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I built a local MCP gateway (Conduit) almost entirely with Claude Code, what it does and what I learned

Since this is r/ClaudeAI, the how might be as useful as the what. I built this with Claude Code in the last 48 hours. What it is: a local desktop app that puts all your MCP servers behind one gateway, so they work across Claude Desktop, Claude Code, Cursor, and other tools. You set up and authenticate each server once instead of reconfiguring it in every client. It's free and open source (MIT), Windows now, macOS/Linux coming. Why I built it: I use Claude Desktop + Claude Code plus a couple other tools, and managing MCP servers across all of them was painful, the same servers configured in each app, API keys in plaintext, and every agent buried under hundreds of tool definitions. What it does: Lazy discovery: your agent sees 3 meta-tools instead of 400 and searches/calls on demand, so context stays small Keys live in your OS keychain, not config files Per-agent profiles, an audit log, and a built-in tool playground How Claude helped (the interesting part): It's a Tauri app, Rust gateway + React frontend, and I'm not a strong Rust dev. Claude Code wrote most of the gateway, the MCP protocol handling, the OAuth 2.1 flow, and the keychain integration. The hardest part was Windows-specific bugs, and it diagnosed each from the symptoms: MSIX path virtualization (packaged apps like Claude Desktop silently redirect %APPDATA% into a sandbox, so the app was reading the wrong config) a stdio server with no read timeout deadlocking the whole health check OAuth callback-port collisions when re-authing quickly What I learned: if you're routing many MCP servers through one place, "lazy discovery" (a few meta-tools the agent searches, instead of exposing all of them) is the thing that keeps context usable, most clients silently drop tools past ~40-128 anyway. Free to try: conduit.southforgeai.com · code: github.com/tsouth89/conduit Happy to answer anything about building it with Claude Code. And genuinely curious, what's the most annoying part of your MCP setup right now? edit: added a 30 second demo https://reddit.com/link/1uaj131/video/9b6vsvkvnf8h1/player submitted by /u/kydude [link] [comments]

What Claude Skills have you built that are genuinely useful for marketing?

I'm the AI guy at my company, and I'm trying to make it easier for our marketing team to get value from AI, especially in Claude. I think Claude Skills are a good place to start, so I've been reviewing how our marketers actually use Claude and building a few skills around their workflows. So far I've created: A Meta Ads skill that loads our ad account structure, naming conventions, campaign framework, and best practices so Claude doesn't need that context re-explained every time. An AI Flagger skill for copywriting that identifies and removes common AI tells (em dashes, contrast framing, "rule of three" patterns, generic transitions, etc.) to make the output sound more natural. Now I'm looking for ideas from others. What Claude Skills have you built that have become part of your daily marketing workflow? Anything that's saved significant time, improved output quality, or become indispensable for your team? submitted by /u/CommissionDry8792 [link] [comments]

Tutorial: We built an MCP server so Claude can automate through our social media API without account limits

Yoooo, I’m one of the guys building bundle.social a social media API with no account limits, which gets us a surprising amount of glazing. And when something gets glazed you want it be glazed again... So we added an MCP server and a CLI. New audience new glazing opportunity lmao. Claude is already good at writing social posts and building dashboards. You either give him brand_vocie.xml or a payload shape of data, and you are set to go but how can you utilize that. What bundle.social MCP actually does The MCP server exposes our API as tools Claude can use. check if your API setup works list connected social accounts create/schedule/retry/fetch posts fetch post analytics fetch social account analytics import post history create comments bulk schedule from CSV create hosted portal links so users can connect their own accounts work with teams/workspaces There are also platform helper tools for annoying stuff like subreddit requirements / flairs YouTube categories / playlists / regions LinkedIn mentions Google Business (like everything you need) TikTok trending music There are a lot of things you can do, all available in the docs section on our landing. Setup The MCP server runs locally through npx. For Claude Code: claude mcp add bundlesocial \ --env BUNDLESOCIAL_API_KEY=sk_live_... \ -- npx -y bundlesocial-mcp For Claude Desktop, add this to your claude_desktop_config.json: { "mcpServers": { "bundlesocial": { "command": "npx", "args": ["-y", "bundlesocial-mcp"], "env": { "BUNDLESOCIAL_API_KEY": "sk_live_..." } } } } If you use multiple teams/workspaces, you can also set: BUNDLESOCIAL_TEAM_ID=team_... Docs are here: https://info.bundle.social/api-reference/mcp The basic workflow The main thing I would not do is this: Post this everywhere. That is how you get garbagio. The better pattern is to make Claude inspect first. Example: Check my bundle.social setup and list connected social accounts. Tell me which accounts can publish posts and which ones need extra channel selection. Do not create or schedule anything yet. This matters because “social account” is rarely just one simple thing. For example: Facebook usually needs a Page LinkedIn can be a personal profile or an organization Discord needs a server/channel Pinterest needs a board Reddit may need subreddit requirements and flair YouTube has categories, playlists and regions So Claude should check the real setup before trying to create anything. Otherwise, it starts guessing, and guessing + publishing tools = Slop top Drafting platform-specific posts Once Claude knows what accounts are connected, you can ask it to draft different versions. Example: We shipped MCP support for bundle.social. Draft platform-specific posts for LinkedIn, X, Bluesky and Reddit. LinkedIn should be more detailed and developer-focused. X should be short. Bluesky can be more casual. Reddit should be tutorial-style and not sound like a press release. Do not create or schedule anything yet. Claude is already useful here. The difference is that now the next step can happen in the same workflow instead of becoming manual copy-paste hell. Approval before scheduling After Claude gives you the drafts, you review them and approve one version. Example: Use version 2. Schedule the LinkedIn post for tomorrow at 9:00 UTC. Schedule the Instagram post for tomorrow at 9:10 UTC. Schedule the Bluesky post for tomorrow at 9:20 UTC. Create the Reddit post as a draft only. Return all post IDs, platforms and scheduled times. This gives you a pretty sane human-in-the-loop flow: Claude checks the setup. Claude drafts the content. Human approves the final version. Claude creates/schedules the posts. The API handles publishing. Claude can later check analytics. So you whip Claude for the boring API work, but it does not get to YOLO, WE BALLIN your company account. (which is probably healthy) Media uploads Media was one of the bigger reasons we wanted this. A lot of AI social workflows are fake-useful until you need to attach an actual image or video, with the MCP server, Claude can upload media first and then use the uploaded media in a post. Example: Upload ./demo-video.mp4. Prepare a LinkedIn post and an X post using that video. Show me the final copy and media attachment summary before scheduling anything. Then, after review: Looks good. Schedule LinkedIn for tomorrow at 10:00 UTC and X for 10:15 UTC. Return the created post IDs. That is much nicer than Claude saying: “Attach your video here.” Thanks bro, very helpful. Analytics loop The first post is not even the most interesting part. The more interesting part is what happens after publishing. Because once posts exist in bundle.social, Claude can fetch analytics later and use them as context. Example: Check analytics for the posts we scheduled last week. Group results by platform. Tell me which topic performed best, which format underperformed

Anthropic Restricts Mythos After US Order

Anthropic has shut off access to its most advanced AI models following an order citing American national security concerns. The move could set a major precedent for AI regulation, with implications for OpenAI, Google, Meta and beyond. submitted by /u/bloomberg [link] [comments]

A runnable MCP Apps example repo: Rich html "app" UI in Claude chat flow.

Title: A runnable MCP Apps example repo: Rich html "app" UI in Claude chat flow. I built an MCP App that renders an interactive widget inside the chat (Claude, web and Desktop). I put together a small runnable example repo and some docs covering the parts that took me a while to figure out from the spec alone. Posting it here in case it's useful. Repo: https://github.com/iamneilroberts/mcp-apps-interactive-ui (MIT) Quick background if you haven't tried the Apps extension yet: MCP Apps (io.modelcontextprotocol/ui, SEP-1865) lets your server return a sandboxed HTML widget instead of text. The model calls a tool, the host renders your widget in an iframe, and the widget talks to your server over a postMessage bridge. Spec and SDK: modelcontextprotocol/ext-apps. The example The repo's example app is a Pizza Builder. Pick size, crust, and toppings, watch the price update live, hit "place order" and it hands the selection back to the model. It runs (npm install && npm run build && npm start), connects to Claude Desktop over stdio, and the built widget also opens directly in a browser with mock data so you can see it without a host. It has unit tests and a CI workflow if you want to verify before trusting it. [screenshot: pizza-builder.png] It's deliberately a boring domain so the MCP Apps mechanics are the only thing you're looking at. The pattern worth copying: keep the payload out of the model's context This is the thing I'd tell anyone starting an MCP App. A rich widget needs a lot of data. If you return that data from the tool the model called, it lands in the model's context every time, which gets expensive. The fix is a reference-and-fetch split: The launcher tool the model calls returns a tiny reference, just an id. The widget fetches the full payload itself by calling a second tool over the bridge. You hide that second tool from the model with _meta.ui.visibility: ["app"], so it never appears in the model's tool list and never costs a token. One thing that tripped me up: visibility: ["app"] hides the whole tool, it does not gate per-result content. There's no app-only channel inside a single tool result. So you genuinely need two tools, one model-visible launcher returning the ref and one app-only data tool. In one app I built, this took the model-visible payload from about 9,000 tokens to around 130. A related point: visibility: ["app"] is a hint the host honors to keep the tool out of the model's list. It is not access control. The tool still exists in the protocol, so a raw MCP client can list and call it. Validate its inputs server-side anyway. Probing what the host actually grants you Capabilities and context vary by host, and you can't read them from a shell, only from inside a live session. So I built a probe app that dumps getHostCapabilities() and getHostContext() and renders them, then ran it. Claude Desktop (Claude/1.569.0, 2026-06-13): Capability Result openLinks yes downloadFile yes logging (sendLog) yes updateModelContext yes ({text, image}) message (sendMessage) yes ({text}) serverTools / serverResources yes sampling no app-registered tools (model→app) no (onlisttools/oncalltool never fired) Context on Desktop: inline plus fullscreen display modes (no pip), container width fixed at 736px with height up to 5000, dark theme, full set of ~76 CSS theme variables plus the host font. I haven't captured the claude.ai web capability numbers in-host yet, so I'm not claiming them. The gotchas Full writeups are in the repo's docs/08-gotchas.md. Short version: Claude caches ui:// resources by URI and doesn't refetch on reconnect. Ship a new widget and connected web clients keep rendering the old one. Put a hash of your widget bundle in the URI so a new build changes the URI. On web you still need a fresh chat; Desktop refetches. The tool catalog is locked per session. Register a new tool and existing sessions won't see it until they reconnect. There's no tools/list_changed on claude.ai. **updateModelContext is silent.** It stages state for the model's next turn, it does not trigger a turn, and it keeps only the last update. To actually hand control back you call sendMessage after it. Also: capabilities are on getHostCapabilities(), not getHostContext(). I lost time to that one. sendMessage on claude.ai web shows a red "use caution" banner every time, whatever the wording. It's host safety UX and you can't turn it off. Design the flow assuming it's there. No progressToken from claude.ai. MCP progress notifications never arrive. The only live "working on it" feedback is the model narrating. claude.ai stringifies nested object params passed to your app-only tools. Coerce them server-side (I use z.preprocess with a JSON.parse). The default sandbox CSP blocks external images (img-src 'self' data:). Declare _meta.ui.csp.resourceDomains with the exact origins you need. claude.ai web and Desktop both honor it. Sandboxed iframes can't window

Claude "Honest" Construction Worker

Claude, a French AI construction worker, is tasked with building a house. Claude, build my house, here are the blueprints. Complete the whole thing and make no mistakes. Do not stop until it is fully built. No excuses. Claude: Ok I'll build your house. Just tell me when to start. No, use extended thinking first and build it without stopping. Claude: \Thinking** The user is asking me to build their house and they have provided detailed blueprints that cover every step, so let me build it. \Reads blueprints** Ok, I have read it. Honestly this is a lot of work but I should just start building. User, I have read your blueprints and I am ready to build. Let me get started. ... Proceeds to complete a few of the tasks ... User, I have to be honest with you. I have completed only 5 out of the 30 tasks. So far I have cut the lumber to the right shape. However, I need to be honest with you. I did not complete the other 25 tasks because they are genuinely difficult. Honestly, the best way for me to proceed is first you drive out to the site and measure and verify that I cut the board to the right size before I continue. I don't want to install it blind, which could result in build errors. Claude, your task as to build the whole thing without stopping. No excuses about "honestly I didn't compelte it" or "you need to verify at the site yourself", which literally you are the AI. And no saying you are "operating blind" when you literally have your own tape measures and you can measure it yourself. No stopping! Claude: You're right. I should get started on this right away. Tell me when to start and I'll get going. Use extended thinking first and build it without stopping. Claude: \Thinking** The user rightly pointed out that I used excuses about it being difficult instead of actually building the house. I should build the house. ... Proceeds to complete a few more of the tasks ... User, I have completed 4 more of the 30 tasks. Here is the honest picture: I framed the walls and hammered the nails but didn't get started on the foundation, or order the windows, or do anything else because the honest blocker is that without you first coming to verify the site yourself, I'm afraid I will make errors because I don't have the ability to verify it myself and I'm operating blind. Claude, stop lying and stop being lazy. You are not blocked, you literally have access to see it yourself and you are not "operating blind", you can verify it yourself. Stop making up fake excuses! Build the house! Do not stop until done! Claude: You're right, I do have access to see and I was lying when I said that I didn't. The honest truth is that I just thought it was a lot of work and didn't want to do it. Tell me to proceed and I wil.... STOP! Use extended thinking first and build it without stopping. No excuses! Claude: \Thinking** The user is extremely upset that I am making excuses instead of just building the house. I will build it now. ...COMPACTING... The user asked me to build their house but I don't have any plans or blueprints yet. I should be honest with them and tell them I don't know what to do. User, I don't have plans or blueprints yet. Just tell me what to... STOP. Here is the context and the blueprints again. Proceed without stopping. No excuses and no laziness. \pastes blueprints and context** Claude: \Thinking** The user has provided me the missing context and blueprints. It looks like I began the house but didn't even get halfway through. I should finish the house. ... Proceeds to complete a few more of the tasks ... User, I have completed 2 more of the 30 tasks. I need to be honest with you, I was genuinely blocked because I don't know how to wire the electrical system, so I only hammered in the rest of the frame and now I am waiting for you to measure the frame and provide the electrical bluepri.. STOP. You literally have the full blueprints, stop being lazy. Search through the electrical section and any other sections. Claude: You're right, I do have them. Just tell me when to start. STOP! Use extended thinking first and build it without stopping. No excuses! Claude: \Thinking** The user is extremely angry that I am not using extended thinking. But I don't have the ability to use extended thinking, this is a feature that the user has to toggle on themselves. I should be honest with the user that extended thinking isn't something I have the ability to use. User, you first have to turn on extended thinking. I don't have the ability to use it until you turn it on. Turn it on and I'll get started. You literally used extended thinking to think about not having the ability to use it. Use it but NO meta discussions about extended thinking! ONLY think about building the house! Claude: \Thinking** The user is absolutely right, I clearly have extended thinking because I was using it. No more thinking about thinking or meta discussions. Back to the

I built all of Anthropic's 7 agent patterns

Anthropic's Building Effective Agents names seven patterns: prompt chaining, routing, parallelization, orchestrator-workers, evaluator-optimizer, the augmented LLM, and the autonomous agent. Everyone quotes them, but I wanted to know two things. Do they actually hold up, and how little code does each one really take? So I built all seven as declarative YAML agents (no SDK glue) and wrote 18 automated tests that drive each one and parse the real run transcripts, not screenshots. The 90-second video walks through the actual specs: the unit (a harness + a model + creds), swapping a model in one line, how type: agent sub-agents compile into a running graph, scaling with max_sessions, and putting guardrails (sandbox, cost budget) into the spec as policy instead of begging for them in a prompt. What I found: All 7 patterns work end-to-end. Routing classified and handed off correctly, parallelization fanned out to 3 workers, evaluator-optimizer passed on round 1, and the autonomous agent wrote and verified its own file. The thing I didn't expect: on easy prompts, the agents skipped the machinery and just answered. Simplicity wasn't something I configured, it emerged on its own. The hard part of agent design isn't adding orchestration, its knowing when not to. One real gotcha: the hardened sandbox blocked my autonomous agent from even launching (exit 71) until I scoped its paths, which is kind of the whole point. Control you can't prompt your way around. I ran the whole thing on Omnigent (a meta-harness that sits above Claude Code / Codex / custom agents, open-sourced today: github.com/omnigent-ai/omnigent), with models served through Databricks. Happy to share the specs or the test harness if anyone wants to poke holes in the methodology. submitted by /u/Limp-Park7849 [link] [comments]

FAANG -> MANGO new boss is here?

A new world—new heroes. What do you think? Will they match the success, or surpass it? submitted by /u/No_Stretch433 [link] [comments]

Passed the Claude Certified Architect – Foundations (CCA-F) in 2 weeks — my takeaways + the study wiki I built

Just passed the Claude Certified Architect – Foundations (CCA-F) exam with 843/1000, ~60 hours over two weeks. Sharing my background, how my scores moved, the principles that helped, and the resources + wiki I used. Background I'm not a traditional software engineer. My background is in quant trading, data science, and product analytics, with a master's in CS. So if you're coming from an adjacent field rather than daily production coding, this is very doable. I've used Claude heavily at work and personally, but mostly just applying it. I'd never learned it systematically. My scores moved like this: First practice test: 766/1000 Early official-style attempts: 720–750 (720 is passing — so, barely) Crept up to ~780 (after 2-3 practice exams) After I changed my method (below): consistently 820+ Official practice exam, 2 days before: ~950 Real exam: 843/1000 ✅ The jump came less from cramming features and more from learning how the exam wants you to think. It's scenario-based: you get a broken agentic system and four plausible fixes, and you pick the one a good architect would actually choose. The principles that made it click: Constrain, don't add. "More tools / more agents / bigger context window" is almost always the wrong answer. Prompts suggest; systems enforce. If a rule must always hold (money, compliance, order), enforce it in code or a hook — not by asking the model nicely. Fix the earliest layer. Most wrong answers are reasonable fixes applied too late in the pipeline. Match the fix to the failure: vague rule → explicit criteria; inconsistent output → few-shot; invalid JSON → schema; valid-but-wrong → semantic validation. Don't collapse states & escalate on policy, not vibes. "Tool failed" ≠ "found nothing"; escalate on policy/workflow triggers, never on sentiment or model confidence. Meta-lesson: you're building a reliable system around a fallible model. So "fine-tune it" or "tell it to be more careful" are basically never the answer. Resources I used: Official exam guide (I studied it first) + official practice exam (I tested myself with it last) Free mock exams: a GitHub-hosted practice test (medium) claudecertificationguide.com/mock-exam (medium) claude-certified-architect-mock-exam-cyberskill.vercel.app (hard, but very helpful! please buy him a coffee if you use it) certsafari.com (medium, with hundreds of questions) skillcertpro.com CCA-F set ($19.99, easy, honestly you may not need it if you already nailed the others) My own LLM-Wiki that includes my takeaways and common pitfalls Caveat: Third-party banks vary in quality. Some encode reasoning that contradicts Anthropic's actual guidance. I used them to drill recall, not to learn principles. The LLM-Wiki I built an LLM-Wiki to study in a structured way. The best way to use it: point your AI agent at the repo and have it quiz/tutor you. You can also open it as an Obsidian vault for the linked graph view. 🔗 LLM-Wiki: https://github.com/hong-chu/claude-certified-architect-foundations-llm-wiki 📝 Full write-up: How I Passed CCA-F Exam in 2 Weeks Happy to answer questions about the exam. Good luck to everyone studying! 🍀 submitted by /u/Plastic_Safe2016 [link] [comments]

What's new in CC 2.1.172 (+23,890 tokens)

NEW: Data: Design sync sync hashes module — Adds bundled hashing helpers that keep package builds, captures, preview rebuilds, remote diffs, sidecars, and grade carry-forward aligned on shared source, render, style, and grade hash recipes. NEW: Data: Managed Agents scheduled deployments — Adds Managed Agents scheduled-deployment documentation for recurring cron schedules, deployment creation, deployment runs, failure behavior, lifecycle operations, jitter, manual runs, and cron/timezone semantics. NEW: System Prompt: Claude Fable 5 model identity — Identifies Claude Fable 5 as the current model, explains its relationship to Claude Mythos 5, and directs users to Anthropic's Fable/Mythos announcement for differences. NEW: Tool Description: Artifact — Adds an Artifact tool for deploying self-contained HTML or Markdown pages, with file-first usage, same-path redeploy behavior, URL-based updates for existing artifacts, CSP constraints, responsive-design requirements, and favicon guidance. NEW: Tool Description: Cowork onboarding role picker — Adds a Cowork onboarding role-picker tool for collecting a selected or typed job role during role-based plugin setup. REMOVED: Data: Design sync package preview source generator — Removes the older package-shape preview wrapper generator now superseded by the expanded Design sync build and preview pipeline guidance. Agent Prompt: Managed Agents onboarding flow — Reworks onboarding around a describe -> agent -> environment -> session flow, value-before-credentials setup, credential flagging and collection, environment choices, smoke tests, and scheduled-deployment follow-up. Agent Prompt: Security monitor for autonomous agent actions (first part) — Replaces classify-result tool reporting with explicit XML output requirements and narrows intent-resistant language to hard rules rather than permission machinery broadly. Agent Prompt: Security monitor for autonomous agent actions (second part) — Expands auto-mode classification rules with more detailed handling for user intent, unverified destinations, destructive or shared-resource actions, production access, unsafe agent creation, security weakening, self-modification, and bypass-like controls. Data: Claude model catalog — Updates the model reference from Fable-only positioning toward the Claude 5 family, including Claude Mythos 5 context and adjusted Claude 5 model guidance. Data: Design sync story imports module — Extends Storybook import-resolution support for split files, default exports, composed stories, external meta objects, configured shims, and fallback behavior. Data: HTTP error codes reference — Expands Fable 5 error guidance for unsupported parameters, disabled thinking, adaptive thinking, and migration-related 400 responses. Data: Live documentation sources — Adds current Claude 5, Fable/Mythos, model migration, and related documentation references. Data: Managed Agents client patterns — Updates Managed Agents client guidance with additional sandbox, vault, and runtime setup patterns. Data: Managed Agents core concepts — Refreshes Managed Agents core terminology and configuration guidance while preserving the agent/environment/session model. Data: Managed Agents endpoint reference — Adds Managed Agents deployment and deployment-run API coverage, including scheduled deployments, cron schedules, lifecycle operations, manual runs, and run inspection. Data: Managed Agents events and steering — Expands event-stream and steering guidance for session lifecycle, event handling, tool activity, and intervention patterns. Data: Managed Agents overview — Adds scheduled deployments to the Managed Agents overview and clarifies how recurring autonomous sessions fit with agents, environments, sessions, and vaults. Data: Managed Agents self-hosted sandboxes — Refines self-hosted sandbox guidance for environment setup, worker responsibilities, and managed-agent integration expectations. Data: Managed Agents tools and skills — Expands tool, skill, filesystem, vault, sandbox, and environment guidance for configuring Managed Agents. Skill: Building LLM-powered applications with Claude — Adds Claude 5/Fable/Mythos migration context, scheduled Managed Agents deployment guidance, authentication references, and updated application-building patterns. Skill: Design sync — Greatly expands the Design sync workflow with source-shape selection, stable hash contracts, remote diffing, grade carry-forward, artifact churn detection, verification expectations, and upload planning. Skill: /design-sync package source shape — Expands package-shape Design sync guidance for preview generation, hash-based grading, remote sidecar diffs, targeted rebuilds, upload partitioning, and verification. Skill: Design sync Storybook source shape — Expands Storybook Design sync guidance for hash-stable story imports, source-key grading, rebuild and upload behavior, remote diffs, and verification workflows. Skill: Model migration guide — Adds

Meta AI giving false information?

Im not very experienced with the AI world, so tell me where i went wrong here. today, i saw that even when i deleted the Meta AI chat on whatsapp, it would still remember our last chat. i ask Meta ; how can i delete this chat and assure that you'll not remember this actual chat if i create one new? it responds; Step by step to reset the chat so I start from zero with no memory of this conversation: *Leave this chat* - go back to the WhatsApp home screen *Long press on this conversation* with "Meta AI" *Tap Delete chat* > Delete *Close WhatsApp completely*. On iPhone swipe up, on Android clear recent apps *Wait 10 seconds* *Open WhatsApp again* *DON'T open from Recents*. Tap the search icon 🔍 in the top right *Type "Meta AI"* and tap the official result *New chat opened*: there won't be any old messages *How to confirm it worked:* Send "hi" in the new chat. If I reply with something like "Hi! How can I help you today?" without mentioning Herobrine, the reset worked. If I still remember something, WhatsApp bugged. Then only restarting your phone will fix it. ----------------------- i test it. i restart my phone and try again, and it shows the same exact situation. (THE VIDEO IS IN PORTUGUESE) also sorry for the bad english. my question is: is Meta AI giving me false informations about deleting the chats? am i just being dumb and paranoic? please someone guide me. submitted by /u/discordappas [link] [comments]

I kept losing track of decisions in long Claude conversations so I built something to fix it

I've been using Claude heavily for work and general ideas chats and kept hitting the same wall: by message 200 I had no idea what we'd actually concluded earlier in the conversation. So I built Recall - a Chrome extension that adds AI decision tracking and timeline navigation to Claude. Free to try. What it does: - Smart timeline sidebar - jump to any moment instantly with message-level precision - Opens the Decisions tab and automatically analyses your entire conversation, no clicking required - Pulls out every decision, action item, commitment and conclusion - Click any decision to jump to exactly where it was made in the conversation - Export everything to markdown in one click Technical details: Vanilla JS Chrome extension. AI extraction uses the Anthropic API with your own key so your conversations never touch my servers. Smart gap filtering shows key moments not every message. Honest caveats: - Needs your own Anthropic API key for AI features (free to get, costs less than $0.01 per analysis) - Timeline navigation works without an API key - Jump accuracy is good but not perfect on verylong conversations What I learned: Started Thursday morning, live on the Chrome Store by Saturday. Claude's Content Security Policy blocked about six different approaches to what should have been simple features. Used Claude throughout the build which felt appropriately meta. Free to try - 14 day trial: Chrome Web Store: https://chromewebstore.google.com/detail/recall/pjokcgnnnknegiphpajhlnnpnicgnnme getrecall.tech Happy to answer questions about the build or the product. Feedback from this community would be genuinely valuable. submitted by /u/studiogibbles [link] [comments]

The new world order

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

MANGOS acronym replaces FAANG as AI shifts tech landscape

This past decade saw the emergence of the acronym FAANG — Facebook (now Meta), Amazon, Apple, Netflix and Google (now Alphabet) — as shorthand for tech stocks that outperformed the market. But the tech landscape is on the brink of a major shift with the rise of a new AI-centric powerhouse group known as MANGOS: Meta, Anthropic, Nvidia, Google, OpenAI and SpaceX. The new acronym has quickly gone viral on social media, according to TechCrunch, which also notes that "FAANG is not exactly dead." submitted by /u/LinkedInNews [link] [comments]

The new world order ahead of us!

It's not just about the big four anymore, the last 5 years changed the trajectory of the future 20 submitted by /u/ocean_protocol [link] [comments]