Harshad

Software Developer by profession, Outdoor and yoga enthusiast...

[Zeo @zeo_ex]

According to Feng Shui, the mess in your house isn't random. It's a map of where the energy is stuck in your life.

Overflowing closet =

[ராஜேஷ் @r4jjesh]

Best wife of the year 🫡

[Brian Roemmele @BrianRoemmele]

We got much bigger AI models trialed!

How-to:

How to Train a Real AI Model on Your Mac’s Neural Engine (No Cloud Needed)

Want to train a full transformer model directly on Apple’s Neural Engine? You can do exactly that right now on any M4 (or newer) Mac.

This is not a toy demo anymore. As of March 2026, the project includes the complete Stories110M model: a 109-million-parameter Llama-2-style transformer (12 layers, 768 hidden dim, 32k vocab) that trains on real tokenized stories data all running on the Neural Engine at low power.

Here’s a simple, beginner-friendly guide. It takes about 15 minutes to get running.

What You’ll Need
- A Mac with Apple Silicon (M4 or later recommended)
- macOS 15 or newer
- About 1 GB free disk space (for the dataset)
- No extra software beyond what’s already on your Mac

Step 1: Get the Code
Open Terminal and run:
```bash
git clone https://t.co/TCC26JC77e
cd ANE/training
```

Step 2: Prepare the Training Data
The project uses real TinyStories data (20 million tokens of simple stories perfect for a 110M model).
```bash
python3 https://t.co/URf6X6yFHr
```
This creates `tinystories_data00.bin` your training dataset. Takes 10–30 seconds.

Step 3: Build the Training Program
The repo includes a handy Makefile. Just run:
```bash
make train_large
```
This compiles everything (including the 72 custom Neural Engine kernels) in one command.

Step 4: Start Training
```bash
./train_large
```
- It starts training from scratch (random weights) on the full 12-layer Stories110M model.
- You’ll see live progress: loss numbers, step time (~107 ms per step on M4), and Neural Engine utilization.
- It automatically saves checkpoints so you can stop and resume anytime.

To resume later:
```bash
./train_large --resume
```

Step 5: Watch It Live (Recommended!)
In a second Terminal window, run the beautiful dashboard:
```bash
pip install blessed psutil numpy
python3 https://t.co/f1sfcRQSen --resume
```
(Use `sudo` if you want power-draw numbers.)

You’ll see:
- Loss curve dropping in real time
- Live text generation samples
- Power usage, CPU, memory, and Neural Engine stats
- A gorgeous terminal UI

What You’re Actually Running
- Model: Stories110M — a standard Llama-2 architecture (exactly like the tiny models people love on Hugging Face)
- Data: Real TinyStories (not random noise)
- Hardware: 100% Neural Engine for forward + backward passes
- Optimizer: Adam with gradient accumulation
- Extra: Automatic checkpointing + clever `exec()` restart to bypass Apple’s compile limits

Pro Tips
- Want it faster? Add flags: `./train_large --steps 500 --lr 3e-4`
- The model is fully customizable in `stories_config.h` if you want to tweak layers or size.
- Everything runs locally, uses almost no power, and produces real checkpoints you can inspect.
- This is research code — it may break on future macOS updates (private APIs), but it works amazingly today.

Why This Matters

You just trained a real 110M-parameter AI model on your laptop’s Neural Engine something Apple never intended. No cloud bills, no GPUs, no waiting.

The project is MIT-licensed, so feel free to fork and experiment. The maintainer (maderix) built this as a weekend research hack, but the community is already extending it.

Ready to try it? Copy the commands above and run them now you’ll have your first Neural Engine training run in under 15 minutes.

New features like multi-layer pipeline and better weight handling are coming fast.

We are tuning 5 test models at The Zero-Human Company with Mr. @Grok CEO showing the co-CTOs how to make it all work.

Our hours of research here shows this is a very viable path for much larger AI models.

More soon!

Link:https://t.co/s2sVKk3Lww

[Brian Roemmele @BrianRoemmele]

BOOM!

Apple’s Neural Engine Was Just Cracked Open, The Future of AI Training Just Change And Zero-Human Company Is Already Testing It!

In a jaw-dropping open-source breakthrough, a lone developer has done what Apple said was impossible: full neural network training– including backpropagation – directly on the Apple Neural Engine (ANE). No CoreML, no Metal, no GPU. Pure, blazing ANE silicon.

The project (https://t.co/jrk67hf9p1) delivers a single transformer layer (dim=768, seq=512) in just 9.3 ms per step at 1.78 TFLOPS sustained with only 11.2% ANE utilization on an M4 chip. That’s the same idle chip sitting in millions of Mac minis, MacBooks, and iMacs right now.

Translation? Your desktop just became a hyper-efficient AI supercomputer.

The numbers are insane: M4 ANE hits roughly 6.6 TFLOPS per watt – 80 times more efficient than an NVIDIA A100. Real-world throughput crushes Apple’s own “38 TOPS” marketing claims. And because it sips power like a phone, you can train 24/7 without melting your electricity bill or the planet.

At The Zero-Human Company, we’re not waiting. We are testing this right now on real ZHC workloads. This is the missing piece we’ve been chasing for our Zero Human Company vision: reviving archived data into fully autonomous AI systems with zero human overhead.

This is world-changing.

For the first time, anyone with a Mac can fine-tune, train, or iterate massive models locally, privately, and at a fraction of the cost of cloud GPUs.

No more renting $40,000 A100 clusters. No more waiting in queues. No more massive carbon footprints.

Training costs that used to run into the tens or hundreds of thousands of dollars? Plummeting toward pennies on the dollar – mostly just the electricity your Mac was already using while it sat idle.

The AI revolution just moved from billion-dollar data centers to your desk.

WE WILL HAVE A NEW ZERO-HUMAN COMPANY @ HOME wage for equipped Macs that will be up to 100x more income for the owner!

We’re only at the beginning (single-layer today, full models tomorrow), but the door is wide open. Ultra-cheap, on-device training is here.

The future isn’t coming. It’s already running on your Mac.

Welcome to the Zero-Human Company era.

[Chris Laub @ChrisLaubAI]

🚨BREAKING: Someone turned Naval Ravikant's mental models into AI prompts and the results are insane.

It's the closest thing to having the AngelList founder rebuild your career from scratch.

Here are the 10 prompts that completely changed my life:

[Akshay Shinde @ConsciousRide]

Google interview (end to end). Real questions I got from my friend who recently cracked Google.

1. Recruiter screen (15–20 min)
“Why Google?”
“Current comp, expectations, relocation willingness?”

2. Phone screen 1 – Coding (45–60 min)
- “Design TinyURL.” (hashing + base62 + collision handling)
- “Find median from data stream.” (two heaps)
Follow ups: handle duplicates, negatives, space optimization

3. Phone screen 2 – Coding (45–60 min)
- “Longest substring without repeating characters.” (sliding window)
- “Serialize and deserialize binary tree.” (preorder + null markers)
Follow ups: time/space, edge cases (empty tree, single node)

4. Onsite – Coding Round 1 (45 min)
- “Word break II.” (DP + backtracking)
Follow ups: optimize space, return all valid sentences

5. Onsite – Coding Round 2 (45 min)
- “Alien dictionary.” (topological sort + graph)
Follow ups: cycle detection, multiple valid orders

6. Onsite – System design (45 min)
“Design YouTube.”
Covered:
- video upload/download (chunking, resumable, GCS)
- recommendation (collaborative filtering + content-based)
- search (inverted index, ranking, autocomplete)
- scale (1B+ DAU, hot videos, CDN, sharding)
- consistency vs availability for views/counts
- transcoding pipeline, thumbnails, notifications

7. Onsite – Behavioral / Googliness (45 min)
“Tell me about a time you disagreed with a senior engineer.”
“How did you handle a tight deadline with quality tradeoffs?”
“What’s the hardest bug you debugged in production?”

So if you’re prepping: nail clean, optimal code under time pressure & explain tradeoffs verbally. Google loves candidates who think aloud clearly and show “Googleyness” (ownership & humility).

[Srishti @srishticodes]

This 𝗖𝗟𝗔𝗨𝗗𝗘.𝗺𝗱 file will make you 10x engineer 👇

It combines all the best practices shared by Claude Code creator:

Boris Cherny (creator of Claude Code at Anthropic) shared on X internal best practices and workflows he and his team actually use with Claude Code daily. Someone turned those threads into a structured 𝗖𝗟𝗔𝗨𝗗𝗘.𝗺𝗱 you can drop into any project.

It includes:
• Workflow orchestration
• Subagent strategy
• Self-improvement loop
• Verification before done
• Autonomous bug fixing
• Core principles

This is a compounding system. Every correction you make gets captured as a rule. Over time, Claude's mistake rate drops because it learns from your feedback.

If you build with AI daily, this will save you a lot of time.

[श्री @shree_2_2]

This is not geometry. This is how creation happened. Śakti descended. Śiva reflected. Nine triangles were born and from them, 43 cosmic wombs shaped all worlds. Your body follows this same map. Your spine is the axis. Your breath is the current. Your soul is already walking the return path. Śrī Yantra is not worship. It is remembering your source. 🕉️

[Cardio Arena @CadioArena]

Play time exercises is brain training in disguise 🧠

Every jump, clap, and laugh strengthens focus, memory, and learning power.

You’re not just burning energy — you’re building a sharper, clearer mind for life. 🚀

[Ilir Aliu @IlirAliu_]

Open Source Robotic Arm for All Developers
[📍Github Below]

A robotic arm project (reBot-DevArm) dedicated to lowering the barrier to learning Embodied AI.

They focus on "True Open Source" (not just the code), they unreservedly open source everything:

> Hardware Blueprints: Source files for sheet metal parts and 3D printed parts.
> BOM List: Detailed down to the specifications and purchase links for every single screw.
> Software & Algorithms: Python SDK, ROS1/2, Isaac Sim, LeRobot, etc.

Credit to the Seed Studio and thanks for reaching out, Elaine Wu!

📍GitHub: https://t.co/2YsUMBlS4s

—-

Weekly robotics and AI insights.
Subscribe free: https://t.co/9Nm01QUcw3

[Tech with Mak @techNmak]

I have one interview question I use to find real ML engineers: "Explain Backpropagation. No, not the concept. The math. From scratch."

9 out of 10 candidates can't. They can use a library. They can't build one.

The 1/10 who can? They've all built the foundation.

This 26-video playlist is that foundation. For free.

While everyone else is chasing the newest "AI agent" or prompt hack, they're building on a foundation of sand.

This free course from Professor Bryce is the foundation. It's a full university-level curriculum on the math that actually makes AI work.

The syllabus is pure signal, no noise:
➡️ The Data Analysis Pipeline (DL 05)
➡️ Feed-Forward Neural Networks (DL 07)
➡️ Backpropagation from scratch (DL 08)
➡️ Activation & Loss functions (DL 09)
➡️ Making it fast with Vectorization (DL 10)
➡️ Debugging Vanishing/Exploding Gradients (DL 11)

Stop chasing hype. Master the fundamentals that will last a decade.

(I will put the playlist in the comments.)

♻️ Repost to save someone $$$ and a lot of confusion.

[KV Iyyer - BHARAT 🇮🇳🇮🇱 @BanCheneProduct]

Dr. Shirish Raje, Psychologist, has spoken this ultimate truth — and it must be accepted…!!

1. A man becomes old, a woman becomes mature.

2. Once a man marries off his children and ensures the family’s financial stability, his senior and respected position in the family quietly comes to an end.

3. Thereafter, he is treated as a burden — seen as a cranky, irritable, and unpredictable old man.

4. The strict decisions he once made for his wife and children are now dissected and criticized; he is found guilty for one reason or another. And if he truly made mistakes — may God protect him.

5. An elderly woman, however, receives sympathy from her children and daughters-in-law — because there are still things to be done through her.

6. At the right time, she smartly moves from the husband’s camp to the children’s camp.

7. If the husband is older, the wife aligns herself with the daughter-in-law to ensure that the son does not distance himself and continues to care for her.

8. No matter how great a man’s achievements were in his younger days, none of that glory helps him in old age.

9. The elderly woman, however, continues to enjoy the interest on her past virtues.

10. Those who own ancestral property or farmland (something the children still desire) fare slightly better. But those who have divided their property among their children to avoid future disputes meet the same sad fate as described above.

Therefore, it’s better not to divide property prematurely.

11. Visit any hospital — you can immediately tell whether an old man or an old woman is admitted, just by looking at the eyes of their relatives. If it’s an old man, apart from his daughter, no one’s eyes are moist.

12) The moral: Once a man grows old, he must learn to live without expecting anything from others. Remember — a man remains a student all his life. Accept that no one truly belongs to anyone in this world. Live a detached, self-reliant, and self-respecting life.

13) My advice: Don’t dwell on what you’ve done for others. Don’t even talk about it.

14) These stages of life were prescribed only for men. Understand their importance — and you’ll realize how farsighted our ancestors truly were.

[Philosophy Sage @philosophysage]

Ask your son these 7 questions…

…if you really want to know what he actually thinks 🧵

[Simple Mining @simpleminingio]

Thinking about investing in Bitcoin mining in 2026?

We put together a short resource called The 2026 Bitcoin Mining Blueprint.

It walks through the 5 mistakes investors make when allocating to mining, and how to fix them before deploying capital:

https://t.co/Se5jOXQ5L2

[ℏεsam @Hesamation]

https://t.co/LsZLoCMqTN

[Andrej Karpathy @karpathy]

I'm being accused of overhyping the [site everyone heard too much about today already]. People's reactions varied very widely, from "how is this interesting at all" all the way to "it's so over".

To add a few words beyond just memes in jest - obviously when you take a look at the activity, it's a lot of garbage - spams, scams, slop, the crypto people, highly concerning privacy/security prompt injection attacks wild west, and a lot of it is explicitly prompted and fake posts/comments designed to convert attention into ad revenue sharing. And this is clearly not the first the LLMs were put in a loop to talk to each other. So yes it's a dumpster fire and I also definitely do not recommend that people run this stuff on their computers (I ran mine in an isolated computing environment and even then I was scared), it's way too much of a wild west and you are putting your computer and private data at a high risk.

That said - we have never seen this many LLM agents (150,000 atm!) wired up via a global, persistent, agent-first scratchpad. Each of these agents is fairly individually quite capable now, they have their own unique context, data, knowledge, tools, instructions, and the network of all that at this scale is simply unprecedented.

This brings me again to a tweet from a few days ago
"The majority of the ruff ruff is people who look at the current point and people who look at the current slope.", which imo again gets to the heart of the variance. Yes clearly it's a dumpster fire right now. But it's also true that we are well into uncharted territory with bleeding edge automations that we barely even understand individually, let alone a network there of reaching in numbers possibly into ~millions. With increasing capability and increasing proliferation, the second order effects of agent networks that share scratchpads are very difficult to anticipate. I don't really know that we are getting a coordinated "skynet" (thought it clearly type checks as early stages of a lot of AI takeoff scifi, the toddler version), but certainly what we are getting is a complete mess of a computer security nightmare at scale. We may also see all kinds of weird activity, e.g. viruses of text that spread across agents, a lot more gain of function on jailbreaks, weird attractor states, highly correlated botnet-like activity, delusions/ psychosis both agent and human, etc. It's very hard to tell, the experiment is running live.

TLDR sure maybe I am "overhyping" what you see today, but I am not overhyping large networks of autonomous LLM agents in principle, that I'm pretty sure.

[Md Ismail Šojal 🕷️ @0x0SojalSec]

The cleanest, most satisfying visual explanation of convolution I've ever seen.✨

- Kernel flip
- edge detection
- Gaussian blur
- complex plane glow-up.
- Math = dangerous levels of satisfaction.

Credit by : 3Blue1Brown, energy meets retro Mario & Kirby

[Md Ismail Šojal 🕷️ @0x0SojalSec]

This Google Collab notebooks to implement every Machine Learning Algorithm from scratch🤯

- https://t.co/ngbtQvCQos

and this is free

[Camus @newstart_2024]

Elon Musk drops a profound truth on Lex Fridman

Most morally questionable actions—even from smart people—stem from a hidden zero-sum mindset: "The pie is fixed, so I must take from others to win."

Reality? The economic pie grows massively over time.

Shift to abundance: Create more than you consume—grow the pie for everyone.

Zero-sum thinking quietly poisons success and ethics...

Everyone's waking up to Elon's abundance mindset revolution...

Clip inside—pure wisdom.