I've been developing an AI product using LLM APIs (from OpenRouter) but want to deploy an open-source LLM in my own Prod env. which I can control. Few reasons behind this are: - I wanna own the complete stack around my product. - Second I wanna fine-tune the model around my usecase. So, what's the most affordable but a good platform for this? I'm not an AI engineer so don't wanna stuck in CUDA or Transformers hell, anything which can give me a straight path towards my private deployment. Thanks, submitted by /u/Necessary_Gazelle211 [link] [留言]

Your AI coworker, in Slack. Just tag @O. Discussion | Link

Mac notch command center for productivity with tool access Discussion | Link

Sharing a project I have been working on called Third Eye. It does visual geolocation. Given a video, it figures out where it was filmed using only the image content, and draws the route on a map. Pipeline in short: per frame place recognition against a street imagery index a trajectory search that stitches the frames into one coherent path a geometric verification step to catch false matches per frame confidence so weak frames are flagged, not faked I ran it on real dashcam footage and it traced the route quite well. Cross domain matching like this is genuinely hard, so a fair amount of the work went into making it honest about uncertainty. Keen to hear feedback on the matching and trajectory side. Video Demo: https://youtu.be/U3sItFlvq6E?si=-KJrwb0gSlk-GxVH The Index was covering a 12KM 2 Area around NYC. submitted by /u/Ok-Apricot956 [link] [留言]

Appaca is my third pivot. A couple of years ago, I started working on an idea on no-code platform that generates code. The goal is to help devs and agencies ship products faster for their clients. I went through Antler startup accelerator and got initial funding. I was working on the right problem, but wrong solution. Instead of no-code, I should have jumped into LLM a lot earlier. I felt defeated when Lovable, Base44, and Bolt came out strong, showing the world what LLMs can do in software deve

Turn your Mac into a time machine. Discussion | Link

I’ve been making massive headway on tgo, my TypeScript to Go compiler library, but it is forcing me to confront some hard realities about how I manage systems, AI, and even people. The Cost of Scaling Complexity Since the last devlog, I’ve added full support for Node libraries— fs , path , process , and a few others. Right now, I’m in the trenches trying to compile Lodash, argparse , and date-fns . I pushed date-fns to the side for a minute because Lodash is proving to be the perfect stress test. It is, frankly, obnoxious. In some cases, the code is just very poorly written. Lodash has 316 different entry points. Right now, 122 are failing. But dealing with this massive, complex library has forced me to completely overhaul my test runner. I’ve built it so that you can choose specific entry points and compile only what you need—similar to how ES bundle works. I’ve also implemented heavy caching. If you are continually rebuilding, it won't re-compile the source to Go every single time; it just handles the binary compilation unless something actually changed. It’s significantly faster. But as this project scales, the sheer complexity is threatening to break the system—and by the system, I mean the AI I am using to build it. Process is Survival I do most of this development through AI, and getting an LLM to consistently output good software engineering without breaking existing features is incredibly difficult. I was constantly blowing out the context window. Even at 200,000 tokens, it wasn't enough. By the time the AI figured out what to do, it would start summarizing the context and immediately start doing a terrible job. This forced me to narrow down all possibilities. I realized there are really only four things I am ever asking the AI to do: Update the test runner. Fix a bug. Implement a new feature. Work on a library. That’s it. I defined strict workflows for those four pathways. If I ask it to fix a bug, it has to run the specific test, read the JavaScript, read

JavaScript Array Search Methods What are Array Search Methods? Array Search Methods are used to: Find the position (index) of an element. Check whether an element exists. Retrieve an element that satisfies a condition. Find the index of an element that matches a condition. Search from the beginning or the end of an array. Common Array Search Methods Method Purpose Returns indexOf() Finds the first occurrence of a value Index or -1 lastIndexOf() Finds the last occurrence of a value Index or -1 includes() Checks whether a value exists true / false find() Finds the first matching element Element or undefined findIndex() Finds the index of the first matching element Index or -1 findLast() (ES2023) Finds the last matching element Element or undefined findLastIndex() (ES2023) Finds the last matching index Index or -1 1. Array.indexOf() Definition The indexOf() method searches an array for a specified value and returns the index of its first occurrence . If the value is not found, it returns -1 . Syntax array . indexOf ( searchElement ) array . indexOf ( searchElement , startIndex ) Parameters Parameter Description searchElement Value to search for startIndex (optional) Index where the search starts Returns Index of the first matching element. -1 if not found. Internal Working Suppose: let fruits = [ " Apple " , " Orange " , " Mango " , " Orange " ]; Memory: Index 0 → Apple 1 → Orange 2 → Mango 3 → Orange When: fruits . indexOf ( " Orange " ); JavaScript starts from index 0 : Apple ❌ Orange ✅ Found Stops immediately and returns: 1 Example let fruits = [ " Apple " , " Orange " , " Banana " ]; console . log ( fruits . indexOf ( " Orange " )); Output 1 Example - Not Found let fruits = [ " Apple " , " Orange " ]; console . log ( fruits . indexOf ( " Mango " )); Output -1 Example - Start Position let fruits = [ " Apple " , " Orange " , " Banana " , " Orange " ]; console . log ( fruits . indexOf ( " Orange " , 2 )); Output 3 Real-Time Example Suppose an e-commerce site wants to

Lately, it feels like my feed is completely flooded with "Become an AI/ML Engineer in 2 Hours!" crash courses and quick certificates promising a golden fast-track into machine learning roles. But let’s be completely real for a second: there are no tutorial shortcuts here. The more I dive into actual system architecture and cloud infrastructure, the more obvious it becomes: machine learning isn't a standalone magic trick. It's built entirely on rock-solid Computer Science, efficient data structures, and heavy-duty software engineering. Software Engineering First, AI Second If you can’t build or scale a reliable backend, manage data pipelines, or understand low-level underlying system logic, you simply cannot scale an AI model in production. Prompt engineering is cool for prototyping, but production-level ML requires real, foundational engineering skills. You have to learn how to be a great software engineer first. Looking Past the Hype (A Solid Structural Roadmap) If you actually want to look past the superficial fluff and understand how real data workloads, model deployments, and ML infrastructure fit into a cloud environment, I found an incredibly solid, structured resource. Instead of hand-waving past the hard parts, Microsoft Learn has an official, step-by-step breakdown on Azure AI and Machine Learning Fundamentals. It actually goes into the core architectural principles and shows you what real cloud-scale infrastructure looks like. Whether you are trying to map out your summer learning roadmap or just want to understand the actual systems backing these models, I highly recommend checking it out. Here is the structured entry point if you want to skip the shortcuts and dive into the real infrastructure: 🔗 Official Azure Machine Learning Technical Hub What are your thoughts? Are you seeing the same "AI shortcut" hype on your feeds, or are people finally starting to focus back on core system fundamentals? Let's discuss in the comments!

What is an Array? An Array is a special object in JavaScript used to store multiple values in a single variable. Instead of creating separate variables, let student1 = " John " ; let student2 = " David " ; let student3 = " Alex " ; we can use an array: let students = [ " John " , " David " , " Alex " ]; Each value inside the array is called an element , and every element has an index starting from 0 . Index : 0 1 2 ------------------------- Array : | John | David | Alex | ------------------------- 1. Array length Definition The length property returns the total number of elements present in an array. It is not a function . It is a property of an array object. It is also writable, meaning you can change the length to increase or decrease the array size. Syntax array . length To modify the array length: array . length = newLength ; Parameters None. Returns Returns a number representing the total number of elements in the array. Internal Working Consider this array: let fruits = [ " Apple " , " Orange " , " Mango " ]; Memory representation: Index 0 → Apple 1 → Orange 2 → Mango length = 3 When JavaScript creates the array, it internally stores a special property: { 0 : "Apple" , 1 : "Orange" , 2 : "Mango" , length: 3 } Whenever you access: fruits . length JavaScript simply returns the value stored in the length property. It does not count the elements every time. This makes length very fast. Example 1 let fruits = [ " Apple " , " Orange " , " Banana " ]; console . log ( fruits . length ); Output 3 Example 2 - Updating Length let numbers = [ 10 , 20 , 30 , 40 ]; numbers . length = 2 ; console . log ( numbers ); Output [ 10 , 20 ] JavaScript removes the remaining elements. Example 3 - Increasing Length let colors = [ " Red " , " Blue " ]; colors . length = 5 ; console . log ( colors ); Output [ "Red" , "Blue" , empty × 3 ] The new positions become empty slots . Real-Time Example Imagine an E-commerce Shopping Cart . let cart = [ " Laptop " , " Mouse " , " Keyboard " ]; co

Most discussions around MCP focus on tool calling. That is natural. When people first see MCP, the obvious use case is simple: Let the AI call external tools. A model can read a GitHub issue. A model can query a database. A model can update a file. A model can call an API. In that sense, MCP looks like an RPC layer for AI agents. That is useful. But I think it may not be the most important use of MCP. The more interesting use is this: MCP can distribute context, rules, skills, and operating contracts to AI clients. In other words, MCP is not only a way for AI to call tools during work. It can also be a way to define the working environment before the work starts. The problem with RAG RAG is usually used to answer this question: What information might be relevant to this request? The system searches documents, retrieves chunks, and gives them to the model. This works well for many cases. But it has structural limits. RAG retrieves likely relevant information. It does not necessarily define how the work should be done. For team-level AI work, this is a problem. A team does not only need information. A team also needs shared rules. For example: What is the authoritative source? What should be treated as unknown? When should the AI stop? When is human confirmation required? What is the closure condition? Which workflow should be used? Which domain skill applies? What evidence must be recorded? RAG can retrieve documents that describe these rules. But retrieval is not the same as governance. A retrieved chunk is just context. It is not necessarily an operating contract. The problem with local prompts Many teams try to solve this with prompts. They write instructions like: Follow our coding rules. Use this design document. Ask questions when unclear. Do not make risky changes. This helps, but it does not scale well. Each developer may have a different local prompt. Each AI client may load a different file. Each repository may contain a slightly different version of the ru