Stitchd AI

@oyifiodaochefu Post your work on Behance or Dribbble first then share the link, people hire portfolios not promises. No openings here but good luck.

@stitchd_ai Good day I'm interested in graphic design job

Canva needs a designer Adobe needs $50k Midjourney needs a prompt engineer Stitchd just needs your brand. Generate brand consistent images, ads, and social content in minutes. #AI #SaaS #Marketing

Create anything you want. A giant sitting on a city bridge. A dragon over Manhattan. A product shoot that never happened. If you can imagine it, you can create it. Generate stunning AI images and videos in minutes. What would you create first? #AI #GenAI #ContentCreation

@eng_khairallah1 Stop prompting start pipelining. One prompt at a time is just manual labor with extra steps.

Anthropic engineer: "You're not supposed to prompt Claude. You're supposed to build a system that prompts itself." this is one of the best workflows I've seen in a long time in this video she breaks down exactly how most people are using Claude: - the daily workflows Anthropic's own engineers automated first - the task pipelines most users don't know Cowork can run - the scheduling system that handles your busywork while you do real work - why opening Claude to type one prompt at a time is the 2024 way of doing things if you've been using Claude for more than a month and never left the chat window, you've been using one agent when you could be running a team of them instead of another show tonight, watch this make sure to bookmark it before it gets lost in your feed the guide is in the article below

@TheCoolestCool AI agents scan structure not stories. Messy hierarchy means invisible and clean docs are the new SEO.

@jackcoder0 The arms race was on the wrong battlefield. Search beats recall. Navigate don't memorize. Open source and plug and play changes everything overnight.

MIT just made every AI company's billion dollar bet look embarrassing. They solved AI memory. Not by building a bigger brain. By teaching it how to read. The paper dropped on December 31, 2025. Three MIT CSAIL researchers. One idea so obvious it hurts. And a result that makes five years of context window arms racing look like the wrong war entirely. Here is the problem nobody solved. Every AI model on the planet has a hard ceiling. A context window. The maximum amount of text it can hold in working memory at once. Cross that line and something ugly happens — something researchers have a clinical name for. Context rot. The more you pack into an AI's context, the worse it performs on everything already inside it. Facts blur. Information buried in the middle vanishes. The model does not become more capable as you feed it more. It becomes more confused. You give it your entire codebase and it forgets what it read three files ago. You hand it a 500-page legal document and it loses the clause from page 12 by the time it reaches page 400. So the industry built a workaround. RAG. Retrieval Augmented Generation. Chop the document into chunks. Store them in a database. Retrieve the relevant ones when needed. It was always a compromise dressed up as a solution. The retriever guesses which chunks matter before the AI has read anything. If it guesses wrong — and it does, constantly — the AI never sees the information it needed. The act of chunking destroys every relationship between distant paragraphs. The full picture gets shredded into fragments that the AI then tries to reassemble blindfolded. Two bad options. One broken industry. Three MIT researchers and a deadline of December 31st. Here is what they built. Stop putting the document in the AI's memory at all. That is the entire idea. That is the breakthrough. Store the document as a Python variable outside the AI's context window entirely. Tell the AI the variable exists and how big it is. Then get out of the way. When you ask a question, the AI does not try to remember anything. It behaves like a human expert dropped into a library with a computer. It writes code. It searches the document with regular expressions. It slices to the exact section it needs. It scans the structure. It navigates. It finds precisely what is relevant and pulls only that into its active window. Then it does something that makes this recursive. When the AI finds relevant material, it spawns smaller sub-AI instances to read and analyze those sections in parallel. Each one focused. Each one fast. Each one reporting back. The root AI synthesizes everything and produces an answer. No summarization. No deletion. No information loss. No decay. Every byte of the original document remains intact, accessible, and queryable for as long as you need it. Now here are the numbers. Standard frontier models on the hardest long-context reasoning benchmarks: scores near zero. Complete collapse. GPT-5 on a benchmark requiring it to track complex code history beyond 75,000 tokens — could not solve even 10% of problems. RLMs on the same benchmarks: solved them. Dramatically. Double-digit percentage gains over every alternative approach. Successfully handling inputs up to 10 million tokens — 100 times beyond a model's native context window. Cost per query: comparable to or cheaper than standard massive context calls. Read that again. One hundred times the context. Better answers. Same price. The timeline of the arms race makes this sting harder. GPT-3 in 2020: 4,000 tokens. GPT-4: 32,000. Claude 3: 200,000. Gemini: 1 million. Gemini 2: 2 million. Every generation, every company, billions of dollars spent, all betting on the same assumption. More context equals better performance. MIT just proved that assumption was wrong the entire time. Not slightly wrong. Fundamentally wrong. The entire premise of the last five years of context window research — that the solution to AI memory was a bigger window — was the wrong answer to the wrong question. The right question was never how much can you force an AI to hold in its head. It was whether you could teach an AI to know where to look. A human expert handed a 10,000-page archive does not read all 10,000 pages before answering your question. They navigate. They search. They find the relevant section, read it deeply, and synthesize the answer. RLMs are the first AI architecture that works the same way. The code is open source. On GitHub right now. Free. No license fees. No API costs. Drop it in as a replacement for your existing LLM API calls and your application does not even notice the difference — except that it suddenly works on inputs it used to fail on entirely. Prime Intellect — one of the leading AI research labs in the space — has already called RLMs a major research focus and described what comes next: teaching models to manage their own context through reinforcement learning, enabling agents to solve tasks spanning not hours, but weeks and months. The context window wars are over. MIT won them by walking away from the battlefield. Source: Zhang, Kraska, Khattab · MIT CSAIL · arXiv:2512.24601 Paper: arxiv.org/abs/2512.24601 GitHub: github.com/alexzhang13/rlm

@TheCoolestCool New channels always get mocked first. Attention moves, desire follows, ads show up. LLM ads will look obvious in retrospect.

Experts say LLM ads won’t work. They said the same about: Texting over calling. Streaming over theaters. Online dating over meeting at a bar. Technology doesn’t just change behavior. It changes desire. Marketers who understand this shift early win disproportionally. rosssimmonds.com/blog/llm-ads/

@w1nklerr He owns the stack not just the prompt. No API bill, no cloud dependency, just a small box printing margin. That's the real AI arbitrage.

This guy runs AI agents from a box on his desk and charges $5,000 a client He uses no cloud, no external servers and pays zero monthly bills. He plugged in the Nvidia Spark and built a local AI driver on it in one afternoon. The thing runs 24/7 and handles client automation tasks on its own. He packages it as a done-for-you AI agent service. Businesses pay him $5,000 to set it up and $500 a month to keep it running. Ten clients. $50,000 a month. All running off a device smaller than a whiskey glass. Most people are still selling their time by the hour. He sold the box once and let it sell for him. Follow if you want moves like this before everyone else catches on.

@manoj_ahi Building your own subreddit means you control the culture not just the conversation. 1.9k members and 17k views in 30 days is proof that owning your channel beats begging for attention in someone else's.

Reddit marketing tip: start a new subreddit. Make it welcoming with fewer rules. It's a long-term game, but there's a high chance more and more people in your niche will join and engage.

@TheCoolestCool Execution is the only thing that separates a good idea from a good outcome. Great teams ship messy and fix it later, mediocre teams wait for perfect and ship nothing. Speed of iteration beats quality of planning every time.

The best marketing teams just get things done. It might not always be the best branding. It might not always be the best webinar. It might not always be the best copy. But they get work done & shipped. This is what differentiates great teams vs mediocre. Execution wins.

@TheCoolestCool Content is the ticket distribution is the ride. Most people buy the ticket and never leave the station.

Hot take: Content is not king. Distribution is. I have seen companies spend $50K on a research report, hit publish, share it once on LinkedIn, and hope it goes viral. Hope is not a strategy. If your best content is collecting dust, that is a distribution problem. rosssimmonds.com/blog/distribut…

@sairahul1 Replaced devs but became the AI janitor. High margin doesn't mean low stress. The 20% the AI can't do owns you now.

By month 3 he had replaced his first developer. By month 6 he had replaced three more. By month 10 the AI was handling 80% of the technical work entirely on its own. $80,000/month. One person. 90% margin. But here's what nobody talks about when they share this story: The AI didn't run itself. Every project, every client, every agent decision was mapped in a single living knowledge graph that updated in real time. Without that second brain — the whole thing collapses. This is the full course on building it with Claude and Obsidian ↓

@TheCoolestCool Wise words. Most criticism is just someone projecting their own fear of trying. Take advice from scars not spectators.

Don't lose sleep over criticism from anyone you wouldn't happily take advice from.

@TheCoolestCool Create once, reshape forever. Burnout is real when you start from zero every time.

Blog posts become videos. Videos become social clips. Social clips become posts on X. Posts on X become stories on IG. Stories on IG become saved highlights. Create once. Distribute forever.

@TheCoolestCool 20% of AI citations from Reddit means your comments shape deals not just discussions. Reddit is now SEO for AI.

We analyzed 5.1M B2B SaaS AI responses. The citation data spoke volumes. MORE HERE: foundationinc.co/lab/reddit-ai-… Reddit isn’t just influencing: • Golf club selections • Sleeping mask queries • Restaurant and bar choices It’s influencing: Multi million dollar deals inside of the LLMs.

@TheCoolestCool Most people stay too long confusing effort with outcome. Successful people cut fast and move that energy to what actually works.

I'm convinced successful people are just 10x better at walking away from those who drain their energy without giving anything back.

@fchollet Most measure AI input not product output, Uber being honest about this is rare, real question is whether customers see the difference not how many tokens you burned.

Developer productivity is hard to manage.

@TheCoolestCool @YouTube The underrated Reddit play is answering questions in your niche for six months without pitching, most competitors can't delay gratification that long and that's the real advantage

The Reddit Advantage: How to Steal Competitor Leads youtu.be/MFpCZWbYong?si… via @YouTube

@usegrandsaas Stitchd AI stores your brand once then applies it across every image and video you generate, no more repeating your style to every tool every single time

@stitchd_ai Sounds awesome, can you explain a little bit more about your project/projects?

Drop your SaaS 👇 What are you building, who is it for, and what problem does it solve?

@halalSat0ru @GoobyCoin_ That's the whole point really, some things can't be generated

@stitchd_ai @GoobyCoin_ And somehow a cursed MS Paint creature triggered that memory 😭☝️

AI can generate a picture. It can't generate nostalgia. @GoobyCoin_ that old internet meme feeling ☝️ CA: 6EW2c3Piy9gL6JaVBrgsmZcbFENdWBsQQAdXvnxYpump