New Relic AI

New Relic AI AI

New Relic AI AI

New Relic AI AI

New Relic AI AI

New Relic AI AI

New Relic AI AI

New Relic AI AI

New Relic AI AI

New Relic AI AI

New Relic AI AI

Can I make a new account on OpenAI ChatGPT from the same email after I deleted the account on the email which I had? Is it possible? Because I have deleted my account previously .

Can I make a new account on OpenAI ChatGPT from the same email after I deleted the account on the email which I had? Is it possible? Because I have deleted my account previously because I used to waste a lot of time on it. Now I need it, but I am making an account, so it is saying that your account is deleted. Can I reuse the email? submitted by /u/questing0 [link] [comments]

I built a local MCP server that gives AI agents on-device Vision OCR no cloud, no API keys

Demo of how it works I got tired of sending documents and images to cloud APIs just to extract text, so I built VisionMCP a standalone MCP server that plugs directly into Apple's Vision Framework for on-device OCR (NOTE: It only works on macOS as it leverages the native on device Vision framework) What it does: PDF ingestion: renders pages to images via PDFKit, then runs RecognizeDocumentsRequest (the macOS 26 structured document OCR API). Extracts text, tables, lists, and paragraphs with confidence scores. Image ingestion: runs VNRecognizeTextRequest on PNG, JPEG, TIFF, BMP, GIF, HEIC, WebP whatever you throw at it (up to 250MB). Both paths return raw text, auto-chunked output (with configurable overlap), per-page confidence scores, and a SHA-256 file hash. Zero persistence, zero database purely read-only extraction. Why MCP? If you're using tools like opencode or any MCP-compatible AI client (like cLaUdEcOdE), you can just register the binary and your agent gets vision capabilities instantly. No wrapping scripts, no REST endpoints it talks over stdio. { "mcp": { "visionmcp": { "type": "local", "command": ["/usr/local/bin/visionmcp"], "enabled": true } } } Your agent can then call ingest_pdf or ingest_image with a file path and get structured text back. Tech: Swift 6.3, strict concurrency (Sendable everywhere) macOS 26 Tahoe + Xcode 26 Two independent parsers, no shared abstractions just direct routing Trade-offs: macOS 26 only (uses new Vision APIs) No Windows/Linux this is deeply tied to Apple's Vision framework Swift 6.3 strict concurrency means it's very safe but also very strict at compile time Repo: https://github.com/br3akzero/vision.mcp Also mirrored on Codeberg: https://codeberg.org/breakzero/vision.mcp Happy to answer questions or take feedback. PRs welcome. submitted by /u/DeChilli [link] [comments]

LLMs are just giant probability machines pretending to think [P]

It’s fascinating that simple mathematics between tokens can eventually become a machine that writes essays, code, poetry, and even reasoning. We usually think probability means uncertainty. But LLMs show something strange: If probability + context + mathematical matching are scaled enough, uncertainty itself starts producing intelligent looking outputs. To understand this better, I tried breaking down an LLM from first principles using only 4 tiny training sentences. Example: The boat floated down to the bank. The investor walked into the bank to open a new account. The fisherman walked along the bank to cast his net. The bank has a vault. Then I asked: “The investor walked to the bank to lock his money in …” Why does the model predict “vault” instead of river-related words? That single question reveals almost the entire architecture of modern LLMs. The most underrated concept here is the LM Head. Most explanations immediately jump into transformers and attention, but almost nobody explains that the LM Head is essentially a gigantic token vocabulary containing all possible next token candidates the model can output. So internally the model is basically solving: “Out of all known tokens, which one best matches this context mathematically?” Then different layers help solve that problem: Embeddings: convert words into mathematical vectors Positional encoding: preserves word order Attention layer: figures out which words are related to each other in context (“investor”, “money”, “bank” become strongly connected) https://preview.redd.it/1vazq7c09t2h1.jpg?width=2299&format=pjpg&auto=webp&s=60544c9dcfd5c04bb02f3d7f72bffb4a3c34f7d1 Feed forward neural networks: act somewhat like massive learned if/else decision systems refining patterns internally And finally the LM Head converts all of that into probabilities for the next token. What surprised me most is: There is no hidden magic moment where the AI “becomes conscious”. It’s an enormous probability engine continuously finding the best contextual token match from its vocabulary. I made a walkthrough explaining this visually without unnecessary jargon. https://www.youtube.com/watch?v=YTV5qUCpu2c Would genuinely love feedback from people learning transformers/LLMs from scratch. submitted by /u/abhishekkumar333 [link] [comments]

LLMs are just giant probability machines pretending to think

It’s fascinating that simple mathematics between tokens can eventually become a machine that writes essays, code, poetry, and even reasoning. We usually think probability means uncertainty. But LLMs show something strange: If probability + context + mathematical matching are scaled enough, uncertainty itself starts producing intelligent looking outputs. To understand this better, I tried breaking down an LLM from first principles using only 4 tiny training sentences. Example: The boat floated down to the bank. The investor walked into the bank to open a new account. The fisherman walked along the bank to cast his net. The bank has a vault. Then I asked: “The investor walked to the bank to lock his money in …” Why does the model predict “vault” instead of river-related words? That single question reveals almost the entire architecture of modern LLMs. The most underrated concept here is the LM Head. Most explanations immediately jump into transformers and attention, but almost nobody explains that the LM Head is essentially a gigantic token vocabulary containing all possible next token candidates the model can output. So internally the model is basically solving: “Out of all known tokens, which one best matches this context mathematically?” Then different layers help solve that problem: Embeddings: convert words into mathematical vectors Positional encoding: preserves word order Attention layer: figures out which words are related to each other in context (“investor”, “money”, “bank” become strongly connected) https://preview.redd.it/wxmpf00g7t2h1.jpg?width=2299&format=pjpg&auto=webp&s=a214113263cf008a759740474fbda4e0b8394ba5 Feed forward neural networks: act somewhat like massive learned if/else decision systems refining patterns internally And finally the LM Head converts all of that into probabilities for the next token. What surprised me most is: There is no hidden magic moment where the AI “becomes conscious”. It’s an enormous probability engine continuously finding the best contextual token match from its vocabulary. I made a beginner-friendly walkthrough explaining this visually without unnecessary jargon. https://www.youtube.com/watch?v=YTV5qUCpu2c Would genuinely love feedback from people learning transformers/LLMs from scratch. submitted by /u/abhishekkumar333 [link] [comments]

The chat box was never the right interface for AI

I've been building with AI every day for over a year. And I keep coming back to the same uncomfortable realization. The chat box wasn't designed because it was the best interface for AI. It was designed because it was the easiest one to ship. Think about what the chat box actually asks you to do. Stop what you're working on. Open a new tab. Explain your entire context from scratch. Ask your question. Wait. Copy the answer back. Return to work. Lose your train of thought in the process. Then do it again ten minutes later. We've been so focused on making the AI smarter that nobody questioned whether the interface itself was broken. The model went from GPT-3 to GPT-4 to Claude 3 to whatever comes next. The interface stayed exactly the same. A box. You type. It responds. That's not a tool that works for you. That's a tool you work for. The next interface already knows what you're working on. It doesn't wait to be asked. It acts before you prompt it. It notices patterns in how you work and handles them automatically. You never have to explain yourself again. OpenClaw proved this demand was real. 247k GitHub stars for a tool that deleted inboxes and ran up API bills while people slept. People installed something genuinely dangerous because the underlying idea was so compelling. The demand exists. The technology exists. The chat box is just a habit at this point. We're building what comes after it. clarko.ai if you want to follow along. What do you think the right interface for AI actually looks like? submitted by /u/JuniorRow1247 [link] [comments]

Weekly usage limits are designed to double dip on customers

I downgraded from ChatGPT Pro to Plus, and immediately after the next billing cycle, I have lost access to Codex for the next 6 days because OpenAI applies the lower Plus weekly limit right away, while still counting the usage I made yesterday under the higher Pro limit during the same rolling weekly window. According to support, I will only get 3 weeks of Codex access for this month that I paid for. Support confirmed this is “expected behavior.” In other words, when you downgrade, the limit changes immediately, but the prior usage does not reset or remain tied to the old Pro allowance. So you can pay for a fresh month of Plus and still be locked out of Codex for most of the first week because of usage that happened before the Plus subscription started. That makes no sense. Weekly usage has two parts: the usage and the limit. If the usage carries over, the limit should carry over too. If the limit changes immediately, the usage should reset against that new limit. Otherwise, OpenAI is effectively selling a month of Plus while withholding part of the included Codex access. Support refused to reset the quota or provide a partial refund. This policy is not clearly disclosed, and it is anti-consumer. People should know how OpenAI handles plan changes before switching tiers. submitted by /u/coder543 [link] [comments]

I built a music notation app with Claude, and Claude is also a feature inside it

I've been building Nubium, an open-source music notation editor, with Claude Code. Check out the README to see how I combined Github issues + Claude skills to find a workflow that worked for me. The app is document-driven, which makes it easy for its AI Chat plugin to use the app and edit the score for you. Aside from that, it's a fully-featured editor that's free, runs standalone or in browser, and requires no account. If you happen to be looking for a new notation editor, I'd love any feedback - I made it easy to send feedback in-app. Lmk what you think! Website: https://nubium.rocks/ Repo: github.com/nth-chile/nubium submitted by /u/Traditional_Dig_6114 [link] [comments]

Lodestone: A SQLite-backed arXiv research paper retrieval system for Claude Code

(No AI-generated text below) I published a new Claude Code plugin called Lodestone -- it's a SQLlite backed arXiv research paper retrieval system that amplifies the agentic search abilities of Claude Code when grounding plans, implementations etc in state of the art research while remaining very token-sensitive. My bet is that, when seeded, it will always beat Claude Code's web search tools for grounding Claude in the latest research in a domain or cross-domain and not spend a ton of $ for the pleasure. This audience is probably painfully aware of Karpathy's LLM wiki tweet and the industry of projects that's popped up from it; I'll paste an excerpt from the blog below that I think addresses what you all might be thinking: The Approach Karpathy’s proposal made a lot of sense. Let Claude be the curator and librarian of all this research and access it using its bash and file manipulation tools when necessary. This approach spawned a cottage industry of projects where people implemented various takes on this direction. In parallel, researchers like those that created the ARA Compiler have been trying to move research itself into more a structured, agentic form. I liked all of these ideas, but there were three principles I wanted to uphold while building in this space: The system itself needed to be extremely portable. I wanted this system to follow me from computer to computer and be easily backed up. When ingesting documents, I wanted the system to be as deterministic as possible and spend the least amount of tokens. I didn’t want to expend hundreds of thousands of tokens before getting anything useful out of the system. The system needed to be extremely flexible in how Claude could use it and not prescribe a single method for retrieval. I can’t predict all the ways Claude might use this type of system so I wanted to provide multiple pathways into the data. Given these principles, I was immediately drawn to SQLlite as a backing DB. The unmatched ease-of-use combined with a single file made it the obvious option for portability. Claude could potentially create a sprawling file system when maintaining its own knowledge wiki and I didn’t want to have to learn it when backing up or porting my knowledge base. I gave the ARA Compiler a try while in the middle of building Lodestone. I ran it over a standard-sized paper I was interested in; it produced some cool outputs, but spent almost 500k tokens for the pleasure. This was my fear with it and the ecosystem of projects emerging from Karpathy’s ideas: I had to spend a fair bit of money before I even knew if the system was useful. I knew a SQLlite-backed agentic search system needed a form of classic retrieval (keyword or similarity based), but I also am painfully aware of all the limitations and failures of these approaches to RAG. I wanted to combine this retrieval approach with a retrieval approach from the emerging category of vectorless RAG — a taxonomy that Claude can drill into to get its bearings before drilling further. What followed was Lodestone. Check out the blog post (which also has no AI generated text) here: https://medium.com/@pierce-lamb/lodestone-a-sqlite-backed-arxiv-research-paper-retrieval-system-for-claude-code-b77de201f0c8 The repo's README is almost entirely AI-generated, so point your Claude Code cannons at that: https://github.com/piercelamb/lodestone submitted by /u/SnappyAlligator [link] [comments]

Building an open library of Design.md files for AI-generated UIs

I have been working on something that might be useful if you are building UIs with coding agents. The idea is simple. Generating decent UI with LLMs is still inconsistent. You can get something working, but getting it to look coherent and reusable is much harder. So I started building an open library of Design.md files. These are structured design systems that agents can follow to generate more consistent interfaces. The format comes from Google Stitch, but it works with any LLM. This is a very early alpha, but it is already usable: GitHub repo (open to contributions): https://github.com/albemala/design-md-library Simple frontend to browse designs: https://design-md-web.pages.dev/ I am adding new design systems regularly, and the goal is to turn this into a solid collection of reusable UI foundations for AI workflows. Before pushing this further, I want to understand if this is actually useful. Would you use this in real projects? What is missing for it to be useful? What would stop you from contributing? Any honest feedback is appreciated. submitted by /u/albemala [link] [comments]

Using Claude ai and Claude code optimally?

So I’m relatively new to using Claude - have some coding experience but by no means anything in terms of building infrastructure. I want to outline how I’ve used Claude so far for my personal projects and see if anyone can help optimise this strategy: I start of outlining a general idea in Claude ai and ask to discuss it and ask me questions about what I want. So for example I built a website where I had a clear idea in my head which I needed to convey to Claude. We talk for a while about it so Claude is on the same page as me as best as I can confirm. Claude then makes a full spec document in pdf from about the project - idea, goal, phases of development etc etc - generally about 25 pages. I review it and let it know any tweaks. I tell Claude I want the document to be a living roadmap i.e. we update it often when I come up with new idea etc. I also tell Claude the work flow is Claude as the architect, Claude code is the builder and I am the go between. I then feed that document to Claude code (as a .md file in git hub) and behind building according to the roadmap in the spec doc. As we go we update the spec doc and re brief Claude code accordingly while it builds the project. Is there more optimal way than this that anyone has used? submitted by /u/DiscombobulatedElk58 [link] [comments]

Ron537/DPlex: Terminal multiplexer for AI-assisted development — manage Copilot CLI, Claude Code, and regular shells across projects in one window.

Hey everyone, Over the last few months, I’ve been heavily integrating terminal-based AI agents like claude-code and github-copilot-cli into my daily development workflow. They are incredibly powerful, but running multiple concurrent sessions across complex codebases quickly hits a major roadblock: workspace fragmentation. If you close your terminal, update your IDE, or reboot, your entire layout of splits, tabs, and active agent states vanishes. Trying to keep parallel feature branches, code reviews, and debugging sessions organized side-by-side gets messy fast. To solve this, I built DPlex—an open-source (MIT), local desktop workspace and terminal multiplexer optimized specifically for structured AI workflows. 💻 Landing Page: https://ron537.github.io/DPlex/ 📦 GitHub Repo: https://github.com/Ron537/DPlex What it does: * Absolute Layout & Tab Persistence: Quit the app, restart your machine, or let it crash—DPlex automatically serializes your exact environment to disk. Every single AI session tab, pane split, and active process restores perfectly back to where you left it. * Deep Git Worktree Integration: It features a project-aware sidebar designed around concurrent development. You can spin up side-by-side AI sessions in separate Git worktrees instantly, keeping your main branch clean while agents work on different features. * Unified Project Organization: Instead of loose terminal windows scattered across your desktop, DPlex groups your workspace by project. Switch between entirely different project environments with a single click. * Zero Telemetry & 100% Local: No cloud wrappers, no analytics, and zero external tracking. The source is completely grep-able and runs entirely on your local machine. Tech Stack & Architecture: It’s built to be modular. Adding support for a new AI agent provider is as simple as implementing a single pluggable TypeScript interface—no core forks required. It's available for macOS (Intel/Silicon), Windows, and Linux. I’d love to get your feedback on the layout workflow, feature requests, or any architectural thoughts. If you find it useful, please consider leaving a ⭐ on GitHub to help other developers discover it! submitted by /u/Ron537 [link] [comments]

Anthropic and OpenAI don't want better models, they want to sell more tokens

There is a saying in auto racing that describes the current state of AI providers: “Go as slow as you can to win”, that translates as “Spend as low as you can on R&D to stay slightly better than average”. Let’s put our tin foil hats on and look at it from the business perspective of an AI provider. Follow the money AI providers do not make money on training models but on selling inference. It means, from a business perspective, if OpenAI could keep selling GPT-3 forever, they would not spend money on training a better model but keep milking the cow they already have. But they couldn’t, because it was still “cheap” ($80–$100 million for GPT-4) to train a better model, and there was a risk someone else would. That fear of losing to the better model got us where we are. Makes sense. But let’s look at modern times. Training a model is not “cheap” anymore, it’s mega expensive (estimated to be $1.5–$2 billion for GPT-5). There is only a handful of companies who can afford such an affair. And a new model will not necessary better (so sell more inference). An expensive gamble. What it means for the business: Training a new model is mega expensive, raising money for that is getting harder Training a new model is not a revenue stream, selling inference is Having somewhat capable models that don’t one-shot prompts but need “prolonged thinking” (self-prompting) is actually better for the business of selling tokens than a great model that one-shots SCREW NEW MODELS, SELL MORE INFERENCE! Better model is not a goal anymore Is that what’s happening? Did Anthropic and OpenAI accept their niche and unspokenly (or spokenly, we don’t know) decide to “go as slow as they can” with creating new models, as they both are winning anyway? That would sound reasonable if the goal is to make money (which is why commercial companies are created). Let’s look back 6 months (eternity in the AI world) at Anthropic’s release history: Nov 2025 Opus 4.5 released. The last model that felt like an improvement compared to its predecessor. Feb 2026 Opus 4.6: no shockwave, some users reverted back to 4.5. Maybe got slightly better, but only because it was “thinking for longer” (e.g. burning more tokens without extra prompting). April 2026 Opus 4.7: same underwhelming release, the biggest improvement is that the model now thinks even longer and prompts the user less, e.g. burns even more of your tokens without you asking it. To sum up: last 6 month we seen no quality improvements, but better token burn without bothering the user. From the other side, they also squeeze developers into using Claude Code (their AI harness): End of 2025: forbade usage of Claude subscription in 3rd party harnesses (OpenCode, etc.) Start of 2026: blocked subscription usage of OpenClaw, Hermes and other agents From June 2026: programmatic usage of their Claude Code (for example in scripts) will be forbidden as well. They force you into their harness, where they do as much as they can to keep the tokens flowing. Cherry on top of the pie: Boris Cherny, the head of Claude Code, stated he sees the AI coding future in “agent loops” — an agent keeps prompting itself until the task is completed. Have you noticed the difference? The goal is not to “one-shot” the answer anymore (that needs improving models) but “a loop” that keeps going until the problem is solved. And that loop is a money-making machine for Anthropic, great for the business. That approach also makes money for the whole AI supply chain: AI providers making margin on selling tokens Data centers selling GPU hours NVIDIA selling GPUs What does that mean? Lots of tech companies financially benefit from somewhat intelligent models but not intelligent enough to one-shot all questions. And those models are already there. So it’s likely we won’t see massive model improvements in upcoming future. There is no point in it. Top LLMs are on a more or less the same level, competition is miles behind. Time to make money on inference, or go IPO. submitted by /u/kgoncharuk [link] [comments]

Which MCP servers are actually changing your Claude workflow? Sharing mine

Running Claude with MCP for a couple months now, it really does feel like a whole new product. The ability to run real tools (file system, API, database, etc.) connected to Claude, and never have to cut/paste from context again, is huge. I'm trying a bunch of servers, some are pretty good and some aren't. My current normal is: filesystem server for docs on my computer; GitHub server for PR context; and a handful of other domain specific ones I found. One of the more interesting MCPs I have come across recently is Walter Writes MCP. This connects two tools directly within Claude, a detection tool that identifies if written content appears to be artificially generated and an application that can make this AI-written material appear to be written by humans. The one thing I keep thinking about is how much better Claude's output gets when you give it the proper context. It seems like less hallucinating, more on point answers. MCP is essentially an answer to "How do I provide Claude with enough information to help me without having to always watch the context box?" What are people running? Specifically looking for underrated or domain specific things that don't come up as often. submitted by /u/Various-Worker-790 [link] [comments]

AI training is becoming the new coding revolution

I genuinely think people are underestimating how fast AI training is becoming accessible. A few years ago training a useful model sounded like something only OpenAI, Google, or Meta could do. Now random developers are renting GPUs for a few dollars an hour, fine tuning open models from their bedrooms, building datasets with APIs, and getting surprisingly good results. The biggest shift isn’t even the models themselves, it’s the removal of gatekeeping around experimentation. Once regular people can train specialized reasoning, coding, or teaching models without billion dollar infrastructure, the AI industry changes completely. We’re slowly moving from “only corporations can build intelligence” to “small teams can build focused intelligence better than giant companies in specific niches.” submitted by /u/Raman606surrey [link] [comments]

Whenever a new advancement comes, there is backlash and the fear of the unknown. Also jobs needs to be saved, humans need to fed and somehow a balance had to be struck. This wave keeps repeating. The same is true for AI too. The future of AI is positive and therefore we should embrace it.

submitted by /u/mybirthdaye [link] [comments]