标签：#RAM

Honestly, bootcamp Grad Dives Into Google vs OpenAI API Pricing When I finished my coding bootcamp three months ago, I thought I understood what an API did. I mean, you send a request, you get a response back, right? What I did not understand was how dramatically the cost could vary depending on which model you picked. I had no idea that a single line of code change could mean the difference between paying pennies and paying hundreds of dollars at scale. That is the rabbit hole I fell down last week, and I want to walk you through everything I learned. This is the post I wish I had read before I burned through my first $50 in API credits. Why I Started Looking At Pricing In The First Place I was building a small app that takes user reviews and summarizes them. Pretty straightforward. I figured I would just plug in the most popular model and call it a day. That model, if you have been paying attention to the news, is GPT-4o. So I wired it up, ran a few tests, and everything looked great. Then I did the math. GPT-4o charges $2.50 per million tokens on input and $10.00 per million tokens on output. I did not even know what a "million tokens" really meant in practice. So I tested my app with maybe 50 reviews and watched my credit balance drop. It was not catastrophic, but it was enough that I started wondering if there was a cheaper way. I was shocked when I found out how big the gap actually is. The Pricing Table That Changed My Whole Plan I stumbled onto a platform called Global API, and honestly, the pricing chart there blew my mind. They give you access to 184 different AI models, with prices ranging all the way from $0.01 to $3.50 per million tokens. Compare that to the GPT-4o output price of $10.00 per million tokens, and you start to understand why I panicked a little when I saw my early numbers. Here are the five models I ended up comparing side by side: Model Input Cost Output Cost Context Window DeepSeek V4 Flash $0.27 $1.10 128K DeepSeek V4 Pro $0.55 $2.20 20

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The ban would apply to a range of social media platforms including Snapchat, TikTok, YouTube, Instagram, Facebook and X.

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Most developers have done this at least once. You get a messy API response. You need to inspect a JWT. You have a webhook payload, a log object, or a config file that is hard to read. So you open a JSON formatter, paste the content, and move on. That habit is convenient. But it also deserves a second look. Not every JSON tool behaves the same way. Some tools process your input entirely in the browser. Some send content to a server. Some store snippets for sharing. Some extensions have permissions that are broader than you expect. The problem is not that every online formatter is unsafe. The problem is that you often do not know what happens after you paste. What you should avoid pasting blindly Before using any random online tool, be careful with: production JWTs API responses containing user data logs from real systems config files webhook payloads database URLs cloud keys internal endpoints tenant IDs error traces from production systems A JSON payload does not need to contain an obvious password to be sensitive. Sometimes the risky part is context: user IDs, internal URLs, tokens, customer data, or system structure. A quick DevTools check You can do a basic check with your browser’s DevTools. Open the JSON tool. Open DevTools. Go to the Network tab. Clear existing requests. Paste a harmless test JSON first. Run format, validate, diff, decode, or whatever action the tool provides. Watch the Network tab. Look for POST, PUT, fetch, XHR, or beacon requests after your input. Inspect request payloads if they exist. Check whether your pasted JSON appears in any request. Do this with harmless test data first. If the tool uploads the test JSON, do not paste production content into it. What to look for A few signs deserve attention: POST requests after you paste or click format request bodies containing your JSON share-link features that save snippets server-side validation APIs analytics events that include pasted content extension background requests that are not clearly

AI coding agents are becoming better at changing software. That is no longer the hardest problem. The harder problem is keeping the repository understandable after those changes land. Code changes quickly. Documentation often does not. Product intent lives in chat history. Architecture notes fall behind. Reviewers can inspect the implementation diff, but they often cannot see whether the product promise, engineering contract, and repository workflow are still aligned. Truthmark is built for that gap. It is a Git-native workflow layer for AI-assisted software development. It installs repository-local truth workflows so AI agents can keep canonical truth docs aligned with functional code changes, while humans still review normal Git diffs. Truthmark 2.2.0 takes a significant step forward: it separates product truth from engineering truth. That may sound like a documentation detail. It is not. It is a workflow boundary for AI coding agents. Why truth needs lanes Most documentation systems treat “docs” as one surface. That works until AI agents start using those docs as operational context. A product promise and an implementation detail are not the same kind of truth. A product doc should say what must be true, why it matters, who benefits, what boundary is being protected, and what success means. An engineering doc should say how the repository currently realizes that promise: the behavior, contract, architecture, workflow, operations, tests, and source-backed implementation facts. When those two kinds of truth collapse into one file, the result is usually weak in both directions. Product truth becomes a summary of implementation mechanics. Engineering truth becomes a detailed version of product rationale. Neither is ideal for humans. Neither is ideal for agents. Truthmark 2.2.0 introduces explicit product and engineering lanes so agents can reason about these surfaces separately. The core rule is simple: Product truth says what must be true and why. Engineering truth

It's not highly theoretical and walks through actual lexer implementation in code submitted by /u/Xaneris47 [link] [留言]

In this session, Sam Newman interviews Kief Morris and Adrian Mouat, both experts in their field. They explore the current reality of security in the container world, how infrastructure automation is impacted by latest trends, and whether platform teams are actually working. submitted by /u/goto-con [link] [留言]

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Introduction It's crazy to me that some GitHub repos, that were just created in the last...

Building software that can change itself without needing to be recompiled is a hard problem, and the reflection architectural pattern is a solid answer to that. I published an article diving into the reflection architectural pattern. If you've ever wondered how Spring Boot uses annotations to magically wire your dependencies, or how ORMs map database fields without explicit code, reflection is the answer. I break down how this pattern actually works, show practical examples, and discuss when you should and shouldn't use it. submitted by /u/Netunodev [link] [留言]

Everyone is hyping up local-first architecture because of Linear’s speed and the flack Notion is getting for its half-baked offline mode. Keeping data on the client sounds amazing for UX, but the engineering trade-offs are brutal. Unless your users are literally working in tunnels or you have strict privacy requirements, local-first feels like a massive over-engineering trap. Managing CRDTs, conflict resolution, and running database schema migrations across thousands of fragmented user devices is an absolute nightmare. Notion's struggles proved that trying to bolt offline support onto a legacy cloud-first DB just doesn't work well. I wrote a deeper breakdown on the strategic trade-offs and what the sync problem actually costs to solve here: https://buildmvpfast.com/blog/local-first-saas-offline-first-vs-cloud-first For most apps, a boring Postgres stack lets you ship fast and validate the product. You can worry about complex sync layers later. For anyone who has shipped a production local-first app, was the snappy UI worth the infrastructure headache? I'd love to get some feedback and hear your war stories on this. submitted by /u/Timely-Ad-2615 [link] [留言]

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Originally published at Perl Weekly 777 Hi there! In the recent weeks I looked at a lot of MetaCPAN profiles (aka. author pages) such as that of MANWAR . If I could also find their LinkedIn profile I invited them to connect via LinkedIn . (If I have not sent you an invitation yet, then I guess I missed your profile. I'd be glad to get a connect request via LinkedIn.) I noticed that a large percentage of the people still have their @cpan.org email address listed. Despite the fact that cpan.org email forwarding has been shut down 6 weeks ago. That means people will get annoyed if hey try to contact you using that address. You could replace that address or hide it and offer other ways for people to contact you. Either of them is better than having a bad address. In addition, I noticed that some of the links people have there are not working. (e.g. incorrect link to their LinkedIn profile, or to their home page etc.) In order to fix these you probably first need to check and update your PAUSE account . After logging in look for the Edit Account Info menu option. There you can list your email address and you can even decide if you'd like to have a visible address or not. Then you could take a look at your MetaCPAN profile. For this visit MetaCPAN . Login in the top-right corner. If you don't remember whether you used GitHub or Google, don't worry. Inside you can connect them in the Identities menu point. Then go to the Profile menu point and update the fields there. Finally, if you have updated your profile after reading this, I'd be glad if you sent me an email so I'll know this messaged had some positive impact. Oh, and if you don't have a CPAN account and you have not uploaded anything yet, then what are you waiting for? Enjoy your week! -- Your editor: Gabor Szabo. Articles Time::Str - Time Zones and Leap Seconds Time::Str parses and formats date/time strings across 20+ standard formats, with an optional C/XS backend and nanosecond precision. The previous post, Intro

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Most bad decisions in software engineering aren't made because the engineer chose wrong between two...

Plug a sensor into a switch, wire up a building full of cameras, or rack a server, and you are using Ethernet. It is the most widely deployed wired networking standard on earth, the quiet backbone under offices, factories, and data centers. And it is named after a scientific idea that turned out to be completely wrong. The name was not an accident or a marketing afterthought. It was a deliberate engineering choice, and the reasoning behind it explains why Ethernet outlived nearly every rival and still underpins industrial IoT half a century later. A memo, a laser printer, and a dead theory The date Ethernet enthusiasts celebrate is May 22, 1973. On that day, a young engineer named Robert Metcalfe, working at Xerox's legendary Palo Alto Research Center (PARC), circulated a memo describing how to connect the Alto - one of the first personal computers - to a new device PARC had built: the laser printer. The problem was getting many machines to share one wire without their messages colliding into noise. Metcalfe needed a name for the shared medium that carried the signals. He reached back into nineteenth-century physics and borrowed the term luminiferous ether . For generations, physicists had assumed that light, being a wave, needed something to wave through - just as sound needs air. They called that invisible, all-pervading substance the ether, and they believed it filled the entire universe as a silent carrier of electromagnetic waves. The trouble is that the ether does not exist. The famous Michelson-Morley experiment of 1887 failed to detect it, and Einstein's special relativity in 1905 made it unnecessary altogether. By the time Metcalfe wrote his memo, the luminiferous ether had been a discredited idea for decades. He used it anyway, and on purpose. Why a debunked idea made for brilliant engineering Metcalfe later explained the choice plainly: "We called it Ethernet because the ether could be coax, twisted pair, radio, optical fibers, power line, whatever you wa

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