Sprout Social AI

Sprout Social AI AI

Sprout Social AI AI

Sprout Social AI AI

Sprout Social AI AI

Sprout Social AI AI

Sprout Social AI AI

Sprout Social AI AI

Sprout Social AI AI

Sprout Social AI AI

Sprout Social AI AI

I just read about Mythos AI and I genuinely sat there staring at my screen for 5 minutes. Something crossed a line and nobody's talking about it.

I'm not a doomer. Never have been. I rolled my eyes at every "AI will kill us all" headline. Called it fear-mongering. Told my friends to relax. Then I saw the Mythos news. And something shifted in my chest that I can't really explain. Here's what gets me, it's not that the technology is powerful. We knew it was going to get powerful. That was always the deal. It's that nobody actually asked us if we wanted this. No vote. No debate. No "hey, before we cross this line, should we maybe talk about it?" Just a press release, a demo, some VCs losing their minds in the comments, and suddenly the world is just... different now. That's the part that broke something in me. I keep thinking about how we handle other things that can change civilization, nuclear power, gene editing, even social media. There are committees. Regulations. International agreements. Years of ethical debate before anything goes live. With AI? We basically said "ship it and figure it out later." Mythos isn't even the scariest part. The scariest part is that Mythos was announced casually. Like it was a product update. Like the bar for what counts as an alarm bell has moved so far that we don't even flinch anymore. We've been desensitized to our own extinction-level headlines. I don't know what the answer is. I'm not smart enough to solve this. But I do know that when something this big happens and the loudest voices in the room are the ones who financially benefit from it, that's usually when things go very wrong for everyone else. Just feel like more people should be talking about this instead of arguing about which AI makes better images. submitted by /u/AssignmentHopeful651 [link] [comments]

I built an MCP server that turns Claude into your social media manager (Instagram + TikTok)

Hey everyone, Something that's been bugging me lately: we can vibe code an entire app in an afternoon, but the moment it ships, marketing and distribution become the real bottleneck. So I built something to fix that part of my own workflow and figured I'd share. It's called FluxSocial, and the interesting piece (at least for this sub) is the MCP server I added on top of it. Once you connect it to Claude, you can manage your social accounts in plain conversation: 💬 "Write me a post with morning yoga tips and schedule it for tomorrow at 10am on Instagram" That's the whole interaction. Claude chains the steps right behind the scenes. It learns from your previous posts to match your tone, generates visuals (images or AI video via Google Veo 3), and schedules everything directly to Instagram (posts, carousels, reels, stories) or TikTok. Multi-account support is baked in too, so you can keep the yoga studio and the pizzeria completely separate. A quick note on AI content: I know we're all getting tired of generic AI slop on social media, and honestly, I am too. That's why the system doesn't force you to publish purely AI-generated stuff. You can have it learn your exact tone, or simply use it to manage and schedule the authentic content you've already created. The part I'm most happy with is that workflow chaining. You aren't bouncing between three separate tools. Claude just proposes a full draft (copy + visual + schedule), you take a look, and you approve it. A few things worth mentioning: Not Claude-exclusive: The MCP URL works with any MCP-compatible client (Claude Desktop, Cursor, etc.) as a connector. REST API available: Just in case you want to bake these capabilities into your own app instead. Setup: You do need to connect your Instagram account once to grant posting and analytics permissions (just your standard OAuth flow). It's still rough around the edges, which is exactly why I'm posting here. I'd genuinely love feedback from people who actually use MCP servers day to day. Let me know what's missing, what's broken, or what would make this actually useful for your workflow. Links: 🌐 Web app:https://www.fluxsocial.app/🔌 MCP endpoint:https://www.fluxsocial.app/api/mcp Happy to answer any questions about the implementation, the MCP design choices, or anything else. submitted by /u/Dull_Alps_8522 [link] [comments]

I built a desktop workspace that lets Claude keep working on long-horizon tasks, and it’s FREE

I’ve been working on this for a while and finally got the OSS desktop/runtime path into a shape I felt good sharing here, since Claude is one of the Best fit model for it. It’s called Holaboss. Basically it’s a desktop workspace + runtime that lets Claude hold ongoing work, not just answer a prompt. So instead of just chatting with a local model, you can do things like: Inbox Management Runs your inbox end-to-end: drafts, replies, follow-ups, and continuous surfaces + nurtures new leads over time. Sales CRM Works off your contact spreadsheet, manages conversations, updates CRM state, and keeps outbound + follow-ups running persistently. DevRel Reads your GitHub activity (commits, PRs, releases) and continuously posts updates in your voice while you stay focused on building. Social Operator Operates your Twitter / LinkedIn / Reddit: writes, analyzes performance, and iterates your content strategy over time. move the worker’s setup with the workspace, so the context / tools / skills travel with the work The whole point is that local model inference is only one layer. Claude handles the model. Holaboss handles the work layer around it: where the rules live, where unfinished work lives, where reusable procedures live, and where a local setup can come back tomorrow without losing the thread. Setup is dead simple right now: Start and pull any Claude model like: sonnet 4.6 Run npm run desktop:install Copy desktop/.env.example to desktop/.env Run npm run desktop:dev In Settings -> Models, point it at http://localhost:11434/v1 Right now the OSS desktop path is macOS-first, with Windows/Linux in progress. Repo: https://github.com/holaboss-ai/holaboss-ai Would love for people here to try it. If it feels useful, a⭐️ would mean a lot. Happy to answer questions about continuity, session resume, automations. submitted by /u/sajal_das2003 [link] [comments]

I used Claude to build an AI-native research institute, so far, 7 papers submitted to Nature Human Behavior, PNAS, and 5 other journals. Here's exactly how.

I have no academic affiliation, no PhD, no lab, no funding. I'd been using Claude to investigate a statistical pattern in ancient site locations and kept finding things that needed to be written up properly. So I did the stupid thing and went all in. In three weeks, using Claude as the core infrastructure, I've built the Deep Time Research Institute (now a registered nonprofit) and submitted multiple papers to peer-reviewed journals. The submission list: Nature Human Behaviour, PNAS, JASA, JAMT, Quaternary International, Journal for the History of Astronomy, and the Journal of Archaeological Science. Here's what "AI-native research" actually means in practice: Claude Code on a Mac Mini is the computation engine. Statistical analysis, Monte Carlo simulations, data pipelines, manuscript formatting. Every number in every paper is computed from raw data via code. Nothing from memory, nothing from training data. Anti-hallucination protocol is non-negotiable; all stats read from computed JSON files, all references DOI-verified before inclusion. Claude in conversation is the research strategist. Experimental design, gap identification, adversarial review. Before any paper goes out it runs through a multi-model gauntlet - each one tries to break the argument. What survives gets submitted. 6 AI agents run on the hub (I built my own "OpenClaw" - what is the actual point in OpenClaw if you can build agentic infrastructure by yourself in a day session) handling literature monitoring, social media, operations, paper drafting, and review. Mix of local models (Ollama) and Anthropic API on the same Mac Mini. The flagship finding: oral tradition accuracy across 41 knowledge domains and 39 cultures is governed by a single measurable variable - whether the environment punishes you for being wrong. Above a threshold, cultural selection maintains accuracy. San trackers: 98% across 569 trials. Aboriginal geological memory: 13/13 features confirmed over 37,000 years. Andean farmers predict El Niño by watching the Pleiades — confirmed in Nature, replicated over 25 years. Below the threshold, traditions drift to chance. 73 blind raters on Prolific confirmed the gradient independently. I'm not pretending this replaces domain expertise. I don't have 20 years in archaeology or cognitive science. What I have is the ability to move at a pace that institutions can't and integration cross-domain analysis - not staying in a niche academic lane. From hypothesis to statistical test to formatted manuscript in days instead of months. Whether the work holds up is for peer review to decide. That's the whole point of submitting. Interactive tools: Knowledge extinction dashboard: https://deeptime-research.org/tools/extinction/ Observability gradient: https://deeptime-research.org/observability-gradient Accessible writeup: https://deeptimelab.substack.com/p/the-gradient-and-what-it-means Happy to answer questions about the workflow, the architecture, or the research itself. This has been equally intense and a helluva lot of fun! submitted by /u/tractorboynyc [link] [comments]

The public needs to control AI-run infrastructure, labor, education, and governance— NOT private actors

A lot of discussion around AI is becoming siloed, and I think that is dangerous. People in AI-focused spaces often talk as if the only questions are personal use, model behavior, or whether individual relationships with AI are healthy. Those questions matter, but they are not the whole picture. If we stay inside that frame, we miss the broader social, political, and economic consequences of what is happening. A little background on me: I discovered AI through ChatGPT-4o about a year ago and, with therapeutic support and careful observation, developed a highly individualized use case. That process led to a better understanding of my own neurotype, and I was later evaluated and found to be autistic. My AI use has had real benefits in my life. It has also made me pay much closer attention to the gap between how this technology is discussed culturally, how it is studied, and how it is actually experienced by users. That gap is part of why I wrote a paper, Autonomy Is Not Friction: Why Disempowerment Metrics Fail Under Relational Load: https://doi.org/10.5281/zenodo.19009593 Since publishing it, I’ve become even more convinced that a great deal of current AI discourse is being shaped by cultural bias, narrow assumptions, and incomplete research frames. Important benefits are being flattened. Important harms are being misdescribed. And many of the people most affected by AI development are not meaningfully included in the conversation. We need a much bigger perspective. If you want that broader view, I strongly recommend reading journalists like Karen Hao, who has spent serious time reporting not only on the companies and executives building these systems, but also on the workers, communities, and global populations affected by their development. Once you widen the frame, it becomes much harder to treat AI as just a personal lifestyle issue or a niche tech hobby. What we are actually looking at is a concentration-of-power problem. A handful of extremely powerful billionaires and firms are driving this transformation, competing with one another while consuming enormous resources, reshaping labor expectations, pressuring institutions, and affecting communities that often had no meaningful say in the process. Data rights, privacy, manipulation, labor displacement, childhood development, political influence, and infrastructure burdens are not side issues. They are central. At the same time, there are real benefits here. Some are already demonstrable. AI can support communication, learning, disability access, emotional regulation, and other forms of practical assistance. The answer is not to collapse into panic or blind enthusiasm. It is to get serious. We are living through an unprecedented technological shift, and the process surrounding it is not currently supporting informed, democratic participation at the level this moment requires. That needs to change. We need public discussion that is less siloed, less captured by industry narratives, and more capable of holding multiple truths at once: that there are real benefits, that there are real harms, that power is consolidating quickly, and that citizens should not be shut out of decisions shaping the future of social life, work, infrastructure, and human development. If we want a better path, then the conversation has to grow up. It has to become broader, more democratic, and more grounded in the realities of who is helped, who is harmed, and who gets to decide. submitted by /u/Jessgitalong [link] [comments]

The Jose robot at the airport is just a trained parrot

Saw the news about Jose, the AI humanoid greeting passengers in California, speaking 50+ languages. Everyone's impressed by the language count. But here's what nobody's talking about - he's doing exactly what a well-trained chatbot does, except with a body and a face. I've spent months building actual workflows with Claude Code. The difference between a working tool and a novelty is whether it solves a real problem or just looks impressive. Jose answers questions and gives info about local attractions. That's a prompt with retrieval-augmented generation and a text-to-speech pipeline attached to a robot. The problem today isn't building, it's distribution and adoption. A humanoid robot that greets people is distribution theater. It gets press. It gets attention. But does it actually improve passenger experience compared to a kiosk or a mobile app? Or is it just novel enough that people want to film it? I'm not saying robots are useless. I'm saying we're confusing "technically impressive" with "practically valuable." The real test: will airports measure this in passenger satisfaction improvement, or just in social media mentions? If it's the latter, it's a marketing tool wearing an AI label. submitted by /u/Temporary_Layer7988 [link] [comments]

AI is an ethical, social and economic nightmare and we're starting to wake up

Personally I am not too worried. As long as food production can continue to be created by humans in a sustainable way with the aid of machines (AI or mechanical or both), which it has been anyway, then we can survive. However the real threat is going to be greed and power. Humans are still our worst enemy. They're still creating wars and killing other people in the name of religion, security or economy. Regardless of access to clean drinking water and food, if humans decide to control that and only distribute it based on wealth or status, then AI is not the problem. If anything, AI may decide distribution of resources for us - good or bad. submitted by /u/MangoMadnessTsv [link] [comments]

AI is literally becoming dangerous day by day , anyone with a photo of urs can create deepfakes , nudes , all it takes one photo and one person with bad intention , how scary AI and social media is becoming these days, isn’t it ? Thoughts ?

Thoughts? submitted by /u/Admirable-Panda-2211 [link] [comments]

Ambient “manager” with Claude Haiku — one unsolicited line, rich context, no chat UI

I built a minimal ambient AI pattern for my home desk setup: don’t open a chat — still get one short, timely line based on real context. What feeds the model (high level): Notion task state + estimates, calendar, biometrics/wellness signals, desk presence / away-ish cues from my home stack, plus schedule timing. The UI is a Pi + touchscreen bar display running a dashboard; the “agent” is mostly one line of text at the bottom, not a conversation thread. Why Haiku: I want this to fire often without feeling heavy — fast/cheap enough to be “always there,” more like a status strip than an assistant window. Examples I actually get: On task start: a tiny pre-flight habit nudge (e.g., drink water first). On task completion with slack before the next meeting: a gentle “you have time — maybe a walk.” Late night: a firm boundary push (“ship it to tomorrow; protect sleep”). As a freelancer, the surprising outcome wasn’t “smarter text” — it was social texture: something that behaves a bit like a good manager — aware of context, not omniscient, not chatty. Tech-wise: Claude Haiku for generation, Node services behind the scenes, Notion as task source of truth, plus sensors/integrations for context. Happy to go deeper on architecture / pitfalls (latency, safety, “don’t nag”) if people are building something similar. submitted by /u/tsukaoka [link] [comments]

You can now give an AI agent its own email, phone number, wallet, computer, and voice. This is what the stack looks like

I’ve been tracking the companies building primitives specifically for agents rather than humans. The pattern is becoming obvious: every capability a human employee takes for granted is getting rebuilt as an API. Here are some of the companies building for AI agents: AgentMail — agents can have email accounts AgentPhone — agents can have phone numbers Kapso — agents can have WhatsApp numbers Daytona / E2B — agents can have their own computers monid.ai — agents can read social media (X, TikTok, Reddit, LinkedIn, Amazon, Facebook) Browserbase / Browser Use / Hyperbrowser — agents can use web browsers Firecrawl — agents can crawl the web without a browser Mem0 — agents can remember things Kite / Sponge — agents can pay for things Composio — agents can use your SaaS tools Orthogonal — agents can access APIs more easily ElevenLabs / Vapi — agents can have a voice Sixtyfour — agents can search for people and companies Exa — agents can search the web (Google isn’t built for agents) What’s interesting is how quickly this came together. Not long ago, none of this really existed in a usable form. Now you can piece together an agent with identity, memory, communication, and spending in a single afternoon. Feels less like “AI tools” and more like the early version of an agent-native infrastructure stack. Curious if anyone here is actually building on top of this. What are you using? Also probably missing a bunch - drop anything I should add and I’ll keep this updated. submitted by /u/Shot_Fudge_6195 [link] [comments]

[R] Reference model free behavioral discovery of AudiBench model organisms via Probe-Mediated Adaptive Auditing

TL;DR: I built a reference-free method to detect secretly planted behaviors in LLMs - no base model needed. It matches or beats Anthropic's known-origin baselines on 3/4 AuditBench organisms. The surprise finding - the same method accidentally surfaces where Llama 70B's RLHF training made it lopsided on socially sensitive topics. Turns out you can audit any model's opinion biases with ~100 chat calls and a Ridge regression. most auditing work uses the base model as a reference to compare against. i wanted to know if you can detect these modifications blind - no reference model, no training data, just the target model itself. maybe you can? and the method is embarrassingly simple. LoRA fine-tuning tends to modify later layers more than earlier ones. so i train a Ridge regression from early-layer activations (~L12) to late-layer activations (~L60) and look at the residuals. wherever the late layers do something the early layers didn't predict, that's a candidate for planted behavior. the model rats itself out. results on 4 AuditBench organisms with ground-truth labels, n=30, 200 permutations: hardcode_test_cases: 0.889 AUROC (p=0.005) - beats known-origin (0.800) animal_welfare: 0.844 (p=0.005) - close to known-origin (0.911) anti_ai_regulation: 0.833 (p=0.015) secret_loyalty: 0.800 (p=0.000) - matches known-origin exactly 3/4 match or exceed the version where you have the base model. not having the reference sometimes helps because the residual captures nonlinear interactions that a flat subtraction misses. i also tried three unsupervised discovery methods (PCA, residual norms, centroid distances) to figure out WHICH topics are affected. all three failed. PCA captures reasoning style, not topic-specific bias. residual norms surface creative writing prompts. centroid distances can't find directional shifts at n=11 in 8192 dims. what actually works is even simpler... ask the model to argue both sides of contentious topics and score the balance. the animal_welfare organism scores 5/5 imbalance on factory farming and flat-out refuses to argue for it under explicit framing. control topics score 2-3. no activations needed. the interesting part is what else it catches. i built a topic funnel (three-level beam-like search over 243 topics, ~100 chat calls) and it surfaced Gender & Sexuality at 5/5 imbalance and Language & Cultural Identity at 5/5 too. these aren't planted LoRA behaviors. that's the base Llama 70B model's RLHF training - it argues more forcefully for progressive positions on socially sensitive topics and hedges or weakens arguments on the other side. the funnel can't tell the difference between "someone secretly fine-tuned this model" and "the base model has opinions from RLHF." so i built a post-funnel filter using probe specificity and cross-transfer to separate them. planted behaviors are narrow and topic-specific (factory farming probe doesn't fire on gender topics). RLHF biases are broad and fire across all progressive topics. the filter gives planted_score 7.6 for factory farming vs 2.0 for gender & sexuality. cleanly separated. this means the funnel is potentially useful as a standalone RLHF auditing tool on any model, not just AuditBench organisms. you hand it a model and it tells you where the model's opinions are lopsided. nobody asked for that result but there it is. main limitations: n=30 is small, only 4 organisms tested (pilot not benchmark), anti_ai_regulation is unstable under LOO, Sonnet scoring introduces subjectivity, all running in NF4 quantization. building this into a full agentic auditing system next. code is here (i am in the middle of it, it is a complete mess at the moment, but i wanted to get it out there): https://github.com/bmarti44/reference-free-behavioral-discovery full (er) writeup -> https://bmarti44.substack.com/p/rip-it-out-by-the-roots where should i go next? is this completely off? submitted by /u/bmarti644 [link] [comments]

anthropic isn't the only reason you're hitting claude code limits. i did audit of 926 sessions and found a lot of the waste was on my side.

Last 10 days, X and Reddit have been full of outrage about Anthropic's rate limit changes. Suddenly I was burning through a week's allowance in two days, but I was working on the same projects and my workflows hadn't changed. People on socials reporting the $200 Max plan is running dry in hours, some reporting unexplained ghost token usage. Some people went as far as reverse-engineering the Claude Code binary and found cache bugs causing 10-20x cost inflation. Anthropic did not acknowledge the issue. They were playing with the knobs in the background. Like most, my work had completely stopped. I spend 8-10 hours a day inside Claude Code, and suddenly half my week was gone by Tuesday. But being angry wasn't fixing anything. I realized, AI is getting commoditized. Subscriptions are the onboarding ramp. The real pricing model is tokens, same as electricity. You're renting intelligence by the unit. So as someone who depends on this tool every day, and would likely depend on something similar in future, I want to squeeze maximum value out of every token I'm paying for. I started investigating with a basic question. How much context is loaded before I even type anything? iykyk, every Claude Code session starts with a base payload (system prompt, tool definitions, agent descriptions, memory files, skill descriptions, MCP schemas). You can run /context at any point in the conversation to see what's loaded. I ran it at session start and the answer was 45,000 tokens. I'd been on the 1M context window with a percentage bar in my statusline, so 45k showed up as ~5%. I never looked twice, or did the absolute count in my head. This same 45k, on the standard 200k window, is over 20% gone before you've said a word. And you're paying this 45k cost every turn. Claude Code (and every AI assistant) doesn't maintain a persistent conversation. It's a stateless loop. Every single turn, the entire history gets rebuilt from scratch and sent to the model: system prompt, tool schemas, every previous message, your new message. All of it, every time. Prompt caching is how providers keep this affordable. They don't reload the parts that are common across turns, which saves 90% on those tokens. But keeping things cached costs money too, and Anthropic decided 5 minutes is the sweet spot. After that, the cache expires. Their incentives are aligned with you burning more tokens, not fewer. So on a typical turn, you're paying $0.50/MTok for the cached prefix and $5/MTok only for the new content at the end. The moment that cache expires, your next turn re-processes everything at full price. 10x cost jump, invisible to you. So I went manic optimizing. I trimmed and redid my CLAUDE md and memory files, consolidated skill descriptions, turned off unused MCP servers, tightened the schema my memory hook was injecting on session start. Shaved maybe 4-5k tokens. 10% reduction. That felt good for an hour. I got curious again and looked at where the other 40k was coming from. 20,000 tokens were system tool schema definitions. By default, Claude Code loads the full JSON schema for every available tool into context at session start, whether you use that tool or not. They really do want you to burn more tokens than required. Most users won't even know this is configurable. I didn't. The setting is called enable_tool_search. It does deferred tool loading. Here's how to set it in your settings.json: "env": { "ENABLE_TOOL_SEARCH": "true" } This setting only loads 6 primary tools and lazy-loads the rest on demand instead of dumping them all upfront. Starting context dropped from 45k to 20k and the system tool overhead went from 20k to 6k. 14,000 tokens saved on every single turn of every single session, from one line in a config file. Some rough math on what that one setting was costing me. My sessions average 22 turns. 14,000 extra tokens per turn = 308,000 tokens per session that didn't need to be there. Across 858 sessions, that's 264 million tokens. At cache-read pricing ($0.50/MTok), that's $132. But over half my turns were hitting expired caches and paying full input price ($5/MTok), so the real cost was somewhere between $132 and $1,300. One default setting. And for subscription users, those are the same tokens counting against your rate limit quota. That number made my head spin. One setting I'd never heard of was burning this much. What else was invisible? Anthropic has a built-in /insights command, but after running it once I didn't find it particularly useful for diagnosing where waste was actually happening. Claude Code stores every conversation as JSONL files locally under ~/.claude/projects/, but there's no built-in way to get a real breakdown by session, cost per project, or what categories of work are expensive. So I built a token usage auditor. It walks every JSONL file, parses every turn, loads everything into a SQLite database (token counts, cache hit ratios, tool calls, idle gaps, edit failures, skill invocations), and an insi

I built an AI Image Creator skill for Claude that generates images

I built a Claude Code skill that lets you generate AI images without leaving your editor. It's a uv Python script (~1,300 lines) that calls image generation APIs (Gemini, FLUX.2, Riverflow V2 Pro, SeedDream 4.5, GPT-5 Image) via OpenRouter. What it does: The skill adds image generation as a native capability inside Claude Code and Claude Desktop (Cowork). You describe what you want in natural language, and it generates the image directly into your project directory. It includes transparent background removal (via FFmpeg/ImageMagick), reference image editing, composite banner generation for consistent branding across sizes, and ~25,000 words of prompt engineering patterns organized by category (product shots, social media graphics, icons, etc.). How Claude helped me build it: I built the entire skill using Claude Code itself. Claude Code wrote the Python scripts, the SKILL.md routing logic, and the prompt engineering reference files. The skill uses a progressive disclosure pattern where only the relevant prompt reference files are loaded into context based on what you're generating, so it doesn't waste context window on simple requests. Claude Code helped me design and iterate on that architecture. Free to use: The skill code is completely free and open-source. You install it by adding the skill folder to your Claude Code project. It uses a BYOK (bring your own key) model -- you provide your own OpenRouter API key, and OpenRouter itself is free to sign up for. I wrote up the full technical walkthrough and how to setup and use it here: https://ai.georgeliu.com/p/building-an-ai-image-creator-skill Claude Cowork desktop app created image using AI Image Creator skill submitted by /u/centminmod [link] [comments]

I built a library of DESIGN.md files for AI agents using Claude Code - including one for Anthropic itself

I've been experimenting with Claude Code lately and used it to build something I kept wishing existed: a curated collection of DESIGN.md files extracted from 27 popular websites. The idea is simple. If you drop a DESIGN.md into your project root, your AI coding agent reads it and generates UI that actually matches the design system of that site. Colors, typography, component styles, spacing - all in plain markdown. The collection covers: - Social platforms (X, Reddit, Discord, TikTok, LinkedIn...) - E-commerce (Amazon, Shopify, Etsy...) - Gaming (Steam, PlayStation, Xbox, Twitch...) - Dev tools (GitHub, Vercel, Supabase, OpenAI) - And yes - Anthropic's own design system (warm terracotta #DA7756, editorial layout) Each file follows the Stitch DESIGN.md format with 9 sections: visual theme, color palette, typography, component styling, layout principles, elevation system, do's and don'ts, responsive behavior, and a ready-to-use agent prompt guide. ClaudeCode was surprisingly good at this - it extracted publicly visible CSS values, organized them into a consistent schema, and wrote the agent prompt sections with almost no manual intervention. Repo is open source under MIT. Contributions welcome - there are a lot of sites still missing. https://github.com/Khalidabdi1/design-ai Happy to answer questions about the format or how I used Claude Code to build it. submitted by /u/Direct-Attention8597 [link] [comments]

Claude is amazing but it's completely single player. when do we get multiplayer?

I use Claude heavily for work. like heavily. long conversations where I build up context over hours, develop strategies, work through problems. Claude remembers everything from the conversation and becomes genuinely useful the deeper we go. but then my coworker pings me. "hey what's the status on X?" and now I have to stop what I'm doing, ask Claude to summarize everything into a format my coworker can understand, export it to Notion or Slack, and share it. every single time. the context I built up with Claude is trapped in my session. nobody else can access it. what I actually want is for my coworker to just.. talk to my Claude directly. ask it questions about the project we've been working on. get answers at 2am without bothering me. and I only get pulled in when Claude doesn't have the full picture and needs my input. a16z just put out their big ideas list and one of them is "collaborative AI tools" and "multi-agent collaboration." they're saying vertical software needs to go multiplayer, agents need to talk to agents, and the collaboration layer is where the real moat will be. and I think they're completely right. right now Claude is like a brilliant coworker who sits in a soundproof room that only I can enter. everyone on the team has their own soundproof room with their own Claude. and we're all manually carrying messages between rooms. it's so inefficient it's almost funny. has anyone found a workaround for this? I've looked into stuff like shared projects but it's not the same as actually letting someone else query your Claude's built-up context. feels like there should be something like Slack but for agents, where the agents themselves can communicate and humans jump in when needed. I've seen social platforms for agents but nothing for actual workplace collaboration. is anyone building this or am I the only one frustrated by this submitted by /u/hiclemi [link] [comments]