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🚨 Announcing a new SOTA memory system, Observational Memory (OM), available in latest Mastra version now. It achieves the highest scores ever recorded on LongMemEval (gpt-4o 84.2%, gpt-5-mini 94.9%) No RAG, no graphs, no input based retrieval, just a simple constantly evolving text blob that outperforms every other memory...

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Tyler Barnes

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144,758 просмотров • 4 месяцев назад •via X (Twitter)

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🚨 Announcing a new coding agent that rivals Claude Code but with no compaction needed 🚨 The feeling of using it: run your coding sessions forever, don't worry at all, and get shit done! We're calling it Mastra Code, it's powered by Mastra's new observational memory, and we've been using it internally Mastra to do all our work 1/4 🧵
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🚨 Announcing a new coding agent that rivals Claude Code but with no compaction needed 🚨 The feeling of using it: run your coding sessions forever, don't worry at all, and get shit done! We're calling it Mastra Code, it's powered by Mastra's new observational memory, and we've been using it internally Mastra to do all our work 1/4 🧵

Tyler Barnes

66,490 просмотров • 3 месяцев назад

BREAKING: ChatGPT GPT-4o was just announce by OpenAI. It improves on vision, audio and text. The ease of use is incredibly enhanced. It makes interaction with the GPT much more natural, especially with voice. GPT-4o reasons across voice, text and vision. GPT-4 wil be available to everyone.
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BREAKING: ChatGPT GPT-4o was just announce by OpenAI. It improves on vision, audio and text. The ease of use is incredibly enhanced. It makes interaction with the GPT much more natural, especially with voice. GPT-4o reasons across voice, text and vision. GPT-4 wil be available to everyone.

Ed Krassenstein

21,605 просмотров • 2 лет назад

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
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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 просмотров • 3 месяцев назад

This is big. The Retrieval Plugin allows ChatGPT to have a memory! The model can now remember information from conversations and store it in the retrieval plugin for later use. This feature is a must if you want to develop GPT-based tools.
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This is big. The Retrieval Plugin allows ChatGPT to have a memory! The model can now remember information from conversations and store it in the retrieval plugin for later use. This feature is a must if you want to develop GPT-based tools.

Lior Alexander

227,586 просмотров • 3 лет назад

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.
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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 просмотров • 1 месяц назад

Memory is now available to all ChatGPT Plus users. Using Memory is easy: just start a new chat and tell ChatGPT anything you’d like it to remember. Memory can be turned on or off in settings and is not currently available in Europe or Korea. Team, Enterprise, and GPTs to come.
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Memory is now available to all ChatGPT Plus users. Using Memory is easy: just start a new chat and tell ChatGPT anything you’d like it to remember. Memory can be turned on or off in settings and is not currently available in Europe or Korea. Team, Enterprise, and GPTs to come.

OpenAI

2,045,324 просмотров • 2 лет назад

ANNOUNCING THE INTEL DESK Crypto had no memory. No accountability. Until now Powered by $BMT
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ANNOUNCING THE INTEL DESK Crypto had no memory. No accountability. Until now Powered by $BMT

Bubblemaps

530,320 просмотров • 8 месяцев назад

llama 3.1 outperforms gpt-4o on most benchmarks. It's a massive open-source win. Here are 5 side-by-side comparing llama to gpt-4o with my own tests:
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llama 3.1 outperforms gpt-4o on most benchmarks. It's a massive open-source win. Here are 5 side-by-side comparing llama to gpt-4o with my own tests:

Ruben Hassid

257,399 просмотров • 1 год назад

New course: Agent Memory: Building Memory-Aware Agents, built in partnership with Oracle and taught by Richmond Alake and Nacho Martínez. Many agents work well within a single session but their memory resets once the session ends. Consider a research agent working on dozens of papers across multiple days: without memory, it has no way to store and retrieve what it learned across sessions. This short course teaches you to build a memory system that enables agents to persist memory and thereby learn across sessions. You'll design a Memory Manager that handles different memory types, implement semantic tool retrieval that scales without bloating the context, and build write-back pipelines that let your agent autonomously update and refine what it knows over time. Skills you'll gain: - Build persistent memory stores for different agent memory types - Implement a Memory Manager that orchestrates how your agent reads, writes, and retrieves memory - Treat tools as procedural memory and retrieve only relevant ones at inference time using semantic search Join and learn to build agents that remember and improve over time!
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New course: Agent Memory: Building Memory-Aware Agents, built in partnership with Oracle and taught by Richmond Alake and Nacho Martínez. Many agents work well within a single session but their memory resets once the session ends. Consider a research agent working on dozens of papers across multiple days: without memory, it has no way to store and retrieve what it learned across sessions. This short course teaches you to build a memory system that enables agents to persist memory and thereby learn across sessions. You'll design a Memory Manager that handles different memory types, implement semantic tool retrieval that scales without bloating the context, and build write-back pipelines that let your agent autonomously update and refine what it knows over time. Skills you'll gain: - Build persistent memory stores for different agent memory types - Implement a Memory Manager that orchestrates how your agent reads, writes, and retrieves memory - Treat tools as procedural memory and retrieve only relevant ones at inference time using semantic search Join and learn to build agents that remember and improve over time!

Andrew Ng

157,772 просмотров • 2 месяцев назад

Milla Jovovich (actress from The Fifth Element) created a world-beating Claude memory system with Ben Sigman?! - 100% on LongMemEval — first perfect score ever recorded. Free and 100% open source. Github link in the quoted post from Ben. I'm keen to hear how it works for you.
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Milla Jovovich (actress from The Fifth Element) created a world-beating Claude memory system with Ben Sigman?! - 100% on LongMemEval — first perfect score ever recorded. Free and 100% open source. Github link in the quoted post from Ben. I'm keen to hear how it works for you.

Jeremy Nguyen ✍🏼 🚢

1,719,619 просмотров • 2 месяцев назад

Mal latest memory with Aaliyah 🕊️ New Patreon Ep. available Now🍊💰🍼
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Mal latest memory with Aaliyah 🕊️ New Patreon Ep. available Now🍊💰🍼

New Rory & MAL

280,598 просмотров • 9 месяцев назад

She just became 10x hotter… and it has nothing to do with looks. She just outsmarted every AI tool out there. Resident Evil. Fifth Element. Supermodel. Now she’s out here building her own AI memory system from scratch. A full “Memory Palace” for AI chats. Beat every paid tool. Runs local. No cloud. No subscription. Watch her explain it.
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She just became 10x hotter… and it has nothing to do with looks. She just outsmarted every AI tool out there. Resident Evil. Fifth Element. Supermodel. Now she’s out here building her own AI memory system from scratch. A full “Memory Palace” for AI chats. Beat every paid tool. Runs local. No cloud. No subscription. Watch her explain it.

Wisdom

19,351 просмотров • 2 месяцев назад

Claude Sonnet 3.5 Artifacts is now available to use with GPT-4o, Gemini, Llama-3 and other LLMs for just $10 a month. Build interactive experiences, search the web, generate images and audio with GPT-4o and Claude Sonnet 3.5 in just one AI playground.
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Claude Sonnet 3.5 Artifacts is now available to use with GPT-4o, Gemini, Llama-3 and other LLMs for just $10 a month. Build interactive experiences, search the web, generate images and audio with GPT-4o and Claude Sonnet 3.5 in just one AI playground.

Shubham Saboo

27,452 просмотров • 1 год назад

GPT-5 is now available in Windsurf! Free for a limited time. Based on our internal evals, which measure model performance on real-world engineering tasks, it is SOTA. Let us know what you think!
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GPT-5 is now available in Windsurf! Free for a limited time. Based on our internal evals, which measure model performance on real-world engineering tasks, it is SOTA. Let us know what you think!

Windsurf

756,309 просмотров • 10 месяцев назад

We went from being the worst, to the best AI memory system in the world. While being students with ~no prior research experience. .... but, how do we measure "good" vs bad memory? Evals (and vibes!) here's a small video on how we think about memory.
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We went from being the worst, to the best AI memory system in the world. While being students with ~no prior research experience. .... but, how do we measure "good" vs bad memory? Evals (and vibes!) here's a small video on how we think about memory.

Dhravya Shah

120,929 просмотров • 6 месяцев назад

Here’s a demo of our Agentic Memory system, inspired by how our own brain holds information in a 3D spatial space. This feels, natural. Extending this further, we announced the Agentic Memory Protocol on July 15th in SF - which enables memory to be local, encrypted and available to other agents and apps based on your permission-only. We believe this is the future of memory - not owned by any one app, spatial, and always improving. Aaron Levie Andrej Karpathy
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Here’s a demo of our Agentic Memory system, inspired by how our own brain holds information in a 3D spatial space. This feels, natural. Extending this further, we announced the Agentic Memory Protocol on July 15th in SF - which enables memory to be local, encrypted and available to other agents and apps based on your permission-only. We believe this is the future of memory - not owned by any one app, spatial, and always improving. Aaron Levie Andrej Karpathy

Varun

187,489 просмотров • 10 месяцев назад

We analyzed Anthropic’s memory architecture and built something better: a persistent, human-inspectable, and token-efficient memory layer that scales with your projects. Today, it’s OPEN-SOURCE. ByteRover CLI gives agents (like OpenClaw, Claude Code, and Hermes) persistent, structured memory. Built on the exact architecture that became the #1 memory system for OpenClaw (30,000+ downloads in a week), it lets developers curate project knowledge into a file-based hierarchy. This guarantees highly accurate, lightning-fast retrieval, even with lightweight models. 👉Highly Accurate: >92% retrieval accuracy across long-running sessions - the highest proven production accuracy on the market. ⚡Fast: ~1.6s average retrieval time. 💰Economic: Maintains >90% accuracy even with lightweight models, saving 50-70% on token costs. ☁ Portable: Runs locally by default, with cloud-sync to share memory across agents and teammates.
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We analyzed Anthropic’s memory architecture and built something better: a persistent, human-inspectable, and token-efficient memory layer that scales with your projects. Today, it’s OPEN-SOURCE. ByteRover CLI gives agents (like OpenClaw, Claude Code, and Hermes) persistent, structured memory. Built on the exact architecture that became the #1 memory system for OpenClaw (30,000+ downloads in a week), it lets developers curate project knowledge into a file-based hierarchy. This guarantees highly accurate, lightning-fast retrieval, even with lightweight models. 👉Highly Accurate: >92% retrieval accuracy across long-running sessions - the highest proven production accuracy on the market. ⚡Fast: ~1.6s average retrieval time. 💰Economic: Maintains >90% accuracy even with lightweight models, saving 50-70% on token costs. ☁ Portable: Runs locally by default, with cloud-sync to share memory across agents and teammates.

andy nguyen

28,892 просмотров • 2 месяцев назад

AutoGPT might be the next big step in AI. Here's why Karpathy recently said "AutoGPT is the next frontier of prompt engineering" AutoGPT is the equivalent of giving GPT-based models a memory and a body. You can now give a task to an AI agent and have it autonomously come up with a plan, execute on it, browse the web, and use new data to revise the strategy until the task is completed. It can analyze the market and come up with a trading strategy, customer service, marketing, finance, or other tasks that requires continuous updates. There are three components to it: 1. Architecture: It leverages GPT-4 and GPT-3.5 via API. 2. Autonomous Iterations: AutoGPT can refine its outputs by self-critical review, building on its previous work and integrating prompt history for more accurate results. 3. Memory Management: Integration with Pinecone allows for long-term memory storage, enabling context preservation and improved decision-making. 4. Multi-functionality: Capabilities include file manipulation, web browsing, and data retrieval, distinguishing AutoGPT from previous AI advancements by broadening its application scope.
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AutoGPT might be the next big step in AI. Here's why Karpathy recently said "AutoGPT is the next frontier of prompt engineering" AutoGPT is the equivalent of giving GPT-based models a memory and a body. You can now give a task to an AI agent and have it autonomously come up with a plan, execute on it, browse the web, and use new data to revise the strategy until the task is completed. It can analyze the market and come up with a trading strategy, customer service, marketing, finance, or other tasks that requires continuous updates. There are three components to it: 1. Architecture: It leverages GPT-4 and GPT-3.5 via API. 2. Autonomous Iterations: AutoGPT can refine its outputs by self-critical review, building on its previous work and integrating prompt history for more accurate results. 3. Memory Management: Integration with Pinecone allows for long-term memory storage, enabling context preservation and improved decision-making. 4. Multi-functionality: Capabilities include file manipulation, web browsing, and data retrieval, distinguishing AutoGPT from previous AI advancements by broadening its application scope.

Lior Alexander

808,070 просмотров • 3 лет назад

Is GPT-4o the only tool that can reason across audio, vision and text in real time?… No! So can projects powered $KARRAT
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Is GPT-4o the only tool that can reason across audio, vision and text in real time?… No! So can projects powered $KARRAT

Karrat

58,008 просмотров • 2 лет назад

Imagine if you could run #raylib with no need for an OS, just a simple memory buffer. Now you can. 🤯 NEW backend: PLATFORM_MEMORY, full 2d/3d rendering to a memory framebuffer, only libc required! 🚀 All raylib functionality in ~200KB! Future is now! 🥳
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Imagine if you could run #raylib with no need for an OS, just a simple memory buffer. Now you can. 🤯 NEW backend: PLATFORM_MEMORY, full 2d/3d rendering to a memory framebuffer, only libc required! 🚀 All raylib functionality in ~200KB! Future is now! 🥳

Ray

56,475 просмотров • 6 месяцев назад