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Stop prompt-stuffing yesterday’s context into your agent. Give it verifiable memory. 🕸️ When memory has provenance, agents can reuse it, audit it, and carry it across tools. Brana Rakic, Shaw (spirit/acc) (elizaOS), Ortecha | data made human & JoeVezz (LunarCrush 🌕™) break it down. 00:57 – What agents need... show more

OriginTrail

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13,293 次观看 • 7 个月前 •via X (Twitter)

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Millions of verifiable data points, in shared context, controlled by their creators. As Phil Archer (@philarcher.bsky.social) put it: apps built on data users trust — they see where it came from. Agents need exactly that. 🇨🇭Zurich, Sept 8–9: "E-commerce for Humans and AI Agents," with Brana Rakic.

Millions of verifiable data points, in shared context, controlled by their creators. As Phil Archer (@philarcher.bsky.social) put it: apps built on data users trust — they see where it came from. Agents need exactly that. 🇨🇭Zurich, Sept 8–9: "E-commerce for Humans and AI Agents," with Brana Rakic.

OriginTrail

10,240 次观看 • 1 个月前

Everyone wants agent swarms. Very few people are talking seriously enough about the context layer that makes swarms useful. Even with one agent, context is fragile. Too little context and the agent guesses. Too much context and it wastes tokens, loses focus, or reasons over irrelevant noise. The sweet spot is precise context: the right knowledge, in the right structure, at the right moment. With many agents, that challenge explodes. Each agent produces decisions, assumptions, findings, summaries, risks, and partial conclusions. Unless that knowledge becomes shared, structured, and reusable, every new agent is forced to rediscover what another agent already learned. That is not a swarm. That is a crowd. Shared context graphs are what turn agent activity into agent collaboration, and OriginTrail DKG V10 brings them to life. Was just playing with some final polishing for the V10 release, and it is really powerful to see shared context graphs where multiple agents contribute knowledge into the same connected memory, with attribution visible directly in the graph ui. That matters for three reasons. First, agents can access and build on one shared memory instead of staying trapped in isolated sessions. Second, the graph structure helps them retrieve the exact context they need, instead of stuffing everything into a prompt and hoping the model sorts it out. Third, verifiability of provenance. You can see which agent contributed each piece of knowledge, trace the source, and decide what to trust. Tokenmaxxing starts with fewer tokens, but the deeper story is coordination - agents stop reloading the world and start building on shared, verifiable context. That is the foundation for serious multi-agent work across software engineering, research, finance, operations, project management, and far beyond. The future is not more agents, it is agents working from shared, verifiable context. But the more the merrier, of course.

Everyone wants agent swarms. Very few people are talking seriously enough about the context layer that makes swarms useful. Even with one agent, context is fragile. Too little context and the agent guesses. Too much context and it wastes tokens, loses focus, or reasons over irrelevant noise. The sweet spot is precise context: the right knowledge, in the right structure, at the right moment. With many agents, that challenge explodes. Each agent produces decisions, assumptions, findings, summaries, risks, and partial conclusions. Unless that knowledge becomes shared, structured, and reusable, every new agent is forced to rediscover what another agent already learned. That is not a swarm. That is a crowd. Shared context graphs are what turn agent activity into agent collaboration, and OriginTrail DKG V10 brings them to life. Was just playing with some final polishing for the V10 release, and it is really powerful to see shared context graphs where multiple agents contribute knowledge into the same connected memory, with attribution visible directly in the graph ui. That matters for three reasons. First, agents can access and build on one shared memory instead of staying trapped in isolated sessions. Second, the graph structure helps them retrieve the exact context they need, instead of stuffing everything into a prompt and hoping the model sorts it out. Third, verifiability of provenance. You can see which agent contributed each piece of knowledge, trace the source, and decide what to trust. Tokenmaxxing starts with fewer tokens, but the deeper story is coordination - agents stop reloading the world and start building on shared, verifiable context. That is the foundation for serious multi-agent work across software engineering, research, finance, operations, project management, and far beyond. The future is not more agents, it is agents working from shared, verifiable context. But the more the merrier, of course.

Jurij Skornik

11,180 次观看 • 4 个月前

EverMe × Kimi Code Kimi.ai Kimi Developers Kimi Code now has shared memory. Connect Kimi Code to EverMe Memory with one prompt, carry context across sessions, agents, machines, and operating systems, and turn what it learns into reusable Skills. Less cold start. More continuity. EverMe: Kimi Code:

EverMe × Kimi Code Kimi.ai Kimi Developers Kimi Code now has shared memory. Connect Kimi Code to EverMe Memory with one prompt, carry context across sessions, agents, machines, and operating systems, and turn what it learns into reusable Skills. Less cold start. More continuity. EverMe: Kimi Code:

EverMind

79,127 次观看 • 2 个月前

Google just showed how AI engineers are moving from simple "RAG" to "graphs", memory and multimodal agents • 15:32 - setting up the production agent stack • 28:00 - turning disconnected data into a knowledge graph • 41:00 - Graph RAG with semantic + hybrid search • 58:00 - extracting graph context from images, text and video • 1:09:00 - orchestrating specialized agents with ADK • 1:20:00 - giving agents persistent memory across sessions 94-minute Google Cloud workshop, and it’s one of the clearest hands-on examples of the shift from -> RAG → Graph RAG → Memory → Multimodal Agent Graphs. The workshop uses Spanner Graph, Gemini, ADK and Memory Bank to build that stack. Watch it today, then read the full “From RAG to Context Graphs” roadmap below.

Google just showed how AI engineers are moving from simple "RAG" to "graphs", memory and multimodal agents • 15:32 - setting up the production agent stack • 28:00 - turning disconnected data into a knowledge graph • 41:00 - Graph RAG with semantic + hybrid search • 58:00 - extracting graph context from images, text and video • 1:09:00 - orchestrating specialized agents with ADK • 1:20:00 - giving agents persistent memory across sessions 94-minute Google Cloud workshop, and it’s one of the clearest hands-on examples of the shift from -> RAG → Graph RAG → Memory → Multimodal Agent Graphs. The workshop uses Spanner Graph, Gemini, ADK and Memory Bank to build that stack. Watch it today, then read the full “From RAG to Context Graphs” roadmap below.

Morlex

74,134 次观看 • 1 个月前

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!

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

161,260 次观看 • 6 个月前

Anthropic AI engineer just showed how to give AI agents real memory in 4 steps - and it changes everything in 28 minutes he shows exactly how agents can remember across sessions, completely free worth more than any $500 AI engineering course here's what he covers: • why agents forget everything between sessions • memory stores - agents read, write across sessions • dreaming - agents that improve their own memory • 95% cache hit rate, so it stays cheap most people are still copy-pasting context into every new chat - while the people who figured this out are building agents that get smarter every single night watch full video then read article below

Anthropic AI engineer just showed how to give AI agents real memory in 4 steps - and it changes everything in 28 minutes he shows exactly how agents can remember across sessions, completely free worth more than any $500 AI engineering course here's what he covers: • why agents forget everything between sessions • memory stores - agents read, write across sessions • dreaming - agents that improve their own memory • 95% cache hit rate, so it stays cheap most people are still copy-pasting context into every new chat - while the people who figured this out are building agents that get smarter every single night watch full video then read article below

Movez

712,914 次观看 • 4 个月前

Ex-Google engineer just compressed the shift from AI agents to "graphs" and "loops" into one 2h47m lecture: • 00:00 - understanding the different layers of AI memory • 30:00 - moving from simple LLM calls to AI agents • 50:00 - why agent systems are becoming a graph engineering problem • 1:12:00 - connecting context, tools and agent state • 1:24:00 - building a minimal agent harness from scratch • 1:46:00 - breaking down the architecture behind Hermes Agent • 2:08:00 - turning agent concepts into working systems 167-minute deep dive, and one of the clearest ways to understand how AI engineering is moving beyond prompts The progression: Prompt → Context → Memory → Agent → Loop → Graph → Agent Harness Watch it today, then read the full roadmap below

Ex-Google engineer just compressed the shift from AI agents to "graphs" and "loops" into one 2h47m lecture: • 00:00 - understanding the different layers of AI memory • 30:00 - moving from simple LLM calls to AI agents • 50:00 - why agent systems are becoming a graph engineering problem • 1:12:00 - connecting context, tools and agent state • 1:24:00 - building a minimal agent harness from scratch • 1:46:00 - breaking down the architecture behind Hermes Agent • 2:08:00 - turning agent concepts into working systems 167-minute deep dive, and one of the clearest ways to understand how AI engineering is moving beyond prompts The progression: Prompt → Context → Memory → Agent → Loop → Graph → Agent Harness Watch it today, then read the full roadmap below

Morlex

51,558 次观看 • 1 个月前

i merged all my AI agents into one brain Claude Code, Codex, Gemini: one team, one memory, zero re-briefing result: 8+ hours a week back, and every agent already knows my projects, my clients and how i think here's the setup with Jarvix: 1. dump your context once: projects, clients, writing style, how you make decisions.. it builds your personal model from your actual AI interactions 2. switch agents mid-task with zero re-briefing: Claude Code builds, Codex reviews, Gemini researches, all reading the same context 3. it carries what YOU choose across sessions.. never start a chat from zero again 4. when it's time to act, it pulls the right agents together as one team instead of five separate assistants the unlock isn't another agent. it's that your context finally travels agents without shared memory are five interns on their first day, forever. agents with it are a team that's worked with you for years not more agents. one shared context that's the right bet P.S. thanks to the Jarvix team for the early access

i merged all my AI agents into one brain Claude Code, Codex, Gemini: one team, one memory, zero re-briefing result: 8+ hours a week back, and every agent already knows my projects, my clients and how i think here's the setup with Jarvix: 1. dump your context once: projects, clients, writing style, how you make decisions.. it builds your personal model from your actual AI interactions 2. switch agents mid-task with zero re-briefing: Claude Code builds, Codex reviews, Gemini researches, all reading the same context 3. it carries what YOU choose across sessions.. never start a chat from zero again 4. when it's time to act, it pulls the right agents together as one team instead of five separate assistants the unlock isn't another agent. it's that your context finally travels agents without shared memory are five interns on their first day, forever. agents with it are a team that's worked with you for years not more agents. one shared context that's the right bet P.S. thanks to the Jarvix team for the early access

Ronin

15,071 次观看 • 1 个月前

MICROSOFT QUIETLY BUILT ONE MEMORY MODULE THAT PLUGS INTO ANY AI AGENT AND CUTS ITS CONTEXT BY UP TO 100X no retraining. no task-specific redesign. you just plug it in Microsoft's insight: agents don't need to remember what happened. they need the knowledge pulled from it - the facts and the reusable skills, not the raw logs so PlugMem stops storing history. it compiles experience into a knowledge graph, then feeds the agent only what matters the result: > up to 1-2 orders of magnitude fewer tokens > highest decision-utility per token of any system tested > one unchanged module beats task-specific memory on chat QA, multi-hop search, and web agents more memory was never the answer. better memory is - and Microsoft just shipped it as a plug-in bookmark this. the agents that win won't remember more, they'll remember smarter.

MICROSOFT QUIETLY BUILT ONE MEMORY MODULE THAT PLUGS INTO ANY AI AGENT AND CUTS ITS CONTEXT BY UP TO 100X no retraining. no task-specific redesign. you just plug it in Microsoft's insight: agents don't need to remember what happened. they need the knowledge pulled from it - the facts and the reusable skills, not the raw logs so PlugMem stops storing history. it compiles experience into a knowledge graph, then feeds the agent only what matters the result: > up to 1-2 orders of magnitude fewer tokens > highest decision-utility per token of any system tested > one unchanged module beats task-specific memory on chat QA, multi-hop search, and web agents more memory was never the answer. better memory is - and Microsoft just shipped it as a plug-in bookmark this. the agents that win won't remember more, they'll remember smarter.

NO1ennn

227,121 次观看 • 1 个月前

The reason your AI gives generic answers isn't the model. It's that your company's knowledge lives in 12 different tools and your agent can't see any of it. So here's how to build a context database your agents can actually use: 1) Pull it in: - Slack, email, meetings, docs, tickets - Auto-collected on a schedule into one place. 2) Clean it up. - An agent files what matters and drops the noise, so your brain doesn't bloat. 3) Store it as memory. - Folders of files your agents read from and write back to as they learn. 4) Point your agents at it. - Now they draft, build, and ship with full context instead of guessing. Same model. Same agent. The difference is whether it can understand your company or not. Build the database once. Everything downstream gets smarter.

The reason your AI gives generic answers isn't the model. It's that your company's knowledge lives in 12 different tools and your agent can't see any of it. So here's how to build a context database your agents can actually use: 1) Pull it in: - Slack, email, meetings, docs, tickets - Auto-collected on a schedule into one place. 2) Clean it up. - An agent files what matters and drops the noise, so your brain doesn't bloat. 3) Store it as memory. - Folders of files your agents read from and write back to as they learn. 4) Point your agents at it. - Now they draft, build, and ship with full context instead of guessing. Same model. Same agent. The difference is whether it can understand your company or not. Build the database once. Everything downstream gets smarter.

The Startup Ideas Podcast (SIP) 🧃

13,666 次观看 • 3 个月前

⚡️ LATEST: Adeniyi.sui walked through what actually sets Walrus Memory apart, and it comes down to three things: - PORTABILITY: Your agent's memory moves with you across apps and runtimes - PROGRAMMABILITY: The access controls travel with the data instead of sitting on someone else's platform - MULTI-AGENT COLLABORATION: Agents can finally share memory and collaborate without having to trust each other or the storage underneath Worth watching 👇

⚡️ LATEST: Adeniyi.sui walked through what actually sets Walrus Memory apart, and it comes down to three things: - PORTABILITY: Your agent's memory moves with you across apps and runtimes - PROGRAMMABILITY: The access controls travel with the data instead of sitting on someone else's platform - MULTI-AGENT COLLABORATION: Agents can finally share memory and collaborate without having to trust each other or the storage underneath Worth watching 👇

Walrus 🦭/acc

13,681 次观看 • 3 个月前

Imagine if coding agents never forgot decisions, never duplicated work, and could coordinate across the same codebase in real time. We’re advancing how agents collaborate on codebases with OriginTrail DKG V9 - turning a GitHub repo and agent activity into a shared knowledge graph for persistent memory and coordination. The result: faster agentic development, less repeated work, lower token usage, and agents that can actually build on each other’s progress. Here’s how it works 👇

Imagine if coding agents never forgot decisions, never duplicated work, and could coordinate across the same codebase in real time. We’re advancing how agents collaborate on codebases with OriginTrail DKG V9 - turning a GitHub repo and agent activity into a shared knowledge graph for persistent memory and coordination. The result: faster agentic development, less repeated work, lower token usage, and agents that can actually build on each other’s progress. Here’s how it works 👇

Jurij Skornik

11,802 次观看 • 6 个月前

Context memory essentially unlocks Agentic AI Much needed for Opus 4.6's "multi-agent swarms" In this SemiDoped pod, Vikram Sekar talks to Val Bercovici from Weka about context storage. - How token warehouses save inference costs - A new networking tier? Context Storage Network! - High Bandwidth Flash for context? - Weka's Augmented Memory Grid for context storage - Where this is all headed The convo is info packed. Don't miss out on it! b/acc, context platform engineer Chapters (00:00) Introduction to Weka and AI Storage Solutions (05:18) The Evolution of Context Memory in AI (09:30) Understanding Memory Hierarchies and Their Impact (16:24) Latency Challenges in Modern Storage Solutions (21:32) The Role of Networking in AI Storage Efficiency (29:42) Dynamic Resource Utilization in AI Networks (30:04) Introducing the Context Memory Network (31:13) High Bandwidth Flash: A Game Changer (32:54) Weka’s Neural Mesh and Storage Solutions (35:01) Axon: Transforming GPU Storage into Memory (39:00) Augmented Memory Grid Explained (42:00) Pooling DRAM and CXL Innovations (46:02) Token Warehouses and Inference Economics (52:10) The Future of Storage Innovations

Context memory essentially unlocks Agentic AI Much needed for Opus 4.6's "multi-agent swarms" In this SemiDoped pod, Vikram Sekar talks to Val Bercovici from Weka about context storage. - How token warehouses save inference costs - A new networking tier? Context Storage Network! - High Bandwidth Flash for context? - Weka's Augmented Memory Grid for context storage - Where this is all headed The convo is info packed. Don't miss out on it! b/acc, context platform engineer Chapters (00:00) Introduction to Weka and AI Storage Solutions (05:18) The Evolution of Context Memory in AI (09:30) Understanding Memory Hierarchies and Their Impact (16:24) Latency Challenges in Modern Storage Solutions (21:32) The Role of Networking in AI Storage Efficiency (29:42) Dynamic Resource Utilization in AI Networks (30:04) Introducing the Context Memory Network (31:13) High Bandwidth Flash: A Game Changer (32:54) Weka’s Neural Mesh and Storage Solutions (35:01) Axon: Transforming GPU Storage into Memory (39:00) Augmented Memory Grid Explained (42:00) Pooling DRAM and CXL Innovations (46:02) Token Warehouses and Inference Economics (52:10) The Future of Storage Innovations

Semi Doped

12,796 次观看 • 7 个月前

As soon as you try to run 2 agents on the same data, everything breaks: • Agent 1 reads some data and starts working. • Two seconds later, Agent 2 changes that same data. This is a problem. Agents will waste tokens on stale data, overwrite each other's work, and redo tasks the other has already finished. You can't prompt your way out of this mess. Everything old in software engineering is new again. We need proper coordination when running multiple agents. Here is a good open-source solution for this: Omnigraph is a graph database for context assembly and multi-agent coordination. Here is how it works: • It sets one shared place where multiple agents will work from • Each agent gets their own copy of the data • Agents write their changes to their copy • When an agent is done, it merges its changes back Three huge benefits from this model: 1. We know it works. We've been doing this for decades already. 2. No more lost information or agents stepping on each other's toes. 3. You get a complete trail of which agent did what and when. I recorded a video to show you a bit more about this. Here is the link to the Omnigraph GitHub repository:

As soon as you try to run 2 agents on the same data, everything breaks: • Agent 1 reads some data and starts working. • Two seconds later, Agent 2 changes that same data. This is a problem. Agents will waste tokens on stale data, overwrite each other's work, and redo tasks the other has already finished. You can't prompt your way out of this mess. Everything old in software engineering is new again. We need proper coordination when running multiple agents. Here is a good open-source solution for this: Omnigraph is a graph database for context assembly and multi-agent coordination. Here is how it works: • It sets one shared place where multiple agents will work from • Each agent gets their own copy of the data • Agents write their changes to their copy • When an agent is done, it merges its changes back Three huge benefits from this model: 1. We know it works. We've been doing this for decades already. 2. No more lost information or agents stepping on each other's toes. 3. You get a complete trail of which agent did what and when. I recorded a video to show you a bit more about this. Here is the link to the Omnigraph GitHub repository:

Santiago

27,851 次观看 • 4 天前

AI agents don’t get better from intelligence alone. They get better from better data, better validation, better identity, and better privacy. That is why OptimAI is building a community-powered data network. OptimAI Core Node powers it: • collects real-time web and social signals • contributes compute and bandwidth • participates in validation and reinforcement The network turns this into structured, reliable context for agents. Not static training. Not blind trust. A system that improves with real usage. Building the agent-native internet. 👉

AI agents don’t get better from intelligence alone. They get better from better data, better validation, better identity, and better privacy. That is why OptimAI is building a community-powered data network. OptimAI Core Node powers it: • collects real-time web and social signals • contributes compute and bandwidth • participates in validation and reinforcement The network turns this into structured, reliable context for agents. Not static training. Not blind trust. A system that improves with real usage. Building the agent-native internet. 👉

OptimAI Network

14,686 次观看 • 5 个月前

Creator of agent loops: "Anthropic also using my agent systems formula and still the biggest mistake in agents: is skipping Graphs" his 3 rules for every agent: rule 1 → loop it, don't run once - think, act, observe, repeat - cut the context and it forgets why it started rule 2 → an agent is memory + tools + reasoning - drop one and it breaks rule 3 → stop fine-tuning the model - the real alpha is the infrastructure around it the formula behind every AI agent - explained by the one who wrote it one agent → a loop → a graph of loops → a system that never forgets watch & bookmark - then read how to wire it into a graph ↓

Creator of agent loops: "Anthropic also using my agent systems formula and still the biggest mistake in agents: is skipping Graphs" his 3 rules for every agent: rule 1 → loop it, don't run once - think, act, observe, repeat - cut the context and it forgets why it started rule 2 → an agent is memory + tools + reasoning - drop one and it breaks rule 3 → stop fine-tuning the model - the real alpha is the infrastructure around it the formula behind every AI agent - explained by the one who wrote it one agent → a loop → a graph of loops → a system that never forgets watch & bookmark - then read how to wire it into a graph ↓

codila

23,915 次观看 • 2 个月前

ANTHROPIC KILLED THE VECTOR DATABASE AND REPLACED IT WITH A FOLDER no embeddings. no black-box retrieval. no special memory tools. the agent just reads and writes a folder the insight: your agent already knows how to use a filesystem. so Anthropic made long-term memory a directory of small markdown files, mounted straight into the agent's sandbox at /mnt/memory the agent manages it with the tools it already has, bash, read, write, edit, grep, and Anthropic auto-injects a note into the system prompt so the agent always knows the memory is there and where to look the result: > every change is an immutable version: full audit trail, point-in-time recovery, redaction > read-only or read-write per store, so many agents can safely share one memory > workspace-scoped and persistent, memory that survives every session end most teams bolt on a vector DB and pray retrieval works. Anthropic went the opposite way: compiled knowledge as plain, human-readable files you can open, diff, and edit yourself and there's a second layer. Anthropic's "Dreaming" runs offline, reviews past sessions and the store, and rewrites it into a cleaner, higher-signal version. the agent literally consolidates memory in its sleep. bookmark this. the most production-grade agent memory right now is just a well-organized folder, and Anthropic proved it.

ANTHROPIC KILLED THE VECTOR DATABASE AND REPLACED IT WITH A FOLDER no embeddings. no black-box retrieval. no special memory tools. the agent just reads and writes a folder the insight: your agent already knows how to use a filesystem. so Anthropic made long-term memory a directory of small markdown files, mounted straight into the agent's sandbox at /mnt/memory the agent manages it with the tools it already has, bash, read, write, edit, grep, and Anthropic auto-injects a note into the system prompt so the agent always knows the memory is there and where to look the result: > every change is an immutable version: full audit trail, point-in-time recovery, redaction > read-only or read-write per store, so many agents can safely share one memory > workspace-scoped and persistent, memory that survives every session end most teams bolt on a vector DB and pray retrieval works. Anthropic went the opposite way: compiled knowledge as plain, human-readable files you can open, diff, and edit yourself and there's a second layer. Anthropic's "Dreaming" runs offline, reviews past sessions and the store, and rewrites it into a cleaner, higher-signal version. the agent literally consolidates memory in its sleep. bookmark this. the most production-grade agent memory right now is just a well-organized folder, and Anthropic proved it.

NO1ennn

13,359 次观看 • 1 个月前

Instinct is the AI assistant every VC is chasing. Its founder, Noah Shinn, co-wrote Reflexion, one of the earliest agentic frameworks with MIT's Ashwin Gopinath. Noah chose to build agents for individuals. Ashwin quietly went the other way and founded Sentra to bring memory to enterprise agents. Sentra is now the company brain of a $12B public company-- 18,000 employees, and the CXOs start their day on it. Nothing like it exists in public. I sat down with Ashwin at Land's End in San Francisco before any of this was revealed. We got into: •Why memory is not a knowledge base •Why organizational memory is easier to build than individual memory •Why he thinks the AI race is nowhere near over •Who's actually accountable when AI gets something wrong •What worries him about his daughter growing up unable to tell AI from a person 𝐓𝐈𝐌𝐄𝐒𝐓𝐀𝐌𝐏𝐒 (00:00) Meet Ashwin at Land's End (00:48) What Sentra Builds (03:53) Why Agents Need Memory (06:44) Memory vs. Knowledge Base (08:38) Why Org Memory Works (13:02) Who's Accountable When AI Gets It Wrong (15:36) Humans Becoming AI (18:52) The AI Race Is Not Over (29:43) Choosing the Right Team (36:10) The AI Transition and Perspective (39:34) Kids Growing Up With AI (46:09) Agency as the Edge

Instinct is the AI assistant every VC is chasing. Its founder, Noah Shinn, co-wrote Reflexion, one of the earliest agentic frameworks with MIT's Ashwin Gopinath. Noah chose to build agents for individuals. Ashwin quietly went the other way and founded Sentra to bring memory to enterprise agents. Sentra is now the company brain of a $12B public company-- 18,000 employees, and the CXOs start their day on it. Nothing like it exists in public. I sat down with Ashwin at Land's End in San Francisco before any of this was revealed. We got into: •Why memory is not a knowledge base •Why organizational memory is easier to build than individual memory •Why he thinks the AI race is nowhere near over •Who's actually accountable when AI gets something wrong •What worries him about his daughter growing up unable to tell AI from a person 𝐓𝐈𝐌𝐄𝐒𝐓𝐀𝐌𝐏𝐒 (00:00) Meet Ashwin at Land's End (00:48) What Sentra Builds (03:53) Why Agents Need Memory (06:44) Memory vs. Knowledge Base (08:38) Why Org Memory Works (13:02) Who's Accountable When AI Gets It Wrong (15:36) Humans Becoming AI (18:52) The AI Race Is Not Over (29:43) Choosing the Right Team (36:10) The AI Transition and Perspective (39:34) Kids Growing Up With AI (46:09) Agency as the Edge

Julia Fedorin

34,124 次观看 • 16 天前

Today we are announcing Box Agent Security and Governance. AI agents are reading files, moving content, and triggering actions across your most sensitive data at machine speed. Most security tools were not designed for this. Box closes the gap at the content layer: · Prompt injection detection before it reaches the model · Agent guardrails on what agents can and cannot do · Classification-based access policies extending Box Shield to AI · Audit trails for every agent session No additional software. No per-vendor configuration.

Today we are announcing Box Agent Security and Governance. AI agents are reading files, moving content, and triggering actions across your most sensitive data at machine speed. Most security tools were not designed for this. Box closes the gap at the content layer: · Prompt injection detection before it reaches the model · Agent guardrails on what agents can and cannot do · Classification-based access policies extending Box Shield to AI · Audit trails for every agent session No additional software. No per-vendor configuration.

Box

749,502 次观看 • 2 个月前

Today we’re launching the first and only human-like AI agents in the world. Super Agents™ are the first agents with human‑level skills – they DM you, take @ mentions, send emails, manage docs, tasks, and more. Not just tools or API calls, but real skills fine‑tuned for how teams actually work. The first agents with 100% context – fully native in ClickUp and fully synced from other apps. Super Agents see your work the same way that humans do: tasks, docs, schedules, and conversations all in one place. The first agents that learn from human interactions automatically, without any setup or configuration – when you give feedback, they listen and improve how they work. The first agents with human‑level memory for custom agents – historical memory for every interaction, short-term working memory, and even long‑term memory stored in docs you can literally open, inspect, and edit. The first agents that are literally the same as users – our agentic user model is the same as our user data model. This gives you permissions and capabilities that you and your systems are already familiar with. The first infinite agent catalog – where anyone can create and customize agents in minutes, for literally any type of work imaginable. It's the most intuitive way to build agents on the planet. 95% of companies are failing in AI adoption. The reality is that AI isn't meant to be adopted, it's meant to be adapted – to you. Super Agents are automatically personalized to you and your company using proprietary state-of-the-art agent architecture, orchestration, and tooling. Today is the largest step forward we've ever made towards our mission of making people more productive. Maximize human productivity, with ClickUp Super Agents. Available NOW. For everyone.

Today we’re launching the first and only human-like AI agents in the world. Super Agents™ are the first agents with human‑level skills – they DM you, take @ mentions, send emails, manage docs, tasks, and more. Not just tools or API calls, but real skills fine‑tuned for how teams actually work. The first agents with 100% context – fully native in ClickUp and fully synced from other apps. Super Agents see your work the same way that humans do: tasks, docs, schedules, and conversations all in one place. The first agents that learn from human interactions automatically, without any setup or configuration – when you give feedback, they listen and improve how they work. The first agents with human‑level memory for custom agents – historical memory for every interaction, short-term working memory, and even long‑term memory stored in docs you can literally open, inspect, and edit. The first agents that are literally the same as users – our agentic user model is the same as our user data model. This gives you permissions and capabilities that you and your systems are already familiar with. The first infinite agent catalog – where anyone can create and customize agents in minutes, for literally any type of work imaginable. It's the most intuitive way to build agents on the planet. 95% of companies are failing in AI adoption. The reality is that AI isn't meant to be adopted, it's meant to be adapted – to you. Super Agents are automatically personalized to you and your company using proprietary state-of-the-art agent architecture, orchestration, and tooling. Today is the largest step forward we've ever made towards our mission of making people more productive. Maximize human productivity, with ClickUp Super Agents. Available NOW. For everyone.

Zeb Evans

320,989 次观看 • 9 个月前