Subhan Qureshi

Sharing insights on AI, Tech, & Empowering driven Founders and CEOs to build impactful Personal Brands on Twitter |📩 DM For Collaboration [email protected]

[Subhan Qureshi @LearnWithSubhan]

You're Wasting Netflix

If you haven't watched these 4 must-see movies yet!

Make sure to check them out!

[Bishal Nandi @LearnWithBishal]

🚨 Breaking: I used an AI agent to apply to 150+ jobs in a single day.

Now it wakes up every morning at 10 AM and keeps applying for me automatically.

If you're job hunting in 2026, you need to see this: 🧵👇

[Alexander @Mdkhurshed76417]

You can make $3,400 per week, If you have:

1. Internet
2. Mobile
3. 1 hour everyday

I have prepared a guide for this. It's absolutely FREE:

Like & reply “Need” and I’ll DM you the document.

(Must follow me to receive it)

[ETHERSON @LiamEtherson]

The difference between a video and a slideshow is rarely the graphics.

It's how scenes are paced, how long moments linger, and how attention is directed.

frame.md bakes those ideas into the instructions rather than leaving them for post-production.

[Rohit @ai_rohitt]

INSTEAD OF WATCHING AN HOUR OF NETFLIX TONIGHT.

This 1 hour Stanford lecture by Joel Peterson will teach you more about negotiation and getting what you want than most people learn in years.

Bookmark it and give it an hour, no matter what.

[Jaymin Shah @JayminSOfficial]

For years, consumer AI has been optimized for one thing: workflow.

But one of the biggest sources of stress for working parents is not the work itself.

It’s the invisible operations and crisis management of family life.

School pickups.
Doctor appointments.
Extracurriculars.
Group chats.
Matching up calendars.
When do they need to be picked up?
Where do they need to be picked up?

The next generation of AI products for families will look very different from the productivity tools we have now.

It goes beyond automated calendars and reminders.

It’s reducing the mental weight that sits on the shoulders of every working parent.

I recently explored this new AI product centered on family life, and what stood out to me was not the automation.

It was the clarity. It was the relief.

A healthy family does more than improve organization.

They reduce tension.
There is space to breathe.
The parents are present with each other and their children.

AI products today optimize for efficiency.

This one optimizes for your peace of mind.

[Jyoti Soni @soni_jyoti_]

Miso One offers a glimpse into the future of voice AI—more expressive, less robotic, and responsive enough for natural, real-time conversations. As the technology evolves, emotional intelligence may prove just as important as raw intelligence

[Kalsoom (ghotai ) @AIwithGhotai]

Email marketing just changed.

@nitrosendx lets you create, design, segment, and send emails from a single prompt in Claude.

No dashboard.
No templates.
No copy-pasting.

Built by a team that has already delivered 6B+ emails and exited two email companies.

See it in action: https://t.co/PT7uJ5J5ka

[Jack @jackcoder0]

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: https://t.co/Z1w6mk0EHd
GitHub: https://t.co/Ko36uDE5XO

[Alex hals @Alex_AI96]

JOB INTERVIEW:
"Tell me about a conflict with a coworker."

Most candidates say:
"We had different working styles, but we sat down, talked it through, and found common ground. It made us stronger as a team."

THE WINNING ANSWER:

[Rajesh Kumar @therjrajesh]

I accidentally built a social media employee with AI.

It works 24/7.

Never sleeps.
Never complains.

And somehow it makes money too.

Thread 👇👇👇

[Amber shaheryar @AmberShaheryar]

💸 Everyone is using AI to save time.
Smart creators are using AI to print money.

Here’s the earning trick most people are missing 👇

Combine powerful AI tools + smart execution = scalable income.

✅ Content Creation

✅ Faceless YouTube

✅ Freelancing

✅ Digital Products

✅ Automation Services

The people who learn AI today won’t just compete tomorrow…
They’ll build faster, market smarter, and earn while others are still figuring things out.

Your next income stream might not need:
❌ Huge investment
❌ Big team
❌ Advanced coding skills

It may just need the right AI stack + consistency.

Stop asking: “Will AI replace jobs?”

Start asking: “How can AI multiply my income?” 🚀

The AI gold rush has already started.
Are you scrolling… or building? 👀

#AI #ChatGPT #AITools #MakeMoneyOnline #DigitalMarketing #Freelancing #YouTubeAutomation #PassiveIncome #OnlineBusiness #Entrepreneurship

[Austin Jordan @austin_jordan12]

How To Use Claude To Start a Business📍
Follow me (@austin_jordan12)

[How 2 AI @How2use_AI]

I turned an AI agent into a social media manager.

Now it works 24/7 and generates income while I sleep.

The use-cases are endless

Check the thread👇

[Kalsoom (ghotai ) @AIwithGhotai]

The most impressive part isn’t the AI video generation.

It’s the ability to bring your own references, assets, threads, videos, and design systems, then iterate directly on the result.

That’s much closer to a real creative workflow.

https://t.co/DEdvlvlwbZ

[Prashant Payasi @payasi_pa66104]

Perplexity AI
📌Fast research
🫰🏻Fact-checking
🪝Finding sources

[Anuj @anujcodes_21]

I already code in Claude.

I research in Claude.

I plan products in Claude.

Now I can build and send entire email flows from Claude too.

Feels obvious in hindsight.

Every software category is eventually becoming "just tell the AI what you want."

@nitrosendx is what that looks like for email.

[Zohaib Ai @AiwithZohaib]

Building an AI Agent in 2026 is no longer just about picking an LLM.

The real magic happens in the system around the model. 🤖

This roadmap perfectly breaks down how modern AI agents are actually built from scratch. 👇

A production-ready AI agent needs 8 core layers:

1️⃣ Define the Purpose
Before writing prompts, define:
• use case
• user needs
• constraints
• success metrics
Most AI projects fail because this step is skipped.

2️⃣ System Prompt Design
Prompts are becoming operating systems for agents.
A strong system prompt defines:
• role/persona
• goals
• instructions
• safety guardrails

3️⃣ Choose the Right LLM
Different models = different strengths.
• GPT-5.5 → versatility & tool usage
• Claude → reasoning & long context
• Perplexity → research & citations

There’s no “best model.”
Only the best model for the task.

4️⃣ Tools & Integrations
This is where AI becomes actionable.
Agents connected to:
• APIs
• MCP servers
• databases
• custom tools
• external apps

Can actually execute workflows instead of just generating text.

5️⃣ Memory Systems
Memory is the difference between:
“a chatbot”
and
“an intelligent assistant.”
Modern agents use:
• working memory
• vector databases
• structured storage
• episodic memory

6️⃣ Orchestration
This is the hidden layer most people ignore.
Workflows, triggers, queues, retries, routing, multi-agent coordination…

This is what turns prompts into systems.

7️⃣ User Interface
The best AI products win on UX, not just intelligence.
Chat apps, APIs, Slack bots, dashboards - interface matters.

8️⃣ Testing & Evaluations
If you don’t measure quality, latency, reliability & hallucinations…
your AI product will eventually break at scale.

The biggest takeaway?
AI Engineering is rapidly becoming a combination of:
Software Engineering + Prompting + Systems Design + Automation.

The engineers who understand orchestration, memory, tools & workflows will dominate the next decade of AI products.

Save this roadmap.

This is basically the blueprint for building AI agents in 2026. 🚀

Follow @AiwithZohaib for more AI engineering breakdowns, prompts, workflows & agent architectures.

[Katyayani Shukla @aibytekat]

Think of it as a compile step.

design.md is the source. frame.md is the target the agent can actually direct from.

You stop translating brand-to-motion by hand every single render.

[Nafi @Nafi_5t]

Stop using Claude on default settings.

This Claude Setup Guide breaks down the exact framework to supercharge your workflow and make your AI 10x smarter.

Unlock the full blueprint below. Follow @Nafi_5t
Comment 'Claude Guide'
Auto Send in Your DM
#ClaudeAI #Ai

[Elara Grace @ElaraGrace_AI]

Finally! No more random AI outputs this Pixar-style workflow with character sheets, storyboards & Seedance 2.0 makes everything feel consistent and alive. The sunbeam girl short is adorable @apob_ai