标签：#AR

The first post went over some of its capabilities. Over the past week Edict went v1.0, adding cursors for reading projections after command dispatch (to close some eventual-consistency gaps), a new type of projection that holds state inside the Orleans grain directly instead of a table, saga timeouts, schedules, an improved skills package and MCP server that ships with Edict, and more. Edict has now grown to over 75,000 lines of code and more than 1000 tests, and contains several deep mechanisms that have been fixed, broken, and fixed again. It is well past the point where I can hold all of Edict in my head. This post is about working with AI on large codebases, which I expect to be the first problem most software engineers have to solve. The context problem Years ago I was talking to a PhD candidate whose area of research was Natural Language Processing (NLP). He explained to me that one of the most difficult NLP problems was context. If a colleague says they need to pop out to pick their kids up from school, a scene can form in your head: one with a school, the layout of the road, people waiting, walking, driving, the environs. You may never have seen the school your colleague mentioned, but you can form a rich scene from your accumulated experience and use it to drive the rest of the conversation with a shared understanding. LLMs ingeniously dodge this entire issue by making it your problem. Just a word-probability machine Strip away the chat window and a Large Language Model (LLM) is doing one thing: predicting the next token. Give it a run of text and it returns a probability distribution over what comes next, samples one, appends it, and repeats. Companies like OpenAI and Anthropic then beat it into shape using techniques like supervised fine-tuning and reinforcement learning, which tune those probabilities in meaningful ways. That is why Claude is always telling me "Good framing" or "You've spotted...". It even called me "Bold" on one occasion. The probabilit

TL;DR As an international student in the United States, I joined tech communities hoping to find...

A few months ago I was trying to prepare for a cloud certification exam. I went looking for practice questions - good ones. Not just answer lists, but questions that actually trained the reasoning the exam tests. I found some scattered GitHub repos, a few YouTube playlists, sites with outdated question dumps. Nothing that felt structured. Nothing that explained why an answer was right, not just what it was. So I started building my own study tool. Mock questions, practice sets, AI-generated explanations. The kind of thing I wished existed. Six weeks later that became ArchReady - a certification prep platform for AWS, GCP, and PSM1. It's live now. What it does Practice questions across AWS (CCP, SAA, DVA, SAP), GCP ACE, and PSM1 Explanations for wrong answers - walks through the reasoning, not just the correct option AI-powered explanations coming soon Claude (Anthropic) Confidence tracking - shows which topics you're weak on Free to practice, no signup required. Pro unlocks full history and tracking. The stack Frontend: Next.js 14 (App Router) Backend: FastAPI (Python) AI: Claude (Anthropic) - explanations launching soon Payments: Dodo Hosting: Vercel (web) + Railway (API) Nothing exotic. I kept it boring on purpose - solo founder, 2-5 hrs/week, I can't afford interesting infrastructure problems. What I actually learned Ship before it feels ready. I had a list of 12 features I thought were "required for launch." I launched with 4. Nobody noticed the missing 8. Questions sourced from open-source + AI is good enough to start. Questions come from curated GitHub repos and AI-generated content built around official exam frameworks. That's enough to be useful. Perfection is a later problem. The hardest part isn't building - it's the first 10 users. The product exists. Getting people to try it is the actual work now. Where it is today Live at archready.io . Early stage. Still building. If you're prepping for AWS, GCP, or PSM1 - try it free, no account needed. Honest feedba

Context engineering matters for two reasons: reliability and cost. If your agent's context window is...

I tried to build one before but I think I’m seeing the same results. Any tips on how to effectively build a gpt in chatgpt? submitted by /u/GlobalOpsNotes [link] [留言]

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https://preview.redd.it/n6rst0kxs66h1.png?width=840&format=png&auto=webp&s=784c711f8efb5234445c68175dab8fde8d1702bc Just wanted some wallpapers lol submitted by /u/MassAppa [link] [留言]

Whenever I am struggling to grasp a tough topic (specifically in math/statistics), I ask ChatGPT to explain it to me like I am in high school. I have my MS in Statistics, so I have a relatively good mind when it comes to numbers/probabilities. However, when ChatGPT can explain a concept to me in simple terms, it really helps me learn the material better. Next time you're working on something and you're going through the struggle to grasp something new, give it a try! Then once you have the groundwork/basics down, you can keep the conversation flowing with more questions/answers. submitted by /u/thecogitobrief [link] [留言]