SearchGPT

SearchGPT AI

SearchGPT AI

SearchGPT AI

SearchGPT AI

SearchGPT AI

SearchGPT AI

SearchGPT AI

SearchGPT AI

SearchGPT AI

SearchGPT AI

Built a free MCP for tracking which URLs Claude (and 5 other engines) cite for any query

We were comparing hosted AI citation dashboards (Profound, AthenaHQ, Otterly) and they all start at $295 to $499 a month. The data they collect is mostly the same data you can pull from each vendor's API. So we built an MCP server that does the same job locally. Citation Intelligence is a stdio MCP server with 12 tools that track what Claude, ChatGPT, Perplexity, Gemini, Google AI Overviews, and Bing cite for any query. Install: npx -y u/automatelab/citation-intelligence Add to .mcp.json: { "mcpServers": { "citation-intelligence": { "command": "npx", "args": ["-y", "@automatelab/citation-intelligence"] } } } Three of the tools run on a local cache and cost zero. The rest are bring-your-own-keys (ANTHROPIC_API_KEY, OPENAI_API_KEY, GEMINI_API_KEY, SERPAPI_API_KEY), about $0.01 to $0.03 per query. The one that actually changed our editorial flow is gsc_citation_gap - it joins Google Search Console data with AI citation status and surfaces pages that rank in Google but are not cited by any AI engine. Those pages are the editorial budget. Repo and full tool list: https://github.com/automatelab/citation-intelligence Launch write-up: https://automatelab.tech/launching-the-citation-intelligence-mcp/ Curious if anyone else here is tracking AI citations in their agent loop rather than in a dashboard, and how you handle the predict-vs-measure tradeoff. submitted by /u/exto13 [link] [comments]

Scattered context was becoming a major bottleneck in my workflow.

I kept running into this problem with Claude where the actual work wasn’t even the hard part anymore. It was managing context. Like half the stuff I needed would be buried somewhere across Slack, Notion, emails, meeting notes, random docs, etc. And every time I wanted Claude to continue a task properly, I had to go dig everything back up again. I tried a few different setups. First I used Claude connectors. They were convenient, but it felt like they were pulling in huge chunks of text first and then searching afterward, instead of actually retrieving only the relevant context. Once you connect a bunch of sources, token usage gets kinda crazy. Then I went down the whole Obsidian + agents + local memory system rabbit hole. Honestly, it worked pretty well at first for static knowledge and notes. The hard part was keeping everything updated once info started changing constantly across Slack, docs, meetings, emails, etc. I spent more time maintaining the system than actually using it. And devs can probably brute force this stuff with scripts and automations, but most people aren’t gonna build an entire personal knowledge infrastructure just to use Claude properly. So I decided to build an MCP setup for non-devs that syncs stuff like Notion, Slack, email, calendar, etc, and maintains a live knowledge graph automatically. When something changes in one of the sources, the graph updates too. Then Claude can pull the relevant context during work sessions without me manually pasting everything in every time. The unexpectedly hard part was avoiding “context rot.” At some point, having more memory/context actually made outputs worse unless retrieval was filtered really aggressively and continuously updated. I ended up having to summarize + index sources ahead of time and keep everything synced almost in real time whenever events changed. I've been going through a ton of trial and error with Graph + vector hybrid retrieval, including RRF, filtering, reranking, etc., and I'm still on it, honestly. Curious how other people here are handling the scattered context problem within the AI workflow. Edit: You can try mine at membase.so for free. Love to hear any kind of feedback. submitted by /u/Time-Dot-1808 [link] [comments]

Claude doesn't generate images?

New to Claude. It can't create images? submitted by /u/Individual-Sell-7022 [link] [comments]

Best AI tool for making compilations and edits?

I like making compilation and edit videos for my friends and I to watch, but I haven’t had enough time to sit down on premiere pro and go through hours of content and cut it down to an hour or less. I know there are tools like clip anything, but, as far as I know, it can only take clips from one video. I’m looking for a tool or workflow where I can basically prompt it to make me a compilation of the best tennis rallies of all time (for example), and it would take the time to go through hours and hours of footage and give me back a consolidated video. I’m assuming a tool like this doesn’t exist yet, at least not with all of these features in one, but is there a good workflow I could use? Right now I’m using ChatGPT to do deep research and find the moments and share the videos and timestamps so that I can clip them myself, but not only is it tedious, but it is also unreliable (it might miss a lot of moments, need very specific instructions, or completely hallucinate and give me fake clips or pretend to search through “every video” and get done in 5 minutes with 13 total examples of something that happens 10 times a video) submitted by /u/KaminariDenki24 [link] [comments]

I built a Chrome extension to navigate long ChatGPT conversations more easily

Hey everyone, I built a small Chrome/Chromium extension called ChronoChat to solve a problem I kept running into: long ChatGPT conversations becoming almost impossible to navigate. ChronoChat adds a sidebar to ChatGPT that turns the current conversation into a searchable map. You can: jump directly to specific messages filter by user or assistant turns search inside the current conversation use keyboard shortcuts for faster navigation export the full conversation as JSON, CSV, Markdown or PDF The extension runs locally in the browser. There is no backend, no analytics, and no remote runtime assets. I made it because I often use ChatGPT for research, coding, planning, and long iterative work, and scrolling through huge threads gets painful fast. GitHub repo: https://github.com/sickn33/chronochat Would love feedback, especially from people who use ChatGPT for long workflows. What would make this more useful for you? submitted by /u/Fickle_Guitar7417 [link] [comments]

Can't uninstall Codex app on Android

Hey everyone! I'm not sure if this is the right subreddit to post this in but I just noticed a new app on my phone called "Codex". I have ChatGPT installed but I don't use Codex. It doesn't let me just uninstall Codex unless I install ChatGPT as well and I haven't been able to find any settings in the ChatGPT app to remove the Codex app. Any suggestions here? It's not the end of the world if I can't remove it but it irks me not being able to uninstall an app I don't use. Non-rooted Pixel phone if that helps. Thanks! submitted by /u/TheDiligentOccasion [link] [comments]

Thinking about getting yearly membership

Hello guys, I am a professional in the airline industry. I need AI for everyday tasks and searches. I am not a heavy coder or image/video creator. I have been using both Claude and ChatGPT for the past few weeks and I seem to like Claude better. Do you guys think getting a yearly subscription makes sense for my case? Please weigh in. submitted by /u/Massive-Guidance5342 [link] [comments]

OpenAI Unethical Billing Practices

I had a $100 budget/month set on my OpenAI API organization. Despite that, OpenAI billed me almost $200. I had signed up for ChatGPT Pro and tried using the Codex App, but it was painfully slow/causing my computer to crash, so I switched to the Codex CLI. I did not realize it was still reading from my API key and not signed into ChatGPT, and I incurred almost $200 in API bills. I contacted OpenAI support and they refused to offer me any sort of refund or credit, even after reaching a human and multiple attempts. This seems really unethical: OpenAI provides no way to stop runaway API billing, and they refuse to refund customers who exceed their defined budget. The "budget" system does not actually stop spending, so it's entirely pointless. After searching extensively through the OpenAI API platform and documentation I see no way to limit your API spending. This is on top of me contacting them asking for a refund of ChatGPT Pro subscription a couple months ago after we had a newborn and I was unable to use it for that month. I forgot to cancel the auto-renew, but contacted them the same day of the renewal. They absolutely refused to give me any sort of refund. I've never had an organization refuse to refund subscriptions before when it was accidentally renewed. So I'm out $400 now, thanks openai. submitted by /u/Direct-Row9073 [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]

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]

Should pay for Claude?

Should I pay for Claude? I’ll be honest, I only use ChatGPT for basic things like asking it for queries to do Google searches with Google dorks. But lately Instagram keeps showing me all the “incredible” things Claude can do. Automations, dashboards, code, images, agents… and I can honestly say I’m getting FOMO. So I want to know if it would actually be useful for me. Right now I’m not running a business yet, but I am interested in learning how to build automations and help businesses automate their processes. On top of that, I want to eventually open a call center and a marketing agency. So I want to know if it’s worth it. What do you guys do with it? How has it helped you? What have you been able to do differently because of it? Please give me some clarity. I was thinking about paying for the Pro plan for now. And honestly, if I do pay for it, it’s because I want to learn how to truly take advantage of Claude and eventually make money with it. submitted by /u/blackmonarc [link] [comments]

memv ships an MCP server — OSS memory layer for agents, now usable from any MCP client

memv (OSS, Python) gained an MCP server today. If you're building on Claude Desktop / Code / Cursor — or your own MCP host — you get persistent, structured memory without writing integration code. bash pip install "memvee[mcp]" memv-mcp --db-url memory.db --llm-model openai:gpt-4o-mini Or mount it inside your own process: ```python from memv.mcp.server import create_server server = create_server( db_url="memory.db", default_user_id="alice", embedding_client=my_embedder, llm_client=my_llm, ) server.run(transport="streamable-http") ``` Surface: - 5 MCP tools: search_memory, add_memory, add_conversation, list_memories, delete_memory - LLM optional — retrieval/add work LLM-free; only add_conversation extraction needs one - Per-user isolation at every tool boundary, including delete_memory ownership check - Concurrent extractions for the same user coalesce onto one task For context if you haven't seen memv before: predict-calibrate extraction (Nemori-inspired) so we don't store everything, bi-temporal model so contradictions expire instead of overwriting, hybrid retrieval (vector + BM25 + RRF). Docs: https://vstorm-co.github.io/memv/advanced/mcp-server/ GitHub: https://github.com/vstorm-co/memv submitted by /u/brgsk [link] [comments]

ChatGPT only lets you delete chats one at a time!! So I built a bulk delete dashboard!!

About a year ago I tried to clean up my ChatGPT chat list. I had something like 800 conversations, two years deep, mostly auto-titled "Untitled chat" garbage that I couldn't tell apart without opening. I sat down to delete the dead ones. Click chat. Click three-dot menu. Click Delete. Confirm. Click the next chat. Same thing. Repeat. After an hour I had deleted maybe 40 chats. Forty!! Out of 800!! That's the rate of clearing a 2-year history in something like three full workdays of just sitting there clicking confirm. I looked for a native bulk option. There isn't one inside ChatGPT itself. The closest is "Delete all chats" in Settings > Data Controls, which is the nuclear all-or-nothing button. There's no "delete the oldest 300" or "archive everything from before March". That's the entire native API. This seemed insane to me given how trivial "Select All plus Delete" is in literally every other product I've used since 2008! So I built the missing piece. What I built It's a Manage Chats modal inside a Chrome extension I ship called ChatGPT Toolbox (also runs on Edge, Brave, Opera, Arc). The modal lists every conversation in your account with checkboxes. Tick what you want gone, click Delete or Archive, and it runs through them in batches of 10 with a progress bar. ChatGPT Toolbox Manage Chats Feature A few details that came out of dogfooding it: Color-coded age badges on every chat. Green for the last week, blue for the last month, amber for the last 6 months, red for older than 6 months. The first thing I realized was that picking what to delete was the hard part, not the deletion itself, and age was the strongest signal for "I will never look at this again". Active vs Archived tabs. Archive ended up getting more use than Delete in my own usage, because I was rarely 100% sure I wouldn't want a chat back. So I made archive a first-class action, not a second-tier option. Live progress bar ("Deleting 23/50") on bulk operations. I tried it without and kept refreshing the page mid-operation thinking it was stuck. Adding the indicator stopped that completely. Search by title to filter the list before you start ticking. Surprisingly useful even on the auto-generated nonsense titles because there's usually some keyword in there. Bulk export to text, markdown, JSON, or PDF. Less critical for cleanup itself, but a few testers asked for it so they could save a chat outside ChatGPT before deleting it. I went from 800 chats to about 60 in 5 minutes using it. Most of those 5 minutes was deciding what to keep, not the deleting itself. How does the workflow look? Open the modal. List loads sorted by recency. Search to narrow it down if you want. Tick checkboxes. Hit Delete or Archive. Confirm. Progress bar runs through them. Done! If you've cleaned up a big ChatGPT history (with or without my tool, or with some clever workflow I haven't seen), would genuinely love to compare approaches in the comments. submitted by /u/Ok_Negotiation_2587 [link] [comments]

Free MCP server that audits pages for AI-citation eligibility (13 tools, no API keys)

I've been thinking about a gap in the MCP ecosystem: there are tools for web search, document reading, and code execution, but nothing that audits a page for the signals AI assistants actually use when deciding what to cite. So I built one. The AI-SEO MCP gives Claude (and any other MCP-compatible agent) 13 tools to audit, score, and rewrite pages for AI-citation eligibility. The things it checks are the ones that matter specifically for AI search - not classic SEO factors: - FAQPage JSON-LD schema (structured answers are what AI assistants extract) - robots.txt posture per AI crawler - GPTBot, OAI-SearchBot, PerplexityBot, ClaudeBot, and 7 more - llms.txt presence and spec compliance - Citation worthiness score broken down by engine (Perplexity, ChatGPT, Google AI Overviews, Claude) - Entity density and sameAs link coverage - Two rewrite tools (rewrite_for_aeo and rewrite_for_geo) that use MCP sampling to have Claude actually do the rewrite under a structured rubric Install is one npx line: ``` npx -y u/automatelab/ai-seo-mcp ``` Then add the usual config block to claude_desktop_config.json. No API keys. No registration. MIT license. It fetches public URLs directly and respects robots.txt by default. One thing I found useful while building it: GPTBot and OAI-SearchBot are separately controllable in robots.txt, but most sites either block both or allow both. The MCP surfaces this - you can block GPTBot (training) while explicitly allowing OAI-SearchBot (ChatGPT search retrieval). That distinction alone has been worth adding to the audit for a few sites I've tested it on. Happy to answer questions about the implementation or what the audit output looks like in practice. Repo: https://github.com/AutomateLab-tech/ai-seo Landing: https://automatelab.tech/products/mcp/ai-seo/ submitted by /u/exto13 [link] [comments]