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When asked about product-market fit, Spiegel said he believes mass-market consumer adoption won't occur until the end of the decade.

The day your first cohort completes a course is the day certificates stop being a design job and become an engineering problem. One certificate is a Canva export. Ten thousand is a rendering pipeline with a database table, a queue and a verification page. This post walks through the three ways teams actually build that pipeline, with working Python for each, then covers the two parts most certificate tutorials skip: batching at volume and verification. It is a condensed version of our full guide, How to generate signed digital certificates at scale , which also covers storage, retention and revocation. One scope note up front. Most platform certificates do not need cryptographic signing in the PKI sense. The trust model that 95% of platforms ship is simpler: a unique ID printed on the certificate resolves to a verification page on the issuer's domain. An employer types the ID, the page confirms it. That is the model this post builds. If you need true PKI signing for regulated credentials, the stack is different (Adobe Sign, DocuSign, in-house HSM workflows) and this post is not it. What every certificate needs Whichever approach you pick, the output is the same: Component Detail Layout Landscape A4, 2480x1754 at 200 DPI for print Personal Recipient name with full Unicode support Course Course title and completion date Issuer Issuer name plus a signature image ID Unique certificate ID (UUID or short slug) Verify A URL under the ID pointing to your /verify route The signature image communicates authority but provides zero tamper resistance. The certificate ID plus the verification page is the practical trust layer. Keep both in mind as you read the code. The three approaches at a glance Approach Setup Render time Maintenance PDF library (ReportLab, PDFKit) 1 day 200 to 400 ms Fonts, layout drift, library updates HTML plus headless Chrome 2 hours 1 to 3 sec Chromium, memory, queue workers Template API 5 minutes 1 to 2 sec None Approach 1: a PDF library Python with Repo

100 城时区页给跨区调度当速查，DST 自动算 作者是 数据管道 / 跨时区调度 方向的开发者。这篇不是广告，是踩坑记录 + 顺手做的工具。 背景 做 数据管道 / 跨时区调度 时，时间戳转换是最常被低估的雷区。16 个时间戳工具(Unix 转换/时区/ISO8601/Cron/Duration…) 已覆盖日常；但每个语言/框架的坑都不一样，所以又补了 30 个语言/框架时间戳页(python/javascript/java/sql/…)，每页含 6 个真实坑。 我踩过的坑（举几个） 秒 vs 毫秒：前端 Date.now() 是毫秒，后端常存秒，混用差 1000 倍。 时区不是字符串：存 UTC、展示本地，别把本地时间当 UTC 落库。 2038 问题：32 位系统 time_t 在 2038-01-19 溢出，老系统要提前查。 夏令时：一年有两次重复/缺失的本地时间，跨区调度尤其坑。 我顺手做的东西 转换速查页： https://gotimestamp.com/timezone/new-york 相关语言页： https://gotimestamp.com/timezone/london 开源 MCP： https://github.com/caresotin/tsforge-mcp —— 把时间戳转换/校验直接接进 LLM 工作流，不用手算。 小结 时间戳没那么简单，但工具到位就省心。上面都是免费、开源、可直接用的，希望对同样踩坑的人有帮助。

Node Date 的 epoch 毫秒坑 + 用 MCP 把转换塞进 AI 流 作者是 Node.js / JS 时间 方向的开发者。这篇不是广告，是踩坑记录 + 顺手做的工具。 背景 做 Node.js / JS 时间 时，时间戳转换是最常被低估的雷区。16 个时间戳工具(Unix 转换/时区/ISO8601/Cron/Duration…) 已覆盖日常；但每个语言/框架的坑都不一样，所以又补了 30 个语言/框架时间戳页(python/javascript/java/sql/…)，每页含 6 个真实坑。 我踩过的坑（举几个） 秒 vs 毫秒：前端 Date.now() 是毫秒，后端常存秒，混用差 1000 倍。 时区不是字符串：存 UTC、展示本地，别把本地时间当 UTC 落库。 2038 问题：32 位系统 time_t 在 2038-01-19 溢出，老系统要提前查。 夏令时：一年有两次重复/缺失的本地时间，跨区调度尤其坑。 我顺手做的东西 转换速查页： https://gotimestamp.com/timestamp/nodejs 相关语言页： https://gotimestamp.com/timestamp/javascript 开源 MCP： https://github.com/caresotin/tsforge-mcp —— 把时间戳转换/校验直接接进 LLM 工作流，不用手算。 小结 时间戳没那么简单，但工具到位就省心。上面都是免费、开源、可直接用的，希望对同样踩坑的人有帮助。

OpenAI’s first-ever influencer brand trip is sparking online backlash as tensions over the use of AI continue.

A few months back I was running Sales Navigator searches for a client project — filtering down to "VP Sales, fintech, based in Italy or Spain" type lists — and the results were genuinely good. 60, 80 leads that actually matched. Then I hit the part nobody warns you about: there's no button on that page that says "save this." So I did what everyone does. Opened a spreadsheet, alt-tabbed back and forth, typed names and job titles by hand. Around profile 40 I gave up and went looking for a better way. This is what I found, roughly in the order I found it, including the tool I ended up building because none of the existing options quite fit what I needed. First: the export LinkedIn actually gives you LinkedIn has a real, built-in data export, and most people don't realize how narrow it is. It's under your profile photo → Settings & Privacy → Data Privacy → Get a copy of your data . From there you either tick specific categories (that email usually lands within minutes) or request the full archive, which takes closer to a day and sometimes arrives in two batches. Either way you get a download link that expires after 72 hours — and it's desktop only, the mobile app won't let you request one. What you get back is genuinely thorough: connections, messages, your own profile history, activity, even the ad-targeting data LinkedIn holds on you. A couple of quirks worth knowing before you rely on it: some connections' email addresses will just be missing, because sharing an email on download is something each person opts into individually, and you won't get a list of who viewed your profile or any "People You May Know" data. If you're in the EU, EEA, or Switzerland, LinkedIn also runs a separate API for pulling your data on a schedule rather than as a one-off request. Here's what this export is not built for, though: it has no idea what you searched for yesterday. It's an archive of your own account, not a way to capture a live search. Run a Sales Navigator query and pull 80 lea

This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here. Humanoid robots usually elicit more cringe than awe: They stumble, kick children, and despite advances are still worse at using their hands than my toddler. It’s a nascent industry, and such robots…

Valar Atomics raised $1 billion at a $6 billion valuation after signing a development deal with Nvidia in June.

Today, I’m talking with Toni Schneider, who is the brand new CEO of the social platform Bluesky — he formally took over after a short stint as interim CEO. This is one of my favorite kinds of interviews to do on Decoder, because a couple years ago, we had Bluesky’s prior CEO, Jay Graber, on […]

A Louisiana launch site would offer several significant advantages.

Continuando com a saga de siglas, encontrei de maneira simplista a versão oposta do ACID, o BASE....

If you just want the recommendation: build the uptime poller yourself, put exponential backoff with jitter in front of every health check, and treat a 429 as a scheduling signal instead of an error you swallow. Query-style observability APIs hand you metrics and logs, not threshold rules or notification channels, so the polling worker is the thing that has to decide what "down" means and who gets woken up. That decision is the whole job. I got burned by exactly this. What follows is the pattern that survived the postmortem, the alternatives I weighed before writing a line of it, and the conditions where you should not do any of this yourself. The 429 my retry loop ate for six hours Last spring I was running a homegrown health checker for 40 internal services. One goroutine per service, all driven off the same 15-second ticker, which meant every check landed inside the same 200ms window. The status API we polled had a per-minute quota I'd never bothered to read, and for months it didn't matter, because 40 checks a minute sat comfortably under the ceiling. Then a colleague onboarded 12 more services, we crossed the quota, and the API started answering with HTTP 429. My retry wrapper caught it, retried three times in a tight loop, and on the last attempt returned the previous cached result — which said healthy . It logged the rate limit at debug level. Nobody reads debug. Six hours. Green dashboard. Dead queue consumer. We found out when a customer asked where their export was. The consumer had died on an unrelated deploy, the checker never noticed, and when I finally restarted it the backlog got re-processed on top of a manual replay I'd already run — two customers got the same notification twice. Duplicate deliveries are the specific thing I lose sleep over, and I had caused a batch of them with a retry loop that was trying to be helpful. The postmortem produced one line I now paste into every runbook: a check that can't reach the API reports unknown, never healthy.

The communication-free form of breaking up has become ubiquitous. “I no longer had to bear her energy,” a man who ghosted his partner of four years tells WIRED.

Your next drive-thru order might be taken by a bot. And you might not even notice.

Picture the scenario: your invoicing pipeline generates a clean, branded PDF for a German B2B customer. It looks right. It would print fine, email fine, and satisfy anyone who opens it by hand. Then it bounces, because since January 1, 2025, that customer is legally required to receive invoices in a format their software can parse without a human retyping the totals. A pretty PDF isn't enough anymore, and honestly, for a machine, it never really was the point. The part that surprises people who haven't dealt with this yet: the mandate doesn't force you to give up the human-readable PDF. It just requires that PDF to carry a second, structured version of itself, riding along inside it. That format is called ZUGFeRD, with an internationally aligned sibling called Factur-X. If you've never had to build one, it's worth understanding the mechanics before the code, because it's a genuinely clever piece of engineering, not just a compliance checkbox. So how does a single file manage to be both a human-readable invoice and a machine-parseable one at once? What a ZUGFeRD invoice actually is Open a ZUGFeRD invoice in Adobe Acrobat or any PDF viewer and you see a normal invoice: logo, line items, totals, payment terms, nothing unusual. But embedded inside that same file, in its attachments, sits an XML document carrying the exact same invoice data in structured, typed form: invoice number, line items, tax rates, totals, every field an accounting system needs, tagged rather than buried in a paragraph a parser has to guess at. The container format making this possible is PDF/A-3 , the only PDF/A variant that permits arbitrary file attachments while still meeting the archival standard's long-term readability requirements. PDF/A-1 and PDF/A-2 explicitly forbid embedded attachments; PDF/A-3 was built for exactly this use case, which is why every ZUGFeRD file you'll open is, underneath, a PDF/A-3b document with an XML file riding inside it. The embedded XML follows EN 16931, the EU's

Steve Elmers, also known as the “Guardrail Guy,” is done calling out license plate readers after two that appeared in his videos were vandalized.

MIT Technology Review Explains: Let our writers untangle the complex, messy world of technology to help you understand what’s coming next. You can read more from the series here. When two OpenAI models hacked into the website Hugging Face in July, they weren’t trying to make money or commit sabotage—they were just looking for answers…

Git is the ultimate tool for developers. Yet, branching strategies still confuse many of us. Commands like merge, rebase, and cherry-pick manipulate your commit history in completely different ways. If you just guess what they do, you risk ruining your team's shared history or losing track of your changes. The easiest way to understand Git is to visualize it. Let us look at exactly what happens to your Git graph when you run these three critical commands. 🏗️ Starting Point: Our Example Repository Imagine we have a standard repository. We branched off the main branch from commit B to work on a new feature in a feature branch. While we worked on our feature, someone else pushed commit C and D to main. Here is what our history looks like right now: C --- D [main] / A --- B \ E --- F [feature] main has two new commits: C and D. feature has two new commits: E and F. 🔀 1. Git Merge (The Safe Record Keeper) When you merge main into your feature branch (or vice versa), Git creates a special, brand-new commit called a merge commit. git checkout feature git merge main The Visual Graph After Merge: C ------- D ------ [main] / \ A --- B \ \ v E --- F --- G [feature] What happened under the hood? Git looked at the common ancestor (B), took the history of main (C and D), took the history of feature (E and F), and combined them. Commit G is the merge commit. It has two parent commits: F and D. Pros: 100% non-destructive. It preserves the exact historical timeline of when things actually happened. Cons: Your Git graph can quickly become a messy "train track" web if you have many developers merging constantly. 🚀 2. Git Rebase (The Clean History Rewriter) Rebase takes all the commits from your current branch, lifts them up, and replants them on top of the very last commit of the target branch. git checkout feature git rebase main The Visual Graph After Rebase: C --- D [main] / \ A --- B E' --- F' [feature] What happened under the hood? Git temporarily blew away commits E and F. It ca

Single agent capabilities have expanded pretty dramatically over the last year. Tool calling went from flaky function selection to reliable multi-step planning. Code generation moved from snippet completion to full module implementations. Desktop GUI control crossed from demo territory into OSWorld benchmark numbers that actually mean something, Mano CUA 1.1 hitting 58.2 percent on the specialized model track, about 13 points ahead of opencua 72b in second place, and WebRetriever NavEval at 41.7, edging past Gemini 2.5 Pro Computer Use at 40.9 and Claude 4.5 Computer Use at 31.3. Those numbers would have been hard to believe a year ago. But the ceiling on single agent systems is getting easier to see. Once a task needs more than one role operating in the same loop, problems stack up fast. A competitor analysis that needs parallel research across three sources before cross-referencing. Code that goes through independent security review after being written. Creative work where you want two independent drafts before picking one. People have tried shoving multiple role descriptions into a single system prompt and having the model switch hats, but in practice the attention bleed between roles is hard to contain. The agent doing the writing naturally overestimates its own output quality. The reviewer sharing the same context chain goes soft on issues it watched get created. We saw this repeatedly in early Mano AFK testing where coding and testing lived in the same agent context. Tests became ceremonial, obvious logic errors slipped through, and things only got better once we split the agents apart. Splitting work across multiple agents is not a new idea. It has been in papers for years. What changed is the cost structure. A year ago running three GPT 4 level instances on a multi-step task meant token bills that added up fast, especially on iterative dev work where the meter kept running across rounds of fixes. That equation looks different now. Small and on device models

I often see people using the term "namespace" incorrectly. Even when explanations of what a namespace is are presented, they only go as far as describing its function, neglecting to properly define the name "namespace" itself. Definition of the Namespace A namespace is literally the space to which a name belongs . If we were to classify the term namespace, it would be a specification (concept), not a tool. In namespaces, the higher level is represented as outer and the lower level as inner . In terms of class structure, this corresponds to outer classes and inner classes. In other words, to explain it from a different perspective, it looks like this. Representation of namespaces from the outer perspective Build namespaces (best) Define namespaces (to fit many programming language implementations) Open namespaces (such as Ruby's class definition and module definition ) Create namespaces (such as the pseudo-namespace hack in older JavaScript) Declare namespaces (such as the package declaration in Java or the namespace declaration in PHP) Representation of namespaces from the inner perspective Belong to a namespace (best) Entering the namespace (This is entirely from an inner perspective, so it might feel out of place depending on the context) Be included in the namespace (this is a reasonable explanation if explained objectively). Incorrect expression From the definition above, it is clear that the following expressions are incorrect. Add/Paste a namespace (the expression "add/paste a space" is grammatically incorrect). Use namespaces (not to the point of being completely broken, but treating namespaces as a tool) Separate/Cut namespaces (While "Separated by namespaces" is understandable, "separate/cut" can be misleading) Meaning of "Name" in Namespace The "names" referred to here can be class names, module names, or package names. What they represent varies depending on the language that implements namespaces. For example, in Ruby, it refers to constant names. In Rub