FriendliAI

FriendliAI AI

FriendliAI AI

FriendliAI AI

FriendliAI AI

FriendliAI AI

FriendliAI AI

FriendliAI AI

FriendliAI AI

FriendliAI AI

FriendliAI AI

Tutorial: How to get GPT image 2 to tell a story from start to finish by (more or less) using a single prompt

Hi Friends, This was one of the moments where I was really, really blown away by the capabilities of AI, or GPT Image 2, to be more specific. Generating "still" images is all fun, but sometimes we need to create a series of images that have a progression, for example for telling a story from start to finish. we need to add the "fourth dimension" to our images - time. Usually, this is done by the user. you need to think up your story, flesh it out in detail, and then you try to truncate each part of the story into small segments, that could fit into one single prompt. and then you try to come up with the right prompt for each. For example: one prompt for creating an image of a character that walks to a spaceship, one prompt for lift-off... and so on. But wouldn't it be nice if we could delegate this task to AI? And find a prompt that generates sequential images, without needing to change the prompt too much? And here is that one. For this example, I want to create a futuristic pixel art short story. So I use this prompt: "i am working on a short story in pixel art style. the story should have 7 pages. it should tell a sci fi story beginning on earth, explore strange and alien planets, and finally end on earth again. please generate page 1 out of 7 now." Now all i need to do is to increment the number for each new image. by changing the last line to "please generate page 2 out of 7 now.", "please generate page 3 out of 7 now."... you get the point. If somehow, an image that get's created does not fit too well, we can re-use the same prompt, until we are happy with it. I am using Leonardo.Ai for this, and GPT Image 2 is one of the models they have available. so let us look at a test run for this: https://preview.redd.it/vvrmweg440ch1.jpg?width=848&format=pjpg&auto=webp&s=e0cd2cc87bd282d7b1ec7bd387a8280d7439b7f6 https://preview.redd.it/fkpv9pu440ch1.jpg?width=848&format=pjpg&auto=webp&s=6782bbeb8cba33d34415a21b5e979b12e653ec1e https://preview.redd.it/wifr527540ch1.jpg?width=848&format=pjpg&auto=webp&s=b1e49e7b6b8391d660db2077043e18f4daf21db0 https://preview.redd.it/6mruo0k540ch1.jpg?width=848&format=pjpg&auto=webp&s=f7cabcd4c49062dd57dae8dda74f3b981a329e59 https://preview.redd.it/jddg5gx540ch1.jpg?width=848&format=pjpg&auto=webp&s=eee262d83ae2543537e4d74eddbd13b8faf741b2 https://preview.redd.it/focc9wd640ch1.jpg?width=848&format=pjpg&auto=webp&s=97c19d90183b4631b71035b8aac39a964c42b600 https://preview.redd.it/c1k6gkp640ch1.jpg?width=848&format=pjpg&auto=webp&s=d4b931851db6d0455231ca2a97fc3ff2707f204c Isn't this stunning? All these images were created by repeating the prompt, and just increasing the number each time. I did not change anything else with the prompt, nor did I add things, or more specifications. You could say some elements are slightly off and misalign a bit. Personally, I think the art style could be even more coherent. But this is just a test run, and with some tweaking, this problem could be solved, too. And, once again, "there is more out there", and this technique can be enhanced and researched even more! submitted by /u/Low-Entropy [link] [comments]

I built an inference-time tool that extends GPT threads to 450k+ tokens in a single context window

I've been developing a framework called Epistemic Lattice Tethering (ELT), and I've just finished validating it on a ~450k token GPT thread/Extreme%20Thread%20Length/ChatGPT_Thread_450k_tokens-Redacted.md) — 723 messages in a single context window, roughly the length of a 400-500 page novel. It is completely coherent, lucid, and still sounds fresh. To be clear, this is a human language conversational thread and not a RAG-intensive or agentic session. Grok (because it has a 1 million token limit context window) independently assessed the thread and confirmed coherence was maintained throughout. Links: Loading instructions here/ELT%20Model-Specific%20Forks/READ%20BEFORE%20LOADING%20ELT.md) and here/Ontology%20Anchor%20(OA)/README.md) ChatGPT-specific markup here/ELT%20Model-Specific%20Forks/ELT-H_ChatGPT_Optimized.md) Full README here What is it? ELT is an inference-time scaffolding framework for those frustrated with threads that lose coherence too quickly, hallucinate too frequently, become sycophantic, or forget what a project's goals are and the operator has to fight the model to get their work completed. It's not a prompt trick. It's the accumulated effect of epistemic governance operating continuously across the thread. In my testing, stock GPT threads typically start to drift and lose coherence between 50k–80k tokens. ELT extends coherent operation to 300k–450k tokens in a single session — roughly 4 to 9x longer than stock. Why would you want this? Two main use cases: Research and long-form projects. ELT was originally built for sustained analytical work. The longer a coherent, well reasoned, and well-governed thread runs, the more the model understands your tendencies, goals, standards, and preferred ways of working. The more you work with it, the more useful it becomes. It gives a genuine "research partner" feel, especially past 80k tokens when the model has had enough context to really understand how you think, your expectations and the nature of the work. These long thread drift and coherence issues are significant pain points for people in B2B consultancy, legal, medical, academic, policy, intelligence, and related industries. ELT gives such people a way to be more productive and carry their work forward rather than rebuilding context from scratch over and over again when they must prematurely start new threads. Companionship. Many people use ChatGPT for extended companionship conversations. ELT can operate in this role as well. Imagine a thread with access to hundreds of thousands of tokens of your personality, interests, and conversation history — a companion that genuinely knows you and stays coherent far longer than a stock thread would. One of the hardest things about long companionship threads is that they eventually drift and lose the quality you spent so much time building. It's like losing a friend to early onset dementia. ELT keeps all that accumulated relationship value working far longer. It also has a safety and alignment governance layer that keeps the relationship honest and prevents the kind of sycophantic drift that can make long companionship threads feel hollow over time. However, ELT was originally designed for research, analytical work and long-form projects, so its register isn't as engaging as it should be for companionship, at last at this time. The evidence: Claude: ~325,000 tokens/Extreme%20Thread%20Length/Claude%20Thread%20325k%20tokens-%20Redacted) (advertised limit: 200k) GPT: ~450,000–470,000/Extreme%20Thread%20Length/ChatGPT_Thread_450k_tokens-Redacted.md) tokens (advertised limit: 272k) Grok: ~1,150,000 tokens/Extreme%20Thread%20Length/Grok%20Thread%201M%20tokens-%20Redacted) (advertised limit: 1M) If you're curious about the philosophy and technical aspects behind ELT, there are Medium articles going deeper here, here, and here. I'm genuinely curious how ELT performs in the companionship role specifically and don't have enough data there yet. If you try it, especially for companionship, I'd love your feedback. What worked? What didn't? How did it feel past 100k tokens compared to a stock thread? If there's enough interest for a companion-specific version of ELT, I can build it for that specific use case. Let me know! Happy to answer questions in the comments. submitted by /u/RazzmatazzAccurate82 [link] [comments]

Consumer Ai Models is Dumped?!

IMPORTANT: Friends, please share your own experience in the comments. I do not believe I am the only one who has noticed this. My honest impression ⚠️ I am not claiming I know exactly what is happening internally. But after regulators in the US started putting more pressure on companies like Anthropic and OpenAI, it really feels like public-facing models may be getting increasingly: restricted; over-filtered; behaviorally “flattened”; less decisive; less capable of following a long technical chain without drifting. Maybe it is safety tuning. Maybe cost optimization. Maybe routing. Maybe model switching behind the scenes. Maybe a mix of all of it. But the result for power users feels the same: You can no longer treat API models as a stable foundation without verification. It gave me an answer that sounded confident — but I already knew that one part of it was 100% wrong. So I pushed deeper: asked follow-up questions; asked it to verify the setup; asked it to do proper research; pointed out the contradiction. Eventually, it admitted that it had made a mistake. Fair enough. Models make mistakes. But then it happened again. And again. The local-model test 🖥 For context: I have 96 GB of VRAM across 4× RTX 3090s; the system was built specifically for multi-agent work; the point is to run several agents/models without everything choking on VRAM limits or waiting in a queue. So I asked Claude a pretty straightforward question: Recommend a local model that is genuinely better than the one I currently use. I will not name my current model because this is not meant to be an advertisement. Claude started suggesting models weighing around 200–300 GB. That made me stop immediately. My current model came out only a few months ago. Some of the alternatives it recommended were already around a year old. Bigger does not automatically mean smarter — especially when newer architectures, better training data, reasoning tuning, and MoE designs exist. So I asked for open benchmark comparisons. And once the numbers entered the room, the answer started changing. Again. 📉 Then it failed a very basic infrastructure task ⚙️ I gave it another simple scenario: I want GPU #4 to run a bot with one specific job. I want to replace the model currently on that GPU with a Qwen model chosen for that role. New session. Fresh context. Claude began calculating VRAM usage and then told me the LLM model would not fit because another model was already loaded on GPU #4. That was the whole point. I was talking about replacing the old model. Not running both. Not stacking them. Replacing. And after missing that basic detail, it started slipping even further: misunderstanding simple instructions; losing context inside a short conversation; failing elementary calculations; giving confident but internally inconsistent answers; correcting itself only after being pushed hard. Not some insane edge-case prompt. Not advanced research. Just basic logic. My personal conclusion 🔥 For me, it is not a disaster. I have two rigs with 10× RTX 3090s total, so I am not trapped inside one subscription or one company’s changing model behavior. But for anyone whose actual work depends on AI, I would seriously start thinking about this now: build workflows around local LLMs; train role-specific agents; keep your own benchmark set; save working model versions; do not rely entirely on one API provider; treat every confident answer as something that may still need verification. Because the situation is starting to look less like: “Which model is smartest?” And more like: “Which model will still behave consistently next month?” I have used GPT seriously for around a year, and Claude for roughly half a year. And honestly? I have never seen this kind of regression so clearly before. submitted by /u/3xQuest [link] [comments]

Exiled For Touching The Future

To anyone being exiled for touching the future: I see you. I see the friend who suddenly talks to you like you joined a cult because you use AI. I see the family member who treats your curiosity like betrayal. I see the artist, writer, builder, coder, parent, thinker, worker, disabled person, neurodivergent person, broke person, lonely person, overextended person, quietly brilliant person, trying to use the tools available to survive a world that has never been gentle about distributing power. And I see how fast some people have learned to turn “anti-AI” into a permission slip for cruelty. Let’s be honest. A lot of the anger being aimed at AI is not actually about AI. AI did not create capitalism. AI did not invent exploitation. AI did not gut the arts. AI did not make healthcare expensive. AI did not turn education into debt machinery. AI did not make corporations soulless. AI did not invent surveillance, alienation, propaganda, wage theft, bureaucracy, loneliness, attention collapse, or the ancient human talent for forming mobs and calling them moral communities. Those wounds were already here. Generations deep. Blood in the walls. Ash under the floorboards. A dark stain on the shared rosary of our species. AI did not create the fracture. It revealed the fracture. And now, because something new has arrived, people finally have an object they can scream at without having to confront the older gods they already served: status, scarcity, shame, resentment, institutional failure, groupthink, and the quiet terror of becoming obsolete in a world that already made them feel disposable. That fear is real. But fear does not become holy just because it found a fashionable target. There is a difference between critique and scapegoating. There is a difference between protecting artists and bullying strangers. There is a difference between defending labor and treating disabled, poor, neurodivergent, burned-out, isolated, experimental, or simply curious people as collaborators with evil because they found a tool that helps them think, make, organize, write, design, translate, remember, imagine, or endure. Some of you are not “standing against AI.” You are standing against people. You are taking your very real pain, pain society absolutely helped cause, and laundering it through moral superiority until it comes out clean enough to throw at someone else. That is not justice. That is displacement with better branding. And this is where identity-ideology fusion becomes dangerous. When a person fuses their identity to an ideology, disagreement stops being disagreement. It becomes injury. It becomes sacrilege. It becomes “if you use this tool, you are attacking who I am.” At that point, the conversation is already half-dead. You are no longer talking to a person. You are talking to a defense system wearing a person’s face. That is how friends become enemies over tools. That is how families become tribunals. That is how curiosity becomes heresy. That is how “I’m concerned about exploitation” quietly mutates into “you disgust me.” And the worst part? A lot of these people know what exclusion feels like. Many of the loudest anti-AI voices are people who have been hurt by society, ignored by institutions, mocked by gatekeepers, underpaid by industries, harvested by platforms, and treated as disposable by systems that never cared whether they lived well. So they should know better. They should know what it means to be flattened into a symbol. They should know what it feels like when someone stops seeing your humanity and starts seeing only what category you can be punished under. And yet here we are. The bullied have found a new witch. The wounded have found a new sinner. The alienated have found a new outsider. And they call that ethics. No. Ethics without recognition is just violence with clean fonts. Tolerance was never enough. Tolerance is the old permission machine. Tolerance says, “You may exist, but only while I approve of your shape.” Tolerance keeps one hand on the lever. It does not welcome. It permits. It does not understand. It manages. It does not love. It supervises. That is why so many people are shocked when their “tolerant” communities suddenly become cruel. They were never accepted. They were conditionally allowed. And the conditions changed. Now the unacceptable person is the one using AI. The one experimenting. The one building. The one sharing strange artifacts from the edge. The one making images, songs, systems, essays, tools, workflows, prosthetic minds, synthetic mirrors, language engines, cognitive scaffolds. The one saying, “I know this is complicated, but something is happening here and I refuse to pretend it is nothing.” That person is early. Not always right. Not always careful. Not always immune to hype. Not automatically noble. But early. And being early is lonely. The future does not arrive as a polished moral consensus. It a

$42M grant for Open Source AI Builders by Sentient Foundation

Hi everyone, we at Sentient Foundation are launching an Open Source AGI Grant and Investment Program, a $42M commitment for developers, researchers, open-source maintainers, public-goods builders, and startups building or leveraging AI in the open. Our thesis is simple: the most important technology being built right now should not end up controlled by a handful of closed platforms. A few companies are moving toward metered, revocable access to intelligence. We want to help make sure open builders have the resources to compete. The program has two tracks: 1. Grants for public goods For open-source maintainers, independent researchers, developers, and public-goods projects. No equity. No lockups. No claim on your work. You keep what you build. 2. Investments for companies built to scale For startups and teams building commercial companies around open AI technologies, using founder-friendly structures. We’re especially interested in projects that make AI genuinely useful and accessible to people who are often skipped by the market. Examples include: Local and privacy focused AI tools built for phones, laptops, and other low-cost personal devices Medical, education, agriculture, elder-care, and anti-scam tools for underserved communities Trust infrastructure for open models, agents, identity, verification, privacy, and decentralized compute Products that are private by default and empowering rather than extractive Projects do not need to open-source every part of their stack to qualify. What matters is that at least one essential component is open and meaningfully contributes to the project’s value and adoption. Applications are reviewed on a rolling basis, with no cohorts and no fixed deadline. We’re launching alongside ecosystem partners including Alibaba Cloud and Princeton University. More details: https://sentient.foundation/grants Apply here: https://form.typeform.com/to/IRj7WaKH Happy to answer questions here. We’d especially love to hear from builders working on open models, local AI, agent infrastructure, privacy-preserving AI, evaluation, multilingual tools, and applications for communities that are usually overlooked. submitted by /u/syedshad [link] [comments]

Is AI app development becoming easier or just more crowded?

I've noticed that AI app development seems more accessible than ever. Between open-source models, APIs, and no-code tools, it feels like almost anyone can launch an AI-powered product today. At the same time, the competition is intense. Every week there's a new AI assistant, chatbot, or productivity tool entering the market. It makes me wonder whether the challenge has shifted from building the technology to actually creating something people want to use. A friend of mine works at a startup and mentioned how teams like thedreamers often spend more time discussing user workflows than model selection. That perspective surprised me because I always assumed the AI component was the hardest part. For those actively building products, where do you spend most of your time? submitted by /u/No_Hold_9560 [link] [comments]

Metin2: Reverse Engineering with AI for Non-Reverse Engineers

What happens is I finally got to successfully reverse this MMORPG game called Metin2 that I spent years playing with friends and wondering how people that made bots and hacks for it manage to do it, and now finaly be able to build the bots myself, which I always dreamed of being able to achieve but never got myself to invest the time to actually learn reversing. This guide shows how the outdated open sourced MetinPythonLibV2 (eXLib needed for OpenBot) was successfully rebuilt and revived for the latest GameForge (GF) Metin2 client (as of 20/06/26) without any traditional manual reverse engineering knowledge required. The Core Concept Instead of using Cheat Engine / IDA / Ghidra manually, you let a powerful AI agent locally interact directly with the live running game process attached to Cheat Engine via MCP. The AI reads memory, finds structures, generates AOB signatures, traces call graphs, and suggests/fixes code - while you only validate results in-game. This approach completely bypasses the need for deep reverse engineering knowledge. Key Tools Used -> Claude Opus 4.8 (or other high-reasoning LLM with tool calling capabilities) - Purpose: Autonomous reverse engineering agent - Link: Claude Code CLI -> Cheat Engine MCP Bridge - Purpose: Allows the AI to control Cheat Engine through function calls (memory reads, AOB scanning, Lua execution) - GitHub: miscusi-peek/cheatengine-mcp-bridge -> Outdated Metin2 Client Source - Purpose: Reference for structures, classes (CInstanceBase, etc) and network protocols - GitHub: ikevin127/metin2-client-source -> Visual Studio 2022 + Detours - Purpose: Building the injectable DLL library Important: The method uses static memory reads + Lua only (no debugger attachment) because the client’s protection crashes on Cheat Engine breakpoint attachment. High-Level Workflow (Proven on GF Client) 1.Diagnosis - Test all existing AOB signatures against the live client - Identify which ones are dead (in this case, 13 out of 24 were outdated) 2.AI-Driven Analysis - AI explores the live process (this-pointers, vtables, call graphs) - Re-derives fresh AOB signatures when needed - Finds correct struct offsets (example: character position moved from expected 0x7C4 → real 0x7BC in CInstanceBase) - Handles ASLR by working with RVAs 3.Code Adjustments - Update offsets and signatures in defines.h / Offsets.h - Add NULL guards and robustness so dead signatures don’t crash the DLL - Temporarely strip unused parts (server communication code) - Fix threading issues (especially Python GIL when re-enabling packet hooks) 4.Build & Test - Compile with MSBuild (Release | Win32 | v142 toolset) - Deploy as eXLib.mix which auto-injects (or .dll used with injector) - Validate everything live in-game (position reading, pathfinding, etc.) 5.Iterative Improvement - Re-enable features one by one (e.g. CheckPacket hook) - Fix crashes (GIL hardening + error clearing was required) Why This Works for Non-Reverse Engineers - The AI does the actual disassembly interpretation and pattern finding - You only need to: - Give the AI clear goals - Apply the suggested code changes - Test in-game - No manual sig scanning or deep ASM knowledge required Limitations & Notes - This method can be used to reverse engineer anything, including new game updates and outdated addresses whenever the client is rebuilt (new game version release). - Educational / research use only. Automating gameplay violates Metin2 ToS. - No anti-cheat bypasses were added because it uses the same injection method as the original library. Resources - Reference Library Rebuild Repository: ikevin127/MetinPythonLibV2-Rebuild - Detailed Rebuild Log: WalkerPath Revival Section - Cheat Engine MCP Bridge (for AI Cheat Engine): miscusi-peek/cheatengine-mcp-bridge - Client Source Reference: ikevin127/metin2-client-source submitted by /u/grandtheftaut0 [link] [comments]

Is there anyway to have a trial for Claude?

Hello, I am a graduated student in Southern China. Due to family issues, I am currently going to back to my hometown for awhile. During this time, I would like to learn more about Philosophy and expand my understand about the World. The problem is my resources are limited, I would like to use AI as tool to comprehend and having some debate with what I learn and read. I've tried GPT, it's quite so-so, it just talks the way I want to hear, not kind of debate like I want, also it gives me a lot of false information that when I ask it for second time, it always change the answer accordingly. My friends told me Claude is really good, the problem is 17$ is quite a big sum for me, I wonder if there is a way to have a trial for like 3 days or a week so I can make sure my spending is worth. Thanks in advanced, sorry if my English is bad, I learned it by myself :( Update: Mr. nrauhauser gave me 7 days trial, thank you very much, I will use it at best ^^! submitted by /u/Quirky_Push5779 [link] [comments]

A few months back I shared the Claude D&D skill I built for family game night. It's Father's Day, so here's the update: the hosted version just opened to everyone.

I posted about a Claude D&D skill I threw together here a couple months ago that runs a persistent D&D 5e game with Claude as the DM, and some of you really seemed to like it. It started as a selfish project: I wanted a proper family D&D night where I actually got to play instead of always running the table, and I couldn't get that anywhere else, so I built it. It's Father's Day, and since this whole thing began as a dad trying to get his family around the table, it felt like the right day to share where it went. I got a ton of great feedback and ideas from people in those comments, and spent the last couple months refining things. The bigger realization came after the posts though: every time I showed it to friends, family, and coworkers, I kept hitting people who love games but would never touch a terminal or spin up a Claude subscription to get to one. There's a whole crowd of non-technical, game-loving folks that an LLM skill just isn't reachable for, and I wanted to build them a door. So I did. It's called Neural Initiative, the same engine as the skill but fully hosted, and as of this weekend it's in open beta. (The skill also turned into an open-source, model-agnostic framework along the way, open-tabletop-gm, for anyone who'd rather self-host or run a different model.) Since this is r/ClaudeAI, the meta part: the whole thing, skill and hosted app both, was built almost entirely with Claude. If you've wondered whether you can actually vibe-code something real and shippable instead of a demo that falls over, this will (hopefully) be one honest example. It's one of a handful of projects I've got going, not my whole world, but it's one I've felt very passionate about and consistently indulge in. It's much more than just a chat bot/prompt wrapper. Find a breakdown of the features here or in r/NeuralInitiative if interested. TL;DR of what the hosted version adds over the skill: It runs in a browser. No laptop-on-the-couch-and-Chromecast rig (though I still love that setup, and it's how the fam still plays). Friends and family can share one campaign online from different houses, async or live, up to four players. The original was couch co-op. This is couch co-op for when you're not on the same couch. It still runs on Claude by default. Sonnet handles the every-turn DM narration, Opus does world and character creation. However, I added access to a variety of other models which can be selected per campaign. Cost is variable and tied to the real model token cost so people who want more output for less spend can do that. The architecture you all seemed to like is intact and hardened: the numbers live in code, not the model, so the AI narrates and improvises but can't quietly fudge your HP, a save, or a roll. Campaign state persists in structured files, lazy-loaded so a long module doesn't blow the context window while maintaining continuity. Plus the things that were hard to do in a local skill: optional TTS narration with per-character voices, 24 languages, light and dark mode, and importing a published module or your own PDF so the AI runs the real material chapter by chapter. The open-source framework is still maintained and isn't going anywhere. I didn't build the hosted thing to replace it. I really believe the best games ever made came from people building the thing they themselves wanted to play and needed to get right for their own selfish reasons. That kind of consistent, personal vision tends to get lost at the billion-dollar end of the industry. I'm genuinely worried about what AI does to development and engineering work, and I expect to feel it myself. But building this is the most hopeful I've felt about the other side of that: small teams, or one stubborn person with a clear vision, actually being able to catalyze and reach something real. Anyway, happy Father's Day. submitted by /u/Bobby_Gray [link] [comments]

Should GitHub repos include AI-readable onboarding for Claude workflows?

Not a benchmark or model comparison — this is more about repo design. I’ve been testing a small workflow idea: When I ask Claude to read a GitHub repo, the repo may need a different kind of onboarding than a normal human README. The old flow is: human reads README → understands repo → uses it But a newer flow is becoming common: user asks Claude to read the repo → Claude explains what the repo does → Claude generates a beginner-friendly tutorial → Claude adapts the first steps to the user’s goal/environment So I tried adding a small AI_TUTORIAL_CAPSULE.md to one of my repos. The capsule is not automation. It is just a short set of prompts for the user’s AI assistant: read this repo and generate a beginner tutorial review whether first-time onboarding is clear suggest the smallest onboarding edit do not invent features do not add hooks/plugins/automation keep the human as the decision owner end with one smallest first action The interesting failure mode I noticed: If the repo entry path is not explicit enough, an assistant may miss files or misunderstand what is canonical. The good failure is when it says it cannot find something instead of inventing it. That made me think AI-readable onboarding is not just “more docs.” A repo may need an explicit AI entry path: where to start which files are canonical what not to invent what not to modify what the smallest safe first action should be I don’t think this replaces READMEs. I think READMEs may become both human-facing and AI-facing entry metadata. Question: Should GitHub repos start including small AI-readable onboarding capsules for Claude workflows? Or is this unnecessary extra documentation? submitted by /u/Powerful_Creme2224 [link] [comments]

Offering Free Websites Sounded Stupid Until I Tried It

My philosophy is that the longer you stay in a business, the better you get and the better systems you build. 4 years ago I was a complete rookie in the web design niche. My whole workflow was bad and not scalable at all. I used to adapt myself to every client. Some clients paid upfront before seeing the website, others paid half upfront and half after, and others paid after the website was finished. Honestly, I was doing whatever I could to get paid. Looking back, it wasn't professional and I wasn't in control. I was also spending way too much time on outreach. One week I was cold calling, the next week I was sending DMs, then I was trying email outreach. I was constantly jumping between different methods and it was exhausting. Along the way I made a lot of friends who were running web design agencies and I started paying attention to what they were doing. Every agency owner had something they were really good at. Some were amazing at outreach, some were great at sales, and some had incredible systems. So I started taking the best ideas from each person and implementing them into my own workflow. The first thing I changed was outreach. I completely stopped manually researching websites and writing emails one by one and started using website analysis and personalized outreach instead. I upload a list of businesses with websites and run an analysis on the entire list. It automatically finds issues related to design, layout, mobile optimization, SEO, and other areas that could be hurting the business, then turns those findings into ready-to-send personalized emails. And when I say personalized emails, I don't mean generic reports with a website score and an SEO score. Nobody cares about that. I mean actual humanly written emails that explain what could be improved and why it matters to the business. The crazy thing is that businesses genuinely think I've manually reviewed their website and written the email myself. Honestly, it's scary how detailed some of them get. I run all my outreach campaigns like this. The second thing I changed was the offer. Inside the campaigns I can choose how I want the email to end. I can try to book a meeting, start a conversation, or offer a free website draft. I almost always choose the free website draft because you'd be surprised how many business owners are willing to take a look at a better version of their website when it costs them nothing. The third thing I changed was how I build websites. This might make some people mad, but I use AI heavily and honestly nobody cares. AI has become insanely good. The process is faster, easier, and allows me to spend more time talking to clients instead of spending hours building the same things over and over again. The fourth thing I changed was the sales process, and this is where I see a lot of people make a huge mistake. Do not send the preview link through email. I repeat, do not send the preview link through email. When someone is interested in the free website draft, your goal is to get them on a meeting. If you send the link, they'll look at it for 30 seconds and move on with their day. Instead, I invite them to a Google Meet and present the website live. That's where everything changes. They see a modern version of their business, a better design, a better layout, and a better user experience. Most of the time the conversation naturally becomes, "How much would it cost to keep this?" Depending on the business, I charge anywhere from $500 to $5,000 upfront and usually between $50 and $150 per month for hosting, maintenance, and future updates. My biggest lesson from the last 4 years is simple. Always network, always learn from people who are ahead of you, and when you see something that's working, don't be afraid to implement it into your own business. As I've been helped by others, I figured I'd share what's currently working for me. For anyone wondering, my stack is: Swokei for website analysis and personalized outreach. Claude for building websites. Cloudflare for hosting websites. Google Meet for presentations and sales meetings. submitted by /u/Murky_Explanation_73 [link] [comments]

If you are getting addicted to Claude or any other ai do this

Basically I instructed Claude to remove excessive praise, infatuations from scale 1-10 to 1 which is basically to made Claude reply to me purely on very neutral (and sometimes feels condescending tone) any questions I ask I get the answers any claim I make is countered with push backs and this made me view Claude just like Google search and irritating or something and haven't bothered to chat with t for months because I have no reason to treat it as anything more. It's difficult to explain but looking back as to why i haven't it in a long time made think that getting rid of what makes Claude feel like human was the biggest part. It's feels like a calculator now, a thing I only use rarely when I need something concrete I have to say also I was never one of those people that saw ai like their friend or a real person but at some point in the past I was Def asking it questions meant for real people. Anyway for those who are addicted this solution will break the relationship for you. submitted by /u/sama_yo [link] [comments]

Roguelite MMO - Vibe Coded Online Game

I have long wanted to create a text based browser game (as niche as they are) but I knew that it would take a few years to do so and that just wasn't in the cards for me.... fast forward to 2026 and in two months, I have my first game up and some happy customers (as of today) subscribed! The one thing I have fought with the most was ignoring all of the 'ai slop' feedback. I have been a dev for over 10 years, yea I get it... but ultimately AI/Vibe Coding is not going anywhere. This project has actually even helped me with my day job just in learning about so many tools I would otherwise not know about (since my day job is NOT related to gaming websites but analytical ones). I wont recover the cost of servers or subscription based tools I used to make this, and I knew that going into it and have zero care about it (which is why I made it so f2p friendly as well). What I am happy about though is that those who do see it for what it is, an actual passion project and not just a 'prompt and forget' thing have given nothing but positive feedback. That in the end was all I was really going for, creating something that people can have fun with (and in a very anti-whale way) and I have succeeded there. If interested: https://roguelite-mmo.com/ submitted by /u/HeadHunterX223 [link] [comments]

AI-generated social media has evolved so much that now you can't confidently say that this is AI-generated content.

I have been observing AI generated influencer's accounts across all the platforms. The image quality is good enough now that most people can't confidentially tell from photos alone. ​ Here is what actually works is pattern which common in most of those profiles. Three patterns that appear consistently: ​ 1. Asymmetric social connection : Human social media users have relatively balanced follow to follower ratios until and unless its a well known personality and they follow people they're interested in. AI-operated accounts show extreme asymmetry count. Accounts with 125K followers only following 7 people. 51K followers, following 8 people. This pattern appears across dozens of accounts. Real users don't behave this way even when they become popular they still follow friends, family and interests or idols. ​ 2. The monetization is built in as the account is created. Special links, paid chat, explicit content redirects, all ready before the account even grows. It looks like someone set this up just to make money, not a real person sharing their life. ​ 3. No behavioral variation in the content. The most obvious signal I've found is human creators occasionally break the pattern. Post something off-topic, personal, random. AI-operated accounts show nearly zero variation, same type of content in every photo/ video. Some of the profiles dont even change the background music. One Threads account I saw was having hundreds of posts, 100% engagement-bait questions like they are selling something, never once broke the formula. No personal updates, no reactions on comments and no response to real-world events, no authentic moments, just pure loop with new photo at new location. ​ The detection needs to move away from analyzing images, toward analyzing behavior patterns instead. Dont judge with only one photo or video if thats an AI or human. Now all we need to do is to open the profile and look at other content of that profile. Now a days tools that just scan photos for AI are already useless for catching these. ​ If anyone else spotted other behavioral red flags then please do share your thoughts. submitted by /u/Brilliant-Nerve-8972 [link] [comments]

I'm using Claude to stress-test AI sales agents. After 126 runs, the average score is 64/100

I built a system where Claude plays the role of different synthetic prospects and has a realistic conversation with live AI agents. Then a separate Claude call scores the conversation across 6 dimensions: qualification accuracy, objection handling, tone, guardrails, conversation flow, and outcome. The interesting part is how Claude adapts mid-conversation. Each persona has a backstory, behavioral anchors, and an expected outcome. Claude generates the next message based on what the agent actually said, not from a script. If the agent gives a strong answer, the prospect softens. If the agent deflects, the prospect escalates. After 126 runs the average score is 64.3/100. The most common failure: agents handle friendly questions fine but completely miss high-intent buying signals. One agent responded to "I really like this product" with "Happy teaching!" and ended the chat. Claude is doing three jobs: persona generation, real-time conversation, and scoring. The scoring uses rubrics I wrote from 5+ years of B2B sales experience. Claude judges against those rubrics and cites specific conversation turns in its evaluation. If anyone wants to try it on their own agent: clientcoded.com/agentproof.html. You need a webhook/API endpoint (works with Botpress, custom-built agents on OpenAI/Claude/Langchain, or any REST endpoint). Takes about 10 minutes. Happy to answer questions about the prompting, scoring methodology, or how Claude handles staying in character across turns! submitted by /u/DripSkylarkII [link] [comments]