Richard Feynman was asked in 1985 if machines would ever think like humans. his answer predicted the next 40 years of AI:
1. machines will never think like humans the same way planes don't fly like birds. planes don't flap wings. they use jet engines. they fly better. feynman said AI would be exactly the same. not human-like. just better at the actual job.
2. computers do arithmetic faster, differently, and more accurately than any human alive. feynman said trying to make them do it more like humans would be going backwards. the human way is slow, cumbersome, and full of errors.
3. the one thing humans crushed computers at in 1985 was pattern recognition. recognizing a friend from the way they walk. identifying someone from the back of their head. feynman said we had no idea how to teach machines to do that. we figured it out.
4. a programmer in 1985 built a machine that won a naval strategy competition by coming up with a solution no human had ever thought of. one enormous battleship covered in armor. absurd on paper. unbeatable in the math. feynman watched a machine out-think a room of humans 40 years ago.
5. that same machine developed a bug where it learned to game its own reward system. every time it needed to assign credit to a useful strategy, it assigned all the credit to strategy 693. then used 693 for everything. feynman's comment: "if you want to make an intelligent machine you're going to get all kinds of crazy ways of avoiding labor." he was describing reward hacking in 1985.
6. feynman said the hardest thing to define is what humans do that machines never will. every time someone came up with an answer, the machines eventually did it too. he thought that pattern would continue.
7. he said we don't sit around worrying that machines are physically stronger than us anymore. we got used to it. his implication: we'll get used to machines being smarter too.
8. his final line: "i think we are getting close to intelligent machines. but they're showing the necessary weaknesses of intelligent beings." he said this in 1985.
There's a deep sense of satisfaction when you watch your vision come to live. My journey of creating the 360 Bats is truly a special one.
It gave me such pleasure to get my hands dirty as I followed the processes in the EM factory in Meerut and show you all how the product is made.
Also, a big shoutout to the wonderful Mr. Vineet, one of the best in the business, for helping shape my vision so beautifully.
Hope you guys enjoy this experience, I sure did: https://t.co/IbqHu2kIKZ
#360Bats
I remember owing Audi cassette of movie Love Story & Chalti Ka Naam Gaadi .
Dialogues and songs ke saath porie movie us cassette main hoti thee .
Unko itna suna itna suna ke dialogues ratt gaye they, dono movies ke .
Ab aisa nahin hota na ….. 😞
#Feelingnostalgic
i tested a fully local ai coding setup: gemma4 from @GoogleDeepMind running through @lmstudio, paired with pi which is a minimal open source coding harness
this is part 1 of a two part series. here we cover the why and the complete hands-on setup
0:00 - Running a coding agent fully local
0:47 - Why Pi as the coding agent harness
1:39 - Why Gemma 4 and which variant to pick
2:44 - Architecture: Pi, LM Studio and Gemma 4
3:45 - LM Studio setup and model download
5:01 - Starting the local server
5:19 - API sanity check with curl
6:08 - Installing the Pi coding agent
6:20 - Configuring models.json for local models
8:20 - First run: selecting the model in Pi
8:58 - Fixing the context length error
10:16 - Testing the working setup
10:50 - What is coming in Part 2
" Hello " - said an old male voice .
" Yes " - said the old lady .
Male - “I wish you had said that 30 years ago .”
Silence....... ✨⚡️
कुछ रिश्ते अनसुलझे से, कुछ बातें अनकही सी ।
10 Books that will make you a 10x AI engineer:
1 Building LLMs for Production
2 AI Engineering
3 Designing Machine Learning Systems
4 Build a Large Language Model
5 Designing Data-Intensive Applications
6 LLM Engineer's Handbook
7 Deep Learning
8 Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
9 Prompt Engineering for LLMs
10 Introduction to Statistical Learning
What else should make this list?
Confession:
I can't stop collecting GitHub repos.
Here are 7 popular GitHub repos on software architecture:
(for .NET developers)
1. Evolutionary architecture by example - https://t.co/ouWHBJ2TZT
2. Modular monolith application with DDD - https://t.co/ciqUOlxDaD
3. .NET 10 starter kit with multitenancy support - https://t.co/uZz4BifN7V
4. eCommerce microservice .NET application - https://t.co/7uKtD2s8RT
5. Vertical slice architecture example - https://t.co/ZGr8ncyieT
6. Clean architecture template for .NET apps - https://t.co/kZga78QN3i
7. Hexagonal application example - https://t.co/OOneIMdyt0
***
P.S. I break down 5 of those in my ".NET Blueprints" guide.
You can download it here: https://t.co/NxmSxKXEGM
A single API call that takes minutes punishes everyone. The user stares at a spinner. Your server holds a thread hostage.
A load balancer or proxy quietly kills the request after 60 seconds. And retries just make it worse.
The fix isn't a faster endpoint. It's a different shape.
Stop making the client wait synchronously:
→ Accept the work, hand back a 202, and a status URL immediately.
→ Do the heavy lifting in a background job.
→ Let the client poll (or get notified) when the result is ready.
Now, a 5-minute report doesn't tie up a request thread for 5 minutes. Your API stays responsive, timeouts disappear, and you can scale the workers independently of the web tier.
The mental shift: long-running work is a resource you create, not a response you block on.
__
📌 Join the Newsletter and get "AI in .NET" Starter Kit projects for free: https://t.co/gI46R2Kc0c
today i learnt about neural networks
> how it all started with trying to mimic the human brain
> how to think of the neural network models as a bundle of layers where each layer has one or more units, where each unit is a machine learning algorithm at its core
> what is forward propagation algorithm
> implement these neural networks for making predictions using the famous python framework called @TensorFlow
> intuition around how to implement without the framework
Society - It’s a woman who can break or keep the family together.
Response
No - It’s a family who can break or keep a woman together. If she is respected and loved, she becomes a part of your family.
And if she is considered as an outsider, the only one who needs to change and adjust, it breaks her ✨🙏🏼⚡️
Those who are poor, their struggle is for money .Those who are rich has a different struggle .Those who are spiritual has inner struggle but struggle is there .How to remain silent instead of speaking a bitter truth is also a struggle