suraj kumar

PYTHON is difficult to learn, but not anymore! Introducing "The Ultimate Python ebook "PDF. You will get: • 74+ pages cheatsheet • Save 100+ hours on research And for 24 hrs, it's 100% FREE! To get it, just: 1. Like & RT 2. Reply "Python" 3. Follow @Surajcoder [MUST]

@HeyRohhit Definitely worth an hour of your time.

Instead of watching an hour of Netflix, watch this 60 minute lecture from Steve Jobs after being fired from Apple. It will teach you more about building companies than most startup books ever will.

@TechByMarkandey The feedback loop is usually the missing piece, not the model itself.

Everyone wants better AI agents. The challenge isn't collecting more data. It's making sense of the massive amount of production traffic, traces, tool calls, and feedback agents generate every day. That's why Adaline is interesting. It turns production behavior into evals by identifying patterns, surfacing failures and drift, generating synthetic scenarios based on real usage, and continuously creating new tests to improve agent performance. What stood out is that humans stay in control. Adaline can build agent candidates from what it learns, but a human reviews and approves changes before anything goes live. The next leap in AI agents may come less from bigger models and more from better feedback loops.

@Ronycoder This feels like a more meaningful direction.

i don't think the next breakthrough in agents is a bigger model. it's giving agents a way to learn from their own production mistakes. reading traces, identifying recurring behaviors, generating evals, and improving from real-world usage feels like a much more important direction than most people realize. this is basically that, shipped.

@Alokkumarzz Great thread! Most people use WhatsApp daily but rarely explore features that can improve privacy and productivity.

WhatsApp has more than 1.9 billion users worldwide. But 97% of people don’t know its true potential. Here are 15 essential tricks you should know:

If you have a Gmail account, you need to read this. Google's AI now scans your emails and attachments, bank statements, tax files, medical letters, all of it. It turned on by default, and there's a class-action lawsuit over how. Here are 5 moves to shut it off, the switch is hidden in two places:

STOP TELLING CHATGPT, "DO THIS." STOP TELLING CHATGPT, "WRITE CODE." STOP TELLING CHATGPT, "FIX THIS ERROR." YOU'RE ACTUALLY TREATING A SENIOR AI LIKE A JUNIOR INTERN. HERE ARE 8 PROMPTS YOU CAN COPY AND PASTE DIRECTLY:

Someone cloned Netflix. Then cloned Spotify. Then cloned Instagram. Then cloned Airbnb. Then cloned WhatsApp. Then cloned TikTok. Then cloned Amazon. Then put all of them in one GitHub repository. For free. Not one clone. Not ten. 100+ open-source recreations of the world's most popular apps. Frontend. Backend. APIs. Authentication. Payments. Real-world architecture. The project is called Clone-Wars. Created by Gourav Goyal. Instead of learning from toy projects, you can study how developers rebuilt products used by billions of people. Inside you'll find: • Netflix clones with streaming interfaces • Spotify clones with playlists and music players • Instagram clones with feeds, stories, and DMs • WhatsApp clones with real-time messaging • Airbnb clones with booking systems and maps • Amazon clones with carts and checkout flows • TikTok clones with short-form video feeds • Twitter clones with posts, follows, and engagement • Slack clones with channels and team chat • Trello clones with boards and drag-and-drop workflows • YouTube clones with video search and playback • And dozens more The best developers don't just watch tutorials. They reverse-engineer real products. They study architecture. They inspect code. They understand how everything connects together. That's exactly what this repository helps you do. 100+ projects. 100+ codebases. 100+ opportunities to learn from production-style applications. GitHub: github.com/GorvGoyl/Clone…

Fable & Mythos just got restricted outside US 🔒 But you can rebuild 95% of the magic inside Claude Opus. Here are 6 prompts that can turn Claude into your own Fable & Mythos engine 🧵 (Save this)

@wiliam23820a Awesome share

I used to blame my MacBook for being slow. Then I found out Apple hides the performance settings. 6 settings that make a $2,000 MacBook run 3x faster:👇

We're reaching a point where AI video quality is already "good enough." Speed and cost might become more important than quality improvements. @capcutapp's upcoming model seems to be betting on exactly that. Agree or disagree?

@Ronycoder Worth watching for anyone serious about YouTube Shorts.

This guy literally shows the easiest way to print with YouTube Shorts in 2026.

@kyronis_talks Excellent work, thanks for sharing! 👏

Google has published a paper that might end the transformer era. For the last 7 years, every major AI, ChatGPT, Claude, Gemini, has been built on the exact same architecture: The Transformer. But Transformers have a fatal flaw. To remember context, they have to process every single word against every other word. It’s called quadratic complexity. As your prompt gets longer, the compute cost explodes. The alternative is the old-school RNN (Recurrent Neural Network). RNNs are incredibly cheap and fast, but they have a fixed memory size. If you give them a long document, they get amnesia. Until today. Google researchers published Memory Caching: RNNs with Growing Memory. And it fixes the biggest bottleneck in AI. Instead of an RNN having a fixed, rigid memory that constantly overwrites itself, Google gave it a "save" button. The technique allows the RNN to cache checkpoints of its hidden states as it reads. The memory capacity of the RNN can now dynamically grow as the sequence gets longer. They built four different variants, including sparse selective mechanisms where the AI actively chooses exactly which checkpoints matter most. The results rewrite the rules of efficiency. On long-context understanding and recall-intensive tasks, these new Memory-Cached RNNs closed the gap with Transformers. They achieved competitive accuracy without the explosive, quadratic compute cost. It perfectly bridges the gap between the cheap efficiency of an RNN and the massive capability of a Transformer. We have spent billions scaling Transformers because we thought they were the only way an AI could remember a long conversation. But Google just proved we don't need to process the whole history every single time. We just needed a smarter cache.

How to master Claude in just 5 days(and save yourself hours every week) ☀️ DAY 1: Train Claude about you Goal: Make responses feel personalized from the start. → Choose your workspace mode (Chat, Code, or Teamwork)→ Enable Memory→ Add a short intro about your work in Preferences Once you do this, Claude starts understanding your workflow and style. ☀️ DAY 2: Create your first workspace Goal: Avoid typing the same instructions again and again. → Set up a project around a repeated task→ Add your custom instructions once→ Upload files, examples, or references Now Claude already has your context before every conversation. ☀️ DAY 3: Set up a reusable Skill Goal: Remove repetitive explanations forever. → Open Settings → Customize → Skills→ Tap "+" and define a task you do often→ Save it and let Claude auto-load it when needed One setup. Reusable forever. ☀️ DAY 4: Link your everyday apps Goal: Work faster without switching platforms. → Connect tools like Gmail, Drive, or Slack→ Let Claude access and work within those apps directly No endless copy-pasting. No tab chaos. ☀️ DAY 5: Automate recurring work Goal: Make Claude handle tasks on autopilot. → Create recurring workflows→ Schedule summaries, updates, or daily briefs→ Let Claude process and deliver automatically At this point, it stops feeling like a chatbot.It becomes your operating system. Five days of setup.Weeks of saved time after that. Instead of doom scrolling today: Bookmark this thread Start with Day 1 (takes barely 10 mins) Finish the full setup this week Small setup. Massive leverage.

so sweet