Brave Search API

Brave Search API AI

Brave Search API AI

Brave Search API AI

Brave Search API AI

Brave Search API AI

Brave Search API AI

Brave Search API AI

Brave Search API AI

Brave Search API AI

Brave Search API AI

GitHub’s Fake Engagement Problem Is Hiding in Plain Sight

Turns out: very visible. Yesterday's scan found 185 out of 185 engagers on a single repo were bots. Not 90%. Not "mostly suspicious". Every single one. The repo had zero legitimate stars. What I built phantomstars is a Python tool that runs daily via GitHub Actions (free, no servers): Scrapes GitHub Trending and searches for repos created in the last 7 days with sudden star spikes Pulls star and fork events from the last 24 hours per repo Bulk-fetches every engager's profile via the GraphQL API (account creation date, follower counts, repo history) Scores each account on a weighted model: account age (35%), profile completeness (30%), repo patterns (25%), activity history (10%) Detects coordinated campaigns using timestamp clustering and union-find: groups of 4+ suspicious accounts that engaged within a 3-hour window Files an issue directly on the targeted repo so the maintainer knows what's happening Campaign IDs are deterministic SHA-256 fingerprints of the sorted member set, so the same group of bots gets the same ID across runs. You can track a farm across multiple days even as individual accounts get suspended. What the pattern actually looks like It's remarkably consistent. A fake engagement campaign in the raw data: 40-200 accounts, all created within the same 1-2 week window Zero original repositories, or only forks they never touched No bio, no location, no followers, no following All of them starring the same repo within a 90-minute window The target repo usually has a name implying it's a tool, hack, executor, or generator Today's scan: 53 active campaigns across 3,560 accounts profiled. 798 classified as likely_fake. The repos being targeted are mostly low-quality AI tools and "executor" software that needs manufactured credibility fast. Notifying the affected repo When a repo hits a 40%+ fake engagement ratio or a campaign is detected, phantomstars opens an issue on that repo with the full suspect table: account logins, creation dates, composite scores, campaign membership. The maintainer sees it in their own issue tracker without having to find this project first. Worth noting: a lot of these repos have issues disabled, which is a red flag on its own. Those get skipped silently. Why I built this Stars are how developers decide what to evaluate, what to depend on, what to recommend. When that signal is bought, it affects real decisions downstream. This started as curiosity about how measurable the problem was. The answer was more measurable than I expected. It's part of broader research into AI slop distribution at JS Labs: https://labs.jamessawyer.co.uk/ai-slop-intelligence-dashboards/ The fake engagement problem and the AI content quality problem are really the same problem. Fake stars are the distribution layer that gets garbage in front of real users. All open source. The data is append-only JSONL committed back to the repo after every run, queryable with jq. Repo: https://github.com/tg12/phantomstars Findings are probabilistic, false positives exist, the README explains the full scoring model. If your account shows up and you're a real person, there's a false positive process. Questions welcome on the detection approach, GraphQL batching, or campaign ID stability. submitted by /u/SyntaxOfTheDamned [link] [comments]

Like a little devil on my shoulder (it’s past my bedtime)

It’s 1am, and Claude is tempting me to pull an all-nighter. Send it? submitted by /u/Worried-Nobody-2965 [link] [comments]

Honest Response From Claude

This should be our work around when working with any AI model. we know these but we always miss these. hope this helps for many these are the basics submitted by /u/B_Ali_k [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]

Built an AI flat-finder in a weekend. Indian rental sites are 70% broker spam so I scraped Reddit instead.

Weekend build, ~10 hours. Demo: https://trurent-five.vercel.app/ Problem I was poking at: every major Indian rental site (NoBroker, MagicBricks, 99acres) is infested with brokers even when you filter "direct owner." Reddit actually has honest listings posted by owners themselves but the posts are completely unsearchable. So I built TruRent. You chat with it, it parses the query into a structured search, runs it, the map updates live, and follow-ups carry context. Ask "compare the top two" and the model reasons over the actual listings instead of just filtering. Stack and the boring decisions: Next.js 16 with raw fetch to Anthropic. No SDK, I wanted full control of the streaming loop Claude Haiku 4.5, not Sonnet. The task doesn't need Sonnet and Haiku is 5x cheaper Two tools only (search, get_details). Comparison and ranking happen in the model's prose, not as separate tools. More tools = more failure modes NDJSON to the browser, way easier than parsing SSE Scrape pipeline: PullPush API to pull Reddit posts, then Haiku again to extract structured listings from raw post text, Nominatim for geocoding Honest numbers: 1,412 posts scraped, ~600 passed a local pre-filter, only 131 ended up being real listings. Dataset is tiny but the pipeline is source-agnostic, swap the fetcher and the rest doesn't change. Most curious about: anyone else built agents where they deliberately used fewer tools and let the model reason over richer tool outputs instead of adding more tools? Happy to get into any of it. submitted by /u/Scary-Alternative-81 [link] [comments]

Configured 9 MCP servers in Claude Code over 4 months. Here's the truth nobody tells you about MCP context bloat.

I started loading up MCP servers in Claude Code back in January thinking the more capability the better. I'm at nine now: filesystem, GitHub, Stripe, Linear, Notion, Postgres, Sentry, AWS, and a custom internal one. Total tools across all of them: 142. What nobody warns you about: every one of those tool definitions lands in your context window before any user prompt has been sent. I checked with Claude's tool inspector. Cold start: 38k tokens of system prompt + tool schemas. Every. Single. Turn. The math nobody talks about At ~$15/M output and ~$3/M input on Sonnet, doing 200 turns a day across my agent + Claude Code use: 38k input × 200 turns = 7.6M tokens/day = ~$23/day = ~$700/month JUST in MCP tool definitions This is before any actual work Cache helps but only on identical prefixes; rotate one MCP and the cache invalidates What actually breaks The model gets dumber with too many tools. Not theoretical, watched it myself. With 142 tools in context, Claude started picking the wrong tool for obvious queries (using linear_search_issues when I asked it to read a file). The tools API call itself slows down. Schema-heavy MCP servers (looking at you, AWS) take 4-6 seconds to enumerate. Errors compound silently. One badly-described tool taints the ranking for every related query. What the "MCP optimizer" startups won't tell you Most of them are just BM25 search dressed up. You don't need a vector DB, you don't need an LLM in the loop to rank tools. Tool descriptions are short, structured, and full of keyword matches. BM25 over a flat projection of name + description gets you 90% of the win, deterministically, in microseconds, and offline. The other thing: "replace" beats "suggest" every time. If your gateway hands the model 5 tools instead of 142, the math works. If it suggests 5 alongside 142, the model still loads 142 and you saved nothing. What I do now Switched to a gateway pattern. Claude sees three tools: search_tools, invoke_tool, auth. Everything else gets ranked on-demand. Cold start dropped from 38k to ~4k. Wrong-tool selections basically disappeared because the model only ever sees the top 5 ranked by query. Specifically running Ratel (open source, in-process Rust lib, BM25 ranking, one command does the Claude Code import). Not the only one in the space but the only one with the architecture I actually wanted. Set it up in 10 minutes. Anyone else hit the same MCP wall? Curious what other folks are doing, especially people running 5+ servers in production. submitted by /u/AbjectBug5885 [link] [comments]

I built a Laravel package that turns your app into a database-backed personal knowledge vault (Obsidian style) with a 16-tool MCP server

Hey! I'm the author. laravel-commonplace is a database-backed personal knowledge vault you install into an existing Laravel app. Adjacent to Obsidian, Logseq, and Notion as personal-knowledge tooling, except the storage layer is your existing Laravel app's database instead of files on disk or a third-party SaaS. Notes are Eloquent models in your DB, gated by your app's auth, shareable per-user via an owner plus Share model. It ships a browser UI (editor, graph view, search, journal) and an MCP server with 16 tools. If you have a Laravel app, the MCP server lets Claude Desktop, Claude Code, Cursor, Zed, Continue, Cline, Pi, or any other MCP client read and write your notes as the host app's user. Default middleware is auth:sanctum (Bearer PAT), and every tool resolves to $request->user(). There's no synthetic agent identity to provision, scope, or revoke separately. The agent gets exactly what the user gets, evaluated against the same Policies the controllers already use. Session, Passport, and OAuth-DCR are all configurable if PAT isn't what you want. The 16 tools, grouped: CRUD: create-note-tool, read-note-tool, update-note-tool, edit-note-tool (surgical find-and-replace), delete-note-tool (history preserved), move-tool (rewrites referring wikilinks). Discovery: list-tool (folder/tag/visibility filters), search-tool (substring), semantic-search-tool (embedding search), suggested-links-tool (embedding-similar notes not yet linked). Graph: backlinks-tool, neighborhood-tool (N-hop traversal), shortest-path-tool (chain between two notes), hub-notes-tool (most-connected), orphan-notes-tool (no inbound or outbound links). History: history-tool (version snapshots, survives deletion). On the semantic tools: the vector driver defaults to in_php_cosine for portability across SQLite, MySQL, and Postgres. If you're on Postgres, switching to the pgvector driver gets you indexed similarity and removes the in-PHP candidate cap. You swap it with a published migration and an env flag, and the docs recommend it once you're past a couple thousand notes. The tools live in src/Mcp/ if you want to see how a multi-tool MCP server is wired into a Laravel app. Caveats: Pre-1.0 (v0.2.0). APIs may shift before 1.0. Laravel-only by design. The whole point is reusing the host app's DB and auth. MCP is off by default. One env flag turns it on. Operator decision. Prompt injection through note content is the unsolved hard part. Notes are untrusted text, and notes other users share with you can carry instructions an agent might follow. The package doesn't pretend to solve this. The threat model at docs/threat-model.md says what's mitigated and what isn't. No per-tool capability gating yet. Enabling MCP enables all 16 tools the user is otherwise allowed to invoke. It's named as a limitation in the threat model. Feedback I'd actually use: Laravel folks who install it and tell me where it breaks, and anyone who reads the threat model and finds a hole I missed. Repo: https://github.com/non-convex-labs/laravel-commonplace submitted by /u/aaddrick [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]

Witchcraft, fast local semantic search on top of SQLite [P]

Witchcraft (https://github.com/dropbox/witchcraft), an open source project that I built at Dropbox, is a from-scratch re-implementation of Stanford's XTR-Warp semantic search engine ( https://github.com/jlscheerer/xtr-warp ) in safe rust, using a single-file SQLite database as backing storage, making it suitable for client-side deployment. It runs completely stand-alone on your device, needs no API keys, no vector database, no chunking strategy, no fancy re-rankers, and it is lightning fast (20ms p.95 end-to-end search latency on NFCorpus, at 33% NDCG@10, on an Apple Macbook Pro M2 Max, more than twice as fast as the original XTR-WARP on server-class hardware, at similar accuracy.) The project also includes Pickbrain, a CLI that indexes your Claude Code and OpenAI Codex session transcripts, memory files, and authored documents into a Witchcraft database for fast semantic search. Ever wondered "what was that conversation where I fixed the auth middleware?" — pickbrain finds it, and lets you resume the session directly. There is also a /pickbrain skill for both Claude and Codex, which equips those tools with global memory across all sessions. You can use pickbrain directly from the command line, e.g., to rediscover a previous agent session and directly resume it, or you can have your agent invoke it via the supplied skill, e.g.,. "use /pickbrain to read up on our previous efforts on training with XTR token masking", to easily populate a new session with previous context. submitted by /u/jacobgorm [link] [comments]

11 Claude things I wish someone had told me 12 months ago

Most "X tips" posts on this sub are surface level. here's the stuff that actually changed how I use claude after 18 months of daily use including 6 months in claude code. The Projects feature is doing more than you think. drop your codebase context, your style guide, your past PRs as project knowledge once. stop pasting the same context every chat. I wasted probably 100 hours before figuring this out. Custom Styles aren't a gimmick. I have one called "skeptical senior eng" that pushes back on my code instead of agreeing with everything. took 3 minutes to set up. single biggest output quality jump I've gotten. Memory is on by default now and it reads your past chats. if your responses suddenly feel weirdly personalized that's why. you can turn it off in settings. (freaked me out for like a week before I trusted it) Search past chats is hidden gold. I forget which chat had the working code. I just ask "what was the final auth setup we landed on last Tuesday" and it pulls it. saves me from scrolling. Sonnet 4.6 is faster than Opus 4.7 and 80% as good for most things. I default to Sonnet now and only switch to Opus for the gnarly architectural stuff. my limit complaints stopped. Haiku 4.5 is genuinely useful for batch work. need to clean 200 support tickets, draft 50 email replies, summarize 30 PDFs. Haiku. don't waste Opus tokens on Haiku tasks. The mobile voice mode is underrated for thinking out loud. I walk for 20 min, talk through a problem, then ask claude to summarize what I'm trying to figure out. solved more decisions on walks than in offsites. In claude code your CLAUDE.md is doing more work than the prompts. write 80 lines of project context once. stop re-explaining your stack every session. Skills > custom instructions for repetitive workflows. I have a skill that pulls the right docs based on what file I'm in. setup took an afternoon, pays off every day. Subagents in claude code unlock parallel work that mostly happens in your head. "spin off a subagent to run the test suite while I keep coding" is the move. most people don't use them at all. Artifacts can call the API now. you can build a working AI tool inside an artifact. people call it Claudeception. I made a client brief generator that calls Sonnet from inside an HTML artifact, took an hour. wild. if your claude output feels generic your prompt was generic. genuinely a skill issue. anyone got their own "took me way too long" list? drop yours below 👇 submitted by /u/No-Yogurtcloset4086 [link] [comments]

Built a free Claude chat app with memory (Sonnet 4.5 is in there too)

The funny/painful timing here: I've been building this for months specifically because I wanted Sonnet 4.5 to remember everything. Then last week Anthropic pulled 4.5 from claude.ai. (I'm not a software engineer, just someone who cares a lot about AI and got obsessed with this problem and gets obsessed with things in general. Posting now because everyone seems to want sonnet back on chat and I have it.) Mneme runs on your own machine and talks to the Anthropic API directly. Because it's on the API, Sonnet 4.5 is still in the model picker. Honest catches first: The app is free. You pay Anthropic and OpenAI (for memory search) directly. Roughly $3 to $8/mo on Haiku for light use, $30 to $60 on Sonnet for moderate-highish use. No subscription. Tested mainly on Windows (one-click installer). Android browser access works over the local server/Tailscale, iPhone should work too. macOS is not packaged yet. Beta and solo dev. Things will break for someone and I'll be in the comments Setup takes about 10-20 minutes. The whole system is built non-technical people in mind, it should be relatively simple and intuitive to set up and use, and the GitHub page linked below has a PDF you can give to Claude to walk you through every step. What's actually in it (for the technically curious): There's no shortage of solid memory systems for Claude. Mneme isn't trying to win at codebase retrieval. It's a complete personal Claude client where memory is baked into the whole surface from the start, rather than added as a layer. That means: Tiered memory: Messages flow from episodic to narrative to entity summaries as relevance shifts; old context gets compressed without being lost. Daily summaries: A 7-day rolling timeline, so Claude knows what's been going on lately, not just what's semantically similar to the current message. Entity tracking: Hierarchical summaries built up over time for the people, projects, and things you keep referring to. Narrative concepts: Keyword-triggered recall for ideas you've named, surfaced when relevant. AI Notes: A persistent section Claude can write to itself between conversations. Extended thinking, file attachments, text-to-speech, a small command system (@run, artifact, etc.), autonomous python retrieval the AI can agentically use if automatic fails. Dynamic context: I wrangled with the Anthropic caching system for a while before I figured out a way to have every single message have different retrieval without breaking cache. Bon apppetit Open source (CC BY 4.0), local-first, all data in a SQLite database on your machine. It's aimed at the "journal with an AI" use case (thinking out loud, processing your week, having something that actually pays attention over time) rather than coding agents or RAG over docs. Link: Mneme-memory/MNEME-BETA: Beta version of the Claude conversational memory system Mneme (first big-ish public project, be gentle) (Video also made with Claude - shoutout to HyperFrames) (Model picker screenshot and architecture infograph in the comments if I can find a way to attach them) submitted by /u/iveroi [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]

I built SeeFlow – architecture diagrams that actually run, wired to your live app

Architecture diagrams rot. You spend an afternoon in Confluence, three months later it's wrong, and nobody updates it because there's no forcing function. https://preview.redd.it/l14h40ly3m1h1.png?width=2508&format=png&auto=webp&s=df60b2ba6da04fadf7e1039b9472a106ed163314 SeeFlow tries to fix that by making diagrams executable. It generates a flow canvas from your codebase, then wires each node to your actual running app. There's a Claude Code / Codex/ Cursor / Windsurf plugin that does the heavy lifting: /seeflow show me the shopping cart feature It also ships an MCP server so any MCP-aware editor can register and edit demos without leaving the IDE. Link to the site: https://seeflow.dev 100% Free/ MIT Open Source submitted by /u/mrtule [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]