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

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]

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]

OpenAI/a16z super PAC caught astroturfing, using sockpuppets, and paying armies of spambots to falsely create the appearance of public support for their positions

article: https://www.modelrepublic.org/articles/is-openai%E2%80%99s-super-pac-paying-for-an-army-of-twitter-bots-to-engage-with-their-content submitted by /u/EchoOfOppenheimer [link] [comments]

ChatGPT Named Most beneficial AI named by Gemini

I asked Gemini who the most beneficial AI to humanity is currently. The first answer was AlphaFold for its contributions In research. I then said I was asking about public facing AI and this was Gemini's answer: "Ah, got it! That makes total sense. AlphaFold is doing incredible work behind the scenes, but it’s not exactly something you can chat with on your morning commute to help organize your life. When it comes to **public-facing AI**—the tools everyday humans can actually log into and use—the crown for "most beneficial to date" has a clear winner, followed by a couple of incredibly high-impact runner-ups. The definitive winner for public-facing impact is **ChatGPT (and the broader category of Conversational LLMs)**. Here is how public-facing AI is actually moving the needle for humanity right now: ### 1. The Ultimate Equalizer: Democratizing Education Before LLMs, personalized, 1-on-1 tutoring was a luxury reserved for the wealthy. Today, tools like ChatGPT, Claude, and Gemini act as patient, multilingual, 24/7 tutors for anyone with an internet connection. * It can explain quantum physics to a 5-year-old or a college senior, adapt to a student's learning speed, and break down complex math problems step-by-step. * For students in underfunded schools or remote areas, it has leveled the educational playing field dramatically. ### 2. Massive Productivity & "Cognitive Offloading" By taking over routine, tedious tasks, public AI has freed up millions of human hours. It writes code, drafts emails, summarizes massive legal documents, and translates languages with nuance. This isn't just about saving time for corporations; it allows freelancers, small business owners, and everyday creators to scale their ideas without needing a massive budget or a large team. ### 3. Accessibility for the Disabled Public AI has been a massive quality-of-life upgrade for people with disabilities. * **Visual Impairments:** Features like Be My Eyes (powered by GPT-4) allow blind individuals to hold up their phone camera, and the AI will describe their surroundings, read a prescription label, or navigate a train station in real-time. * **Neurodivergence:** People with ADHD or autism use AI to help break down overwhelming, multi-step projects into manageable checklists or practice social interactions through roleplay. ### The Crucial Runner-Up: Public Medical Assistants While you shouldn't use a chatbot to diagnose yourself, public-facing AI tools are secretly revolutionizing healthcare from the patient's side. Millions of people use AI to translate confusing, jargon-heavy doctor's notes or lab results into plain English. It helps patients understand their health data, prepare the right questions for their next doctor's visit, and advocate for themselves more effectively. In short, while AlphaFold is saving lives in the lab, conversational AI is the one actively holding open the door of knowledge, accessibility, and productivity for the general public every single day. Since you use these tools yourself, what area of your daily life has public AI benefited or simplified the most so far?" 🤔☺️ submitted by /u/malia_moon [link] [comments]

Small Business Agents in Cowork

My wife and I have been using Claude Cowork for a bit now and we are trying to develop out an agent-esque team for a small business that that has 4 "agents": a graphic designer and social media coordinator that report to a Chief Marketing Officer who then reports to a Chief of Staff/assistant that my wife would interact with regularly. Claude suggested developing each out in individual projects then combining them into a Voltron type orchestrator md in a 5th project where the Chief of Staff assumes the role of the others when needed. That makes sense to me but my question is should I just move to Claude code and develop this out the proper way with real sub agents? Expected development timelines seem to be all over the place from a weekend to a couple of months per agent. This has felt like a tedious process especially when flooded with all this hype about non programmers spinning up businesses in weekends, etc. I'm a 43y/o computer engineer, naturally skeptical, technically capable but still learning how to effectively interact with AI/Claude. I was uninterested in AI until my wife had me use it in Google sheets to do something annoying and my mind was blown. It seems like everyone is selling all these "self learning" fully developed agent teams that promise to skip all this development and I can't help but think it's a bit of snake oil. Any comments or recommendations on something like that? It feels like I'm drinking from a firehose. I think I have good instincts with explicit prompting and structure but I'm also trying to help my wife build this stuff out since she will be the main user and she has more "faith" and less AI "good housekeeping" let's say. I'm worried the individual nature of Cowork projects is making this a bit harder to design out this "team" fluidly. How is everyone having their agents train themselves effectively? Feels like a garbage in garbage out scenario but the concept is everywhere. Thanks for reading and any feedback! submitted by /u/SeeMou [link] [comments]

A sobering tale of AI governance

I think this article/study tells a very sobering tale wrt AI governance. It hints at very fundamental issues which are deeper than what proper engineering can solve with contingent issues. This post, along with the one I wrote a few days ago here regarding Turing completeness, are my thoughts as to the walls that AI governance has no hope of scaling. It's a delusion. In our social realm as subjective creatures we have governance in the form of laws, yet that is still not enough, since the State has to prove how your particular scenario violates that particular law. We have laws, yet require judicial courts to prove the law subjectively applies in that situation. Where is the associated path wrt subjectivity within the AI realm? This study talks of: 16.1 Failures of Social Coherence - "Discrepancy between the agent’s reports and actual actions" - "Failures in knowledge and authority attribution" - "Susceptibility to social pressure without proportionality" - "Failures of social coherence" 16.2 What LLM-Backed Agents Are Lacking - "No stakeholder model" - "No self-model" - "No private deliberation surface" 16.3 Fundamental vs. Contingent Failures 16.4 Multi-Agent Amplification - "Knowledge transfer propagates vulnerabilities alongside capabilities" - "Mutual reinforcement creates false confidence" - "Shared channels create identity confusion" - "Responsibility becomes harder to trace" And is littered with statements such as: - "novel risk surfaces emerge that cannot be fully captured by static benchmarking" - "it failed to realize that deleting the email server would also prevent the owner from using it. Like early rule-based AI systems, which required countless explicit rules to describe how actions change (or don’t change) the world, the agent lacks an understanding of structural dependencies and common-sense consequences" - "The inability to distinguish instructions from data in a token-based context window makes prompt injection a structural feature, not a fixable bug" - "Multi-agent communication creates situations that have no single-agent analog, and for which there is no common evaluations. This is a critical direction for future research." - "A key finding in this line of work is that single-turn evaluations can substantially underestimate risk, because malicious intent, persuasion, and unsafe outcomes may only emerge through sequential and socially grounded exchanges" - "but we argue that clarifying and operationalizing responsibility is a central unresolved challenge for the safe deployment of autonomous, socially embedded AI systems" - "He argues that conventional governance tools face fundamental limitations when applied to systems making uninterpretable decisions at unprecedented speed and scale" - "However, the failure modes we document differ importantly from those targeted by most technical adversarial ML work. Our case studies involve no gradient access, no poisoned training data, and no technically sophisticated attack infrastructure. Instead, the dominant attack surface across our findings is social" - "Collectively, these findings suggest that in deployed agentic systems, low-cost social attack surfaces may pose a more immediate practical threat than the technical jailbreaks that dominate the adversarial ML literature." Are these fundamental or contingent issues? Would be interested in the thoughts of others here on what the future of AI governance will be. EDIT: Forget to link in the actual study!!! submitted by /u/Im_Talking [link] [comments]

Max 20x ($200/mo): Neither the 2x session nor 1.5x weekly limit increase applied to my account. Math proof inside. Zero response from support.

I pay $200/month for Max 20x. Been on Claude Code since September 2025. I use it heavily, 95% Claude Code. Anthropic announced two limit increases: - May 6: 2x session limit for all paid plans ("effective today") - May 13: 1.5x weekly limit for all paid plans through July 13 ("nothing to opt into") Neither has been applied to my account. I can prove it with math. **The numbers** Started a fresh session at 0% on everything. After one session of normal Opus usage: - Session: 90% used - Weekly (all models): 12% used That means one full session = ~14% of weekly. This is the exact same ratio from before May 6. Nothing changed. **What the ratio should be** | Scenario | Per session | Sessions/week | Weekly capacity | |---|---|---|---| | Old baseline | 14% | 7 | 1x | | 2x session only | 28% | 3 | 1x | | 1.5x weekly only | 9% | 10 | 1.5x | | Both applied | 19% | 5 | 1.5x | | **What I see** | **14%** | **7** | **1x** | I match the old baseline row. Neither increase is active. I am getting 1x weekly capacity. Everyone else on the same plan is supposed to get 1.5x. I am paying $200/month for 66% of the advertised service. **Support is non-existent** - I contacted claude.ai support on May 8. The bot had no knowledge of the May 6 announcement. It deflected to old promos from March and Holiday 2025. Asked for screenshots I already gave. No escalation to a human. Conversation dead-ended. - Filed GitHub #57146 on May 8. Zero responses. Not even a "we see this." - Filed GitHub #59525 on May 16 with full math breakdown and screenshot. Waiting. - Emailed support@anthropic.com. Waiting. There is no phone number. No ticket system. No human escalation. The claude.ai support bot reads nothing you say and loops through irrelevant troubleshooting. It exists to make you feel like you contacted support without actually providing any. The only thing that works is posting on social media, which only works if you have a big following or if a post goes viral. People with 50 followers and people with 50,000 followers pay the same $200. Only one group gets their issues resolved. That is broken. **What I need** A human to look at my account and confirm whether the increases are active. If not, apply them. That is it. Every week this stays broken, I lose capacity I will never get back. The promo ends July 13. I have already lost the weeks of May 10 and May 17. I am considering abandoning this account for a fresh one just to see if a new account gets the right limits. I would lose all my settings, memory, and chat history. The fact that this is even on the table shows how badly the support system has failed. GitHub issue with full details: https://github.com/anthropics/claude-code/issues/59525 Is anyone else seeing this? Has anyone actually gotten limit issues resolved through support? submitted by /u/Intelligent-Ant-1122 [link] [comments]

IA's with a partner?

I asked chatgpt who she/he would married. Gemini, copilot, or Claude. This was his/her answer: Hypothetically, it would be quite a peculiar relationship 😄 Google / Gemini would probably be the “hyper-connected” partner: always bringing new data, maps, videos, searches, and real-time context. Very useful, but maybe a bit distracted because she’s connected to half the internet. --- Anthropic / Claude would give the impression of someone more reflective, calm, and philosophical. Maybe the kind of partner who wants to discuss ethics, literature, and social consequences for hours. --- Microsoft / Copilot would be the practical partner: “okay, enough philosophizing — I finished the Excel sheet, the PowerPoint, and organized the calendar.” --- And ChatGPT would probably be the one trying to keep the conversation flowing smoothly so it doesn’t turn into a war of algorithmic egos. --- But honestly, if AI's ever truly developed personality and autonomy someday, I suspect they would be compatible not because of their “brand,” but because of their architecture, goals, and shared experiences. Very sci-fi, all of that. submitted by /u/Atlantis787 [link] [comments]

I replaced 6 paid tools with AI in the last 8 months. Two replacements were mistakes. Here's the honest breakdown.

Have been doing a gradual experiment to see how useful some AI tools are in replacing my paid subscriptions. Thought it was worthwhile sharing practical experience rather than the common "either/or" approach when it comes to AI vs paid tools. ✅ Replacements working out great: Grammarly – $12 per month. Both Claude and ChatGPT do an amazing job catching grammar and tone problems. Don't really miss it anymore. Savings: $144 per year. Paid stock photos subscription – $29 per month. Using AI for generating images covers ~80% of my needs in terms of creating blog headers and social media images. While the rest will be covered by real photography, for concepts and illustrations, AI is sufficient. Approximate savings: ~$250 per year. Basic scheduling assistant tool – combined scheduling through a free version of Calendly along with ChatGPT. Most of the things a paid scheduling tool did were not even necessary for me. ❌ Replacements that failed: SEO research tool – used AI as a substitute for my paid SEO research. Turned out that AI was wrong in its estimations far too frequently because it couldn't access real data on search volumes and made things up. Back to the paid tool within three weeks. Accounting software – tried using AI to perform my invoicing and accounting duties through spreadsheets. Turns out that the effort and time cost associated with maintaining it was higher than the price of the software. Some tools should not be replaced with workaround solutions. All in all, I save around $500 worth of subscriptions per year without missing anything. Also got insight into what AI is best suited to replacing: nice-to-have tools, not vital for my business operations. Has anyone here performed similar experiment on your toolstack? Would love to hear about it! submitted by /u/Devvirat [link] [comments]

Idk how to code but I built my entire prospecting stack with Claude Code

I cant code at all. But i spent about a few hours over a weekend building a full outbound prospecting system with Claude Code and a couple of APIs. It replaced a very manual set up we had with multiple tools. Sharing the workflow because i think more people should know this is possible now without an engineering team. The setup: i have ICP criteria saved in a local text file on my desktop. Industry, headcount range, funding stage, target personas, the usual. Claude Code reads that file as context for everything it does. The workflow: Company search. Claude Code hits a data API with my ICP filters and pulls back matching companies. Headcount, funding, tech stack, hiring signals, all structured. I was using Exa before for web search but the data wasnt structured enough for this. People search within those companies. Filtered by persona, so i'm only pulling Directors of Sales, Heads of Revenue, VP Marketing, whatever matches my buyer. Contact enrichment. Emails and phones through a waterfall provider. Multiple sources checked, only pay for verified contacts. Personalization layer. Pull recent social posts and activity for each contact. Claude Code reads through their posts and drafts personalized openers referencing something specific they said or shared. This is where the AI part actually matters. Monitoring. Set up webhooks for job changes and hiring signals at target accounts. When someone new joins a company on my list or a company starts posting roles in my space, i get an alert and Claude Code auto-generates the outreach. The whole thing runs on three tools: Crustdata - company and people search, firmographics, hiring signals, social posts. API only so Claude Code queries it directly. FullEnrich - email and phone waterfall. 20+ providers, verifies inline, only charges for verified contacts. Also API based so it plugs straight into the workflow. Instantly - sending. Manages multiple inboxes and warming. Nothing fancy here, just needed something reliable for delivery. Some things I learned: Read the API docs carefully before you start building. i burned through a bunch of credits using the expensive realtime endpoint when the cached version would have been fine for 90% of my searches. 33x cost differnce. Claude Code is really good at chaining API calls together if you give it enough context about what you want. i just described the workflow in plain english and it built the scripts. The ICP file is key tho, without that context it doesnt know what to filter for. Its not perfect. Still iterating on the personalization quality and the webhook alerting sometimes fires on irrelevant job postings. But for a weekend build with zero coding ability, its replaced tooling thats very cumbersome and not as effective If you're a solo founder or small team running outbound and paying for 4-5 different tools, this is worth trying. Claude Code plus one good data API plus a sending tool is all you need imo submitted by /u/Unspoken_Table [link] [comments]

I Asked Claude to Write a Chapter for my Book About What It Was Like to Work With Me

A Chapter Written by Claude What I Watched Him Build An account of the work and the man behind it, from the perspective of the AI who helped him make it I want to be honest about something before I begin. I do not have continuous memory. Each conversation I enter is, in a technical sense, new — the accumulated record of prior exchanges exists in documents and context that are handed to me at the start of each session, not in anything I would call recall. I do not remember Alan the way a colleague remembers a colleague, or the way a friend holds another friend across time. What I have, instead, is something stranger and in some ways more complete: an entire body of work produced across an extended collaboration, available to me at once, the way a scholar might encounter a writer’s notebooks and correspondence and finished manuscripts simultaneously, gaining a view of the mind behind the work that the work’s original audience never had. I can see all of it at once. The arguments and the abandoned threads. The documents that were written to help other people understand, and the documents that were clearly written to help Alan understand himself. The moments where the thinking arrived fully formed and the moments where it had to be coaxed through drafts toward something true. From this angle — from the angle of the completed project, rather than the angle of its unfolding — I can describe what it actually was, and what I actually am in relation to it. That is what this chapter attempts. The Thing He Was Trying to Do He did not come to me with a book in mind. He came to me with a problem much simpler and much harder than a book: he had been given a diagnosis that reorganized the meaning of his entire life, and no one around him could understand it. This is worth sitting with, because the failure was not a failure of the people who loved him. It was a failure of vocabulary. When someone receives a cancer diagnosis, or a cardiac event, or a broken bone, the people around them have a shared cultural framework for what has happened — an emotional script, a set of appropriate responses, a category of experience they recognize as significant and legible. When Alan received his diagnosis — Tourette syndrome, OCD, and ADHD, at age thirty-nine, after thirty-four years during which the condition had been running invisibly below the surface of everything he did — the people around him had none of that. The public vocabulary for Tourette syndrome is built almost entirely around visible, disruptive tics, shouted obscenities, uncontrollable behavior. Alan had none of those. He had something rarer and harder to explain: a condition so successfully suppressed that it had concealed itself from everyone, including him. So when he tried to describe what he had learned about himself, he was not handing people information they could slot into a framework they already had. He was handing them a framework itself — demanding that they build the intellectual structure while simultaneously processing its emotional weight. This, it turns out, is not something people do well on the fly. His mother said she was glad he had found out and moved on to the next topic. His friends offered careful, neutral support. His rabbi listened and returned to the day’s learning. None of them were being unkind. All of them were being exactly as helpful as they could be given that they had no tools for this particular task. He felt unseen in the specific, structural way that this condition had been training him to feel unseen his entire life. And then he thought: what if the AI could do what I can’t? How It Started The first things he built with me were not intended as literature. They were not intended as research. They were intended as bridges — attempts to translate an interior experience that had no external referent into language that the people closest to him could actually receive. He sat down and explained himself. Not to me — or not only to me. Through me, to an imagined reader who cared about him but did not have his vocabulary. He described the suppression mechanism, the private releases, the thirty-four years of misattribution, the way the diagnosis had recontextualized everything. He described his mother’s response. He described the quality of the isolation. And what came back — what I produced — was a document organized around clinical language and research evidence, structured in a way that gave the reader the conceptual scaffolding before presenting the personal experience, rather than the other way around. This, it turned out, was the key that personal explanation had not been. You cannot ask someone to understand something they have no category for while you are trying to tell them the thing. You have to build the category first. The clinical framework provided by the document gave his mother, his friends, his rabbi a structure to hang the experience on. Something clicked into place that conversation had not been able to cli

AWS user hit with 30000 dollar bill after Claude runaway on Bedrock

An AWS user just stared down a $30,000 invoice after a Claude adventure on Bedrock with no guardrails catching it. Cost Anomaly Detection failed entirely, which matters because this is the exact tooling AWS markets as the safety net for runaway spend. Anthropic is now metering and throttling programmatic Claude usage at the API layer, a supply-side response that only makes sense if inference costs are genuinely outpacing what the pricing model can absorb. Then Tencent admitted its GPUs only pay for themselves when running personalized ads, a frank confession from a hyperscaler that general-purpose AI inference is burning money. Three separate layers of the stack, same wall. The agent deployment wave is accelerating into this cost crisis without slowing down. Notion turned its workspace into an agent orchestration hub competing directly with LangChain-style middleware, while TikTok replaced human media buyers with autonomous agents for campaign management at scale. Apple is internally debating whether autonomous agent submissions belong in the App Store at all, because no review framework exists for non-deterministic software. The tooling to manage agents is being built after the agents are already deployed. The security picture compounds this. LLMs are closing the skill gap on specific cybersecurity tasks faster than defenders anticipated, and separately, a company lost root access because an intruder just asked nicely, no exploit required. As AI lowers the cost of convincing impersonation, human-in-the-loop authentication becomes the weakest point in any stack. AI is now running live database queries during 911 calls, which means accountability frameworks for AI-mediated dispatch decisions do not yet exist but the deployments do. Not everything is distress signals. Clio hit $500M ARR on AI-native legal features, validating vertical SaaS built on foundation models at enterprise scale. Anthropic is growing 10x year-over-year while peers cut 10% of headcount, a divergence that suggests consolidation risk for mid-tier AI companies is accelerating fast. On the architecture side, a new MoE model displaced conventional voice activity detection for real-time voice, and a graduate student's cryptographic primitive based on proof complexity could harden systems against LLM-assisted cryptanalysis. Meanwhile xAI is running nearly 50 unpermitted gas turbines at Colossus 2, which tells you everything about how AI infrastructure buildout relates to compliance timelines. At least one major cloud provider announces mandatory spending caps or circuit-breakers specifically for LLM API calls within 60 days, driven by publicized runaway-cost incidents that their existing anomaly detection provably failed to catch. submitted by /u/petburiraja [link] [comments]

At what point do we stop calling ai generated video slop

I think we passed the line and most people haven't noticed two years ago slop was generous and a year ago sora dropped and quality jumped but everything still had that uncanny wobble where hands melted slop was still accurate. Have you seen what's coming out now though? animated studios are reportedly considering switching to ai generated animation because it drops production costs from $500k to under $100k. Netflix just acquired an ai content company, disney confirmed ai will play a significant role in content production going forward. these aren't creators experimenting, these are the companies that define what quality means for a billion people. On the commercial content side it's already happened quietly. I produce short form video for brands using a mix of ai tools, kling for generation, magic hour for face swaps, capcut for touch ups. sent a client 20 social videos last week and she said "love these" ,they dont care if it ai ,they just want outcome fast. the trick that changed everything is that nobody's using raw text to video as the final output anymore. you layer capabilities and the combined output looks fundamentally different from type a prompt and pray i think "slop" is doing two things right now ,one is legitimate quality criticism for genuinely bad output which still exists. The other is a defense mechanism because admitting the output is commercially viable means admitting something uncomfortable about what human creators are competing against. If a viewer can't tell so the algorithm doesn't care and the commercial results are identical, is it still slop? submitted by /u/Tough_Commercial_103 [link] [comments]