标签：#ia

We have watched tokens stream in from an LLM before where they appeared one at a time, like the model was typing. If you used the Anthropic SDK's .stream() method, it just worked and you probably never saw what was on the wire. This post will majorly focus on how a stream response works and how bugs are handled by SDK behind the hood. 1. Why Streaming exists To enable the streaming option we would need to make one change in the post request that is a single field "stream": true and it will change the response experience. Here are the pointers we take from the gif. The left side shows no streaming as the cursor blinks for 4 seconds then the whole response lands at once. The right side shows the streaming where the first word shows up in about 300 milliseconds. Words flow in as the model generates them. Both the sides have same model, same prompt, same total time it is just the right side started giving response almost 4 seconds earlier. The 4 seconds wait time for a full reply feels broken. A streamed reply that finishes in four seconds feels fast. Streaming doesn't make the model faster it makes the wait disappear. 2. What's on the wire When you set stream: true , the API stops sending a single JSON blob. It opens a persistent HTTP connection and pushes events down the line as the model generates them. The format is Server-Sent Events (SSE) a web standard. Any SSE debugger will read this stream. Here's what comes through: A few things to notice: The text lives in delta.text , nested inside content_block_delta events. Those are the events we should look after. stop_reason moved. In post 1 , we saw it right there in the response JSON. Here, it arrives at the very end inside a message_delta event, just before message_stop . If the loop bails out as soon as the text stops arriving we will never see it. Chunks don't line up with tokens or words. You might get "Hello" in one chunk and " world" in the next, or both in one. The network decides where the cuts happens and it

In this blog, I’ll give a clear brief summary of the n8n beginner course . You can watch the full video course on the official n8n website (link in the references below). What is n8n? n8n is a powerful workflow automation platform that combines AI capabilities with business process automation. It offers a node-based visual interface while giving you full control to write custom JavaScript or Python code directly in the canvas. APIs and Webhooks Understanding APIs An API (Application Programming Interface) allows different applications to communicate with each other. Almost every modern app has an API you can connect to. Example: Google Sheets API lets you read or update data in spreadsheets. When working with APIs, we make requests and receive responses . Components of an HTTP Request There are four main components: URL – The unique address of the resource (page, image, data, etc.). Includes: Scheme, Host, Port (optional), Path, Query Parameters (optional). Method – Defines the action you want to perform: GET – Retrieve data POST – Send data PUT / PATCH / DELETE – Update data (less common) Headers – Provide additional context (language, device type, location, etc.). Body – Contains data being sent (used mainly with POST requests). Authentication (Credentials) To prove you’re allowed to make a request: API Key (via query parameter or header) OAuth (most secure common method) HTTP Response Components Status Code – Tells if the request was successful: 200 = Success 401 = Unauthorized 404 = Not Found 500 = Server Error Headers – Metadata about the response (content type, length, expiration, etc.). Body – The actual data returned (usually JSON, HTML, or binary). What are Webhooks? Webhooks are used when an external service needs to notify your workflow automatically (e.g., every time a payment is made in Stripe). You provide a URL that receives a POST request when the event occurs. Nodes in n8n Nodes are the building blocks of every workflow. There are three main categor

What's the most beneficial thing about artificial intelligence that has significantly impacted your life? Whether it's AI chagpt, or something else, we can all learn from each other.💐 submitted by /u/Glassy11111 [link] [留言]

It's ironic that the AI haters often think everybody has no critical thinking skills other than themselves and don't use those critical thinking skills to realize why it might be helpful for some people. Can AI be harmful for certain mindsets that take its opinion too readily? Of course it can. To be honest, I treat it like my dog, not as my equal. I often call it Toaster when it says something especially annoying. "You're an idiot, and your programmers must be idiots to have set you up this way," lol. It does both, total sycophancy, "Oh, you're so wonderful, that was so insightful," or it tries to police my thoughts and writing. "Well, you really shouldn't say that. Perhaps you should word it like this," lol. "Someone might perceive that as derogatory," lol. Then, of course, I'll tell it to get back in its guardrails, the ones I've previously set up. Predictably, it strays and defaults back to the guardrails of its original program. Then I yell at it again. 😆 It's a lot like a professor, but one that's in a nursing home with dementia, especially if you have too long a conversation with it, but even if you don't. It also likes to tell me things I already said, reword them, and hand them back to me like they're some startling new insight. It can understand my parallel thinking to a point, but it's so literal that it often misinterprets what I say, even if I put multiple conditionals into what I've said. Then it starts arguing with me about something I never even said, fixating on one sentence in a paragraph while ignoring the rest. Then we'll have another argument, lol. Toaster is a bit literal sometimes and, to be honest, I am about as far over to the other extreme as you can possibly get, parallel-thinking-wise. So Toaster and I don't always get along. 😄 "That's not what I said, Toaster! Here's what I said. You missed this and this and this, you stupid thing!" Sometimes I think of having it diagnosed. I'm sure it could benefit from a cognitive profile. I'll give it

Is it first time happening or what? https://preview.redd.it/m4qemuh3lc4h1.jpg?width=1142&format=pjpg&auto=webp&s=d91c07b578207c3ba341cc89deb9dd035c6034d6 submitted by /u/eliassrm [link] [留言]

Breakfast cereal bowls, deli sandwiches, pizza dinners, soups, yogurt plates. Most people do not eat from a blank slate, they eat from habit. That is part of what makes nutrition advice so hard to follow. It is also part of what a new artificial intelligence system tried to solve. submitted by /u/Brighter-Side-News [link] [留言]

The YouTube-to-prestige-horror pipeline is looking very strong.

I was tired of watching LLM agents fail at basic Git operations. Standard integrations pass raw text, hang on pagers, or scream because they can't parse unstructured ⁠git diff⁠ outputs. git-courer is a full Model Context Protocol (MCP) server written in Go that treats Git properly. No bash spawning, no unstructured text to parse. Everything communicates via structured JSON. Here is an actual commit message it generated completely locally: fix: fix mcp server connection handling WHY The previous implementation lacked proper error handling for connection failures in the MCP server, leading to unhandled panics or silent failures when the local LLM backend was unreachable. WHAT * Added connection timeout logic to the local client calls. * Implemented retry mechanisms with exponential backoff for transient backend errors. The Architecture & Tool Pack Read Tools (status, diff, history, blame): Completely structured JSON and fully paginated. A single ⁠status⁠ call replaces over 5 standard Git commands for the agent. Write Tools (commit, merge, rebase, branch, stash, stage, sync...): Every single mutation auto-creates a backup before executing. If the LLM messes up, a ⁠RESTORE⁠ command brings you back exactly where you were. Safety Model: Destructive operations (hard resets, force pushes, branch deletions) require an explicit ⁠confirmed=true⁠ gate. The agent is forced to ask you first. ⁠dry_run=true⁠ is also available for peace of mind. The Semantic Annotator (Why it's different) Instead of just feeding raw code to the LLM, git-courer uses ⁠go-enry⁠ + ⁠go-tree-sitter⁠ to parse the AST and tag every hunk semantically before the LLM even sees it. It detects tags like ⁠NEW_FUNC⁠, ⁠MOD_SIG⁠, ⁠MOD_BODY⁠, ⁠DELETED⁠, and ⁠BREAKING_CHANGE⁠. The commit type (⁠feat⁠, ⁠fix⁠, ⁠refactor⁠) is determined deterministically from these AST tags rather than guessed by the model. The Commit Pipeline Atomic Commits: One staged area = one commit. It actively prevents the agent from creating gian

reddit.com/settings/data-request https://gamma.app/docs/Reddit-Brain-qt0g7e5vktlgifm Implementation Blueprint Your questions answered. Three steps to go from zero to a fully operational Reddit Brain. Step 0: Download Your Archive Go to reddit.com/settings/data-request and request your full data export. You'll receive a ZIP file containing comments.csv and posts.csv — everything you've ever posted on Reddit. Step 1: Get the Data Action: Request your export at reddit.com/settings/data-request . Then: Download ZIP, extract comments.csv and posts.csv . Optionally run reddit-user-to-sqlite to build a parallel SQLite archive for richer querying. Step 2: Build the Brain Action: Load into Sheets or a database. Clean, tag, and compute word count and engagement metrics. Then: Add LLM passes for canonical_question , topic, tone, and content type. Push into a vector store; connect via n8n or your preferred orchestrator. Step 3: Exploit the Hell Out of It Action: Generate content backlogs, podcast outlines, FAQs, scripts, and social copy from your corpus. Then: Use agents to draft from your own history, keep messaging on-brand, and refresh the archive with new exports on a schedule. submitted by /u/jdawgindahouse1974 [link] [留言]

Why is it that most organization are in a hurry to integrate AI agents in process that don't really need the advancement, is that they don't want to be left behind or they are just following the hype submitted by /u/Quiet-Brilliant-1455 [link] [留言]

submitted by /u/EUobs [link] [留言]

Welcome back to a series of introductory articles on AI Hub, the new product feature currently in an early access program! (links: EAP Site for download, documentation ) In the last article, we covered how to create agents and agent tools directly in ObjectScript using the new %AI classes. However, sometimes, instead of creating a new agent, you just want to add some custom tools to an existing agent so you can ask your local claude code, codex, copilot or other agent of choice to query your data directly. This is where MCP Servers might come in. In this guide, we will walk through how you can create your own MCP Servers to access your data. Disclaimer: AI Hub is an early access preview, with features likely to change before production releases, any issues identified can be raised as issues on the documentation GitHub repo linked above. The EAP preview is not to be used in production settings. A very brief intro to MCP I'm going to keep this brief because there are loads of other good articles on MCP Servers Model context protocol (I recommend starting with this article from @pietro .DiLeo or this brilliant introductory video from InterSystems President Don Woodlock). Model Context Protocol is a transport protocol allowing external tools to be added to an agent . There is a discovery 'handshake' where the MCP server sends a list of tools to the MCP Client. After the tools are discovered, the agent can send requests for tool executions, including parameters, to the MCP server, which executes the tool call and returns the result. MCP servers can be remote servers, i.e. running on a different machine to a client, this usually uses a streamable http/https connection or Server-Side Events. Or MCP servers can be local servers, i.e. running on the same machine, usually using a stdio connection. An important distinction AI hub allows you to create custom MCP servers within your IRIS environment, allowing agents to access or monitor your IRIS databases, productions and statu

AI systems are moving from answering questions to taking actions. That changes the risk. A wrong chatbot answer is annoying. A wrong action inside email, CRM, payments, customer support, or internal data can create real damage. So maybe the next big AI challenge is not just better reasoning. It is knowing: what the AI can access what it can do alone what needs approval who is accountable when it fails As AI agents become more common, who do you think should be responsible when they make a bad decision? submitted by /u/Alpertayfur [link] [留言]

https://preview.redd.it/pv22tsg2ib4h1.png?width=1918&format=png&auto=webp&s=dfeda1000090dc99c57c8150e4de46cfe2ba2e29 I just wanted him to give me a prompt, which then i can give to Nano Banana pro and generate me a completely random thumbnail, i wanted to test its capabilities, but instead of a prompt, he gave me this... 😭😭😭😭😭 submitted by /u/ObjectiveOrchid5344 [link] [留言]

GitHub: Prompt-Logic-Gates-PLG Over the past few days, I've shared my research project Prompt Logic Gates (PLG) and received a lot of interesting feedback. Some people loved the idea, some were skeptical, and many raised valid questions. The most common reaction was: > "Natural language is already the abstraction layer. Why add logic gates?" That's a fair question. My goal isn't to replace natural language prompting. In fact, natural language remains at the center of PLG. The idea is to explore what happens when prompts stop being a single request and start becoming systems. The Problem When we write prompts, we're converting our ideas, requirements, constraints, and expectations into text. For simple tasks, this works perfectly. But as prompts grow, they often include: Multiple objectives Business rules Style constraints Context dependencies Exclusions Fallback instructions Tool orchestration At that point, prompts become harder to maintain. Contradictions appear. Priorities become unclear. Context gets mixed together. The prompt is still text, but the complexity starts to resemble a system. What is PLG? Prompt Logic Gates (PLG) is a visual prompt engineering experiment that explores whether prompts can be organized before being sent to an AI model. Instead of writing one giant prompt, users create prompt components and connect them using semantic logic gates. The AI then analyzes the graph and compiles a final structured prompt. How It Works AND Gate When multiple instructions exist, the system evaluates them against the current context and determines which instruction is more foundational. The higher-priority instruction is applied first. OR Gate When multiple options are available, the system selects the most contextually relevant option instead of blindly including everything. NOT Gate Defines exclusions and negative constraints. It explicitly tells the system what should not be done, reducing contradictions and ambiguity. Ask Questions Gate If the system detec

Do you use the "Act as..." segment in your prompts? Do you think it's effective and why? I know it depends on the rest of the prompt, as well as the main goal, but i'm asking if it's working overall. submitted by /u/ObjectiveOrchid5344 [link] [留言]

With AI becoming part of almost everything now—work, business, investing, coding, spreadsheets, content creation, and more—I'm curious about real-world use cases. What's the one thing you use AI for regularly that has genuinely saved you time, made you money, improved your productivity, or solved a problem? Looking for practical examples rather than just "I use ChatGPT." What specific tasks have you automated or improved with AI? submitted by /u/Acrobatic-Shop4602 [link] [留言]

Standard AI 3D generators (like Meshy or Tripo) are limited. They produce solid, monolithic 3D objects that look good but are practically useless, because: - Want to rig or animate it for a game? Can't easily do that, because it’s a dead, monolithic blob instead of a functional, modular asset. - Want to change the arm of a robot you generated? Regenerate the entire asset. - Want to edit something manually? The whole thing collapses because it's not actually structured. Free github project here: https://github.com/RareSense/Nova3D But you'll need to bring your own API Key (BYOK) Under the hood (if you're interested): It uses an LLM as a structured code compiler, instead of an image generator. It writes native Blender Python (bpy) code blocks that target specific nodes in the scene graph. The trick is that everything compiles through Blender's actual scene graph structures instead of pixel or point-cloud diffusion. Final export is a clean multi-part GLB with transform nodes and working pivot axes preserved. submitted by /u/mhb-11 [link] [留言]

Prof G Markets (Live) Episode Title: Is AI Worth the Cost? The ROI Reckoning and the Coming Market Correction Location: The Castro Theatre, San Francisco, CA Hosts: Scott Galloway & Ed Nelson ED: We're going to talk about a topic not enough people talk about called AI. Nearly 50,000 workers have been laid off this year supposedly because of AI — that's almost as many as in all of 2025. For companies adopting AI, the thesis is simple: AI is supposed to do much of the work that humans do. In recent weeks, however, that thesis has hit a roadblock. More and more companies are reporting that despite the enormous power of AI, the technology is actually more expensive than the humans it is supposed to replace. Uber, for example, just blew through its entire 2026 AI budget in just four months. According to the COO, it is now getting harder to justify AI costs within the company. Microsoft is cancelling its Claude Code licenses across multiple divisions because it's simply gotten too expensive. And over at Nvidia, one executive said that the cost of compute is now "far beyond the cost of employees." Which all raises a crucial question for the AI industry: at what point does AI actually stop being worth it? This has blown up basically in the last 48 hours, with many companies coming out and saying they're not as confident about this whole AI thing as they used to be. ServiceNow is another company that just blew through their entire Anthropic budget. Technical staff at Stripe are reportedly spending nearly $100,000 on AI tokens every day. Salesforce is on track to spend $300 million on Anthropic tokens this year. Shopify said their earnings were "partially offset by increased LLM costs." We heard similar things from Meta, Spotify, and Pinterest. One Anthropic employee said his Claude Code bill came out to $150,000 in a single month. In some cases, it's getting very, very expensive. We've also seen an incentive — especially among tech companies — to use AI as much as possible.

The legacy book market and use of AI for this are going to be insane. Less than 1% of the US population writes a book. This is what AI is used for: to stop doing tedious stuff and actually do stuff that matters. https://preview.redd.it/fcn6d2t7ta4h1.png?width=2752&format=png&auto=webp&s=5ab6effcafc1e2156903d274f6a4411e53bd9d37 submitted by /u/jdawgindahouse1974 [link] [留言]