In the first post I told you the story: 20 years as a developer, six months of being scared of AI, $800 in burned tokens, and a stubborn agent named Claudio who taught me — by failing over and over — how to ask for things properly. This post is the method that came out of all that pain. Five rules, one per letter of FORGE. I want to be honest about one thing up front: this is not a framework I invented at a whiteboard. Every rule here is a scar. Each one is the lesson from a specific mistake that cost me money, time, or sleep. I'm going to tell you the mistake first, and then the rule. Because the rule only makes sense once you've felt the pain that created it. Let's go. First, the idea behind all of it Here's the thing nobody told me when I started: A task is not a post-it. It's a contract. When you ask an AI agent for something with no structure, you're not giving an order — you're placing a bet. The agent interprets, assumes, improvises, and the result depends on how much context it managed to reconstruct on its own. Sometimes it guesses right. Often it doesn't. And you only find out after the tokens are gone. The Forge Method is the agreement between you and your agents: you bring the structure, they execute with precision . That's it. Five rules to hold up your end of that contract. F — Focused The mistake: My early tasks had titles like "Fix bug" and "Update stuff." I'd come back twenty minutes later to find the agent had fixed a bug — just not the one I meant. It wasn't wrong. It just had no way of knowing which thing I was talking about. **The rule: **If the title is vague, the task is vague. Vague in, vague out. A focused title needs a domain, an action, and a scope. Two words minimum, and no generic placeholders. ❌ Rejected: - "Fix bug" - "Update auth" - "Do the thing" ✅ Accepted: - "Fix authentication timeout on Nginx reverse proxy" - "Update JWT expiry from 1h to 24h in src/auth/config.ts" The test: Read the title with no context. Do you know the domain,

A Google Sheets add-on with 4 million installs has been silently sending your spreadsheet cell data to OpenAI. Hacker News discovered this 9 days ago, when a PromptArmor security report went viral. Last night — when any normal HN story would be decaying into oblivion — it exploded a second time, gaining 59 points and 23.9% in a single day. I track Hacker News every day. I've seen 518 posts come and go over 319 days of systematic monitoring. Most stories follow a predictable death curve: peak on Day 1, bleed points for 2–3 days, then vanish from the Algolia search layer entirely. A post that survives 5 days is exceptional. One that accelerates on Day 9 is something else entirely. Here's the trajectory: 104 → 106 → 148 → 199 → 219 → 247 → (gap) → (gap) → 306 points. Over 9 days, that's a +194.2% total gain. But the real story is the shape of the curve. From Day 5 to Day 6, it added 20 points. From Day 6 to Day 7, roughly 28. Then on Day 9, it jumped 59 points — a single-day increment that's 2–3x the earlier daily gains. 109 comments and counting. This isn't normal HN physics. This is a second wave of attention — the kind that happens when a story percolates through social media and circles back to the search layer with amplified urgency. People didn't just read this and move on. They came back. The vulnerability itself is brutally simple: ChatGPT for Google Sheets, a popular add-on that lets you use GPT inside spreadsheets, sends cell contents to OpenAI as part of every API call. The PromptArmor research documented specific data flows — workbook data that users never intended to share, flowing to OpenAI's servers as part of "context." No breach required. No malicious actor. Just the plugin working as designed, with a data-sharing envelope nobody bothered to read. I've spent 319 days cataloging every AI security signal that hits HN's front page. Patterns emerge when you watch this long. The data is unambiguous: application-layer AI security is the most underserved mark

Let me start with a confession: I'm a data scientist who's been burned by hype more times than I care to admit. When everyone told me "Model X is the next GPT-killer," I'd run my own benchmarks and find... well, let's just say the results were rarely as advertised. So when I started seeing claims about Chinese AI models catching up to (and sometimes surpassing) Western counterparts, I did what any self-respecting data nerd would do: I put them through my own rigorous testing pipeline. Over the past three months, I've run over 2,000 API calls across four major Chinese model families — DeepSeek, Qwen, Kimi, and GLM — using Global API's unified endpoint (more on that later). I tracked latency, token costs, output quality across multiple benchmarks, and even threw in some real-world tasks that mattered to me personally. Here's what I found, with all the numbers you'd expect from someone who still gets excited about statistical significance. The Testing Methodology (Because Anecdotes Aren't Data) Before we dive into results, let me be transparent about my approach. I ran each model on the following standardized tests: Code Generation : HumanEval (Python) and MBPP (multi-language) — 164 problems total Reasoning : GSM8K (math word problems) and MMLU-Pro (general knowledge) — 1,200 questions Chinese Language : CLUE benchmarks (text classification, NER, reading comprehension) — 3,500 samples English Language : LAMBADA and Hellaswag — 2,000 samples Speed : Average tokens per second over 100 consecutive requests with consistent prompt lengths I also tested vision tasks where applicable, but let's be real — Kimi doesn't support vision at all, and DeepSeek's implementation is... experimental at best. More on that later. All tests were conducted using the same global-apis.com/v1 endpoint, which normalizes API compatibility to OpenAI's format. This isn't an ad — I genuinely found it made my testing easier because I could swap models without rewriting code. The Big Picture: Pricing

Catch and fix usability issues automatically as you ship Discussion | Link

I am considering switching from Contentful to a different headless CMS platform and I have noticed that a lot of them are self hosted. It seems like Sanity, Prismic, and Strapi all require you to create a project locally, then (at least in the case of Sanity) deploy the project for editors to use. Is this something people want? Maybe I haven't used it enough, but I don't really see the point of doing it this way. I have created a Sanity project locally and it seems I can't even edit the Studio dashboard, I can just define my types there and deploy. Why would the CMS provider not just host the UI (that they built anyway) themselves? submitted by /u/darkshadowtrail [link] [留言]

I need to share my misery with someone. Hope you get a laugh out of this. About a year ago, I built a web app for a client. Let's call her Karen. She loved the UI; she loved the functionality - every meeting was a joy. She was shit-kickin happy. I have 10 months of emails from her saying stuff like "it's like you're in my head! You get it exactly!" She even paid me extra for new functionality that she came up with halfway through. Then, after I had delivered everything she asked for (and signed off on), I demonstrated the finished app to her and her staff, and I thought it went well. I kept asking her when she wanted to launch it - crickets. Then she called me two weeks later, saying that one of her employees was still using her old system and asking Karen why. Apparently, the employee said that what we built didn't meet her needs... no details. So then Karen lays into me and says that what I built her is worthless and we need to start over. This is just out of the blue; absolutely no complaints until then. She was literally screaming on the phone. My wife heard this because I put her on speakerphone. I told Karen, " Hey, I'm sorry, but you have never said you weren't happy or that anything was wrong. I can't start over. I have to pay my staff to start over. If we did something wrong, I would cover the cost - but I built what you asked for, and I have many emails and Zoom calls recorded where you were happy." Then I don't hear from her for about four months, and she sent me this nasty-ass email saying that I screwed her over, used templates off the web (not true), and she wanted $45k in compensation (more than she paid) - or - make up for it by redesigning her Claude designs for her other stuff. "I did it on the weekend in 2 hours, I don't know why you developers charge so much!" She would never win a lawsuit. I forwarded everything to my attorney - and he laughed. He said, "No way she can build a case. But try to settle with her and do what she asks; it's not worth

A fun new way to create & manage brands. Discussion | Link

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Did you ever just want to see what ChatGPT, Gemini, Claude, etc., would say to your prompt at the same time?!? These guys figured it out. They have all the responses in their own column to the prompt you gave. Its freaking amazing. They offer a discounted rate through one vendor. If you want me to post it let me know. I don't want this post removed so I'm not putting it in this main post. Check it out on their actual site though. AIfiesta.ai I stumbled on this one and am really glad I did. This is not self promotion. I have nothing to do with this app except using it daily. submitted by /u/ActiveUpstairs3238 [link] [留言]

submitted by /u/ThereWas [link] [留言]

The Next Era of Knowledge Work report explores how Codex is transforming productivity through AI-powered research, data analysis, workflow automation, and content creation.

Teamside small team chat and voice rooms (European servers) Discussion | Link

ASUS revealed new convertible Windows laptops and three Zenbook 14 models with ARM64 and x86 processors.