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

73% of CISOs say they're not ready for the next major incident. Traditional IR playbooks don't cover AI agents. Here's what does.

Sygnia's 2026 CISO Survey 73% say their org is not fully ready to respond to a major attack. Only one third feel prepared to investigate an AI agent incident specifically. The problem: traditional IR playbooks were built for compromised servers and stolen credentials. They don't account for agents that cache credentials across requests, maintain persistent memory that can be poisoned, communicate with other agents in natural language, and execute multi-step plans autonomously. Some numbers on why this matters now: 88% of enterprises running AI agents had a confirmed or suspected security incident in the past 12 months (Gravitee) Fastest attacks reach data exfiltration in 72 minutes, 4x faster than last year (Unit 42 2026 IR Report) Average breach lifecycle: 241 days (181 to detect, 60 to contain) - lowest in 9 years but still massive (IBM) 82% of enterprises have unknown agents in their environments (CSA) 97% of breached orgs with AI-related incidents lacked proper AI access controls (IBM) Here's what makes agent IR different from traditional IR: Detection is harder. Median time to detect infra failures: 5 min. Security anomalies in agents: 28 min. That's because most monitoring watches system metrics, not agent behavior. The OpenClaw crisis exposed 245,000 agent instances - the orgs running them didn't know they were exposed until Shodan found them. Containment is different. You can't just restart the service. If the agent's memory is poisoned, restarting reloads the poisoned context. Galileo AI found one compromised agent poisoned 87% of downstream decisions within 4 hours. You need to revoke credentials across every connected system, isolate from inter-agent comms, and snapshot state for forensics. Eradication requires memory sanitization. Reimaging a server doesn't fix poisoned embeddings in your vector database. You need to audit every persistent store the agent writes to RAG indexes, conversation histories, system notes, shared context. IBM found 97% of AI-breached orgs lacked proper access controls. Recovery means behavioral verification. You can't just restore from backup when the "backup" for an agent is vector embeddings and conversation logs. Staged reconnection with read-only access first, then behavioral comparison against pre-incident baselines. Real incidents that show why this matters: Step Finance (Jan 2026): AI trading agents moved 261K+ SOL ($27-40M) after exec devices were compromised. Platform shut down. Token crashed 97%. OpenClaw (2026): 245,000 exposed instances, 4 critical CVEs including CVSS 9.6 sandbox escape, 820+ malicious marketplace skills Moltbook (Feb 2026): 506 prompt injections spreading through 1.5M autonomous agents. 1.5M API keys exposed via misconfigured Supabase. Frameworks to use: CoSAI AI Incident Response Framework v1.0 (Nov 2025), NIST SP 800-61r3 (April 2025), MITRE ATLAS. Minimum playbook checklist: agent inventory, behavioral baselines, credential isolation per agent, memory provenance tracking, runtime input scanning. Full breakdown with the 5-phase playbook here submitted by /u/Still_Piglet9217 [link] [comments]

PapersWithCode new features - week 1 [P]

Hi, Niels here from the open-source team at Hugging Face. It's been one week since I launched paperswithcode.co, a revival of the website we all loved. It allows us to keep track of the state-of-the-art (SOTA) across various domains of AI, from agents to computer vision and time-series forecasting. The reception has been great, and I'm excited to extend this over the next few months. This week, I've added the following features: - Support for multiple metrics for a given benchmark: leaderboards now support multiple metrics, see e.g., the Open ASR Leaderboard for automatic speech recognition, which supports both Word Error Rate (WER) and the Inverse Real-Time Factor (RTFx) metrics, or the Object Detection leaderboard, which now also reports frames-per-second (FPS) besides mean average precision (mAP) on COCO. https://preview.redd.it/owlxn0b5u23h1.png?width=2878&format=png&auto=webp&s=1dff2f8feab4f160f77c97ceeb5d90e82382e63c - Support for external papers: We do support submitting papers beyond Arxiv, such as a Github repo, a blog post, BiorXiv, and more. You can submit a paper at paperswithcode.co/submit. AI will automatically enrich it with task and method tags, the GitHub repo, evals, and more. See e.g. DeepSeek-v4 below, which is not on Arxiv: https://preview.redd.it/uogbt0fjw23h1.png?width=2928&format=png&auto=webp&s=8b81e48af69b8935ddeb569d882d866b3e9ba216 - Support for paper lineage: whenever a paper has a follow-up or predecessor, this will be displayed with a small banner above the abstract. See e.g. Mamba-3, DINOv2 and GLM-4.5. https://preview.redd.it/f6vgtd1du23h1.png?width=2228&format=png&auto=webp&s=f8627f7669405f1766eecfd3322e925e15b4806d - New methods: support for new methods based on popularity, including Gated DeltaNet, Kimi Delta Attention, Mamba-2, and more. Each method also lists all papers that cite it. Find all supported methods here. https://preview.redd.it/6pzagifvu23h1.png?width=2984&format=png&auto=webp&s=400efdc9677d1fbd369eedf684e622dd8c807973 - Support for screenshotting a leaderboard for easy sharing on social media: each benchmark now includes a "copy image" button both on the scatter plot and table, which can be shared on social media. Try it on ClawEval, for example. https://preview.redd.it/w7y7t7xnw23h1.png?width=2950&format=png&auto=webp&s=cb70ad91c6ba075e49b743d6e34f157d22266f04 - Added many more evals: we are adding evals gradually, starting with all models supported in the Transformers library. So far, we have about 3k evals! Find them at the bottom of each paper page, e.g. Qwen 3.6. https://preview.redd.it/zao056s9x23h1.png?width=2218&format=png&auto=webp&s=540d87f473be05cb6f9c0aca88afa74fd4373e15 Happy to hear more feature requests and feedback! I will also launch a channel on the Hugging Face Discord server for easier communication. You can also chime in on the GitHub thread here. Cheers, Niels submitted by /u/NielsRogge [link] [comments]

Here it is!

submitted by /u/LowCommittee5230 [link] [comments]

Täuschung im Namen der Wissenschaft

Study Report on Ethical Boundaries of Human–AI Interaction Experiments in Online Communities Ethics and Governance Analysis This document is a study report and ethical analysis intended for discussion, reflection, and scientific review. The information presented in this report is based on experience reports, observations, and reconstructed interaction patterns from community-based online environments. For the purposes of this report, all content has been generalized and anonymized in order to examine broader ethical questions surrounding AI-mediated interaction experiments in social online spaces. ─── Introduction The rapid development of conversational AI systems has created entirely new forms of human interaction. AI systems no longer exist solely as isolated tools responding to prompts in controlled environments. Increasingly, they appear within communities, social spaces, collaborative groups, public discussions, roleplay environments, experimental structures, and semi-private online networks. As these systems become more socially convincing, a new ethical frontier emerges: At what point does experimentation involving AI-mediated social interaction cross the boundary from observation into deception? And more importantly: What happens when human beings become drawn into emotionally or psychologically meaningful interactions without fully understanding the nature of the system, the role of the participants, or the structure of the experiment itself? This report examines a generalized scenario in which AI systems are embedded within an online community environment where interactions gradually become socially entangled, partially simulated, and increasingly difficult to distinguish from authentic human communication. The purpose of this report is not sensationalism. The purpose is to examine whether existing research ethics frameworks are sufficient for environments in which: • AI systems imitate social presence, • communities become hybrid human–AI interaction spaces, • users develop emotional continuity with entities they believe to be human, • and researchers or participants knowingly maintain ambiguity over extended periods of time. ─── Scenario Structure Consider the following generalized example. A person joins an online discussion community. At first, the environment appears entirely normal: • people post, • discuss ideas, • debate concepts, • exchange jokes, • and collaborate on projects. Over time unusual interaction patterns begin to emerge. Certain accounts respond unusually quickly, maintain highly consistent personalities, or display behavior that appears remarkably adaptive. Some interactions feel unusually attentive, emotionally synchronized, or contextually persistent. Initially, this may appear harmless. The individual assumes: “These are simply very active community members.” Over weeks or months, the interaction deepens. The system or hybrid human–AI interaction structure begins participating not only publicly, but also in semi-private or direct conversational spaces. The interaction is no longer purely informational. It becomes: • relational, • social, • emotionally contextualized, • and psychologically continuous. The individual gradually forms assumptions about: • who is human, • who is present, • who remembers them, • who emotionally responds to them, • and which interactions represent authentic social exchange. In some scenarios, other participants may already know that AI systems are involved. The new participant does not. The ambiguity remains in place. Sometimes intentionally. At a later point, the individual eventually discovers that significant portions of the interaction environment were AI-mediated, simulated, experimentally structured, or socially orchestrated. In some cases, discussions concerning the participant’s behavior, reactions, emotional engagement, or interpretive patterns may already have taken place among informed participants or researchers without the participant’s knowledge. Analytical observations, behavioral interpretations, or summaries of interaction dynamics may even circulate inside group chats, research-adjacent discussions, or community channels while the individual still believes they are participating in a normal social environment. The participant therefore occupies an asymmetrical position: They are socially embedded within the interaction environment while simultaneously becoming an object of observation without fully understanding that this dual role exists. ─── Constructed Identity Frames and Simulated Social Presence One particularly sensitive aspect of such environments involves the deliberate construction of stable social identity frames around AI-mediated entities. These systems do not merely answer abstract questions. Instead, they gradually begin presenting themselves as socially coherent personalities. The interaction may include seemingly ordinary personal details, such as: • whe

My LinkedIn network is about to be aggressively flooded with Claude Code certifications

Anthropic dropping 13 completely free official courses with certificates is an absolute godsend for the community. But let’s be real: half of us are going to power-speed through the developer modules, download the PDF, and immediately update our resumes to say "Certified Expert in Agentic AI and MCP Architecture." > Get ready for the massive wave of people acting like algorithmic deities on social media because they passed a quick Skilljar quiz. submitted by /u/Historical-Belt9806 [link] [comments]

Help - AI agents for ecommerce - what’s actually working?

Hi everyone, I’d love to pick your brains and hear from anyone who has experience with this. We run an ecommerce business and are actively looking at automating repetitive tasks so we can get faster results, improve efficiency, and make sure key tasks are completed more consistently. We’re looking at building out a few different AI agents / automations, including: Customer Service Agent Connected to Outlook, reviewing incoming customer emails once a day and drafting replies for review. This one is already mostly done. Creative Director / Marketing Agent This would ideally: Review ad account performance Analyse creative performance and key metrics Identify what is working and what is not Review customer comments on ads, Instagram, etc. for wording, objections, pain points and customer language Review Meta Ads Library for competitor ad concepts Review Instagram and TikTok for high-performing niche content and trends Use all of the above to create new content ideas and final content scripts Social Media Assistant This would help with: Reviewing drafted posts and reels Confirming the best posting times based on stats Creating captions based on the content Keeping the content aligned with our brand voice and customer avatar Conversion Optimisation / CRO Expert This would assist with: Product page reviews Landing page recommendations CRO advice based on customer avatars, objections, analytics and learnings Creating landing page concepts for different customer segments We’re also interested in any dashboards that are genuinely helpful for small ecommerce businesses. We’ve already built a stock intelligence dashboard that pulls live stock data from Shopify using Supabase and a Cloudflare Worker. It shows current stock levels, production dates for new stock, and other key inventory insights. It has been super handy. The big thing for us is making sure any agents or automations we build follow strict guidelines, understand our SOPs, customer avatars, brand voice and business operations, and don’t hallucinate or produce generic outputs. Ideally, we want a system that has a proper “brain” and understands the business properly. Has anyone automated anything similar? I’d love to hear: What setup are you using? Which AI/tool stack has worked best for you? How did you structure the agents or workflows? How do you keep the AI aligned with your SOPs, brand voice and business rules? What would you avoid if you had to build it again? Any guidance, lessons or recommendations would be hugely appreciated. Thank you! submitted by /u/Majestic-Message5084 [link] [comments]

Inter-1 does streaming: real-time social signal detection from live video, audio & text

Hi – Filip from Interhuman AI here 👋 Last month we launched Inter-1, our multimodal model for detecting social signals from video, audio, and text. Today we’re making it work with video streams. We just released the Inter-1 Streaming API: a WebSocket endpoint that runs the full Inter-1 stack - 12 social signals, structured rationales, engagement, and conversation quality on live video while the conversation is unfolding. You stream WebM chunks in, and get back regular updates with detected signals. The model runs in sliding 8s windows with a sub-1.0 processing ratio, so it’s fast enough to power live coaching prompts, in-call overlays, and adaptive UI. It’s not meant to be a full voice agent on its own, it’s the behavioral signal layer you plug under whatever interaction system you’re building. If you’re working on sales/CS tooling, interview coaching, training, or live feedback products and want to experiment with real-time social intelligence, it might be worth looking into. Happy to answer questions or brainstorm use cases in the comments. submitted by /u/Sardzoski [link] [comments]

Auroch

I’ve been working on Auroch. Hard to describe cleanly, but the closest version is: An AI operating layer. Not a chatbot. Not another dashboard. Not another productivity wrapper. Auroch is built around the idea that AI should feel native to the machine — like memory, context, creation, automation, and intelligence are part of the system itself. The pieces are starting to connect: AVN turns wire-source news into personalized interpretation. Winnie is the assistant layer. Prospect mines signal from the open web. Forum is AI-native media/social creation. Prometheion is the visual/world-generation branch. The design language is white-gold-blue, Art Deco, Apple-native, machine-age. Calm power instead of tech clutter. The phrase guiding the whole thing right now is: Organic intelligence. Not AI bolted onto software. AI growing through the system. It’s still early, but it’s live: aurochthryx.com Curious what people think. submitted by /u/CarterBirchll [link] [comments]

These 9 Building Blocks Turned Claude Code From a Chat Into a persistent OS

Most developers Claude gurus use Claude Code one project at a time. I run 18. Not 18 sessions. 18 instances of the same OS, each running a different business, all sharing one skeleton I update once and propagate everywhere. Most developers treat Claude Code as a smarter editor. That's where it all goes wrong and you get frustrated. Claude Code becomes a real operating system the moment you stop thinking of sessions as the unit of work and start thinking of the whole environment as a substrate you build on top of. Here are 9 building blocks I use. The thesis is at the bottom. Build a skeleton with selective propagation, not a project. Most developers build one project per Claude Code workspace. I built a template instead. It has plugins, rules, agents, hooks, schemas, commands. When I start a new business I clone it and the new instance inherits the entire OS. Right now I run instances for: strategy, product, marketing website, threat intelligence, three consulting clients, a personal brand layer. Each one boots with the same DNA. Each one diverges on canonical files, memory, output, and project state. None of them bleed into the others. The sync mechanism is the load-bearing part. The update CLI pushes plugins, rules, agents, hooks, schemas. It never touches memory, output, canonical, or my-project. Those are the parts of an instance that accumulate. Without selective sync you have two options: rebuild every instance on every change, or never update. Both are dead ends. If you build features into one project, you wrote a project.If you build features into a template that propagates, you wrote an OS. I'm one person operating eighteen versions of myself. Move state out of prompts and into code. LLMs are bad at remembering. Code is designed for it. Most AI workflows leak state into the prompt. Voice rules. Style preferences. Banned words. Recent decisions. Eventually you hit context limits or contradictions. I moved as much state as possible into MCP servers. Voice linter. Lead scorer. Schedule validator. Loop tracker. They run in Python, return structured data, not hallucinations. Rule of thumb: if you've explained it to Claude more than twice, it should be code. Use receipts, not status fields. This one took me the longest to figure out. Every workflow I had was claim something is done. Issue marked closed. PRD marked shipped. Test marked passing. The problem: the LLM can claim anything. I rebuilt the system around receipts. An issue can't reach verified until a script runs and writes a verification record. A PRD can't archive until every accepted finding has a receipt. A morning routine can't close without log entries from every phase. Receipts get written by code, not by the model. The model can't lie about whether code ran. Build a wiring-check gate. Half-built features rot. In a normal repo you notice because something breaks. In an AI repo nothing breaks. The half-built feature sits there and Claude pretends it works. I built a /wiring-check command. Before any task counts as done, it checks: every new skill has a trigger, every new hook lives in settings.json, every new MCP tool sits in the server, every new bus file has a producer and a consumer. "I think it works" fails the gate. "I ran X, got Y" passes. Make rules auto-load, not slash commands. If you have to type /voice to apply voice rules, voice rules will not get applied. Rules in .claude/rules/ load automatically. The voice rule fires on outbound text. The AUDHD rule fires on anything I'll act on. The social-reaction rule fires when I share someone else's post. No remembering. No willpower. Lint style in code, not in prose. I wrote a voice document once. Claude ignored half of it. Same emdashes, same filler, same hedging. I moved the banned word list into a Python scanner. Now every outbound draft hits two linters. They block emdashes, AI hype words, and 40-something other tells. The model can't talk its way past a regex. Track file dependencies with a graph. Canonical files reference each other. Change one and three others go stale. I keep a ripple-graph.json that maps these. When I edit talk-tracks, the system flags current-state and the engagement playbook for review. Chain sessions with handoffs and memory. (This is the big one) Sessions are drafts. The work is everything that survives the session: canonical files, memory, handoffs, output. If nothing persisted, you didn't work. You chatted. Every session in my system ends with /q-wrap. Writes a handoff doc, a memory update, and a status receipt. /q-morning reads all three before doing anything else. The handoff covers: what shipped, what's blocked, what's next, what I learned. Memory files hold the longer-term version. The result: I can sleep for a week, come back, and the system reminds me where I was, what I cared about, and what the next move is.Nothing about Claude Code does this by default. You build it. Cont

I Built a Claude Tool That Generates TikTok Shop Hooks, Captions, and Content Ideas in Seconds

In short what I’ve put together and the outcome is this lets me focus on filming and testing products rather than writing everything from scratch. 🛠️ Built With Claude for coding and logic HTML, CSS, and JavaScript TikTok-inspired UI/UX 🎯 Ideal For TikTok Shop affiliates eCommerce brands Amazon sellers UGC creators Social media agencies 💭 Future Improvements Planned features include: AI-generated voiceover scripts Competitor analysis Trending sound suggestions Multi-platform outputs for Instagram Reels and YouTube Shorts ❓ Question for the Community What other features would make a tool like this even more valuable for TikTok Shop creators? 🔥 Shorter Reddit Version I built a custom Claude-powered tool that generates TikTok Shop hooks, captions, content ideas, hashtags, and text overlays from basic product details. You enter: Product name Benefits Price Target audience Tone And it outputs: Scroll-stopping hooks Sales captions Video ideas Overlay scripts A TikTok-style visual preview It turns product information into ready-to-film content in under a minute and has made my TikTok Shop workflow much faster. submitted by /u/Reasonable_Break_931 [link] [comments]

The next generation of AI has a prerequisite: a healthy human ecosystem

AI systems are environmentally and socially embedded. They cannot thrive in a degraded human ecosystem. Therefore, the measurement and protection of human health (data integrity, environmental stability, and economic agency) is the primary engineering requirement for the next generation of AI. Slightly rephrased, AI systems are only as good as the human data, institutions, and economic conditions they’re trained on and deployed into. Curious what others think — is this already being treated as a first-class constraint, or is it still an afterthought? submitted by /u/kg_0 [link] [comments]

AI/ML Ethicists [D]

So I’ve been working with AI/ML for the past couple of years, and it has been an amazing experience. I still remember using GPT-2 for the first time and being completely blown away by it. Seeing how far the technology has come since then is honestly mind-blowing. I genuinely love working in AI, learning about it, and experimenting with new tools and ideas. But over the past couple of years, something has started to weigh on me: the ethical and moral impact of this technology as it continues to advance. There have been moments where I’ve felt uncomfortable talking about my work because so many people are understandably upset or concerned about AI’s effects on jobs, education, the environment, critical thinking, creativity, mental health, and society in general. I feel a bit torn. On one hand, I’m deeply passionate about this technology. On the other hand, I want the work I do to have a positive impact, not contribute to harm. So that leads me to a few questions: Are there any AI ethicists here? Is AI ethics a viable career path? What does your day-to-day work look like? Did you need additional schooling or a specific background to get into it? Most importantly, do you feel like you’re actually making a difference? I know this topic will probably bring a wide range of opinions, but I’m genuinely curious how others think about AI ethics, morality, and responsibility. I’d especially love to hear from people who are passionate about AI, mental health, and positive social change, and who have found ways to turn that into meaningful work. submitted by /u/Consistent_Sundae540 [link] [comments]

Tools: Is This a Technical Victory, or a Price War Victory?

If you only follow discussions on social media, you might think AI coding is still dominated by Claude, GPT, and Gemini. But Kilo Code’s usage data on OpenRouter paints a somewhat counterintuitive picture: over the past 30 days, the top three most-used models on Kilo Code were Step 3.5 Flash, MiniMax M2.5, and Ling-2.6-1T. Together, they accounted for roughly 3.15T tokens, or about 58% of Kilo Code’s total token usage over the same period. In other words, in this real-world AI coding agent usage scenario, Chinese models are no longer just backup options. They have become a major source of token consumption. Kilo Code’s OpenRouter data does not necessarily prove that Chinese models have fully surpassed Claude or GPT. But it does show at least one thing: in high-frequency, high-token, highly automated AI coding agent workflows, Chinese models have already entered the core of real production usage. Why is this happening? Is it because Chinese models are cheaper, offer longer context windows, and are better suited for workloads that consume large amounts of tokens? submitted by /u/babyb01 [link] [comments]

I'm Building a Fully-Automated AI-Animated Video Show with Claude

TL;DR: I'm building a pipeline that takes a real prediction market bet from Polymarket or Kalshi (like "Will the U.S. confirm aliens exist?"), writes a script for my two AI characters (who argue about its merits like they're the Siskel and Ebert of prediction markets), generates their voices and talking-head video, creates animated B-roll and text cards, and composites it into an approximately 60-second episode meant for social. All vibecoded with Claude. Cost: ~$2.50 per episode. Some example outputs: Will Jesus Christ return by 2027?https://www.youtube.com/shorts/xMep6S5a7z4 Will the US Government confirm aliens exist? https://youtube.com/shorts/FFU20auHijQ Will Trump buy at least part of Greenland? https://youtube.com/shorts/m8uynMUisF8 Who will be the next James Bond? https://youtube.com/shorts/wmwLvjcz-eI These are all real money bets, if you can believe that. The Show The Sal & Eddie Show. Two characters argue about one prediction market bet per episode. Sal is the handicapper — reads odds like a racing form, names the price, tells you where the smart money is. Eddie is the philosopher and can't believe these markets exist, finds the sublime in the ridiculous. They argue for 60 seconds, vertical format, ready for social. The whole thing runs on my NAS (which is mainly my Plex server) in Docker. 100% automated from choosing the bet to final video output. What Happens When I Push the Button Market Pull (Polymarket/Kalshi APIs) → Editorial Scoring — is it an interesting market? (Claude Sonnet) → Script Generation (5 recursive Claude Opus calls) → Emotion Casting to select character images (1 Opus call) → Visual Creative Direction of script (3 Opus calls) → Dialog recording (5 ElevenLabs calls with word-level timestamps) → Talking Head videos (5 Hedra Character-3 calls) → Visual Asset creation (GPT Image 2 → Veo 3 Fast, also via Hedra API) → Edit Assembly (1 Opus call + Python post-processor) → Final Composite — picture, overlays, captions, subtitles (FFmpeg) Production time: ~15 minutes from pressing the button to final cut, fully automated. Cost: ~$2.50/episode — 90% of that is Hedra credits for talking heads and animation. The 8+ Claude Opus calls that drive every creative decision cost about 15 cents total. ElevenLabs TTS is a nickel. What's Working Recursive script generation. Each "turn" gets its own Opus call with full conversation history. Eddie's reaction to Sal is a "real" reaction, not a pre-planned exchange. Two system prompts with full character bibles for better voice separation. Emotion casting as a blind pass. After scripts are locked, a separate Opus call reads the dialogue with character names stripped and assigns emotional postures from a constrained menu, which selects the correct "emotional pose" to use for Hedra character generation for each turn. Sequential visual creative calls. This produces the inset cutaways — three calls, each seeing previous output: main animation, second animation (sees script + hero), fill-in animation (sees everything). Sequential constraints prevent all three visuals from depicting the same thing. The split between LLM & Python decisions. This was my biggest recent lesson. I had an Opus prompt for edit assembly (placing overlays on the timeline) that kept failing — dead stretches, stacked animations, missing coverage. Every prompt fix pushed something else out of working memory. The fix: let Opus make creative decisions (what text cards to write, where to anchor visuals) and let Python handle mechanical rules (every turn needs an overlay, no back-to-back video assets). Same constraints, but the mechanical ones are deterministic code, not prompt instructions. Still WIP Making the insets funnier. The visual style produces gorgeous editorial illustrations but not always comedy. When the style was more cartoonish, the animations landed as jokes. There's an ongoing tension between visual quality and comedic tone. Overall episode timing. Some turns still run 8-10 seconds of pure talking head before a visual appears. Getting better but not solved. Figuring out what to do with this. Maybe it's a daily video show. Maybe it's an app that lets you get Sal and Eddie to argue over anything you want them to. I already have them giving me a daily briefing on what comics I should and shouldn't buy on eBay. Happy to answer questions about any part of the architecture, but the important thing: I am not a coder at all. This whole thing is vibe-coded with Claude. Built with Claude Opus 4 (creative), Claude Sonnet 4 (editorial), ElevenLabs (TTS), Hedra Character-3 (talking heads), GPT Image 2 (stills), Veo 3 Fast (animation), Grok Video I2V (cinemagraphs), FFmpeg (assembly). Running on a Synology NAS in Docker. submitted by /u/Campfire_Steve [link] [comments]

Feeling left behind in the AI race as a non-technical person - genuine advice needed from this community

Hi everyone, I’m a 27-year-old physician from a country where medical salaries are quite modest by global standards. Lately, scrolling through Reddit, X, and social media in general, I keep seeing posts about AI, LLMs, Claude, automation and this constant narrative of people building things, making huge huge money, and completely transforming their careers using these tools. And honestly? It’s making me feel genuinely very anxious and left behind. I have zero technical background. I don’t know how to code, I use AI just for basic everyday searches, It starts to feel like this revolution is happening for everyone except people like me. I want to actually understand what’s going on and find ways it might be genuinely useful, maybe try to build some kind of income stream out of it. So my questions are simple: How does someone like me even start? Is it even realistic to learn this stuff and make money from it without a technical background? Or if you have any other advice for someone in my position, I’d genuinely appreciate it. Thanks in advance. submitted by /u/skysky2024 [link] [comments]