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Stilla Agents are live. AI teammates that run on autopilot. Shared real-time context. Plain English setup. Auto-triage issues. Post standups. Draft PRs. Send updates. Already running at some of the fastest-growing companies.

Stilla

1,340 subscribers

88,419 次观看 • 5 个月前 •via X (Twitter)

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The reason we’re all in on AI agents at Box is because agents can now process all of the unstructured data in an enterprise at scale. Companies are sitting on mountains of valuable information that they can tap into for the first time.

The reason we’re all in on AI agents at Box is because agents can now process all of the unstructured data in an enterprise at scale. Companies are sitting on mountains of valuable information that they can tap into for the first time.

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Your AI is only as smart as the context it runs on. Have you built your shared enterprise brain yet? At Databricks Data+AI Summit, I spoke with Prukalpa ✨, co-founder and co-CEO of Atlan, to go deeper on the AI Context Layer in practice. The concept of a shared enterprise brain resonated with me. It is a foundation that every AI agent draws from, learns from, and contributes back to. "The future of organizations are going to be the fastest learning organizations." - Prukalpa ✨ Sankar, co-founder and co-CEO of Atlan Watch the full interview on YouTube: Check out the live demo by Atlan on Building Context for Your AI Agents - #EnterpriseAI #ContextLayer #Data #AI #AtlanPartner Matthias Heidrich

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Helen Yu

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Async, automatic, non-blocking context compaction for long-running agents. Last week I gave a talk called Space Machine Sandboxes at the Daytona AI builders meetup about patterns for long-running agents. I work a lot on voice AI agents, which are fundamentally multi-turn, long-context loops. I also build lots of other AI agent stuff, often as part of bigger systems that include voice. One of the patterns I showed in the talk is non-blocking compaction. Here's a short clip.

Async, automatic, non-blocking context compaction for long-running agents. Last week I gave a talk called Space Machine Sandboxes at the Daytona AI builders meetup about patterns for long-running agents. I work a lot on voice AI agents, which are fundamentally multi-turn, long-context loops. I also build lots of other AI agent stuff, often as part of bigger systems that include voice. One of the patterns I showed in the talk is non-blocking compaction. Here's a short clip.

kwindla

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At #RenderCon Jules Urbach shared a view for the future of AI and decentralized compute, discussing how the industry is shifting from focusing on pre-training models towards inference time AI applications and agents that can run on The Render Network high performance consumer GPUs.

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The Render Network

10,904 次观看 • 1 年前

Today, we're thrilled to announce our $3.3M seed round at Delve. AI makes millions of decisions every second. But it’s still regulated with spreadsheets and screenshots. When AI moves at the speed of thought, protecting it with yesterday's tools isn't just inefficient, it's dangerous. We built Delve to help secure the future of AI. Our AI agents turn hours of manual clicks and screenshots into seamless automation and are already securing 100+ of the fastest-growing AI companies. Welcome to the modern age of compliance.

Today, we're thrilled to announce our $3.3M seed round at Delve. AI makes millions of decisions every second. But it’s still regulated with spreadsheets and screenshots. When AI moves at the speed of thought, protecting it with yesterday's tools isn't just inefficient, it's dangerous. We built Delve to help secure the future of AI. Our AI agents turn hours of manual clicks and screenshots into seamless automation and are already securing 100+ of the fastest-growing AI companies. Welcome to the modern age of compliance.

Karun Kaushik

174,804 次观看 • 1 年前

MAC MINI + IPAD + CLAUDE OPUS 4.8 = THE MOST UNDERRATED SETUP OF 2026 > $600 Mac Mini M4 in the closet. no monitor. autonomous agents running 24/7 > reviewing code, managing files, handling workflows while you sleep > you wake up, open the iPad, connect to the full desktop, see what got done coffee shop. lid open. check on the agents. nudge one. close it. your AI keeps working > Mac Mini M4: $600 > Claude: $20/month > iPad you already own this is a remote AI office that fits in a shoebox demo below. Claude skills that run on this setup are in the article above

MAC MINI + IPAD + CLAUDE OPUS 4.8 = THE MOST UNDERRATED SETUP OF 2026 > $600 Mac Mini M4 in the closet. no monitor. autonomous agents running 24/7 > reviewing code, managing files, handling workflows while you sleep > you wake up, open the iPad, connect to the full desktop, see what got done coffee shop. lid open. check on the agents. nudge one. close it. your AI keeps working > Mac Mini M4: $600 > Claude: $20/month > iPad you already own this is a remote AI office that fits in a shoebox demo below. Claude skills that run on this setup are in the article above

Mr. Buzzoni

47,978 次观看 • 2 个月前

50k a month from ai theme pages built to sell affiliate offers automatically each page posts daily ai videos that get views, clicks, and sales on autopilot some pages are also getting monthly retainers just for running the traffic this works across any niche — health, skincare, relationships, fitness, more i wrote a guide showing the full setup and posting system rt + comment "ai pages" and i’ll send it (must follow for dm)

50k a month from ai theme pages built to sell affiliate offers automatically each page posts daily ai videos that get views, clicks, and sales on autopilot some pages are also getting monthly retainers just for running the traffic this works across any niche — health, skincare, relationships, fitness, more i wrote a guide showing the full setup and posting system rt + comment "ai pages" and i’ll send it (must follow for dm)

Daffy

13,189 次观看 • 9 个月前

We asked Eno Reyes (Co-Founder & CTO Factory) why legacy companies struggle to integrate modern AI agents. "We deploy into companies that have already tried an AI-native IDE or have an autocomplete tool deployed." "There is a workflow change required to adopt agents in the development life cycle." "If you accelerate individual developers, you go a little faster. But if you parallelize and automate at scale, that's the bigger change." "Imagine companies where 5-10% of global payments are still on COBOL. It's a ticking time bomb that the market can't ignore."

We asked Eno Reyes (Co-Founder & CTO Factory) why legacy companies struggle to integrate modern AI agents. "We deploy into companies that have already tried an AI-native IDE or have an autocomplete tool deployed." "There is a workflow change required to adopt agents in the development life cycle." "If you accelerate individual developers, you go a little faster. But if you parallelize and automate at scale, that's the bigger change." "Imagine companies where 5-10% of global payments are still on COBOL. It's a ticking time bomb that the market can't ignore."

TBPN

12,346 次观看 • 1 年前

E-commerce just got its first real AI agents. Not chatbots. Not dashboards. Actual AI that runs your business operations 24/7. I tested Triple Whale 🐳's Moby Agents for a couple weeks across our brands and just told it what to watch in plain English, it: → Caught 2 failing ad sets burning $200/day with zero sales → Spotted creative fatigue 2 days before our usual check → Found a customer segment worth $15K we'd been ignoring → Flagged inventory issues 2 weeks before stockout Zero setup. These Agents are trained on the largest data in e-commerce worth $55B+ So they understand your ROAS drops, seasonal patterns, and attribution better than any generic AI. Comment "MOBY" and I'll send you Triple Whale's 100+ specialized agents that top brands use to automate operations.

E-commerce just got its first real AI agents. Not chatbots. Not dashboards. Actual AI that runs your business operations 24/7. I tested Triple Whale 🐳's Moby Agents for a couple weeks across our brands and just told it what to watch in plain English, it: → Caught 2 failing ad sets burning $200/day with zero sales → Spotted creative fatigue 2 days before our usual check → Found a customer segment worth $15K we'd been ignoring → Flagged inventory issues 2 weeks before stockout Zero setup. These Agents are trained on the largest data in e-commerce worth $55B+ So they understand your ROAS drops, seasonal patterns, and attribution better than any generic AI. Comment "MOBY" and I'll send you Triple Whale's 100+ specialized agents that top brands use to automate operations.

Kody Nordquist

204,649 次观看 • 1 年前

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

11,070 次观看 • 2 个月前

The future of ecommerce intelligence is here. Meet Moby Agents 🤝 AI agents will redefine how teams operate, and we’re leading the charge with the first AI agents built for ecommerce intelligence. Imagine AI agents supporting your entire business with… 🧠 Deep ecommerce expertise 💻 Full access + context to all your business data 👀 Real-time insights, recommendations, and anomaly detection on autopilot Deploy 24/7 AI agents that unlock insights you couldn’t access before—making your business smarter, faster, and more efficient. Want in? We’re opening 100 exclusive spots for Triple Whale customers. Check out the comments to request a demo now.

The future of ecommerce intelligence is here. Meet Moby Agents 🤝 AI agents will redefine how teams operate, and we’re leading the charge with the first AI agents built for ecommerce intelligence. Imagine AI agents supporting your entire business with… 🧠 Deep ecommerce expertise 💻 Full access + context to all your business data 👀 Real-time insights, recommendations, and anomaly detection on autopilot Deploy 24/7 AI agents that unlock insights you couldn’t access before—making your business smarter, faster, and more efficient. Want in? We’re opening 100 exclusive spots for Triple Whale customers. Check out the comments to request a demo now.

Triple Whale 🐳

20,668 次观看 • 1 年前

Mark Cuban says AI will split every company into 2 categories within 3 years "When it's all said and done over the next 3 years, there's going to be two types of companies. Those who are great at AI and those who went out of business" "The biggest challenge for a company is going to be for the CEO to make that decision that we're going to have to blow up a lot of what we do to recreate our company" "We hear a lot of stories about huge public companies that spend a lot of money to try to implement AI and it doesn't really get them a return on investment." "Because you already are running your business the way you've always run it." "The things you've considered outsourcing, your customer call centers, AI agents are perfect for. The stuff you would give to an intern, AI agents are perfect for" "But to really take advantage of AI you have to reformulate your business completely to build it on AI"

Mark Cuban says AI will split every company into 2 categories within 3 years "When it's all said and done over the next 3 years, there's going to be two types of companies. Those who are great at AI and those who went out of business" "The biggest challenge for a company is going to be for the CEO to make that decision that we're going to have to blow up a lot of what we do to recreate our company" "We hear a lot of stories about huge public companies that spend a lot of money to try to implement AI and it doesn't really get them a return on investment." "Because you already are running your business the way you've always run it." "The things you've considered outsourcing, your customer call centers, AI agents are perfect for. The stuff you would give to an intern, AI agents are perfect for" "But to really take advantage of AI you have to reformulate your business completely to build it on AI"

Jaynit

196,708 次观看 • 2 个月前

The highest leverage thing you have to optimize for AI agents is their context. If you’re not getting enough out of AI agents, you probably have a context problem. By default the AI model knows absolutely nothing relevant to perform the task successfully. The whole job of context engineering is to prioritize what the model has as information to work with. Small deviations in the goals, instructions, domain understanding, or underlying data you supply the model will lead to wildly different outcomes. This is where you should spend almost all your time and energy in a workflow. We will start to religiously document more of our workflows and business processes, create clearer goals for people and agents, and ensure our tech infra is setup to get AI agents the right information. The lessons are currently being learned the fastest with coding agents, but the same lessons will apply to every field of knowledge work going forward. dex has a great talk on this that’s absolutely worth your time to watch.

The highest leverage thing you have to optimize for AI agents is their context. If you’re not getting enough out of AI agents, you probably have a context problem. By default the AI model knows absolutely nothing relevant to perform the task successfully. The whole job of context engineering is to prioritize what the model has as information to work with. Small deviations in the goals, instructions, domain understanding, or underlying data you supply the model will lead to wildly different outcomes. This is where you should spend almost all your time and energy in a workflow. We will start to religiously document more of our workflows and business processes, create clearer goals for people and agents, and ensure our tech infra is setup to get AI agents the right information. The lessons are currently being learned the fastest with coding agents, but the same lessons will apply to every field of knowledge work going forward. dex has a great talk on this that’s absolutely worth your time to watch.

Aaron Levie

87,086 次观看 • 11 个月前

"In 2022, we made a bet. We said @Ethereum is all we need to build real-time, first-class DeFi products. Today we are proud to say that the MetaDEX protocols run on some of the most sophisticated DeFi apps that live entirely onchain."

"In 2022, we made a bet. We said @Ethereum is all we need to build real-time, first-class DeFi products. Today we are proud to say that the MetaDEX protocols run on some of the most sophisticated DeFi apps that live entirely onchain."

Dromos

35,278 次观看 • 8 个月前

Real-time knowledge bases are the future of RAG! Today, I'll show you how to build agents that can search across any app, database, or document store in real time. We'll be using Airweave, an open-source tool that builds live, bi-temporal knowledge bases so your agents always reason on the freshest facts. It seamlessly connects to tools like Notion, Google Drive, and SQL databases, transforming their contents into searchable knowledge. The best part? The entire setup runs locally inside a Docker container on my machine. You can also expose it via an API and an MCP server. In the video below, I provide the complete setup and a live demo of its capabilities. I've also shared a link to Airweave's GitHub repo in the next tweet!

Real-time knowledge bases are the future of RAG! Today, I'll show you how to build agents that can search across any app, database, or document store in real time. We'll be using Airweave, an open-source tool that builds live, bi-temporal knowledge bases so your agents always reason on the freshest facts. It seamlessly connects to tools like Notion, Google Drive, and SQL databases, transforming their contents into searchable knowledge. The best part? The entire setup runs locally inside a Docker container on my machine. You can also expose it via an API and an MCP server. In the video below, I provide the complete setup and a live demo of its capabilities. I've also shared a link to Airweave's GitHub repo in the next tweet!

Akshay 🚀

66,067 次观看 • 10 个月前

Goodbye manus The next generation of ai agents wont be judged by how smart they are they will be judged by ROTI, return on token investment AllyHub just turned that into a real metric, the same task costing less every single time you run it A thread on the agent that gets cheaper the more you use it

Goodbye manus The next generation of ai agents wont be judged by how smart they are they will be judged by ROTI, return on token investment AllyHub just turned that into a real metric, the same task costing less every single time you run it A thread on the agent that gets cheaper the more you use it

Nawi

56,327 次观看 • 1 个月前

You probably know OpenClaw🦞, one of the fastest-growing open-source projects around. An OpenClaw harness lets you build, code, analyze, and take real control of your machine. But the more access an agent has to your machine, the more it matters where your data goes & who can see it. With QVAC, you can run your OpenClaw agent fully on your own machine in a few minutes. Nothing leaves your computer. Full setup guide on the blog, see first comment below. 💡 Pro tip: already running OpenClaw? Send the blog article to your agent and let the it do the set up.

You probably know OpenClaw🦞, one of the fastest-growing open-source projects around. An OpenClaw harness lets you build, code, analyze, and take real control of your machine. But the more access an agent has to your machine, the more it matters where your data goes & who can see it. With QVAC, you can run your OpenClaw agent fully on your own machine in a few minutes. Nothing leaves your computer. Full setup guide on the blog, see first comment below. 💡 Pro tip: already running OpenClaw? Send the blog article to your agent and let the it do the set up.

QVAC

15,019,651 次观看 • 1 个月前

We've built 40+ AI agents and internal tools. The hardest part is Context Creation. AI runs playbooks and makes judgment calls for you. But without your company's context, you get slop. Context Creation means extracting the subject matter expertise and playbooks that live in people's heads, not in LLM training data, or even your tools. As forward deployed engineers (FDEs), we create context and turn it into code. We evaluate the business impact, how it aligns with the dev roadmap, and come up with creative solutions. We built The FDE Factory to replace ourselves. It drives AI adoption inside our clients' companies by running discovery sessions using prototypes to create context. Here's how it works: We put a prototype in front of a stakeholder. The stakeholder gives feedback via voice while they're using or reviewing it. Then our FDE Factory Agents builds in their expertise in minutes: > Context Agent reviews the codebase and feedback, extracts the requirements, and creates a spec > Scope Agent checks the spec against the development roadmap, validates it, and hands it off > Engineering Agent builds a new feature and wires the integration > QA Agent runs tests to prove to itself it works > PR merges, feature goes live, product updates itself in real time It's like the nontechnical stakeholder wrote the code without even knowing it. Coding agents are great at turning good development plans into code, and they're getting better at turning context into good development plans in collaboration with professional engineers. But nontechnical people are capped on what they can build without product people and engineers. The bridge that takes nontechnical people from vibe coding basic apps to building production AI tools that run on first party context is FDEs. Our new FDE Factory gives you the system to go from idea to production. Context Creation is the first and most important step in our FDE lifecycle, and we just automated it. Now clients get the right agents and tools built for them, customized to their unique business and encoded with their expertise. PS: If you're building AI agents within your company, reply "Playbook" and I'll DM you the entire FDE playbook we've run with 30+ companies. It covers finding high-impact AI use cases, building them, and deploying them across the org.

We've built 40+ AI agents and internal tools. The hardest part is Context Creation. AI runs playbooks and makes judgment calls for you. But without your company's context, you get slop. Context Creation means extracting the subject matter expertise and playbooks that live in people's heads, not in LLM training data, or even your tools. As forward deployed engineers (FDEs), we create context and turn it into code. We evaluate the business impact, how it aligns with the dev roadmap, and come up with creative solutions. We built The FDE Factory to replace ourselves. It drives AI adoption inside our clients' companies by running discovery sessions using prototypes to create context. Here's how it works: We put a prototype in front of a stakeholder. The stakeholder gives feedback via voice while they're using or reviewing it. Then our FDE Factory Agents builds in their expertise in minutes: > Context Agent reviews the codebase and feedback, extracts the requirements, and creates a spec > Scope Agent checks the spec against the development roadmap, validates it, and hands it off > Engineering Agent builds a new feature and wires the integration > QA Agent runs tests to prove to itself it works > PR merges, feature goes live, product updates itself in real time It's like the nontechnical stakeholder wrote the code without even knowing it. Coding agents are great at turning good development plans into code, and they're getting better at turning context into good development plans in collaboration with professional engineers. But nontechnical people are capped on what they can build without product people and engineers. The bridge that takes nontechnical people from vibe coding basic apps to building production AI tools that run on first party context is FDEs. Our new FDE Factory gives you the system to go from idea to production. Context Creation is the first and most important step in our FDE lifecycle, and we just automated it. Now clients get the right agents and tools built for them, customized to their unique business and encoded with their expertise. PS: If you're building AI agents within your company, reply "Playbook" and I'll DM you the entire FDE playbook we've run with 30+ companies. It covers finding high-impact AI use cases, building them, and deploying them across the org.

Mike Fishbein

10,141 次观看 • 2 个月前

The Ex-VP of Engineering at Shopify has entered the AI race on Thursday, raising at a $40M valuation for his new start-up using only a txt file called "future of work". The founders of Lovable (Anton Osika), ElevenLabs (@matiii), Hugging Face (Thomas Wolf), and Synthesia (Victor Riparbelli & Steffen Tjerrild) all sit on the cap table as well as angels from Shopify. They've quietly been working with some of fastest growing companies in the world, like Spotify, Shopify, Lovable, Legora and Ramp - where the product is now live. "We got term sheets at much higher valuations, but we went with General Catalyst - I knew from that meeting this is a company we want to partner with" We sat down with Siavash who broke down why everyone is looking at Stilla right now. Stockholm is truly on fire.

The Ex-VP of Engineering at Shopify has entered the AI race on Thursday, raising at a $40M valuation for his new start-up using only a txt file called "future of work". The founders of Lovable (Anton Osika), ElevenLabs (@matiii), Hugging Face (Thomas Wolf), and Synthesia (Victor Riparbelli & Steffen Tjerrild) all sit on the cap table as well as angels from Shopify. They've quietly been working with some of fastest growing companies in the world, like Spotify, Shopify, Lovable, Legora and Ramp - where the product is now live. "We got term sheets at much higher valuations, but we went with General Catalyst - I knew from that meeting this is a company we want to partner with" We sat down with Siavash who broke down why everyone is looking at Stilla right now. Stockholm is truly on fire.

etn.

81,334 次观看 • 6 个月前

AI agents are getting smarter fast. But intelligence is not what stops them inside real companies. The harder question is: What are they allowed to do - and who can prove they followed the rules? In our new conversation, Arcade CEO Alex Salazar (Alex Salazar) explains why permissions are the real enterprise bottleneck, why Human + Agent is beating full autonomy, and why his team is already trying to live in a post-browser world. YouTube link in comments as well! Really cool conversation we had with him. My partner Peter and I have already conducted some cool interviews with experts in the AI field and will continue to do so. So feel free to follow us on our channels!

AI agents are getting smarter fast. But intelligence is not what stops them inside real companies. The harder question is: What are they allowed to do - and who can prove they followed the rules? In our new conversation, Arcade CEO Alex Salazar (Alex Salazar) explains why permissions are the real enterprise bottleneck, why Human + Agent is beating full autonomy, and why his team is already trying to live in a post-browser world. YouTube link in comments as well! Really cool conversation we had with him. My partner Peter and I have already conducted some cool interviews with experts in the AI field and will continue to do so. So feel free to follow us on our channels!

Chubby♨️

16,081 次观看 • 15 天前