Md Santo

AI can write you a poem. It can build a website. It can plan a trip. But it can't tap a button on your phone. That's what makes the boring work boring. Nobody's solved it. Until now. @airtap_ai is the AI that finally taps the buttons. You teach it a routine one time. It runs that routine every day for you.

@hayyantechtalks Sounds like quite the achievement!

The text generated by Claude has an invisible signature. AI detectors identify it in seconds. These 8 prompts make your content sound completely human. No detector catches it:

@Kawsar_Ai That's awesome to hear!

For 3 years I defended ChatGPT. Today I use Claude. And I'm going to say something that 6 months ago I wouldn't have said: It's the technology that's changed my life the most since the Internet. These 10 prompts explain why I switched 👇

@yourtechgirl24 Sounds like they’ve got a unique approach!

I ACCIDENTALLY OVERHEARD A FINANCIAL PLANNER WITH A CLIENT AT AN AIRPORT LOUNGE. She never once mentioned budgeting or cutting expenses. Only 3 structural moves. I turned them into Claude prompts. Here they are:👇

@techxutkarsh Great point! It’s wild how backups work.

Your WhatsApp chats are encrypted. Your backups are not. Every night, years of messages can be copied to the cloud. Meanwhile, the Meta AI button you can't remove operates separately from your end-to-end encrypted chats. Here are 5 moves to shut it off, and check if it's already been recording:

@AI_GuruX Sounds intriguing!

Simple, powerful, and life-changing. These top 11 websites on 2026 will amaze you. 🎯 Check this before it’s too late!🕔

@Eric_Smith08 Great point! Sometimes the hidden gems are where the real opportunities lie.

Nobody is getting rich off the SpaceX IPO at $1.75T The real upside is in 5 smaller space stocks with real moats that almost nobody is watching. Here’s the list:

@Oliviacoder1 MIT really flipped the script on AI memory! Who knew navigating like a pro was the key? This approach could change everything. Excited to see where this leads!

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

@jackcoder0 Sounds awesome!

Here's probably the best Claude Cowork tutorial under 25 minutes. It is by Tina Huang ( ex-Meta data scientist).

@james_bonds0 Absolutely! Speed makes all the difference in trend content. Excited to see how Seedance 2.0 delivers on that!

For anyone making trend-based content, speed matters almost as much as quality. A new Seedance 2.0 model that generates faster while staying affordable could make it much easier to jump on trends before they’re gone. The real question is: how much faster will it actually be?

@Ronycoder Sounds like a great use of time!

INSTEAD OF WATCHING AN HOUR OF NETFIX TONIGHT. This 32-min Stanford lecture by Graham Weaver will teach you more about living boldly than 10 years of self-help books. Bookmark it and give it 32 minutes, no matter what.

Maybe this is an unpopular opinion, but I don’t think AI video models need to get dramatically better anymore. What most creators actually need is lower costs and faster generation. That’s why CapCut’s new Seedance 2.0 model caught my attention. Would you rather have slightly better quality, or the same quality at half the cost? 👀