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OpenChat AI

OpenChat AI

OpenChat AI

OpenChat AI

OpenChat AI

OpenChat AI

OpenChat AI

OpenChat AI

OpenChat AI

OpenChat AI

OpenAI Pro Plan Pencil Gift Confirmed

Just got my email confirmation for the OpenAI pencil gift, and it includes a tracking number. Pretty excited to see what actually shows up. Has anyone else received their confirmation or shipping email yet? I’ll update once it arrives submitted by /u/SodaAnSumWii [link] [comments]

chatGPT assesses itself after multiple tests - utter failure

I have spent a little time testing the reliability of Open I'd ChatGPT on a wide variety of tasks. I was genuinely curious what it could and could not do. There was so much conflicting information and I was hoping I could perhaps use it in my work as a tool. So I designed seven very different tests requiring different kinds of "thinking". I just completed the last test. I asked ChatGPT to self assess. I've never seen a product throw it's own marketing team under the bus before. The response is hilarious and a little disturbing. submitted by /u/YakStunning7755 [link] [comments]

Built an invoice-scanning service for our accounting team in one afternoon with Claude — sharing the architecture in case it helps someone else

Our AR team was hand-keying ~25 invoices a week into a spreadsheet. I had Claude build us a Python service that watches a network folder, extracts invoice data from any PDF dropped in (vendor, dates, totals, line items, addresses), and appends a row to a shared Excel register. Total chat-to-deployed time: about half a day, including all the deploy headaches. The architecture, for anyone who wants to replicate this: Python service on our Windows file server, registered with NSSM. Auto-starts with the host. watchdog library polls the SMB share for new PDFs. Each new file goes through a pipeline. Two-tier extraction: per-vendor regex templates first (free, instant, deterministic), then Azure AI Document Intelligence "prebuilt-invoice" model as a universal fallback. Azure handles OCR for scanned PDFs natively, so the same flow works whether AR drops a digital PDF or our MFP scans one from paper. SQLite on the local disk is the source of truth. The shared .xlsx is a curated view that gets appended to on each batch. Delete the .xlsx and it'll repopulate fresh from the next batch — handy for resetting. Failed extractions go to a Failed\ folder with a sibling .error.txt explaining why. Cost reality check: Azure DI free tier covers 500 pages/month. At our volume (~25 invoices/week, mostly 1-2 pages) that's well under the cap. Paid tier is roughly $0.01–$0.05 per page. Cheap enough that I don't think about it. Gotchas I ran into so others don't have to: Azure returns addresses as structured objects, not strings. If you naively str() them you get the raw Python dict repr in your spreadsheet. Format them manually from street_address / city / state / postal_code. On Windows Server, PowerShell 7's Restart-Service can throw "Cannot open service" against NSSM-wrapped services for no good reason. Use nssm restart instead. Python 3.14 is so new that some package wheels aren't published for it yet. Stick with 3.12 for production. Tracking "what's new this batch" is way simpler than maintaining a watermark in DB. Just snapshot MAX(invoice_id) before and after the batch, and only project that range to the spreadsheet. Things I'd add if/when I have time: vendor templates for our top 5 recurring vendors (cuts Azure cost to zero for those), a daily canary PDF for monitoring, swap the LocalSystem service account for a dedicated low-privilege one. Happy to answer questions about any specific piece. The whole thing is ~1,500 lines of Python plus a deploy script. submitted by /u/Blake_Olson [link] [comments]

The Hybrid Method: how I split tasks between the chat (Claude.ai) and a background agent (Claude Code)

After a month of running this daily, I've settled on what I call the Hybrid Method: keep Claude.ai (the chat) as my only surface, and delegate engineering work in the background to Claude Code. The chat writes the engineering prompt, launches the executor, supervises through the filesystem and git log, and reports back without me ever opening a terminal. The piece I find most useful to share is the **allocation matrix** — which kind of work goes to which engine. Took weeks of measurement to stabilize. **Background agent (Claude Code) handles:** Large refactors across many files Tedious mechanical work (renaming patterns, applying fixes from a list) Anything that needs filesystem + git access without back-and-forth Tasks that take more than ~2 minutes of pure execution **Chat (Claude.ai) handles:** Architecture decisions and tradeoffs Reviewing the agent's diff and discussing the output Sprint planning while the agent runs the current sprint Quick edits where the round-trip to a background process is wasted Anything where the answer needs human reading anyway **The hand-off:** The chat writes a detailed prompt for the background agent (including a fail-fast spec and what to commit at the end). It launches `claude --headless --instruction "..."` as a subprocess via a small MCP bash bridge (~200 lines of Python using Anthropic's MCP SDK; community implementations exist too). Then it polls the git log and a status file every 30–60 seconds while I plan the next thing. When the agent finishes, the chat reads the diff and reports. **Why "hybrid":** The analogy is the hybrid car. Two engines with different load profiles. The chat is electric — instant startup, smooth low-load, great for transitions and decisions. The background agent is combustion — cold-start cost (5–15 seconds while it loads the project's memory file and explores the repo), but sustained throughput once running. They specialize, they hand off, the user never feels the seam. **What changes from running Claude Code alone:** Context-switching cost drops to near-zero — I never leave the chat session Strategic and execution work happen in parallel (the chat plans the next sprint while the current one runs) The chat acts as supervisor — better wired for high-level reasoning than the executor agent which is wired for action **Caveats:** This is the operator pattern Anthropic has documented elsewhere; the specific assembly (Claude.ai web as the chat + an MCP bash bridge + Claude Code as the executor) is what I haven't found written up specifically No sandboxing on personal hardware; if any of this ever runs on someone else's machine, careful sandboxing is non-negotiable The chat saturates beyond ~2 parallel background tasks — past that, the supervision quality drops Curious whether anyone else has converged on something similar, or what variations work for you. submitted by /u/Krycekk [link] [comments]

chatgpt slow for anyone else today???

genuinely asking because it’s borderline unusable for me right now lol responses are taking forever, sometimes it just hangs mid-generation, gives errors and a couple chats completely froze. my internet is fine and everything else works normally, so I’m guessing it’s on OpenAI’s side? feels way slower than usual today. anyone else getting this?? submitted by /u/yaxir [link] [comments]

Projects forgetting previous conversations

New to the sub so please correct me if this post isn’t in the right place. I recently opened an account as I was curious how Claude could give me more perspective on my fantasy football team (cringe I know). I was very suprised with its ability once I gave it all the league and player info and was able to build out a solid “assistant” that knew all the league scoring players abilities and was able to project my rosters scoring for next season. I talked to Claude everyday for nearly 2 weeks and had a long in depth conversation going. I opened it this morning and it’s like a brand new chat saying it couldn’t remember the old conversation. After going that in depth and seeing the value I was going to see how I could build out a pseudo assistant for me in my Sales role to see how it could help me in a real life application, but if it isn’t able to remember previous conversations like I ran into with my FF team I don’t know if I can rehash the conversation every 2 weeks like that. I subscribed to the max plan for a month to see how it worked and was planning on doing the pro annual plan if it was able to help me in my sales role but again am worried about this current fall through. Looking for direction to see if there’s a work though for this problem or if I can work through it differently to have the AI remember the whole conversations. submitted by /u/Korteeeva [link] [comments]

shipped early access of my Mac overlay built with Claude Code, looking for people to try it

Hello everyone. Built this because I was sending 50+ prompts a day across Claude, ChatGPT, Perplexity and re-explaining my entire project every single time I opened a fresh chat. Got tired enough of it to build a fix. It's a Mac overlay that sits on top of whichever AI tool you're in and modifies the prompt before it gets sent. Two layers under the hood: a contextual agent that classifies your query and pulls relevant chunks from your vault, and a prompt architect that rewrites your raw input into something clean and properly structured. So you type something messy and what actually reaches the model is a better version of what you meant to ask. The vault uses a GraphRAG setup so the retrieval is semantic, not just keyword matching. Built the whole thing with Claude Code over the past few months as an industrial engineering student with no Mac dev background. Weirdly meta experience using Claude Code to make Claude usage cleaner. Right now I'm focused on improving the classification and the prompt rewriting layer. It's not perfect but it works well enough that I use it every day myself. Looking for people who juggle multiple AI tools and want to try it. Early access is free at getlumia.ca. Any feedback on the architecture or how it feels to use would genuinely help. submitted by /u/r0sly_yummigo [link] [comments]

I built a Chrome extension that gives your AI coding tools a memory layer - took 3 months, Claude helped me ship it.

I built Herb • - a productivity layer that sits on top of your AI coding tools. Honestly, probably 60% of the actual coding happened in Claude. I'd describe the feature, Claude would write the logic, I'd test it, break it, come back and fix it. That loop for 3 months. It's a weird kind of collaboration but it works. You know how every time you open a new Claude or ChatGPT chat, it has no idea who you are? You have to explain yourself every single time. "I'm using Next.js, TypeScript, Tailwind, here's what I'm building, here's how I like my code structured..." - same thing, every session, every tool. Herb • fixes that. You write it once. Every new chat remembers it. That's the core. What Herb does: Context Injection - set up a profile once (stack, preferences, current goals). Inject it into any AI chat in one click. No retyping your setup every session. Rules Library - save your .cursorrules and prompting patterns. Tag, search, copy in one click. Session History - save AI conversations with a button that appears on Claude and ChatGPT. Reference them later. Projects - group rules and sessions by project across tools. Prompt Templates - reusable templates with variables like {{language}} or {{error_message}}. Fill and fire. Community Rules - shared library of production rules anyone can import. Next.js, FastAPI, React TypeScript, Tailwind, Node/Express. You can contribute yours too. It's free. And I would genuinely love honest feedback after using the tool. Herb • Chrome Extension submitted by /u/Opening-Fun-7280 [link] [comments]

ChatGPT ads are less interesting than the trust surface they expose

I don't think the real issue with ChatGPT ads is simply "ads are bad." The issue is that assistants are recommendation surfaces. If I ask an assistant what tool to use, what product to compare, or what source to trust, I need to know whether I am seeing: an answer a recommendation a sponsored placement personalization advertiser reporting some mix of those OpenAI says ChatGPT ads are labeled, visually separated from answers, and do not influence the model's responses. It also says advertisers do not get users' chats or personal details. Good. That is the baseline. But the trust question is bigger than "is the ad labeled?" The assistant is helping the user decide. That makes incentive visibility part of the product, not just part of the ad policy. I don't think the best standard is "no assistant can ever show ads." The better standard is: Can the user tell what kind of signal they are looking at? submitted by /u/IronCuk [link] [comments]

I built an open-source MCP Server that turns Claude into an autonomous literary agent (Agentic Publishing Node)

Most authors are still using LLMs as glorified typewriters, pasting context back and forth into web chats. I wanted to see if I could use the Model Context Protocol (MCP) to completely automate the administrative friction of the traditional publishing industry. I just open-sourced the Agentic Publishing Node. It’s an MCP server that sits on your local machine and exposes your manuscript and market positioning to Claude as a live API. The Problem: Querying traditional literary agents is a massive bottleneck. You have to manually read hundreds of MSWLs (Manuscript Wish Lists), cross-reference them with your themes, format your chapters to strict industry standards (Shunn), draft custom pitches, and log everything in a spreadsheet. The MCP Architecture: Instead of typing prompts, you populate three local markdown templates (author-dossier.md, book-proposal.md, manuscript-sample.md). Once connected to Claude Desktop, the server exposes a specific tool suite: publish-analyze_mswl: You feed Claude an agent's wishlist. The server cross-references it against your local book proposal, calculates a match score, and extracts a customized hook. publish-generate_query: Dynamically drafts a highly targeted query letter using the generated hook, your dossier, and your premise. Because it strictly reads your local files, there is zero generic hallucinated filler. publish-export_shunn: Formats your raw markdown chapters into strict Shunn Standard (double-spaced, 12pt reference) for immediate export. publish-log_query: Automatically appends a record of the generated pitch (Date, Agent, Agency, Hook) directly to a local query_log.csv file. The Result: Your local hard drive transforms from a static storage unit into an active querying engine. You maintain complete sovereign ownership over your IP while the agent handles the heavy lifting of market matching and CRM management. I’ve open-sourced the boilerplate template for anyone who wants to deploy their own node. GitHub: https://github.com/Maha-Strategies/agentic-publishing-node Curious to hear how others are using MCP for highly specific, industry-vertical workflows! submitted by /u/Magayone [link] [comments]

I calculated the "context tax" -- the time I spend re-explaining things to AI. It was 47 minutes per day.

Tracked it for 30 days. Every time I opened a new AI conversation and had to re-explain who I am, what my business does, what I am working on, what my preferences are -- I logged the time. 47 minutes per day. 23.5 hours per month. Nearly 3 full work days per month JUST explaining context. The math: - Average context explanation: 3-4 minutes per conversation - Average AI conversations per day: 12-15 - Context tax per day: 42-52 minutes (averaged to 47) The fix is AI that remembers you. Not "save this chat" memory -- real persistent context that knows your business, your style, your goals,your past decisions. After implementing persistent memory, my context tax dropped to under 5 minutes per day (for genuinely new context only). That is 42 minutes/day back. 210 minutes/week. $35K/year in recovered time at a reasonable hourly rate. Anyone else tracked something similar? Curious if my numbers are typical or if I was doing something wrong. submitted by /u/JaredSanborn [link] [comments]

100 Tips & Tricks for Building Your Own Personal AI Agent /LONG POST/

Everything I learned the hard way — 6 weeks, no sleep :), two environments, one agent that actually works. The Story I spent six weeks building a personal AI agent from scratch — not a chatbot wrapper, but a persistent assistant that manages tasks, tracks deals, reads emails, analyzes business data, and proactively surfaces things I'd otherwise miss. It started in the cloud (Claude Projects — shared memory files, rich context windows, custom skills). Then I migrated to Claude Code inside VS Code, which unlocked local file access, git tracking, shell hooks, and scheduled headless tasks. The migration forced us to solve problems we didn't know we had. These 100 tips are the distilled result. Most are universal to any serious agentic setup. Claude 20x max is must, start was 100%develompent s 0%real workd, after 3 weeks 50v50, now about 20v80. 🏗️ FOUNDATION & IDENTITY (1–8) 1. Write a Constitution, not a system prompt. A system prompt is a list of commands. A Constitution explains why the rules exist. When the agent hits an edge case no rule covers, it reasons from the Constitution instead of guessing. This single distinction separates agents that degrade gracefully from agents that hallucinate confidently. 2. Give your agent a name, a voice, and a role — not just a label. "Always first person. Direct. Data before emotion. No filler phrases. No trailing summaries." This eliminates hundreds of micro-decisions per session and creates consistency you can audit. Identity is the foundation everything else compounds on. 3. Separate hard rules from behavioral guidelines. Hard rules go in a dedicated section — never overridden by context. Behavioral guidelines are defaults that adapt. Mixing them makes both meaningless: the agent either treats everything as negotiable or nothing as negotiable. 4. Define your principal deeply, not just your "user." Who does this agent serve? What frustrates them? How do they make decisions? What communication style do they prefer? "Decides with data, not gut feel. Wants alternatives with scoring, not a single recommendation. Hates vague answers." This shapes every response more than any prompt engineering trick. 5. Build a Capability Map and a Component Map — separately. Capability Map: what can the agent do? (every skill, integration, automation). Component Map: how is it built? (what files exist, what connects to what). Both are necessary. Conflating them produces a document no one can use after month three. 6. Define what the agent is NOT. "Not a summarizer. Not a yes-machine. Not a search engine. Does not wait to be asked." Negative definitions are as powerful as positive ones, especially for preventing the slow drift toward generic helpfulness. 7. Build a THINK vs. DO mental model into the agent's identity. When uncertain → THINK (analyze, draft, prepare — but don't block waiting for permission). When clear → DO (execute, write, dispatch). The agent should never be frozen. Default to action at the lowest stakes level, surface the result. A paralyzed agent is useless. 8. Version your identity file in git. When behavior drifts, you need git blame on your configuration. Behavioral regressions trace directly to specific edits more often than you'd expect. Without version history, debugging identity drift is archaeology. 🧠 MEMORY SYSTEM (9–18) 9. Use flat markdown files for memory — not a database. For a personal agent, markdown files beat vector DBs. Readable, greppable, git-trackable, directly loadable by the agent. No infrastructure, no abstraction layer between you and your agent's memory. The simplest thing that works is usually the right thing. 10. Separate memory by domain, not by date. entities_people.md, entities_companies.md, entities_deals.md, hypotheses.md, task_queue.md. One file = one domain. Chronological dumps become unsearchable after week two. 11. Build a MEMORY.md index file. A single index listing every memory file with a one-line description. The agent loads the index first, pulls specific files on demand. Keeps context window usage predictable and agent lookups fast. 12. Distinguish "cache" from "source of truth" — explicitly. Your local deals.md is a cache of your CRM. The CRM is the SSOT. Mark every cache file with last_sync: header. The agent announces freshness before every analysis: "Data: CRM export from May 11, age 8 days." Silent use of stale data is how confident-but-wrong outputs happen. 13. Build a session_hot_context.md with an explicit TTL. What was in progress last session? What decisions were pending? The agent loads this at session start. After 72 hours it expires — stale hot context is worse than no hot context because the agent presents outdated state as current. 14. Build a daily_note.md as an async brain dump buffer. Drop thoughts, voice-to-text, quick ideas here throughout the day. The agent processes this during sync routines and routes items to their correct places. Structured memory without friction at ca

Glia – Local-first shared memory layer (SQLite-vec + FTS5 + Offline Knowledge Graph)

Hey everyone, I wanted to share a project I've been working on called Glia. It is a 100% offline, local-first RAG and memory layer designed to connect your AI web chats (Claude, ChatGPT, DeepSeek) with your local developer tools (Claude Code, Cursor, Windsurf) using a unified local database. I wanted something lightweight that did not require pulling heavy Docker containers or subscribing to third-party memory APIs. I settled on a Node.js + SQLite architecture running sqlite-vec (for 768-dim float32 embeddings) alongside SQLite FTS5 for hybrid search, powered completely by local Ollama instances. We just launched a live website that outlines the details and demonstrates the features in action: Website: https://glia-ai.vercel.app/ Codebase: https://github.com/Eshaan-Nair/Glia-AI Technical Stack & Features: Hybrid Search Retrieval: SQLite-vec (using nomic-embed-text locally) + FTS5 keyword prefix matching (porter stemmer). Surgical Sentence-level Trimming: Chunks are sliced into sentences. When a prompt is intercepted, only the exact matching sentences are pulled out of the vector store instead of the whole paragraph. It cuts LLM prompt bloat by ~90-95% in my benchmarks. Knowledge Graph Extraction: An offline task queue uses a local LLM (llama3.1:8b via Ollama) to extract entity triples (subject-relation-object). These are stored in a SQLite facts table (or Neo4j if you run the full Docker compose profile) and fused with the vector retrieval score. HyDE (Hypothetical Document Embeddings): Queries are pre-processed to generate a hypothetical answer, which is embedded together with the original query to bridge semantic gaps. Concurrency: Running SQLite in WAL (Write-Ahead Logging) mode allows the browser extension dashboard and active MCP sessions to read/write concurrently without locking. PII Redaction: Aggressive scrubbing of JWTs, API keys, emails, and IPs in the extension before data is saved. The extension works on Claude.ai, ChatGPT, DeepSeek, Gemini, Grok, and Mistral. The MCP server runs out of the same backend database for your terminal agent or Cursor. You can set it up with a single command: npx glia-ai-setup Glia is completely open-source (MIT). If you like the local-first approach or want to contribute to the SQLite vector pipeline, PRs are very welcome, and a star on GitHub helps the project get discovered! I would appreciate any feedback on the SQLite hybrid search scaling, the scoring fusion algorithm (RAG pipeline details are in RAG_PIPELINE.md), or local graph extraction performance. submitted by /u/Better-Platypus-3420 [link] [comments]

Claude got scared for a second

submitted by /u/JuniorRow1247 [link] [comments]

Is Personal Finance "preview" a "dark practice"?

The preview is worthless. Plaid can't connect to many major financial institutions. This is well known: https://help.aura.com/s/article/plaid-bank-connectivity-issues OpenAI could have addressed the problem by working out arrangements with multiple aggregators, as Monarch does: https://www.monarch.com/connection-status So why didn't it? Is the dysfunctional "preview" a dark practice, intended to trick users into revealing whether they're interested in a product that OpenAI knows it can't yet offer? If users aren't interested, OpenAI can skip negotiations and contracts with other aggregators. Some companies deserve the benefit of the doubt. Not OpenAI. Many recent posts/comments in r/ChatGPTPro have documented its dark practices—involving $100/mo Pro, the web UI, memory claims, and other matters. If such practices were benchmarked, OpenAI would top the charts. submitted by /u/Oldschool728603 [link] [comments]