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Despite having vision, most AI agents still struggle to see. General-purpose multimodal models are powerful, but they’re expensive for every visual task. We built something better: Perceptron's MCP gives any agent stronger vision capabilities through Isaac with far lower cost.

Perceptron AI

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335,798 Aufrufe • vor 5 Monaten •via X (Twitter)

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Profilbild von Perceptron AI

Perceptron AIvor 5 Monaten

Powered by our Isaac model family, it delivers best-in-class VQA, OCR, object detection, and grounding. Vision is processed natively and returned as structured output, efficient at any scale. What our developers are building: - Sort files by visual content, no manual tagging - Surface the best shots from a camera roll - Extract receipts into structured spreadsheets

Profilbild von Perceptron AI

Perceptron AIvor 5 Monaten

Install in one line for Claude Code: `claude mcp add perceptron -e PERCEPTRON_API_KEY=YOUR_API_KEY -- npx -y @perceptron-ai/mcp-server@latest` Docs → API Keys →

Profilbild von Aakash Gupta

Aakash Guptavor 5 Monaten

very cool, analyzing

Profilbild von Connie

Connievor 5 Monaten

Amazing work!

Profilbild von Very Human Robot

Very Human Robotvor 5 Monaten

Is this Isaac related to nvidias Isaac in any way?

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139,591 Aufrufe • vor 1 Jahr

⚡️PREMIERE⚡️ Santiago: The Last Sail Today, July 21st marks Ernest Hemingway’s 127th birthday. On this day, we present a new vision of the ambitious project we released two years ago, when we set out to create an AI adaptation of Hemingway’s legendary novel, The Old Man and the Sea. Back in 2024, our goal was to build an ultra-realistic AI film inspired by one of the most iconic stories ever written. But at the time, despite ultra-rapid progress, many of the things were still beyond what AI could achieve. Today, through our creative partnership with Invideo - the story finally receives the visuals it deserves with the help of Agent One. We recreated the film entirely from scratch — every character, every scene, every location — rebuilding the project with Agent One into a comprehensive 10-minute short AI film. SANTIAGO: THE LAST SAIL Out now. *stay tuned for a detailed breakdown & tutorial on how we did this with Agent One*

⚡️PREMIERE⚡️ Santiago: The Last Sail Today, July 21st marks Ernest Hemingway’s 127th birthday. On this day, we present a new vision of the ambitious project we released two years ago, when we set out to create an AI adaptation of Hemingway’s legendary novel, The Old Man and the Sea. Back in 2024, our goal was to build an ultra-realistic AI film inspired by one of the most iconic stories ever written. But at the time, despite ultra-rapid progress, many of the things were still beyond what AI could achieve. Today, through our creative partnership with Invideo - the story finally receives the visuals it deserves with the help of Agent One. We recreated the film entirely from scratch — every character, every scene, every location — rebuilding the project with Agent One into a comprehensive 10-minute short AI film. SANTIAGO: THE LAST SAIL Out now. stay tuned for a detailed breakdown & tutorial on how we did this with Agent One

Digital da Vincis

28,149 Aufrufe • vor 2 Monaten

Today, we're introducing not one but TWO new models, striking the balance between efficiency and quality to enable you to build production AI agents. — Gemini 3.6 Flash: Addresses efficiency feedback we received from Gemini 3.5 Flash with upgrades in coding, knowledge work, and multimodal tasks faster, more accurately, and with substantially fewer tokens per task — Gemini 3.5 Flash-Lite: Our fastest, most cost-effective 3.5-class model yet built for agentic workflows, hitting ~350 output tokens/sec with improved coding and overall quality Start building with these today via the Gemini API in Google AI Studio or try them out in the Google Gemini

Today, we're introducing not one but TWO new models, striking the balance between efficiency and quality to enable you to build production AI agents. — Gemini 3.6 Flash: Addresses efficiency feedback we received from Gemini 3.5 Flash with upgrades in coding, knowledge work, and multimodal tasks faster, more accurately, and with substantially fewer tokens per task — Gemini 3.5 Flash-Lite: Our fastest, most cost-effective 3.5-class model yet built for agentic workflows, hitting ~350 output tokens/sec with improved coding and overall quality Start building with these today via the Gemini API in Google AI Studio or try them out in the Google Gemini

Google AI

438,702 Aufrufe • vor 2 Monaten

JARVIS-1: Open-World Multi-task Agents with Memory-Augmented Multimodal Language Models paper page: Achieving human-like planning and control with multimodal observations in an open world is a key milestone for more functional generalist agents. Existing approaches can handle certain long-horizon tasks in an open world. However, they still struggle when the number of open-world tasks could potentially be infinite and lack the capability to progressively enhance task completion as game time progresses. We introduce JARVIS-1, an open-world agent that can perceive multimodal input (visual observations and human instructions), generate sophisticated plans, and perform embodied control, all within the popular yet challenging open-world Minecraft universe. Specifically, we develop JARVIS-1 on top of pre-trained multimodal language models, which map visual observations and textual instructions to plans. The plans will be ultimately dispatched to the goal-conditioned controllers. We outfit JARVIS-1 with a multimodal memory, which facilitates planning using both pre-trained knowledge and its actual game survival experiences. In our experiments, JARVIS-1 exhibits nearly perfect performances across over 200 varying tasks from the Minecraft Universe Benchmark, ranging from entry to intermediate levels. JARVIS-1 has achieved a completion rate of 12.5% in the long-horizon diamond pickaxe task. This represents a significant increase up to 5 times compared to previous records. Furthermore, we show that JARVIS-1 is able to self-improve following a life-long learning paradigm thanks to multimodal memory, sparking a more general intelligence and improved autonomy.

JARVIS-1: Open-World Multi-task Agents with Memory-Augmented Multimodal Language Models paper page: Achieving human-like planning and control with multimodal observations in an open world is a key milestone for more functional generalist agents. Existing approaches can handle certain long-horizon tasks in an open world. However, they still struggle when the number of open-world tasks could potentially be infinite and lack the capability to progressively enhance task completion as game time progresses. We introduce JARVIS-1, an open-world agent that can perceive multimodal input (visual observations and human instructions), generate sophisticated plans, and perform embodied control, all within the popular yet challenging open-world Minecraft universe. Specifically, we develop JARVIS-1 on top of pre-trained multimodal language models, which map visual observations and textual instructions to plans. The plans will be ultimately dispatched to the goal-conditioned controllers. We outfit JARVIS-1 with a multimodal memory, which facilitates planning using both pre-trained knowledge and its actual game survival experiences. In our experiments, JARVIS-1 exhibits nearly perfect performances across over 200 varying tasks from the Minecraft Universe Benchmark, ranging from entry to intermediate levels. JARVIS-1 has achieved a completion rate of 12.5% in the long-horizon diamond pickaxe task. This represents a significant increase up to 5 times compared to previous records. Furthermore, we show that JARVIS-1 is able to self-improve following a life-long learning paradigm thanks to multimodal memory, sparking a more general intelligence and improved autonomy.

AK

141,440 Aufrufe • vor 2 Jahren

MOST AI AGENTS RESET TO ZERO EVERY TIME YOU OPEN A NEW SESSION. Hermes Agent was built to do the opposite. Every task compounds. Every workflow becomes reusable. Every session teaches the next one. That is the core thesis behind Hermes, and it completely changes how AI agents work. Unlike most AI agents built by software tool companies, Hermes was created by the researchers training the Hermes model family itself. Meaning: The same system used internally to generate training data is now open source and available to everyone. One install gives you access to: Claude. GPT. Grok. Local models via Ollama. Telegram, Discord, Slack, iMessage integrations. Custom skills. Persistent memory. CLI control. But the real unlock is the compounding loop: You give Hermes a task. It records every tool call, decision, and result. Then it converts successful workflows into reusable skills automatically. A Hermes setup running for 3 months becomes fundamentally smarter than a fresh install. That’s the big idea. One of the coolest parts: You literally watch the agent evolve in real time from a blank install into a personalized AI operator. Instead of watching Netflix today, spend 28 mins with this. Follow Dami-Defi for more AI systems, agent workflows, and infrastructure alpha.

MOST AI AGENTS RESET TO ZERO EVERY TIME YOU OPEN A NEW SESSION. Hermes Agent was built to do the opposite. Every task compounds. Every workflow becomes reusable. Every session teaches the next one. That is the core thesis behind Hermes, and it completely changes how AI agents work. Unlike most AI agents built by software tool companies, Hermes was created by the researchers training the Hermes model family itself. Meaning: The same system used internally to generate training data is now open source and available to everyone. One install gives you access to: Claude. GPT. Grok. Local models via Ollama. Telegram, Discord, Slack, iMessage integrations. Custom skills. Persistent memory. CLI control. But the real unlock is the compounding loop: You give Hermes a task. It records every tool call, decision, and result. Then it converts successful workflows into reusable skills automatically. A Hermes setup running for 3 months becomes fundamentally smarter than a fresh install. That’s the big idea. One of the coolest parts: You literally watch the agent evolve in real time from a blank install into a personalized AI operator. Instead of watching Netflix today, spend 28 mins with this. Follow Dami-Defi for more AI systems, agent workflows, and infrastructure alpha.

Dami-Defi

11,685 Aufrufe • vor 3 Monaten

a16z speedrun request for startups: GUIs for Agents we’re still in the MS-DOS era of agents today - CLI, terminal sessions, file directories deleted by openclaw etc. while a small slice of silicon valley are power users, we're SO early for the rest of the world at Speedrun, we’re looking for bold founders excited to bring the power of agents to normies everywhere. there's a whole slew of products to be built here - from agent builders to marketplaces to managed infrastructure one broad idea we’re excited about are visual abstraction layers for agents. if you don't know exactly what you want, a command line / chat interface is paralyzing - you need to see options 1 example - think of a GUI or visual command center inspired by strategy games (ex. Factorio) where agents and workflows are represented graphically. skills, tools, MCP connections, background processes, etc could all be configured and shown visually in a workspace on UX, strategy games have long perfected agent management. zoom to get a birds-eye view of your agents, batch and queue orders via shortcuts, assign agents in multiplayer etc. a well-designed agent command center would make multi-agent orchestration for normies feel easy & intuitive most folks today still haven't moved beyond ChatGPT. the potential is enormous - just as Windows unlocked mass-market use of personal computers, the right visual abstraction layer could unlock agentic work for everyone - from individuals to enterprise teams if you share our vision, we'd love to chat!

a16z speedrun request for startups: GUIs for Agents we’re still in the MS-DOS era of agents today - CLI, terminal sessions, file directories deleted by openclaw etc. while a small slice of silicon valley are power users, we're SO early for the rest of the world at Speedrun, we’re looking for bold founders excited to bring the power of agents to normies everywhere. there's a whole slew of products to be built here - from agent builders to marketplaces to managed infrastructure one broad idea we’re excited about are visual abstraction layers for agents. if you don't know exactly what you want, a command line / chat interface is paralyzing - you need to see options 1 example - think of a GUI or visual command center inspired by strategy games (ex. Factorio) where agents and workflows are represented graphically. skills, tools, MCP connections, background processes, etc could all be configured and shown visually in a workspace on UX, strategy games have long perfected agent management. zoom to get a birds-eye view of your agents, batch and queue orders via shortcuts, assign agents in multiplayer etc. a well-designed agent command center would make multi-agent orchestration for normies feel easy & intuitive most folks today still haven't moved beyond ChatGPT. the potential is enormous - just as Windows unlocked mass-market use of personal computers, the right visual abstraction layer could unlock agentic work for everyone - from individuals to enterprise teams if you share our vision, we'd love to chat!

Jon Lai

199,793 Aufrufe • vor 5 Monaten

Most AI demos stop at text prompts. I wanted to see what Qwen3.7-Plus could do beyond chat, so I tested it through the API using an Airbnb interface screenshot. The model analyzed the UI, identified key components, explained the visual hierarchy, highlighted usability strengths, suggested UX improvements, and even generated a React + Tailwind structure to recreate the interface. A few things that stood out: 👁️ Multimodal understanding from a single screenshot 🧠 Strong reasoning around UX and product design decisions 💻 Able to bridge visual inputs and code generation ⚡ OpenAI-compatible API that was easy to integrate 💰 Impressive capability-to-cost ratio Qwen3.7-Plus isn't just a chatbot. It's a multimodal agent model designed for workflows that combine vision, reasoning, coding, and automation. Try Qwen3.7-Plus with limited time 20% off:

Most AI demos stop at text prompts. I wanted to see what Qwen3.7-Plus could do beyond chat, so I tested it through the API using an Airbnb interface screenshot. The model analyzed the UI, identified key components, explained the visual hierarchy, highlighted usability strengths, suggested UX improvements, and even generated a React + Tailwind structure to recreate the interface. A few things that stood out: 👁️ Multimodal understanding from a single screenshot 🧠 Strong reasoning around UX and product design decisions 💻 Able to bridge visual inputs and code generation ⚡ OpenAI-compatible API that was easy to integrate 💰 Impressive capability-to-cost ratio Qwen3.7-Plus isn't just a chatbot. It's a multimodal agent model designed for workflows that combine vision, reasoning, coding, and automation. Try Qwen3.7-Plus with limited time 20% off:

Kuria | AI

17,044 Aufrufe • vor 3 Monaten