Video wird geladen...

Video konnte nicht geladen werden

Beim Laden dieses Videos ist ein Problem aufgetreten. Dies könnte an einem vorübergehenden Netzwerkproblem liegen oder das Video ist möglicherweise nicht verfügbar.

Career update: i'm joining supermemory to lead Dev GTM. supermemory is the memory layer for AI products, we got the best public results on AI memory by a wide margin, 95% on the leading benchmarks. my role will be to make it easy for everyone to use. demos, walkthroughs,... show more

Muskan Jain

20,948 subscribers

109,386 Aufrufe • vor 7 Tagen •via X (Twitter)

Bildung Nachrichten & Politik Wissenschaft & Technologie

Anya Rossi• Live Now

Private livecam show

0 Kommentare

Keine Kommentare verfügbar

Kommentare vom Original-Post werden hier angezeigt

Ähnliche Videos

You can now run supermemory locally. Introducing the supermemory local - Fully self-contained. Comes with our graph engine, embedding model, etc. - Run on any machine, with your OpenClaw🦞, hermes, claude, etc. - SDKs to add memory to your agent, or build your company brain.

You can now run supermemory locally. Introducing the supermemory local - Fully self-contained. Comes with our graph engine, embedding model, etc. - Run on any machine, with your OpenClaw🦞, hermes, claude, etc. - SDKs to add memory to your agent, or build your company brain.

Dhravya Shah

596,907 Aufrufe • vor 7 Tagen

Introducing the all new supermemory 💥 we killed the AI memory lock-in. A universal memory for all the apps you use: - MCP connect to Claude / Cursor / ChatGPT - Graph view of your life - Projects to organize your life free to use. $9/m for first 100 pro subs. demo below

Introducing the all new supermemory 💥 we killed the AI memory lock-in. A universal memory for all the apps you use: - MCP connect to Claude / Cursor / ChatGPT - Graph view of your life - Projects to organize your life free to use. $9/m for first 100 pro subs. demo below

Dhravya Shah

170,604 Aufrufe • vor 10 Monaten

He sold a company at 16. Then raised $3M at 19 without a co-founder. Today he gave the whole thing away. supermemory now runs fully local, self-contained, and the binary is open source. Solo Founders Podcast ep 14 is live with Dhravya Shah of Supermemory. 00:49 How a side project became a company 08:57 "Building was my way of doing art" 14:35 Saying no to VCs for 9 months 24:44 Launching Supermemory Local 29:29 Killing his own viral hit, mid-fundraise 46:39 Why ChatGPT "fails" memory benchmarks on purpose 52:57 The co-founder breakup that made him go solo 01:10:19 The case for solo founding

He sold a company at 16. Then raised $3M at 19 without a co-founder. Today he gave the whole thing away. supermemory now runs fully local, self-contained, and the binary is open source. Solo Founders Podcast ep 14 is live with Dhravya Shah of Supermemory. 00:49 How a side project became a company 08:57 "Building was my way of doing art" 14:35 Saying no to VCs for 9 months 24:44 Launching Supermemory Local 29:29 Killing his own viral hit, mid-fundraise 46:39 Why ChatGPT "fails" memory benchmarks on purpose 52:57 The co-founder breakup that made him go solo 01:10:19 The case for solo founding

weisser

135,992 Aufrufe • vor 7 Tagen

For years, I have been building the perfect knowledge assistant. I mean, that's what supermemory originally launched as! Today, we are relaunching the consumer side of supermemory. Fully powered by the supermemory API. After our Claude code, OpenClaw and other launches blew up to millions of views, it was clear: Consumers WANT their context to be interoperable within models they use. They want their AI to truly understand them, have great memory, and build over time. They want to use this truly wherever they are - or with every AI they use - Claude code, or OpenClaw, or Raycast. They want to bring their memories from chatgpt, claude, twitter bookmarks, and teach their AI, about them. This is your knowledge, and supermemory is the assistant. evolving, understanding, growing with time.

For years, I have been building the perfect knowledge assistant. I mean, that's what supermemory originally launched as! Today, we are relaunching the consumer side of supermemory. Fully powered by the supermemory API. After our Claude code, OpenClaw and other launches blew up to millions of views, it was clear: Consumers WANT their context to be interoperable within models they use. They want their AI to truly understand them, have great memory, and build over time. They want to use this truly wherever they are - or with every AI they use - Claude code, or OpenClaw, or Raycast. They want to bring their memories from chatgpt, claude, twitter bookmarks, and teach their AI, about them. This is your knowledge, and supermemory is the assistant. evolving, understanding, growing with time.

Dhravya Shah

36,546 Aufrufe • vor 4 Monaten

Introducing Supermemory - an AI hub for all your bookmarks and notes. content you consume often gets lost. put it in your second brain to make use of it. me and my friends have been working on this open source project for months now - but the story is actually crazy 🧵

Introducing Supermemory - an AI hub for all your bookmarks and notes. content you consume often gets lost. put it in your second brain to make use of it. me and my friends have been working on this open source project for months now - but the story is actually crazy 🧵

Dhravya Shah

73,249 Aufrufe • vor 1 Jahr

Excited to announce that mem0 is now powering the memory for MultiOn AI, the leading AI agent out there in the world. The memory is a key component in the agent’s learning process and is crucial for its performance. Memory enables the agent to remember past experiences and improve from them over time.

Excited to announce that mem0 is now powering the memory for MultiOn AI, the leading AI agent out there in the world. The memory is a key component in the agent’s learning process and is crucial for its performance. Memory enables the agent to remember past experiences and improve from them over time.

Taranjeet

21,786 Aufrufe • vor 1 Jahr

build your own memory... only if: - you can top the benchmarks - want to spend more money - have more time. - want to deal with infrastructure, and not actually build a product but for most people, you should just... use supermemory

build your own memory... only if: - you can top the benchmarks - want to spend more money - have more time. - want to deal with infrastructure, and not actually build a product but for most people, you should just... use supermemory

Dhravya Shah

58,959 Aufrufe • vor 5 Monaten

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 :)

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

82,607 Aufrufe • vor 3 Monaten

Most "Book a call" buttons are SHIT. I hate them too I will personally onboard everyone who wants to try supermemory, or answer any questions. Every AI should have a memory that works perfectly for that use case. And we're here to help with that.

Most "Book a call" buttons are SHIT. I hate them too I will personally onboard everyone who wants to try supermemory, or answer any questions. Every AI should have a memory that works perfectly for that use case. And we're here to help with that.

Dhravya Shah

29,372 Aufrufe • vor 6 Monaten

Introducing the supermemory CLI. MCP isn't dead - but CLIs can be more powerful. So we built the most advanced CLI for agents. EVERY single thing you can do on the supermemory platform can be done by Agents. just prompt them. They are first class users. > npx supermemory

Introducing the supermemory CLI. MCP isn't dead - but CLIs can be more powerful. So we built the most advanced CLI for agents. EVERY single thing you can do on the supermemory platform can be done by Agents. just prompt them. They are first class users. > npx supermemory

supermemory

96,291 Aufrufe • vor 3 Monaten

Memory for OpenClaw is now Native! Our first OpenClaw Memory Skill was a massive success: 30k+ downloads in a week and 500k+ organic impressions overnight for launch post. But we knew memory needed to be native. On March 21, OpenClaw merged PR #50848, allowing us to go beyond the skill layer and integrate directly into the agent’s context assembly flow. We try to make OpenClaw a truly 24/7 employee capable of complex workflows. The technical setup isn’t the hardest part but the real challenge is giving it a "brain" that remembers exact project details, past decisions, and team changes over time. The Native Memory Plugin is now live on NPM & ClawHub. Here is what it brings to your OpenClaw agents: 👉 Native Integration: Automatically manages a Three-Layer Memory architecture (Context Tree, Workspace Memory, Daily Memory). 👉 Git-like Stateful Memory: Organizes memory into a semantic hierarchy of human-readable, diffable Markdown files. You always get updated knowledge and can actually see and fix what your agent learns. 👉 Top Market Accuracy: Achieves an industry-leading 92.2% retrieval accuracy (LoCoMo & LongMemEval benchmarks), maintaining 90% accuracy even with cheap, lightweight models. 👉 Local-first & Portable: Local-by-default, fully portable for multi-agent teams. 👉 Super Easy Setup

Memory for OpenClaw is now Native! Our first OpenClaw Memory Skill was a massive success: 30k+ downloads in a week and 500k+ organic impressions overnight for launch post. But we knew memory needed to be native. On March 21, OpenClaw merged PR #50848, allowing us to go beyond the skill layer and integrate directly into the agent’s context assembly flow. We try to make OpenClaw a truly 24/7 employee capable of complex workflows. The technical setup isn’t the hardest part but the real challenge is giving it a "brain" that remembers exact project details, past decisions, and team changes over time. The Native Memory Plugin is now live on NPM & ClawHub. Here is what it brings to your OpenClaw agents: 👉 Native Integration: Automatically manages a Three-Layer Memory architecture (Context Tree, Workspace Memory, Daily Memory). 👉 Git-like Stateful Memory: Organizes memory into a semantic hierarchy of human-readable, diffable Markdown files. You always get updated knowledge and can actually see and fix what your agent learns. 👉 Top Market Accuracy: Achieves an industry-leading 92.2% retrieval accuracy (LoCoMo & LongMemEval benchmarks), maintaining 90% accuracy even with cheap, lightweight models. 👉 Local-first & Portable: Local-by-default, fully portable for multi-agent teams. 👉 Super Easy Setup

andy nguyen

169,075 Aufrufe • vor 2 Monaten

Introducing memorybench by supermemory 🔬 The standard, open source evaluation framework for all context systems, across any benchmark. Easily run and compare multiple memory providers. batteries included - comes with a Web UI, CLI, checkpoints and everything else.

Introducing memorybench by supermemory 🔬 The standard, open source evaluation framework for all context systems, across any benchmark. Easily run and compare multiple memory providers. batteries included - comes with a Web UI, CLI, checkpoints and everything else.

Dhravya Shah

44,293 Aufrufe • vor 5 Monaten

The Context Cloud for agents is here. Introducing the new supermemory. A new way to do context, for your agents Everything you need for context - Memory, RAG, Filesystems, Profiles, are now available as blocks you can compose to build something for your use case

The Context Cloud for agents is here. Introducing the new supermemory. A new way to do context, for your agents Everything you need for context - Memory, RAG, Filesystems, Profiles, are now available as blocks you can compose to build something for your use case

Dhravya Shah

88,072 Aufrufe • vor 22 Tagen

New short course: Long-Term Agentic Memory with LangGraph. Learn to build an agent with long-term memory in this course developed in collaboration with taught by its Co-Founder and CEO, Harrison Chase! Personal assistance and productivity tasks have become important use cases for agents. An important feature of an AI assistant, such as a coding or calendar assistant, is its ability to keep improving over time from its experience. Agent memory is the key capability that enables this. To add memory to an agent, you must first figure out what to store and what to retrieve when it is time to use the information. Additionally, you’ll have to decide when to update the stored information. For example, you might update in each iteration loop of the agent or perform updates in the background, with a helper agent. In this course, you will learn a mental framework to build agents with long-term memory. You'll create a useful email assistant that can respond, ignore, and notify using writing, scheduling, and memory-management tools. You’ll develop your agent's memory by adding facts to its memory store, provide examples to learn the user's preferences, and optimize system prompts to evolve instructions based on previous responses. In detail, you’ll: - Learn how the three types of memory--semantic, episodic, and procedural–and the two update mechanisms–via hot path and in the background–apply to your agents. - Build an email agent with writing, scheduling, and availability tools, along with a router that triages incoming email and handles it accordingly by ignoring, responding, or notifying the user. - Add tools to your email agent that allow it to operate on semantic memory by learning facts about the user, storing them in a long-term memory store, and searching over them in future interactions. - Incorporate episodic memory, in the form of few-shot examples, in the triage step of your agents to help them learn and update user preferences. - Add procedural memory as system prompts, optimized with feedback to improve the instructions the agent follows. Learn how to approach memory in agents, and start building agents with long-term memory with LangGraph! Please sign up here:

New short course: Long-Term Agentic Memory with LangGraph. Learn to build an agent with long-term memory in this course developed in collaboration with taught by its Co-Founder and CEO, Harrison Chase! Personal assistance and productivity tasks have become important use cases for agents. An important feature of an AI assistant, such as a coding or calendar assistant, is its ability to keep improving over time from its experience. Agent memory is the key capability that enables this. To add memory to an agent, you must first figure out what to store and what to retrieve when it is time to use the information. Additionally, you’ll have to decide when to update the stored information. For example, you might update in each iteration loop of the agent or perform updates in the background, with a helper agent. In this course, you will learn a mental framework to build agents with long-term memory. You'll create a useful email assistant that can respond, ignore, and notify using writing, scheduling, and memory-management tools. You’ll develop your agent's memory by adding facts to its memory store, provide examples to learn the user's preferences, and optimize system prompts to evolve instructions based on previous responses. In detail, you’ll: - Learn how the three types of memory--semantic, episodic, and procedural–and the two update mechanisms–via hot path and in the background–apply to your agents. - Build an email agent with writing, scheduling, and availability tools, along with a router that triages incoming email and handles it accordingly by ignoring, responding, or notifying the user. - Add tools to your email agent that allow it to operate on semantic memory by learning facts about the user, storing them in a long-term memory store, and searching over them in future interactions. - Incorporate episodic memory, in the form of few-shot examples, in the triage step of your agents to help them learn and update user preferences. - Add procedural memory as system prompts, optimized with feedback to improve the instructions the agent follows. Learn how to approach memory in agents, and start building agents with long-term memory with LangGraph! Please sign up here:

Andrew Ng

131,640 Aufrufe • vor 1 Jahr

I turned AI agent orchestration into a full fledged game. To start this launch, I’m also hosting a tournament with a growing prize pool (starting at $3k) for the first 24 hours. Two players face off a team of AI agents, armed with strategy prompts - utilizing memory, tool-calling, and inter-agent communication to win. Each agent has a battle memory - they learn and synergize with the playstyle of their commander to produce the best results. I’ve been working on this for the past 2 months, so please check it out, my dms are open for feedback :D Documentation: Project Twitter: aiarena Website:

I turned AI agent orchestration into a full fledged game. To start this launch, I’m also hosting a tournament with a growing prize pool (starting at $3k) for the first 24 hours. Two players face off a team of AI agents, armed with strategy prompts - utilizing memory, tool-calling, and inter-agent communication to win. Each agent has a battle memory - they learn and synergize with the playstyle of their commander to produce the best results. I’ve been working on this for the past 2 months, so please check it out, my dms are open for feedback :D Documentation: Project Twitter: aiarena Website:

𝕱𝖔𝖗𝕷𝖔𝖔𝖕

10,867 Aufrufe • vor 2 Monaten

Autonomous AI agents can’t afford a memory problem. After OpenClaw, insane numbers of autonomous agents are coming online, but all of them have a memory layer that's breaking. I have been playing around with SureThing these days. It is an autonomous AI agent designed with a native memory layer. → it learns your patterns → integrates with 1000+ apps → fully autonomous They actually benchmarked memory depth. SureThing: 84.8, ChatGPT: 40, OpenClaw: 60. The gap speaks for itself. SureThing is invitation-only. The crazy part is that you have to talk to SureThing agent itself to get the invite code. The onboarding is the product demo. You feel the memory and judgment before you even have an account. Try it here: Partnered with SureThing for this post. Thanks to the team for the support.

Autonomous AI agents can’t afford a memory problem. After OpenClaw, insane numbers of autonomous agents are coming online, but all of them have a memory layer that's breaking. I have been playing around with SureThing these days. It is an autonomous AI agent designed with a native memory layer. → it learns your patterns → integrates with 1000+ apps → fully autonomous They actually benchmarked memory depth. SureThing: 84.8, ChatGPT: 40, OpenClaw: 60. The gap speaks for itself. SureThing is invitation-only. The crazy part is that you have to talk to SureThing agent itself to get the invite code. The onboarding is the product demo. You feel the memory and judgment before you even have an account. Try it here: Partnered with SureThing for this post. Thanks to the team for the support.

Pratham

80,634 Aufrufe • vor 4 Monaten

Yesterday, I joined TBPN to talk about what it really means to give AI a memory. And, why that’s becoming the next big moat. We discussed: - Why today’s AI apps are stateless - How memory is quickly becoming the real moat for AI systems - How Mem0 is decoupling memory from models - Why users should be able to own and carry their AI memories across apps - And, how 200 cold emails changed my life🙂 Appreciate John Coogan, Jordi Hays & the TBPN team for having me and for such a thoughtful discussion around the future of AI infrastructure.

Yesterday, I joined TBPN to talk about what it really means to give AI a memory. And, why that’s becoming the next big moat. We discussed: - Why today’s AI apps are stateless - How memory is quickly becoming the real moat for AI systems - How Mem0 is decoupling memory from models - Why users should be able to own and carry their AI memories across apps - And, how 200 cold emails changed my life🙂 Appreciate John Coogan, Jordi Hays & the TBPN team for having me and for such a thoughtful discussion around the future of AI infrastructure.

Taranjeet

14,881 Aufrufe • vor 7 Monaten

I spent today turning a blank Hermes install into a sovereign local agent on my RTX 5090. always-on, reachable over telegram, fully local. here's everything it can do. it runs Qwen3.6-27B on the 5090 and answers from my phone over telegram. ~~~ I first made it faster. switched to the MTP build for self-speculative decoding: 62 > 115 tokens/sec. ~~~ it now benchmarks models on command. I ask it to speed-test a gguf and it stops its own model server to free the gpu, runs llama-bench, renders a card, sends it to my phone, then restarts itself. it's never left offline. ~~~ it also knows me very well. I pointed it to my private HF repo where I store all my AI traces since I started using it. I created a local markdown memory vault, seeded from my own history, with semantic search over it. ~~~ the whole stack stays home: weights on disk, inference on the 5090, the agent in my pocket. here is the video it created to showcase everything it has now.

I spent today turning a blank Hermes install into a sovereign local agent on my RTX 5090. always-on, reachable over telegram, fully local. here's everything it can do. it runs Qwen3.6-27B on the 5090 and answers from my phone over telegram. ~ I first made it faster. switched to the MTP build for self-speculative decoding: 62 > 115 tokens/sec. ~ it now benchmarks models on command. I ask it to speed-test a gguf and it stops its own model server to free the gpu, runs llama-bench, renders a card, sends it to my phone, then restarts itself. it's never left offline. ~ it also knows me very well. I pointed it to my private HF repo where I store all my AI traces since I started using it. I created a local markdown memory vault, seeded from my own history, with semantic search over it. ~ the whole stack stays home: weights on disk, inference on the 5090, the agent in my pocket. here is the video it created to showcase everything it has now.

witcheer

14,947 Aufrufe • vor 16 Tagen

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.

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 Aufrufe • vor 6 Monaten

to be clear, i didn't leave esport and i'll never quit without achieving great success, i just had to get off season cause of my surgery. (and after all the EMEA tryouts, i declined the other VCL offers because i believed playing just to play on any team wouldn't lead to success, and playing on a team where i wouldn't be successful could harm my career.) i continued and still continue my training throughout the off-season, preparing for the upcoming season in the best possible way. no matter what i encounter, no matter how unlucky i may be, i will not give up striving to be the best.

to be clear, i didn't leave esport and i'll never quit without achieving great success, i just had to get off season cause of my surgery. (and after all the EMEA tryouts, i declined the other VCL offers because i believed playing just to play on any team wouldn't lead to success, and playing on a team where i wouldn't be successful could harm my career.) i continued and still continue my training throughout the off-season, preparing for the upcoming season in the best possible way. no matter what i encounter, no matter how unlucky i may be, i will not give up striving to be the best.

BURZZY

242,581 Aufrufe • vor 1 Jahr