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Cloud agents keep working while you go get pizza. “It has done excellent work across six different repositories, fixing quite a big issue in our system.” Dhravya Shah from supermemory gave Vorflux three engineering tasks before heading out to eat. He came back to changes and a migration file... show more

Prasanna S

46,798 subscribers

14,827 просмотров • 28 дней назад •via X (Twitter)

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Комментарии: 6

Фото профиля weisser

weisser28 дней назад

@DhravyaShah @supermemory great video. really captured the vibe of the space!

Фото профиля Dhravya Shah

Dhravya Shah28 дней назад

@supermemory great working with you guys!

Фото профиля Andrei Shemet

Andrei Shemet28 дней назад

@DhravyaShah @supermemory The fact that it worked across six repos and still left clean changes is crazy!

Фото профиля aniketh

aniketh28 дней назад

@DhravyaShah @supermemory @DhravyaShah lessgo

Фото профиля dCodes

dCodes28 дней назад

@DhravyaShah @supermemory Crazy

Фото профиля Kinjal Goswami

Kinjal Goswami28 дней назад

@DhravyaShah @supermemory coming back from lunch to a fully fixed codebase is literally the dream.....

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Auto-review is a new mode that lets Codex work longer with fewer approvals and safer execution. It helps Codex keep moving through tests, builds, and more, including during long tasks and automations, while a separate agent checks higher-risk steps in context before they run.

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Dhravya Shah

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Cloud agents work while you eat, sleep, and take the subway. "Every morning I'll spin up a session on the way to work." Melius is a small and nimble startup, and Devin gives them the engineering capacity of a 100-person team. That's why the fastest moving startups use Devin.

Cloud agents work while you eat, sleep, and take the subway. "Every morning I'll spin up a session on the way to work." Melius is a small and nimble startup, and Devin gives them the engineering capacity of a 100-person team. That's why the fastest moving startups use Devin.

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122,114 просмотров • 2 месяцев назад

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.

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Dhravya Shah

701,290 просмотров • 4 месяцев назад

The more agents and projects you have running, the harder it gets to keep track of everything through chat alone. This is where Hermes Kanban comes in. Here's how Hermes Kanban works, why it's worth using, and how I'd recommend setting it up: Chat is where you work with an agent. Kanban is where you manage the work across agents. Hermes Kanban gives you a visual dashboard for tracking tasks across your agents and projects. But it's much more than a task board. Here's what you can do with it: • Create tasks and assign them to specific Hermes agents. • Track what's ready, running, blocked, or completed. • Set dependencies so tasks wait for other work to finish. • Coordinate handoffs between specialized agents. • Review progress and see where something needs your attention. And with the dispatcher, Hermes can pick up tasks that are ready to run and launch the appropriate agent to handle them. Now, here's something I'd recommend: **Create a separate Kanban board for each major project.** For example: 📌 Content production Research → Drafting → Review → Assets → Done 📌 Software development Bugs → Features → Testing → Review → Done 📌 Business operations Research → Waiting → In Progress → Blocked → Done Hermes supports multiple isolated boards, so each project can have its own tasks, workspaces, logs, and agent workflows. You can also connect a board to a Hermes Project to keep the work tied to the right project context. The more work you hand off to Hermes, the more valuable this becomes. Instead of digging through conversations trying to figure out what every agent is doing, give each project its own board and manage the work from one place. 🎥 Video credit: Tom

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Hermes Release Watch

27,640 просмотров • 17 дней назад

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:

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Santiago

29,335 просмотров • 1 месяц назад

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Aaron Levie

103,880 просмотров • 1 год назад

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ZaraAI

28,680 просмотров • 18 дней назад

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Gipp 🦅

19,071 просмотров • 3 месяцев назад

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Muskan Jain

20,335 просмотров • 4 месяцев назад

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, educational content, and the cleanest possible path from "i need memory in my agent" to shipping. pumped to be working with the coolest team oh, and the team just shipped supermemory local. fully self-contained, open source, runs on any machine in a minute!!

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Muskan Jain

113,703 просмотров • 4 месяцев назад

🎉 supermemory plugins are now FREE TO USE! Magical unified memory that is automatic, that works across agents and teams. just do `npx supermemory plugin` to set up. This is powered by a new engine we're working on and many breakthroughs in making the plugins feel magical.

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Dhravya Shah

34,545 просмотров • 2 месяцев назад

I’ve been waiting for something like this. Using multiple AI agents sounds great until you have to keep giving each one the same context. Jarvix Jarvix is built to solve that： It carries your selected context across the AI tools you already use and brings everything together in one place. #JarvixOS You can use Jarvix Home to see your context and connections, Jarvix Search to find past information without remembering exact file names, and Self Reflection to spot patterns in the way you work. There’s also Jarvix Radio for personalized content discovery and Next Move to surface a useful direction based on your context. And the part I find most interesting: Agent Team Mode. Give Jarvix one goal, choose the agents you want to use, and it divides the work while keeping everyone on the same project context. You can watch the progress, review the result, and approve what happens next. No need to keep briefing every agent from scratch. One goal. Shared context. Multiple agents. You stay in control. That’s a much cleaner way to work with AI.

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Aaliya

23,225 просмотров • 2 месяцев назад

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Vaibhav (VB) Srivastav

37,684 просмотров • 3 месяцев назад

The CTO of a $1.5B agent-native software company says we're moving towards a world where the entire team builds the product engineering system that powers your product instead of directly working on the product. They will modify the agents and the constitution of the agents to help them prioritize tasks per Eno Reyes. They will also modify the review system so that it reviews more accurately. It will be akin to a dark factory where the lights are off because it's all robots with no humans inside. In that world, there will be way more things for humans to be involved in because the debates around prioritization/product will be harder and require stronger judgment.

The CTO of a $1.5B agent-native software company says we're moving towards a world where the entire team builds the product engineering system that powers your product instead of directly working on the product. They will modify the agents and the constitution of the agents to help them prioritize tasks per Eno Reyes. They will also modify the review system so that it reviews more accurately. It will be akin to a dark factory where the lights are off because it's all robots with no humans inside. In that world, there will be way more things for humans to be involved in because the debates around prioritization/product will be harder and require stronger judgment.

Ivan Burazin

64,568 просмотров • 6 месяцев назад

How did Dhravya Shah get a Cloudflare internship at 18? "I built something, it got DDoSed, so I reverse-engineered Cloudflare to protect it — wrote a blog about it, it went viral and the CTO just said: do you want to work here?" Dhravya (now founder of supermemory) didn't apply. He just built in the open until the right person noticed. Full interview about his journey below.

How did Dhravya Shah get a Cloudflare internship at 18? "I built something, it got DDoSed, so I reverse-engineered Cloudflare to protect it — wrote a blog about it, it went viral and the CTO just said: do you want to work here?" Dhravya (now founder of supermemory) didn't apply. He just built in the open until the right person noticed. Full interview about his journey below.

Julia Fedorin

168,322 просмотров • 6 месяцев назад

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Hanako

27,608 просмотров • 1 месяц назад

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Yarchi

463,029 просмотров • 4 месяцев назад

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Replit ⠕

29,944 просмотров • 6 месяцев назад

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14,690 просмотров • 17 дней назад