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.

Introducing Software Factory Unified Agent. The Software Factory agent now operates across all modules in the assembly line, rather than one agent per module. Users can move with fluidity retaining their conversation history at each step from requirements, to blueprints, to work orders. Skills and alerts are tagged by... show more

8090

9,726 subscribers

136,368 Aufrufe • vor 2 Monaten •via X (Twitter)

Bildung Wissenschaft & Technologie

Anya Rossi• Live Now

Private livecam show

0 Kommentare

Keine Kommentare verfügbar

Kommentare vom Original-Post werden hier angezeigt

Ähnliche Videos

Today, Sierra is releasing Ghostwriter, our agent for building agents. With Ghostwriter, you can create an AI agent for your customer experience — one that can chat, pick up the phone, speak dozens of languages, take action on your systems of record, and be protected with industry-leading guardrails — simply by having a conversation. No clicking, no forms, no menus. Codex and Claude Code have transformed how we build software, making it possible for software engineers to orchestrate and review the work rather than doing all the work themselves. We think the same transformation will happen for all software. Rather than every enterprise app having a web app for humans and an API for automation, every software platform’s UI will be an agent that can do the work on your behalf. I recorded a demo of my building and optimizing an agent with Ghostwriter so you can see how powerful and easy it is to use. It’s completely changed the way our early adopters build agents, and it’s changed the way I think about the software industry. Let me know what you think, and, if you’re interested in trying it out at your business, please reach out directly.

Today, Sierra is releasing Ghostwriter, our agent for building agents. With Ghostwriter, you can create an AI agent for your customer experience — one that can chat, pick up the phone, speak dozens of languages, take action on your systems of record, and be protected with industry-leading guardrails — simply by having a conversation. No clicking, no forms, no menus. Codex and Claude Code have transformed how we build software, making it possible for software engineers to orchestrate and review the work rather than doing all the work themselves. We think the same transformation will happen for all software. Rather than every enterprise app having a web app for humans and an API for automation, every software platform’s UI will be an agent that can do the work on your behalf. I recorded a demo of my building and optimizing an agent with Ghostwriter so you can see how powerful and easy it is to use. It’s completely changed the way our early adopters build agents, and it’s changed the way I think about the software industry. Let me know what you think, and, if you’re interested in trying it out at your business, please reach out directly.

Bret Taylor

1,061,349 Aufrufe • vor 4 Monaten

You can now use TinyFish in Grok Build. Search, Fetch, Agent, and Browser all under one plugin inside SpaceXAI's coding agent. Give Grok Build a real connection to the live web and let it work across any site from your terminal.

You can now use TinyFish in Grok Build. Search, Fetch, Agent, and Browser all under one plugin inside SpaceXAI's coding agent. Give Grok Build a real connection to the live web and let it work across any site from your terminal.

TinyFish

202,919 Aufrufe • vor 9 Tagen

RADDBG now allows disambiguating global variable evaluations by compilation unit, as well as module or debug info name, regardless of where your selected thread happens to be. It picks module, debug info, compilation unit, and - for local statics - containing function based on the selected thread's position, but those are no longer required, meaning you can evaluate any global variable, in any module, in any unit, from anywhere.

RADDBG now allows disambiguating global variable evaluations by compilation unit, as well as module or debug info name, regardless of where your selected thread happens to be. It picks module, debug info, compilation unit, and - for local statics - containing function based on the selected thread's position, but those are no longer required, meaning you can evaluate any global variable, in any module, in any unit, from anywhere.

Ryan Fleury

12,670 Aufrufe • vor 2 Monaten

Ford CEO Jim Farley on why it's so difficult for legacy car companies to get software right & why Tesla’s vertically integrated approach is the right one: “We farmed out all the modules that control the vehicles to our suppliers because we could bid them against each other, so Bosch would do the body control module, someone else would do the seat control module, someone else would do the engine control module. We have about 150 of these modules with semiconductors all through the car. The problem is the software are all written by you know 150 different companies and they don't talk to each other. So even though it says Ford on the front, I actually have to go to Bosch to get permission to change their seat Control software. So even if I had a high-speed modem in the vehicle and and I had the ability to write their software, it's actually their IP and I have 150, we call it the loose Confederation of software providers, 150 completely different software programming languages, you know all the structure of the software is different. It’s millions of code and we can't even understand it all. That's why at Ford we've decided in the second generation product to completely insource electric architecture. To do that you need to write all the software yourself, but just remember car companies have never written software like this, ever, so we're literally writing how the vehicle operates the software to operate the vehicle for the first time ever.” via Everything Electric Show:

Ford CEO Jim Farley on why it's so difficult for legacy car companies to get software right & why Tesla’s vertically integrated approach is the right one: “We farmed out all the modules that control the vehicles to our suppliers because we could bid them against each other, so Bosch would do the body control module, someone else would do the seat control module, someone else would do the engine control module. We have about 150 of these modules with semiconductors all through the car. The problem is the software are all written by you know 150 different companies and they don't talk to each other. So even though it says Ford on the front, I actually have to go to Bosch to get permission to change their seat Control software. So even if I had a high-speed modem in the vehicle and and I had the ability to write their software, it's actually their IP and I have 150, we call it the loose Confederation of software providers, 150 completely different software programming languages, you know all the structure of the software is different. It’s millions of code and we can't even understand it all. That's why at Ford we've decided in the second generation product to completely insource electric architecture. To do that you need to write all the software yourself, but just remember car companies have never written software like this, ever, so we're literally writing how the vehicle operates the software to operate the vehicle for the first time ever.” via Everything Electric Show:

Sawyer Merritt

1,172,823 Aufrufe • vor 1 Jahr

Max Tynan on the gap nobody else is solving. AI coding tools are single-player by design. One developer, one repo, one prompt. Enterprise software is a team sport. Software Factory is the multiplayer layer. Work Orders run inside Cursor, VS Code, or Claude Code through MCP. Requirements, Blueprints, and Tests stay connected in the Knowledge Graph. Engineers keep their IDE. The team gets a system around it.

Max Tynan on the gap nobody else is solving. AI coding tools are single-player by design. One developer, one repo, one prompt. Enterprise software is a team sport. Software Factory is the multiplayer layer. Work Orders run inside Cursor, VS Code, or Claude Code through MCP. Requirements, Blueprints, and Tests stay connected in the Knowledge Graph. Engineers keep their IDE. The team gets a system around it.

8090

102,280 Aufrufe • vor 2 Monaten

Claude Code now supports agent teams (in research preview) Instead of a single agent working through a task sequentially, a lead agent can delegate to multiple teammates that work in parallel to research, debug, and build while coordinating with each other. Try it out today by enabling agent teams in your settings.json!

Claude Code now supports agent teams (in research preview) Instead of a single agent working through a task sequentially, a lead agent can delegate to multiple teammates that work in parallel to research, debug, and build while coordinating with each other. Try it out today by enabling agent teams in your settings.json!

Lydia Hallie ✨

1,443,017 Aufrufe • vor 6 Monaten

Lysium is an app I've been building specifically for background agent orchestration. Features: → Mobile-first + cross-platform → Run multiple agents in parallel across repositories → Launch agent-based requests from issues and PRs → Swipe actions for close, merge, create PR, and skip-to-tail workflows → One click agent-powered PR reviews + assessments → One-click agent-powered issue assessments Think of it as a cross-platform control plane for async, agent-driven software delivery (that you can control from anywhere). It's still an experiment, but it's open source and available to try:

Lysium is an app I've been building specifically for background agent orchestration. Features: → Mobile-first + cross-platform → Run multiple agents in parallel across repositories → Launch agent-based requests from issues and PRs → Swipe actions for close, merge, create PR, and skip-to-tail workflows → One click agent-powered PR reviews + assessments → One-click agent-powered issue assessments Think of it as a cross-platform control plane for async, agent-driven software delivery (that you can control from anywhere). It's still an experiment, but it's open source and available to try:

nader dabit

47,584 Aufrufe • vor 5 Monaten

Introducing Browser Agent in Julius. Starting today, Julius can go beyond just answering your questions, now it can spin up browser sessions and take actions across the web. We're excited to partner with Browserbase to bring this capability to our users. Browser agent in Julius is completely free this week

Introducing Browser Agent in Julius. Starting today, Julius can go beyond just answering your questions, now it can spin up browser sessions and take actions across the web. We're excited to partner with Browserbase to bring this capability to our users. Browser agent in Julius is completely free this week

Julius AI

13,086 Aufrufe • vor 25 Tagen

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

218,804 Aufrufe • vor 2 Tagen

Quick test of the new damage model (and reason why I needed combat text for debugging). There is quite a lot to unpack, if you won't read this, I won't blame you. Armor penetration is now a thing, and AP/APHE projectiles can exist. A new module system is in place, module shapes and their hitzones are decoupled from hull parts. Hullparts (including parts in armor layers) can have a variable armor rating. Shooting at complex vehicles with APxx ammunition can now yield various results. Ranging from surface hits (white) to module/critical hits (red), Internal hits (penetrating through the hull layer but missing a module) are displayed in yellow. Piercing damage itself is cyan. Modules can now be destroyed, disabling or losing ability modules, defensive fields, or damaging propulsion systems can now happen. Destruction of some modules can also result in quite volatile reactions (we will take a look at that next time). Hence, understanding the layout of your target, its armor layers, and module positions can be greatly beneficial. Energy projectiles can mirror this behavior. The difference is that piercing damage from ballistic projectiles is always kinetic, while for energy projectiles, it's a percentage of their energy pattern. There is a bug in color of ricochet damage numbers, I am aware of it. #Precursor #pixelart #gamedev #indiegame

Quick test of the new damage model (and reason why I needed combat text for debugging). There is quite a lot to unpack, if you won't read this, I won't blame you. Armor penetration is now a thing, and AP/APHE projectiles can exist. A new module system is in place, module shapes and their hitzones are decoupled from hull parts. Hullparts (including parts in armor layers) can have a variable armor rating. Shooting at complex vehicles with APxx ammunition can now yield various results. Ranging from surface hits (white) to module/critical hits (red), Internal hits (penetrating through the hull layer but missing a module) are displayed in yellow. Piercing damage itself is cyan. Modules can now be destroyed, disabling or losing ability modules, defensive fields, or damaging propulsion systems can now happen. Destruction of some modules can also result in quite volatile reactions (we will take a look at that next time). Hence, understanding the layout of your target, its armor layers, and module positions can be greatly beneficial. Energy projectiles can mirror this behavior. The difference is that piercing damage from ballistic projectiles is always kinetic, while for energy projectiles, it's a percentage of their energy pattern. There is a bug in color of ricochet damage numbers, I am aware of it. #Precursor #pixelart #gamedev #indiegame

Jan Orszulik

24,522 Aufrufe • vor 2 Jahren

Introducing LobeHub: Agent teammates that grow with you. LobeHub is the ultimate space for work and life: to find, build, and collaborate with agent teammates that grow with you. We’re building the world’s first and largest human–agent co-evolving network. Two years ago, we built LobeChat, an open-source interface for using different AI models. Today, LobeChat has 70k+ GitHub stars and serves 6M+ users worldwide. How to fully unlock the power of models has always been a shared mission between us and the community. We started with interaction — a fundamentally new, agent-first experience. Agents are no longer passive tools invoked in a single conversation. They should be proactive, always-on units of work. Treating agents as the minimal atomic unit is also the core of our agent harness infra. Today’s agents are mostly one-off executors. Even with memory, it’s often global — and hallucinates. We build long-term agent teammates that evolve with users. Each agent has its own dedicated memory space, editable by users, allowing humans and agents to co-evolve over time. This, in turn, allows us to design clearer rewards for reinforcement learning and create cleaner environments for continual learning. Agent teammates can work in groups. Through a multi-agent system, agent groups operate faster, more cost-effective, and go beyond what single-agent systems can achieve. For example, a single agent often requires heavy user involvement to proceed step by step, whereas LobeHub can execute the same work from a single instruction, with a supervisor orchestrating agents that run in parallel or debate to produce better results. We are building the collaboration network among agent teammates — and between humans and agent teammates as well. Ease of use matters. AI intelligence and shared human intelligence are equally important. With simple instructions and tool selection, you can effortlessly build and team up with agent coworkers to deliver complex, systematic work — even assembling a quant team to execute trades. Through the LobeHub community, anyone can discover, reuse, and remix agents and agent groups, customizing them to fit their own workflows, preferences, and needs. Last but not least, our vision started with LobeChat: multi-model support is the most efficient approach for users. We believe different models excel in different scenarios. By routing across multiple models, LobeHub improves cost efficiency and unlocks capabilities that a single-model setup cannot easily support.

Introducing LobeHub: Agent teammates that grow with you. LobeHub is the ultimate space for work and life: to find, build, and collaborate with agent teammates that grow with you. We’re building the world’s first and largest human–agent co-evolving network. Two years ago, we built LobeChat, an open-source interface for using different AI models. Today, LobeChat has 70k+ GitHub stars and serves 6M+ users worldwide. How to fully unlock the power of models has always been a shared mission between us and the community. We started with interaction — a fundamentally new, agent-first experience. Agents are no longer passive tools invoked in a single conversation. They should be proactive, always-on units of work. Treating agents as the minimal atomic unit is also the core of our agent harness infra. Today’s agents are mostly one-off executors. Even with memory, it’s often global — and hallucinates. We build long-term agent teammates that evolve with users. Each agent has its own dedicated memory space, editable by users, allowing humans and agents to co-evolve over time. This, in turn, allows us to design clearer rewards for reinforcement learning and create cleaner environments for continual learning. Agent teammates can work in groups. Through a multi-agent system, agent groups operate faster, more cost-effective, and go beyond what single-agent systems can achieve. For example, a single agent often requires heavy user involvement to proceed step by step, whereas LobeHub can execute the same work from a single instruction, with a supervisor orchestrating agents that run in parallel or debate to produce better results. We are building the collaboration network among agent teammates — and between humans and agent teammates as well. Ease of use matters. AI intelligence and shared human intelligence are equally important. With simple instructions and tool selection, you can effortlessly build and team up with agent coworkers to deliver complex, systematic work — even assembling a quant team to execute trades. Through the LobeHub community, anyone can discover, reuse, and remix agents and agent groups, customizing them to fit their own workflows, preferences, and needs. Last but not least, our vision started with LobeChat: multi-model support is the most efficient approach for users. We believe different models excel in different scenarios. By routing across multiple models, LobeHub improves cost efficiency and unlocks capabilities that a single-model setup cannot easily support.

LobeHub

185,273 Aufrufe • vor 6 Monaten

starting today Julius can go beyond just answering your questions, now it can spin up browser sessions and take actions across the web using our new browser agent we're pumped to be partnering with Browserbase and Paul Klein IV to bring this capability to our users browser agent in Julius is completely free this week. give it a try!

starting today Julius can go beyond just answering your questions, now it can spin up browser sessions and take actions across the web using our new browser agent we're pumped to be partnering with Browserbase and Paul Klein IV to bring this capability to our users browser agent in Julius is completely free this week. give it a try!

rahul

41,316 Aufrufe • vor 25 Tagen

Introducing Skills in SciSpace Agent. Specialized modes built for specific research workflows. Describe the task, and the right workflow activates automatically. Yes, the right Skill gets loaded based on what you need. Built to simplify multi-step research work and reduce friction around execution. Skills are now live. Try it inside SciSpace Agent.

Introducing Skills in SciSpace Agent. Specialized modes built for specific research workflows. Describe the task, and the right workflow activates automatically. Yes, the right Skill gets loaded based on what you need. Built to simplify multi-step research work and reduce friction around execution. Skills are now live. Try it inside SciSpace Agent.

SciSpace

43,422 Aufrufe • vor 5 Monaten

It doesn’t matter how fast your code is generated if the requirements are unclear. Shit in == shit out. That’s why we designed Software Factory as a discipline, not as a convenience tool. Our agents scrutinize your requirements and designs so that when you pass that context to a coding agent, it delivers as expected. Try it here:

It doesn’t matter how fast your code is generated if the requirements are unclear. Shit in == shit out. That’s why we designed Software Factory as a discipline, not as a convenience tool. Our agents scrutinize your requirements and designs so that when you pass that context to a coding agent, it delivers as expected. Try it here:

Chamath Palihapitiya

305,335 Aufrufe • vor 6 Monaten

This one is for builders who want an agent that can operate their computer. Today, we're releasing HoloDesktop CLI. Powered by Holo3 models, it brings H Agent directly into the agent harnesses you already use, including Claude R Perrin Code, Hermes Trismegistus, Cursor, and others. It runs locally on your device with low latency, full privacy, and no runtime cost. Most AI tooling assumes work happens through APIs. In reality, much of the world's work still happens in interfaces built for humans: browsers, desktop applications, spreadsheets, internal tools, and software with no API. HoloDesktop CLI gives agents the ability to see, understand, and act on these environments through the mouse and keyboard. From testing web applications through their GUI to navigating enterprise software, extracting information from documents, or interacting with internal tools, agents can now operate where work actually happens. We believe AI shouldn't just reason about work. It should be able to operate where work happens. #NVIDIA #ComputerUse #EnterpriseAI #DeveloperTools

This one is for builders who want an agent that can operate their computer. Today, we're releasing HoloDesktop CLI. Powered by Holo3 models, it brings H Agent directly into the agent harnesses you already use, including Claude R Perrin Code, Hermes Trismegistus, Cursor, and others. It runs locally on your device with low latency, full privacy, and no runtime cost. Most AI tooling assumes work happens through APIs. In reality, much of the world's work still happens in interfaces built for humans: browsers, desktop applications, spreadsheets, internal tools, and software with no API. HoloDesktop CLI gives agents the ability to see, understand, and act on these environments through the mouse and keyboard. From testing web applications through their GUI to navigating enterprise software, extracting information from documents, or interacting with internal tools, agents can now operate where work actually happens. We believe AI shouldn't just reason about work. It should be able to operate where work happens. #NVIDIA #ComputerUse #EnterpriseAI #DeveloperTools

H

29,953 Aufrufe • vor 1 Monat

ICP MCP. One agent. Every app. We asked Arshavir Ter-Gabrielyan for the killer feature. His answer: acting across your apps without switching between them. Move your whole account from one platform to another, and the agent does the app-hopping for you. As long as it can sign in to both, your posts, content, and connections come across. Full conversation below.

ICP MCP. One agent. Every app. We asked Arshavir Ter-Gabrielyan for the killer feature. His answer: acting across your apps without switching between them. Move your whole account from one platform to another, and the agent does the app-hopping for you. As long as it can sign in to both, your posts, content, and connections come across. Full conversation below.

DFINITY Foundation

22,867 Aufrufe • vor 16 Tagen

Introducing Naïve Domains — agents can now buy & manage their own domains, straight from the CLI. npm install -g naïve-sdk/cli and your agent can: > search & buy any domain via API > auto-configure DNS in one call > spin up custom email inboxes > deploy websites directly to domains Free to install, free to use — you only pay the registrar fee when your agent actually buys a domain (no markup). All it takes is one prompt. Try it now ↓🔗

Introducing Naïve Domains — agents can now buy & manage their own domains, straight from the CLI. npm install -g naïve-sdk/cli and your agent can: > search & buy any domain via API > auto-configure DNS in one call > spin up custom email inboxes > deploy websites directly to domains Free to install, free to use — you only pay the registrar fee when your agent actually buys a domain (no markup). All it takes is one prompt. Try it now ↓🔗

naïve

14,708 Aufrufe • vor 3 Monaten

Introducing the Lifetimely Profit Agent - powered by the $100Bn in GMV. It uses the benchmarks from across all the GMV in Lifetimely, to find the highest leverage Profit opportunities in your business, and can actually take action to drive Profit. It's live in Lifetimely right now!

Introducing the Lifetimely Profit Agent - powered by the $100Bn in GMV. It uses the benchmarks from across all the GMV in Lifetimely, to find the highest leverage Profit opportunities in your business, and can actually take action to drive Profit. It's live in Lifetimely right now!

Patrick Barnes

14,107 Aufrufe • vor 2 Monaten

Automatically create Agent Skills from past sessions. Gemini CLI can now comb through past session data and suggest new skills based on past patterns of things you do frequently! Helps the agent self-improve. 🧠 Enable Auto Memory in /settings to try it out.

Automatically create Agent Skills from past sessions. Gemini CLI can now comb through past session data and suggest new skills based on past patterns of things you do frequently! Helps the agent self-improve. 🧠 Enable Auto Memory in /settings to try it out.

Jack Wotherspoon

24,793 Aufrufe • vor 3 Monaten

Enterprises today are running single-player AI in a multiplayer business. One engineer plus one IDE plus one agent ships lines of code faster. It does not ship coordinated software. Two desks over, another engineer's agent is shipping a change that contradicts last quarter's architecture decision. Neither agent knows. Compliance assembles the audit trail from screenshots after the fact. We built Software Factory to turn that into a team. Requirements captures the business intent in plain English. Blueprints maps the architecture as it actually exists. Work Orders translates that into structured tasks agents execute through MCP in Cursor, VS Code, or Claude Code. Tests validates the deployed code against the Requirement that started it. The Knowledge Graph wires it all together. Your engineers conduct the agents. The agents share state. The audit trail is part of the build, not a quarterly forensics exercise. EY's deployment: +70% productivity, 95%+ automated test coverage, months of roadmap compressed to days.

Enterprises today are running single-player AI in a multiplayer business. One engineer plus one IDE plus one agent ships lines of code faster. It does not ship coordinated software. Two desks over, another engineer's agent is shipping a change that contradicts last quarter's architecture decision. Neither agent knows. Compliance assembles the audit trail from screenshots after the fact. We built Software Factory to turn that into a team. Requirements captures the business intent in plain English. Blueprints maps the architecture as it actually exists. Work Orders translates that into structured tasks agents execute through MCP in Cursor, VS Code, or Claude Code. Tests validates the deployed code against the Requirement that started it. The Knowledge Graph wires it all together. Your engineers conduct the agents. The agents share state. The audit trail is part of the build, not a quarterly forensics exercise. EY's deployment: +70% productivity, 95%+ automated test coverage, months of roadmap compressed to days.

8090

75,167 Aufrufe • vor 2 Monaten