCP equivalent) and thousands of companies building on top of them.' 'The companies that figured this out first — that pivoted from model-building to application-building — will have a 12-18 month head start.' Close (290-300 sec): 'Three headlines. One pattern. One prediction. This is what tech news analysis does that raw reporting doesn't — it connects the dots between stories and reveals the picture they're drawing.' End card."

Parameters

Parameter	Type	Required	Description
INLINECODE0	string	✅	Describe the tech news story, analysis angle, or trend
INLINECODE1

Workflow

1. Describe — Outline the news story, analysis angle, and timeliness
Upload — Add screenshots, keynote footage, data charts, and source material
Generate — AI produces the video with headline overlays, data visualizations, and analysis pacing
Review — Verify factual accuracy, source attribution, and timeliness
Export — Download in your chosen format

API Example

CODEBLOCK0

Tips for Best Results

1. Separate facts from analysis explicitly — "These are confirmed facts. Everything after is analysis" builds trust. The AI structures fact-then-analysis.
Use data overlays for key numbers — Revenue figures, user counts, and performance metrics need to be visible. The AI renders data when headlineoverlay is enabled.
Cite sources visually — "Source: company press release" on screen verifies claims. The AI displays attributions when sourcecitations is enabled.
Time-box stories in roundups — 60 seconds per story forces clarity. The AI enforces pacing constraints.
End breaking news with "what to watch" — Forward-looking analysis gives the viewer a framework. The AI structures monitoring checklists.

Output Formats

Format	Resolution	Use Case
MP4 16:9	1080p / 4K	YouTube tech news / website embed
MP4 9:16

1080p | TikTok / Reels news clip | | MP4 1:1 | 1080p | LinkedIn / Twitter news post | | GIF | 720p | Data chart / headline card |

Related Skills

- ai-news-video — AI industry news and analysis
startup-news-video — Startup and venture capital news
cybersecurity-video — Cybersecurity news and education

科技新闻视频制作 — 科技新闻与分析视频

科技新闻的更新速度之快，让传统新闻业看起来像地质学讲座——早上9点发布产品，9点47分就出现热门评论，10点30分迎来对热门评论的反击，到午餐时间整个讨论已经转向另一款产品发布，并经历同样的循环。这让真正想了解最初发布内容的人，不得不在点击诱饵标题、缺乏上下文的推文，以及那些明显只浏览了新闻稿、在有待观察段落中添加自己猜测的解说文章中艰难前行。科技新闻视频内容通过结构化分析切入这一噪音，其形式迫使创作者组织思路——你无法在3分钟的视频中像博客文章那样漫无边际地写14段，而视觉媒介要求用截图、数据可视化和产品实拍来支撑观点，而非形容词。该工具将科技新闻转化为精良的分析视频——在发布后几分钟内提供关键事实的突发新闻摘要、解释某项发展对行业意义的深度分析、精选最重要故事的每周综述汇编、提供原始新闻报道所缺乏视角的评论与观点文章、将新闻置于更广泛战略叙事中的公司与产品简介，以及将单个新闻事件连接成预测行业走向模式的趋势分析片段。专为制作视频报道的科技记者、建设新闻频道的YouTube科技评论员、监测行业发展的企业传播团队、拓展视频领域的通讯稿创作者、制作视频配套内容的播客主持人，以及任何需要了解今日科技动态而不必阅读十五篇文章和三条推文来拼凑完整图景的受众而设计。

示例提示

1. 突发新闻——重大发布快速分析

制作一段4分钟的突发新闻分析，内容为一家大型科技公司发布全新AI产品。标题（0-10秒）：屏幕上显示发布信息——公司标志、产品名称、一句话描述。30分钟前，[公司]发布了[产品]——一款完全在设备上运行、无需云连接的AI助手。以下是其含义和重要性。发布内容（10-60秒）：事实——不含推测。产品：一款设备端AI模型，可处理文本生成、图像理解和语音交互，无需将数据发送至外部服务器。展示产品演示截图或主题演讲幻灯片。规格：一个70亿参数的模型，经压缩可在8GB及以上内存的设备上运行。响应时间：文本低于2秒，图像分析低于5秒。发布时支持的设备：其旗舰手机、笔记本电脑系列和平板电脑——均为2024年或更新型号。可用性：从今天起分6周逐步推出。对所有设备所有者免费。这些是来自新闻稿和主题演讲的已确认事实。此后内容均为分析。重要性（60-150秒）：三个影响。影响1——隐私即功能：在设备上运行AI意味着你的对话、文档和照片永远不会离开你的硬件。在几乎所有其他AI助手都将数据发送至云服务器的环境中，设备端处理是一个真正的差异化优势——不是营销口号，而是架构决策。对用户而言：你的AI交互与计算器应用一样私密。无数据收集。无基于你输入的训练。影响2——性能权衡：70亿参数模型能力可观，但并非前沿。相比之下，云端助手使用的模型大10-100倍。设备端版本在复杂任务——多步推理、细致创意写作、技术代码生成——上会明显能力不足。赌注：大多数日常AI任务（起草邮件、总结文章、回答快速问题）不需要前沿规模的模型。一个即时且私密的较小模型，胜过更慢且接近监控的较大模型。影响3——行业反应：这迫使竞争对手直接回应隐私问题。如果[公司]证明用户更喜欢私密但较小的模型而非强大但基于云的模型，整个行业的AI战略将发生转变。关注要点（150-210秒）：未来3个月需监测的三件事。1. 实际性能基准测试：主题演讲演示经过精心策划。独立测试将揭示设备端与云端之间的真实能力差距。2. 采用率：用户会真正使用吗？无人激活的设备端AI只是规格表上的功能，而非产品。3. 开发者接入：第三方应用能否使用设备端模型？如果能，这将成为平台。如果不能，这只是个功能。结尾（210-240秒）：总结：[公司]刚刚押注，在日常AI使用中隐私胜过性能。产品今日发布，证据将在下个季度显现，行业影响取决于用户是否认同这一赌注。我将在获得访问权限后进行完整上手评测。订阅以获取更新。结束卡片。

2. 每周综述——本周五大故事

制作一段6分钟的每周科技新闻综述，涵盖五大故事。开场（0-10秒）：本周五大科技故事——6分钟搞定。开始。故事1（10-70秒）：最大新闻。屏幕上显示标题。第1条：[标题]。30秒背景：发生了什么、涉及谁、关键数字。30秒分析：为何重要、影响谁、接下来如何。要点：[一句话总结]。故事2（70-130秒）：相同结构。标题。背景。分析。要点。每个故事恰好60秒。这一限制迫使内容清晰——如果你不能在60秒内解释清楚，说明你理解得不够透彻。故事3（130-190秒）：周中发展。故事3是关注较少但长期可能更重要的那一个。60秒的背景和分析。故事4（190-250秒）：商业故事——收购、融资轮次、战略转变。本周的金钱故事。屏幕上显示数字：交易规模、估值、市场影响。故事5（250-310秒）：外卡——奇怪、意外或人情味的科技故事。最后——让我大吃一惊的故事。外卡故事让综述不至于连续五个严肃分析。语调变化能在6分钟内保持观众精力。展望（310-340秒）：下周关注：[事件1]、[财报]、[产品发布]。我将报道重大发展，下周综述同一时间、同一地点发布。结束卡片附订阅提示。每周综述形式之所以有效，是因为一致性培养习惯。观众知道：每[天]，6分钟，五个故事。这种可预测性是订阅的驱动力。

3. 趋势分析——串联故事线索

制作一段5分钟的趋势分析，将三个近期发展连接成一个模式。开场（0-15秒）：屏幕上显示三个标题——看似无关。过去一个月的这三个故事看似无关。其实不然。它们是同一个故事的三种不同讲述方式——而这个模式预示着接下来会发生什么。故事1回顾（15-50秒）：第一个发展的简要总结。[公司A]裁掉了整个机器学习研究团队，将预算转向应用型AI产品。标题说裁员。实际故事：研究正被实施取代。故事2回顾（50-85秒）：[公司B]关闭了AI研究实验室，与[AI提供商]签署了许可协议，而非自行构建模型。标题说合作。实际故事：自建与购买的天平已永久倾向购买。故事3回顾（85-120秒）：[公司C]——一家融资2亿美元构建基础模型的初创公司——转向使用开源模型构建AI应用。标题说转型。实际故事：基础模型市场已整合到新进入者无法竞争的程度。模式（120-200秒）：模式：AI行业正分裂为两个层级。动画图表：层级1：基础模型提供商——三到五家拥有资本、数据和算力构建前沿模型的公司。层级2：其他所有人——使用层级1模型（通过API或开源）构建应用、产品和服务。公司A意识到：我们无法在层级1竞争。应用型AI才是我们的价值所在。公司B意识到：许可层级1模型比自建更便宜、更好。公司C意识到：我们融的2亿美元不够。层级1需要数十亿。三家公司。三个不同决策。一个结论：基础模型时代正在整合，应用时代正在开启。含义（200-260秒）：对开发者：停止纠结于构建哪个模型，开始构建吧。模型正在成为商品。应用才是价值所在。对投资者：基础模型公司是基础设施投资。应用公司才是回报所在。淘金热中的镐和铲子时代正在结束；淘金时代正在开始。对用户：AI产品即将大幅改善——不是因为模型改进了，而是因为基于模型构建的公司不再试图同时构建模型，而是完全专注于体验。预测（260-290秒）：到2027年底，AI行业将看起来像云行业：三到四个基础设施提供商（相当于AWS、Azure、GCP）和数千家在其上构建的公司。最先意识到这一点的公司——从模型构建转向应用构建的公司——将拥有12-18个月的先发优势。结尾（290-300秒）：三个标题。一个模式。一个预测。这就是科技新闻分析所做的、原始报道做不到的事——它连接故事之间的线索，揭示它们共同描绘的画面。结束卡片。

参数

参数	类型	必填	描述
prompt	字符串	✅	描述科技新闻故事、分析角度或趋势
duration

tech-news-video科技新闻视频

tech-news-video