Loading video...

Video Failed to Load

Go Home

I built ChatGPT's memory system into Apple's LLM

stevenzhang's profile picture

Steven Zhang

11,215 subscribers

119,810 views • 8 months ago •via X (Twitter)

EducationScience & Technology
LIVE

Anya Rossi• Live Now

Private livecam show

0 Comments

No comments available

Comments from the original post will appear here

Related Videos

I turned my iPad into Tom Riddle’s diary with Apple’s new Foundation Models, an LLM built into iOS. It’s lightning fast!
0:34

I turned my iPad into Tom Riddle’s diary with Apple’s new Foundation Models, an LLM built into iOS. It’s lightning fast!

Ethan Saadia @ SIGGRAPH

330,762 views • 1 year ago

Holy shit... One month before Andrej Karpathy dropped the “LLM Wiki” idea… someone had already built it. Not theory. Not hype. A working system. LLMs today restart from zero every time. No memory. No evolution. No learning. That’s broken. Most people use CLAUDE.md / AGENTS.md → Manual updates → Easy to forget → Knowledge gets lost That’s not intelligence. That’s a notepad. So I built dynamic memory for LLMs: • Learns automatically • Stores without manual effort • Weakens irrelevant data over time • Strengthens what actually matters Before the “LLM Wiki” idea went viral, this already had: • Brain-like decay/boost memory • Smart deduplication (no noise) • Experience tracking per tech • Cross-referenced knowledge graph • Loss-proof backups • Auto-grouped tool sessions Then I added: • memory_audit (clean + optimize memory) • Memory injection directly into tools Now this isn’t just a concept. It’s a live system that turns LLMs from stateless tools → evolving intelligence. Using it daily: → ~5K tokens → Zero manual work → Memory that actually compounds Link in comments. RT & Bookmark — this will be standard soon.
0:32

Holy shit... One month before Andrej Karpathy dropped the “LLM Wiki” idea… someone had already built it. Not theory. Not hype. A working system. LLMs today restart from zero every time. No memory. No evolution. No learning. That’s broken. Most people use CLAUDE.md / AGENTS.md → Manual updates → Easy to forget → Knowledge gets lost That’s not intelligence. That’s a notepad. So I built dynamic memory for LLMs: • Learns automatically • Stores without manual effort • Weakens irrelevant data over time • Strengthens what actually matters Before the “LLM Wiki” idea went viral, this already had: • Brain-like decay/boost memory • Smart deduplication (no noise) • Experience tracking per tech • Cross-referenced knowledge graph • Loss-proof backups • Auto-grouped tool sessions Then I added: • memory_audit (clean + optimize memory) • Memory injection directly into tools Now this isn’t just a concept. It’s a live system that turns LLMs from stateless tools → evolving intelligence. Using it daily: → ~5K tokens → Zero manual work → Memory that actually compounds Link in comments. RT & Bookmark — this will be standard soon.

Nainsi Dwivedi

15,726 views • 4 months ago

How to hack apple's emoji system:
0:23

Sensitive content

How to hack apple's emoji system:

AlphaFox

4,388,428 views • 1 year ago

I built an LLM from scratch. Here's everything I learned along the way.
1:54

I built an LLM from scratch. Here's everything I learned along the way.

CJ (Coding Garden)

257,086 views • 1 month ago

I needed a quick tool to convert any sort of GitHub links into a format an LLM could read. So I built it. GitHub → LLM It's completely free and works with projects, folders, or files 🪄 Deployed on Replit ⠕
0:28

I needed a quick tool to convert any sort of GitHub links into a format an LLM could read. So I built it. GitHub → LLM It's completely free and works with projects, folders, or files 🪄 Deployed on Replit ⠕

Pietro Schirano

120,827 views • 1 year ago

He is a 1st year AI engineering student at Stanford - and already launched an automated system making $2.1M a year: 00:20 - how Anthropic engineers make millions with LLM and Transformers 38:34 - In-Context Learning makes AI smarter for free 59:47 - 1st year student made $2.1M using LLM from scratch after watching I gave LLM the idea I had been holding in my head for 3 years - it built it in one evening. save & watch - the article below is a step by step guide to build your automated LLM system.
1:16:48

He is a 1st year AI engineering student at Stanford - and already launched an automated system making $2.1M a year: 00:20 - how Anthropic engineers make millions with LLM and Transformers 38:34 - In-Context Learning makes AI smarter for free 59:47 - 1st year student made $2.1M using LLM from scratch after watching I gave LLM the idea I had been holding in my head for 3 years - it built it in one evening. save & watch - the article below is a step by step guide to build your automated LLM system.

Noisy

42,621 views • 28 days ago

I built the LLM interface I wished existed. interface0 is for power users: cross-provider memory & chat, multi-model synthesis, sharing & team features, granular context engineering, template prompts, forking, prompt enhancing, & more Here's a demo—see 🧵 for the rest
3:43

I built the LLM interface I wished existed. interface0 is for power users: cross-provider memory & chat, multi-model synthesis, sharing & team features, granular context engineering, template prompts, forking, prompt enhancing, & more Here's a demo—see 🧵 for the rest

Andy Bromberg

25,766 views • 1 year ago

Used Karpathy's LLM Wiki to turn Claude Code into a self-evolving system. Fed it my frontend & CRM sources, and it built a fully updating dashboard with Shadcn packages and constantly improving and linking everything for smarter outputs! Full setup video:
0:25

Used Karpathy's LLM Wiki to turn Claude Code into a self-evolving system. Fed it my frontend & CRM sources, and it built a fully updating dashboard with Shadcn packages and constantly improving and linking everything for smarter outputs! Full setup video:

WorldofAI

133,481 views • 4 months ago

CLAUDE COWORK TOUR: I built my own rules and memory system (harness) so that Claude Cowork remembers everything I do. In this video I show you the specific folder structures, files, and why I did it this way. I use this same system for all 3 of my companies.
13:09

CLAUDE COWORK TOUR: I built my own rules and memory system (harness) so that Claude Cowork remembers everything I do. In this video I show you the specific folder structures, files, and why I did it this way. I use this same system for all 3 of my companies.

Justin Brooke

26,611 views • 4 months ago

The whole session ranks among my favorite David talks. Built friction out of the system not into the system and the XRP Ledger is built for business!
0:27

The whole session ranks among my favorite David talks. Built friction out of the system not into the system and the XRP Ledger is built for business!

Vet

35,717 views • 1 year ago

SCIENTISTS TURN LIGHT INTO A MEMORY SYSTEM Scientists built a tiny photonic chip and sent identical photons through it, expecting messy quantum behavior. Instead, the light started acting like a memory system, forming patterns that match a classic AI model used for associative memory. In simple terms, the interference between photons did the math needed to store and recall patterns. No brain, no computer, just light physics doing the computing. Source: NewsForce
2:34

SCIENTISTS TURN LIGHT INTO A MEMORY SYSTEM Scientists built a tiny photonic chip and sent identical photons through it, expecting messy quantum behavior. Instead, the light started acting like a memory system, forming patterns that match a classic AI model used for associative memory. In simple terms, the interference between photons did the math needed to store and recall patterns. No brain, no computer, just light physics doing the computing. Source: NewsForce

NewsForce

29,578 views • 5 months ago

LLM + Memory + Planning + Tools = Agents 🤖 Last month, Job and I discussed how generative AI is shifting how companies offer customer support. How can we add more layers to our RAG apps to make it more agentic? LLM: Large language model alone Memory: Short-term and long-term memory Planning: Breaking down questions into sub-questions, plan the steps needed before taking action, etc. Tools: Calculator, code executor, weather API, etc. I hope you find this small clip interesting! The link to the full recording is below.
2:19

LLM + Memory + Planning + Tools = Agents 🤖 Last month, Job and I discussed how generative AI is shifting how companies offer customer support. How can we add more layers to our RAG apps to make it more agentic? LLM: Large language model alone Memory: Short-term and long-term memory Planning: Breaking down questions into sub-questions, plan the steps needed before taking action, etc. Tools: Calculator, code executor, weather API, etc. I hope you find this small clip interesting! The link to the full recording is below.

Erika Shorten

32,443 views • 2 years ago

Fellas, stop guessing. I built a system that turns content into income, and I’m teaching you step-by-step. This ain’t luck, it’s strategy!
0:30

Fellas, stop guessing. I built a system that turns content into income, and I’m teaching you step-by-step. This ain’t luck, it’s strategy!

Tysin Black 2.0

11,485 views • 10 months ago

Introducing Brain in Computer. Brain is a continuously learning memory system. Every task on Computer plugs into a context graph built by Brain. It makes Computer more stateful with every run. Available as a research preview for all Perplexity Max subscribers.
1:06

Introducing Brain in Computer. Brain is a continuously learning memory system. Every task on Computer plugs into a context graph built by Brain. It makes Computer more stateful with every run. Available as a research preview for all Perplexity Max subscribers.

Perplexity

610,760 views • 2 months ago

I built the best memory for voice agents while sponsoring @YCombinator hackathon. Introducing: Voice AI + Memory! We built a deep supermemory integration for Pipecat AI, so now even your agents have great memory With user profiles, these are almost-instant latency as well :)
2:11

I built the best memory for voice agents while sponsoring @YCombinator hackathon. Introducing: Voice AI + Memory! We built a deep supermemory integration for Pipecat AI, so now even your agents have great memory With user profiles, these are almost-instant latency as well :)

Dhravya Shah

83,405 views • 6 months ago

How I Built Grey Matter, A Procedural Data System in Blender
1:12

How I Built Grey Matter, A Procedural Data System in Blender

Sam Hox — Second Eight

82,876 views • 10 months ago

Just built a chat with Excel app using Qwen 3 LLM (100% local):
0:28

Just built a chat with Excel app using Qwen 3 LLM (100% local):

Avi Chawla

64,104 views • 1 year ago

“I hated school, I really hated school. I hated school generally.” “I wanted to pursue understanding, I was built this way. I have a great conceptual memory but a terrible rote memory. I always like understanding.” - Ray Dalio
0:55

“I hated school, I really hated school. I hated school generally.” “I wanted to pursue understanding, I was built this way. I have a great conceptual memory but a terrible rote memory. I always like understanding.” - Ray Dalio

Ronit Pereira

82,888 views • 2 months ago