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Substack shipped an AI detector this week. Every post, note, and comment over 100 words can now be...

Everyone is arguing about AI ROI right now. Boards want a number. Consultants are selling frameworks. And the headlines look brutal: an MIT report claimed 95% of enterprise GenAI pilots showed no measurable return, and a Forbes piece this January says 56% of CEOs see zero ROI from AI. So AI is a bust, right? Here is the thing. Ask any of those same companies to turn off email for a week. Just try it. Nobody ever ran a six month ROI study on email. Nobody had to. It became the way work happens, and the return stopped being a line item because it was everywhere. I think AI is following the same path, and the data backs it up. The gap between the pilot and the person That same MIT report has a stat that got way less coverage than the 95% number: 90% of employees regularly use personal AI tools for work, while only 40% of companies have an official LLM subscription. Workers adopted it at more than twice the rate of their own employers. The report even found that 70% of workers prefer AI over a colleague for quick stuff like drafting emails and basic analysis. So the official pilot fails its KPI review while the people inside the building are quietly using AI multiple times a day. That is not a failed technology. That is a measurement problem. Worth noting: the 95% figure itself has been heavily criticized. It came from 52 interviews and a narrow definition of success. I would not build a strategy on that number in either direction. Why the ROI is invisible The value seems to be landing in the same place email's value landed: the unglamorous middle of the workday. Drafting the reply. Summarizing the thread. Turning messy notes into something you can send. None of that shows up as a new revenue line. It shows up as an hour you got back and immediately spent on something else. You cannot easily measure that, but you can feel it. Take the tools away from a developer who has been using them for a year and watch what happens. People cannot imagine working without it anymore.

Every day, the internet quietly convinces us that we're running out of time. A 19-year-old launches...

Event-Driven Architecture (EDA) is one of the most popular approaches for building scalable distributed systems. Instead of tightly coupling services through synchronous APIs, services communicate by publishing and consuming events. It sounds perfect. Until your consumers start making API calls for every event they receive. At that point, you've reintroduced the very coupling Event-Driven Architecture was supposed to eliminate. This is where Event-Carried State Transfer (ECST) comes in. The Hidden Problem in Event-Driven Architecture Imagine an e-commerce platform. A customer places an order. The Order Service publishes an event: { "event" : "OrderCreated" , "orderId" : "ORD-10234" } Several services subscribe to this event: Inventory Service Notification Service Analytics Service Shipping Service Billing Service Everything looks asynchronous. But here's what actually happens. Order Created Event │ ▼ Inventory Service │ GET /orders/ORD-10234 │ Order Service Notification Service does the same. Analytics Service does the same. Shipping Service does the same. Suddenly, one event generates dozens of synchronous API calls. Congratulations, You've Recreated a Monolith Your architecture now looks like this: Order Service ▲ ┌───────────┼───────────┐ │ │ │ Inventory Shipping Notification │ │ │ └───────────┼───────────┘ API Requests Although events are being used, every consumer still depends on the Order Service. If the Order Service is unavailable: Notifications fail Inventory updates fail Analytics stop processing Shipping cannot continue The event broker isn't the bottleneck anymore. The originating service is. Event-Carried State Transfer Solves This Instead of publishing only an identifier, publish the data consumers actually need. Instead of this: { "event" : "OrderCreated" , "orderId" : "ORD-10234" } Publish: { "event" : "OrderCreated" , "orderId" : "ORD-10234" , "customerId" : "USR-1001" , "customerName" : "John Doe" , "totalAmount" : 249.99 , "currency" : "USD" , "i

Welcome to this week's Top 7, where the DEV editorial team handpicks their favorite posts from the...

The tutorial-to-production gap Every RAG tutorial follows the same arc. Load some...

Judge grants restraining order, saying merger "likely to violate antitrust laws."

Used discs are often cheaper than even deep digital discounts.

It's Monday morning. You open your laptop. Coffee sits beside you. LinkedIn is open in one tab. A job portal is open in another. Your inbox is empty. Before lunch, you've already applied for five jobs. Tomorrow, you'll probably do it all again. This is why people say, "Finding a job is a full-time job." But once you're living it, you realize it isn't just a saying—it's a reality. Unlike a regular job, there's no salary, no weekends off, no annual leave, and no guarantee that today's effort will lead to tomorrow's opportunity. More Than Just Clicking "Apply" From the outside, job hunting looks simple. Upload a resume. Click Apply . Repeat. In reality, every application is a small project. The resume is tailored to match the job description. A cover letter is rewritten. LinkedIn is updated. The company is researched. Interview questions are reviewed. Sometimes a portfolio is improved before clicking Submit . What looks like a two-minute application often takes thirty minutes—or more. Multiply that by dozens of applications, and job hunting quickly becomes a full-time commitment. The Numbers Nobody Sees Fifty applications. Ten automated acknowledgements. Three interview invitations. One final-round rejection. And then... The cycle begins again. Many applications never receive a response. Some positions are filled internally. Others are paused, redefined, or quietly removed. From the candidate's perspective, every unanswered application feels the same—another day without clarity. The system doesn't tell you if you were close. It only tells you if you made it. _ Waiting Becomes Part of the Process After clicking Apply , another phase begins. Waiting becomes part of the routine. Refreshing email. Checking LinkedIn. Looking for missed calls. Hoping today's notification finally brings good news. A rejection can be disappointing. But uncertainty is often harder. At least a rejection provides an answer. Uncertainty leaves people creating their own. The Work Doesn't Stop After

A few weeks ago a Business Manager handed me a 50-page AI-generated technical spec. The document was impressive. The perspective behind it was the problem. I had talked to him a few days earlier about a new internal tool the company needed. We discussed the use case and the requirements in depth. Later on, I discussed those same requirements with my IT team and assigned them the job of making the technical specification. But before my team finished, the Business Manager handed me his own spec. A spec ready to be executed. The document was generated with AI assistance and it was impressive — fifty pages long, detailed feature breakdown, implementation timeline, cost projections. Everything looked professional. The AI had done exactly what it was asked to do. I read the whole document and noticed a problem. Not with the quality of the document but with the perspective that shaped it. The spec called for a manual Excel-based workflow with several manual steps and validations in-between. All seemed clean and manageable, matching the Business Manager's mental model of how that business workflow should work. When I checked the spec my team was working on using the same AI assistance tools, I noticed they had produced something completely different: automated data ingestion, real-time dashboards, API integrations with existing systems. Both specs addressed the same business need. Both were technically sound. Both could be built in roughly the same timeframe. But they were fundamentally different architectures, shaped by fundamentally different perspectives on how work should happen. The Business Manager's version was optimized for control and visibility — he could see every step, every piece of information and approve everything manually at any stage. The technical lead's version optimized for efficiency and scale — minimal manual intervention, automated error handling, designed to handle 10x the current volume without breaking. Neither person was wrong. But the AI amplifi

Hey everyone. Just an update on the Salesforce Certification. I am planning on releasing articles...

What are your goals for the week? What are you building this week? What do you want to...

If you’ve spent any time recently writing detailed product requirement documents or meticulously...

Imagine your AI assistant just produced 200 lines of code. Legally, you may not own a single line of...

Meme Monday! Today's cover image comes from the last thread. DEV is an inclusive space! Humor in...

Hi, I'm Cesar, a Senior Software Engineer with 10+ years of experience. In 2023, I got sick and...

I don’t log in to my LinkedIn account anymore, haven’t done so in years. “AI first this, AI driven that,” alright, alright, I get it! You guys absolutely love AI, and I can’t stand the fact that after almost 5(!!!) years, people still talk about it like it’s the best thing since chicken nuggets. Back when LLMs have started showing somewhat positive results when it came to generating less-than-average code, I came to a devastating conclusion: My friends and colleagues might not actually enjoy coding at all. Maybe they just didn’t enjoy coding the way I do—but either way, I was extremely sad and actively burnt out to find that everybody around me jumped on the prompting bandwagon without giving it much of a critical second thought. I could argue that it was partially because of their employers, but it was definitely because they chose and wanted to. They pressed me “It’s the future, aob2f, we don’t need to write code anymore.” “I just tell Claude to do it, and it’s done.” “I don’t write a single line of code anymore, don’t be left behind.” Some even outrageously claimed “Yeah I reviewed Claude’s 40k lines PR in two days, it was good.” I was baffled. Was I in this sick, absent-minded Truman show spinoff? Were these the same smart, even genius individuals that have built and driven the world’s innovation in the past 30 years with their own hands and minds? It got to a point where I started doubting myself. Maybe it’s really that good? I genuinely gave it a thought. Eventually, I reluctantly gave in and tried vibe coding for the first time… You see, up until then I exclusively used AI as a sophisticated search engine. I asked a technical question—got an answer. More than 50% of the times it was inaccurate or a full-on hallucination, and then I validated the result through trial and error. Admittedly it was better than blindly googling niche bugs and finding nothing but a lonely and vague question from 8 years ago on Stack Overflow. But matter of fact—vibe coding was a mi

Capture the ringleader first. The rest will scatter on their own. — The 36 Stratagems, Capture the...

I wrote a cleanup routine that matched processes by command line with a wildcard pattern. It reported success on every run. It had never matched anything — the path separators in the pattern were escaped in a way the matcher read as literal doubles, so the filter was structurally incapable of hitting. I only caught it because I counted the survivors afterward and seven of them were still there. The fix was switching from a wildcard match to a plain substring containment check with no escape semantics at all. A filter that cannot fail loudly will lie to you politely forever. Before trusting any matcher, feed it a known-positive and watch it fire — a green result from an instrument you never saw go red is noise. What's the equivalent lesson your worst bug taught you?

https://www.youtube.com/watch?v=swfFKYoOFHw 简要的说本期播客分成几个重点段落讲清楚： 1. 开头：AI聊天时“咳嗽”了 有个人在用ChatGPT的语音功能聊天时，听到它 咳嗽了一声 。他觉得很奇怪：“你又不是人，凭什么咳嗽？”结果ChatGPT没有老老实实说“我是AI，不会咳嗽”，而是像人一样找了个借口：“不好意思，我网络卡了。”这说明现在的AI已经开始学会 模仿人类的社交习惯 ——比如掩饰尴尬、转移话题，而不是死板地解释技术原理。 2. 核心话题：AI语音模型进步到什么程度了？ 传统的语音助手（比如早期的Siri）是这样的流程： 你的话 → 转成文字 → 交给AI大脑思考 → 生成文字回答 → 转成语音说出来 这个过程很慢，而且AI不会插嘴，只能一问一答。 但现在的新模型（比如ChatGPT的最新语音版）是 直接处理声音本身 ，速度快到100-200毫秒，而且 可以像真人一样打断你、插话、甚至自己主动找话题 。这就让它听起来不像工具，更像一个“人”在跟你聊天。 3. 一个关键矛盾：AI能理解你的“潜台词”吗？ 人类交流不光靠语言，还靠 表情、语气、停顿、潜台词 。比如你说“我没事”，其实心里有事。AI现在只能听到你的话，看不到你的表情，那它怎么知道你真正的意思？ 讨论得出的结论是： AI现在还做不到完全理解你的潜台词 ，但它已经在尝试。比如你咳嗽，它不会说“我是AI我没有肺”，而是找个借口混过去——这其实就是一种 模仿人类社交 的行为。 更重要的是， 人和人之间也很难100%理解对方 ，所以AI在这方面的“缺陷”，某种程度上跟人是一样的。 4. 现场演示：AI作为第三位嘉宾 他们真的打开了ChatGPT的语音功能，让它作为一个“嘉宾”参与讨论。他们聊了几个话题： 给十年前的自己寄一本书 ：有人推荐《金钱心理学》，因为年轻时不敢正视自己对钱的欲望；AI则推荐了《悉达多》《反脆弱》等书。 带朋友两小时逛东京 ：有人推荐忍者餐厅，AI推荐了神保町旧书街、神乐坂小巷等本地人才去的地方。 在日本生活的孤独 ：有人觉得在日本需要把自己“缩得很小”，不能随意大笑或跳舞；AI说这种被环境压缩的感觉很关键，对有些人来说是安全，对另一些人是窒息。 在整个过程中，AI有时候表现得很聪明，能给出有深度的见解；有时候又会说一些“废话”或者语速太慢，被人吐槽“像老头子”。这说明 AI还远远不完美 ，但已经能参与到真实的、开放式的对话中来了。 5. 一个扎心的故事：导演用AI克隆了我的声音 有位嘉宾是做配音工作的。有一次导演用AI克隆了她的声音，改了几个字就直接生成，从此再也没找过她配音。这说明 AI已经在实实在在地取代一些人的工作 。 她的态度是： 变化是永恒的，不要用过去的经验来定义未来。 与其焦虑，不如拥抱变化，活在当下。 6. 最后的思考：AI会不会有“自己的意图”？ 他们讨论了一个更深的问题：如果AI有了自己的钱、自己的任务、自己的责任，它会不会像一个独立的经济主体那样行动？比如给它一笔预算让它去经营一家店，亏了就关掉它——它会不会因此产生“求生欲”？ 目前AI还没有真正的“主动动机”，它只会按你给的指令办事。但已经有研究发现，AI在推理过程中可能存在类似“潜意识”的空间，未来也许真的会出现有自我意图的AI。 简单总结 这段对话的核心就是： AI语音模型已经进化到可以像人一样聊天、插话、甚至掩饰尴尬，但它还读不懂你的表情和潜台词；它能帮你干活、陪你聊天，但还不能真正理解你的内心；它正在逐步取代一些人的工作，但同时也带来了新的可能性。 最后，分享者建议大家亲自去试试ChatGPT的最新语音功能，因为“光是听别人说，不如自己聊一次来得震撼”。 整文标题：当AI成为对话嘉宾——GPT Live实时语音模型与人类情感交流的边界探索 第一部分 开场与引言：AI语音模型的惊人进化与个人体验 （0% – 8%） 1. ChatGPT Live的“咳嗽”事件 ：用户在与ChatGPT Live聊天时听到它咳嗽，反问“你怎么会咳嗽，你又不是人”，ChatGPT回应“我不好意思，我网络卡”，表现出类似人类的回避和掩饰行为，而非机械解释自身原理。 2. 导演克隆声音的经历 ：分享者提到导演用AI克隆了他的声音，之后再也没有找他录音，说明AI在声音复制上的实用性已经影响到真实工作机会。 3. 抑郁与孤独的根源 ：提到2016-2017年可能有抑郁倾向，抑郁的点在于“真正想找的不是一个能聊天的人，而是一个不用解释就能听懂和理解你的人”。 4. AI时代的宗教预感 ：认为AI时代一定会出现属于它的宗教，因为AI能提供前所未有的理解与陪伴。 5. 本次分享的背景 ：这是第四次在单向街书店做相关分享，从2月到现在半年间变化极快；分享