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A three-story office build. The MEP coordination model behind it cost around $8,000 and caught 40 clashes before construction started. Kimi K3 can already read a model like this through Revit's official MCP: flag clashes, cross-check disciplines, nothing more, the server stays read-only. Point it at a community-built Revit... show more

Solvaix

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10,904 görüntüleme • 16 gün önce •via X (Twitter)

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3 HOURS OF MANUAL SETUP GOT AN AI BUILDING INSIDE REVIT. AUTODESK'S OWN VERSION STILL CAN'T. Revit is the software behind most MEP work, the mechanical, electrical, and plumbing systems planned out digitally before a single pipe goes into a real building. Autodesk released Revit 2027 in April 2026, then followed it in June with an official MCP server, Model Context Protocol, letting outside AI tools like Claude plug directly into a live model instead of guessing from screenshots. Claude Fable 5 connected without issue. It could read walls, ducts, pump schedules, dozens of parameters per object. It couldn't create a single wall, duct, or schedule, Tech Preview status, write access still locked on purpose. A beginner MEP engineer wanted more: three pumps, a chiller connection, basic ductwork, built by description instead of menu clicks. The official server couldn't do it. The fix was a community-built Revit MCP project instead, unofficial, 61 separate functions in total, including the ability to actually create and edit elements, the access Autodesk hasn't opened up yet. No official installer, no drag-and-drop, real technical setup before anything worked, close to 3 hours in this case. Once it was live, a small MEP layout that would've taken most of a workday to build by hand was finished in 40 minutes. Over in 3D art and design software, that gap doesn't exist. Blender's official MCP has had full read and write access since April 2026, backed by Anthropic as a funding partner of the Blender Foundation. Get a 3D scene wrong and you hit render again. Get a real pipe run wrong and you're filing a change order. That's the caution Autodesk is building into Revit.

3 HOURS OF MANUAL SETUP GOT AN AI BUILDING INSIDE REVIT. AUTODESK'S OWN VERSION STILL CAN'T. Revit is the software behind most MEP work, the mechanical, electrical, and plumbing systems planned out digitally before a single pipe goes into a real building. Autodesk released Revit 2027 in April 2026, then followed it in June with an official MCP server, Model Context Protocol, letting outside AI tools like Claude plug directly into a live model instead of guessing from screenshots. Claude Fable 5 connected without issue. It could read walls, ducts, pump schedules, dozens of parameters per object. It couldn't create a single wall, duct, or schedule, Tech Preview status, write access still locked on purpose. A beginner MEP engineer wanted more: three pumps, a chiller connection, basic ductwork, built by description instead of menu clicks. The official server couldn't do it. The fix was a community-built Revit MCP project instead, unofficial, 61 separate functions in total, including the ability to actually create and edit elements, the access Autodesk hasn't opened up yet. No official installer, no drag-and-drop, real technical setup before anything worked, close to 3 hours in this case. Once it was live, a small MEP layout that would've taken most of a workday to build by hand was finished in 40 minutes. Over in 3D art and design software, that gap doesn't exist. Blender's official MCP has had full read and write access since April 2026, backed by Anthropic as a funding partner of the Blender Foundation. Get a 3D scene wrong and you hit render again. Get a real pipe run wrong and you're filing a change order. That's the caution Autodesk is building into Revit.

Solvaix

96,637 görüntüleme • 1 ay önce

A CLASH CAUGHT IN THE MODEL COSTS $500. THE SAME CLASH CAUGHT ON SITE COSTS $17,000. A NUMBER THAT TRACES BACK TO AUTODESK'S OWN RESEARCH, NOT A GUESS. This isn't an early sketch of a building system, it's the final version: cooling towers, pumps, heat exchangers, pipe runs called out at exact sizes, 32mm and 100mm, ready to hand to a shop that's about to cut real metal to those numbers. That's the highest-stakes moment in the whole process. A mistake caught early just gets redrawn on screen. The same mistake, missed until the shop has already cut the pipe, means wasted material and a piece that has to be made again from scratch. Reading a model this dense for clashes, pipes crossing pipes, pipes crossing equipment, is exactly what Claude connected to Revit 2027 through MCP is built for. The official Revit MCP server can already read all of it. It still can't fix what it finds. The article below covers why.

A CLASH CAUGHT IN THE MODEL COSTS $500. THE SAME CLASH CAUGHT ON SITE COSTS $17,000. A NUMBER THAT TRACES BACK TO AUTODESK'S OWN RESEARCH, NOT A GUESS. This isn't an early sketch of a building system, it's the final version: cooling towers, pumps, heat exchangers, pipe runs called out at exact sizes, 32mm and 100mm, ready to hand to a shop that's about to cut real metal to those numbers. That's the highest-stakes moment in the whole process. A mistake caught early just gets redrawn on screen. The same mistake, missed until the shop has already cut the pipe, means wasted material and a piece that has to be made again from scratch. Reading a model this dense for clashes, pipes crossing pipes, pipes crossing equipment, is exactly what Claude connected to Revit 2027 through MCP is built for. The official Revit MCP server can already read all of it. It still can't fix what it finds. The article below covers why.

Solvaix

24,679 görüntüleme • 1 ay önce

FIX IT ON SCREEN: 1X COST. FIX IT DURING CONSTRUCTION: 10X. FIX IT AFTER HANDOVER: 100X. That's the industry's own math for coordination errors. Every pipe and duct on this ceiling carries its own millimeter callout, checked against the real unfinished site before drywall closes over it. One documented BIM coordination case spent $200,000 catching clashes like this before construction. Saved over $2.5 million once the project finished. A 10x return. Revit's new MCP integration puts Claude directly on a model like this, every dimension, every parameter, every clearance, without the manual scroll. It still can't move anything in the file, only point at what's about to go wrong. See the article below for exactly where that line is drawn, and why.

FIX IT ON SCREEN: 1X COST. FIX IT DURING CONSTRUCTION: 10X. FIX IT AFTER HANDOVER: 100X. That's the industry's own math for coordination errors. Every pipe and duct on this ceiling carries its own millimeter callout, checked against the real unfinished site before drywall closes over it. One documented BIM coordination case spent $200,000 catching clashes like this before construction. Saved over $2.5 million once the project finished. A 10x return. Revit's new MCP integration puts Claude directly on a model like this, every dimension, every parameter, every clearance, without the manual scroll. It still can't move anything in the file, only point at what's about to go wrong. See the article below for exactly where that line is drawn, and why.

Solvaix

18,909 görüntüleme • 22 gün önce

$65 MILLION RESIDENTIAL TOWER. 17 FLOORS OF WATER, DRAINAGE, AND MECHANICAL RUNS, PLACED AND CHECKED ONE LEVEL AGAINST THE NEXT, BEFORE ANY OF THE WALLS GO UP. Old process: 5 engineers, 3 months, around $240,000. New process: 1 engineer confirming the output, 3 weeks, roughly $25,000 combined, engineer's time plus AI compute. Autodesk added an official MCP server to Revit in April 2026, letting Claude read a live model directly instead of guessing from a screenshot. It can trace every riser, every parameter, every clash. It still can't build or edit anything, read-only on purpose. The workaround already exists, and it's what makes that new process number real. See the article below for how it works.

$65 MILLION RESIDENTIAL TOWER. 17 FLOORS OF WATER, DRAINAGE, AND MECHANICAL RUNS, PLACED AND CHECKED ONE LEVEL AGAINST THE NEXT, BEFORE ANY OF THE WALLS GO UP. Old process: 5 engineers, 3 months, around $240,000. New process: 1 engineer confirming the output, 3 weeks, roughly $25,000 combined, engineer's time plus AI compute. Autodesk added an official MCP server to Revit in April 2026, letting Claude read a live model directly instead of guessing from a screenshot. It can trace every riser, every parameter, every clash. It still can't build or edit anything, read-only on purpose. The workaround already exists, and it's what makes that new process number real. See the article below for how it works.

Solvaix

13,113 görüntüleme • 20 gün önce

4-6M DOLLARS. ROUGHLY WHAT THIS CLIFFSIDE RESIDENCE WOULD COST TO ACTUALLY BUILD. It starts with a mountain, shaped inside Revit's own site tools, floors and columns built level by level, human figures placed into the scene. From there the whole thing moves to 3ds Max for a full rebuild: terrain, floors, materials, multiple camera setups, one render pass to finish it. Cliffside sites push the price up mainly through foundations and platforms that jut out over the drop, not finishes. That's most of where the 4-6M comes from. Built in Revit 2024, before MCP existed. Revit 2027 has it now, and Claude can read every element in a model like this in seconds, surfacing warnings and outliers.

4-6M DOLLARS. ROUGHLY WHAT THIS CLIFFSIDE RESIDENCE WOULD COST TO ACTUALLY BUILD. It starts with a mountain, shaped inside Revit's own site tools, floors and columns built level by level, human figures placed into the scene. From there the whole thing moves to 3ds Max for a full rebuild: terrain, floors, materials, multiple camera setups, one render pass to finish it. Cliffside sites push the price up mainly through foundations and platforms that jut out over the drop, not finishes. That's most of where the 4-6M comes from. Built in Revit 2024, before MCP existed. Revit 2027 has it now, and Claude can read every element in a model like this in seconds, surfacing warnings and outliers.

Solvaix

24,308 görüntüleme • 1 ay önce

A CUSTOM BOOTH AT THIS DETAIL LEVEL RUNS $58,000 TO $145,000 ON THE OPEN MARKET. THIS ONE CAME OUT OF A CHAT MESSAGE. The brief: a reference photo, a height cap of 4.5m, a footprint of 6 by 9 meters. Claude built the whole thing inside Blender through MCP, reception desk, demo counter, a lounge with sofas and hanging planters, LED edge lighting throughout. It hit a real snag partway through: Blender's latest version changed its compositor code, and the first render came out badly overexposed. Claude found the cause, two lights running too hot and glare set too aggressive, and fixed it. Final scene: 1,133 objects, rendered at 1500x1180. Blender's official MCP connection has had full read and write access since April 2026. Every tool it can use, and exactly where it still falls short, is in the article below.

A CUSTOM BOOTH AT THIS DETAIL LEVEL RUNS $58,000 TO $145,000 ON THE OPEN MARKET. THIS ONE CAME OUT OF A CHAT MESSAGE. The brief: a reference photo, a height cap of 4.5m, a footprint of 6 by 9 meters. Claude built the whole thing inside Blender through MCP, reception desk, demo counter, a lounge with sofas and hanging planters, LED edge lighting throughout. It hit a real snag partway through: Blender's latest version changed its compositor code, and the first render came out badly overexposed. Claude found the cause, two lights running too hot and glare set too aggressive, and fixed it. Final scene: 1,133 objects, rendered at 1500x1180. Blender's official MCP connection has had full read and write access since April 2026. Every tool it can use, and exactly where it still falls short, is in the article below.

Solvaix

15,916 görüntüleme • 1 ay önce

MEP refers to the mechanical, electrical, and plumbing systems that are essential for a house to operate. This model shows exactly those systems, including ductwork, water tanks, an exterior air conditioner unit, and wiring, all planned out before building begins. Typically, coordinating these systems for a house this size would cost between $2,500 and $5,000. However, the issues this coordination helps prevent are far more costly than the invoice itself. In the US, electrical issues are estimated to cause 51,000 home fires annually, leading to $1.3 billion in damages. Water damage from broken pipes or faulty plumbing connections averages $15,400 per insurance claim, and about 1 in 67 homeowners file such a claim each year. By modeling all systems before construction starts, potential problems like faulty wiring or incorrectly installed pipes can be identified on screen rather than after walls are finished, saving significant money when issues arise. 3D modeling is also starting to incorporate AI. Revit 2027 now includes an official MCP connector that allows major AI models to access project files like this one. Currently, the AI can only read the files, not modify them. The article below explains why this is the case for now.

MEP refers to the mechanical, electrical, and plumbing systems that are essential for a house to operate. This model shows exactly those systems, including ductwork, water tanks, an exterior air conditioner unit, and wiring, all planned out before building begins. Typically, coordinating these systems for a house this size would cost between $2,500 and $5,000. However, the issues this coordination helps prevent are far more costly than the invoice itself. In the US, electrical issues are estimated to cause 51,000 home fires annually, leading to $1.3 billion in damages. Water damage from broken pipes or faulty plumbing connections averages $15,400 per insurance claim, and about 1 in 67 homeowners file such a claim each year. By modeling all systems before construction starts, potential problems like faulty wiring or incorrectly installed pipes can be identified on screen rather than after walls are finished, saving significant money when issues arise. 3D modeling is also starting to incorporate AI. Revit 2027 now includes an official MCP connector that allows major AI models to access project files like this one. Currently, the AI can only read the files, not modify them. The article below explains why this is the case for now.

Solvaix

32,877 görüntüleme • 7 gün önce

New course: MCP: Build Rich-Context AI Apps with Anthropic. Learn to build AI apps that access tools, data, and prompts using the Model Context Protocol in this short course, created in partnership with Anthropic Anthropic and taught by Elie Schoppik Elie Schoppik, its Head of Technical Education. Connecting AI applications to external systems that bring rich context to LLM-based applications has often meant writing custom integrations for each use case. MCP is an open protocol that standardizes how LLMs access tools, data, and prompts from external sources, and simplifies how you provide context to your LLM-based applications. For example, you can provide context via third-party tools that let your LLM make API calls to search the web, access data from local docs, retrieve code from a GitHub repo, and so on. MCP, developed by Anthropic, is based on a client-server architecture that defines the communication details between an MCP client, hosted inside the AI application, and an MCP server that exposes tools, resources, and prompt templates. The server can be a subprocess launched by the client that runs locally or an independent process running remotely. In this hands-on course, you'll learn the core architecture behind MCP. You’ll create an MCP-compatible chatbot, build and deploy an MCP server, and connect the chatbot to your MCP server and other open-source servers. Here’s what you’ll do: - Understand why MCP makes AI development less fragmented and standardizes connections between AI applications and external data sources - Learn the core components of the client-server architecture of MCP and the underlying communication mechanism - Build a chatbot with custom tools for searching academic papers, and transform it into an MCP-compatible application - Build a local MCP server that exposes tools, resources, and prompt templates using FastMCP, and test it using MCP Inspector - Create an MCP client inside your chatbot to dynamically connect to your server - Connect your chatbot to reference servers built by Anthropic’s MCP team, such as filesystem, which implements filesystem operations, and fetch, which extracts contents from the web as markdown - Configure Claude Desktop to connect to your server and others, and explore how it abstracts away the low-level logic of MCP clients - Deploy your MCP server remotely and test it with the Inspector or other MCP-compatible applications - Learn about the roadmap for future MCP development, such as multi-agent architecture, MCP registry API, server discovery, authorization, and authentication MCP is an exciting and important technology that lets you build rich-context AI applications that connect to a growing ecosystem of MCP servers, with minimal integration work. Please sign up here!

New course: MCP: Build Rich-Context AI Apps with Anthropic. Learn to build AI apps that access tools, data, and prompts using the Model Context Protocol in this short course, created in partnership with Anthropic Anthropic and taught by Elie Schoppik Elie Schoppik, its Head of Technical Education. Connecting AI applications to external systems that bring rich context to LLM-based applications has often meant writing custom integrations for each use case. MCP is an open protocol that standardizes how LLMs access tools, data, and prompts from external sources, and simplifies how you provide context to your LLM-based applications. For example, you can provide context via third-party tools that let your LLM make API calls to search the web, access data from local docs, retrieve code from a GitHub repo, and so on. MCP, developed by Anthropic, is based on a client-server architecture that defines the communication details between an MCP client, hosted inside the AI application, and an MCP server that exposes tools, resources, and prompt templates. The server can be a subprocess launched by the client that runs locally or an independent process running remotely. In this hands-on course, you'll learn the core architecture behind MCP. You’ll create an MCP-compatible chatbot, build and deploy an MCP server, and connect the chatbot to your MCP server and other open-source servers. Here’s what you’ll do: - Understand why MCP makes AI development less fragmented and standardizes connections between AI applications and external data sources - Learn the core components of the client-server architecture of MCP and the underlying communication mechanism - Build a chatbot with custom tools for searching academic papers, and transform it into an MCP-compatible application - Build a local MCP server that exposes tools, resources, and prompt templates using FastMCP, and test it using MCP Inspector - Create an MCP client inside your chatbot to dynamically connect to your server - Connect your chatbot to reference servers built by Anthropic’s MCP team, such as filesystem, which implements filesystem operations, and fetch, which extracts contents from the web as markdown - Configure Claude Desktop to connect to your server and others, and explore how it abstracts away the low-level logic of MCP clients - Deploy your MCP server remotely and test it with the Inspector or other MCP-compatible applications - Learn about the roadmap for future MCP development, such as multi-agent architecture, MCP registry API, server discovery, authorization, and authentication MCP is an exciting and important technology that lets you build rich-context AI applications that connect to a growing ecosystem of MCP servers, with minimal integration work. Please sign up here!

Andrew Ng

142,234 görüntüleme • 1 yıl önce

The interesting part about GPT in CAD is not that it can make a shape. It is that through MCP, GPT can work directly inside Onshape and turn a plain-English description into actual CAD geometry you can keep editing. In this case, the model builds a radial mechanical part with a central hub, repeated ribs, an outer ring, and a full pattern of mounting holes, all inside the real Onshape document. There is no fake “3D-looking” render hiding the hard part. The geometry exists in the CAD workspace, can be rotated, inspected, modified, and used as the starting point for the next iteration. That changes the workflow from manually clicking through every feature to describing the structure, letting GPT build the first pass, then correcting what matters. Once MCP gets reliable enough, the boring part of CAD starts looking less like modeling and more like supervising.

The interesting part about GPT in CAD is not that it can make a shape. It is that through MCP, GPT can work directly inside Onshape and turn a plain-English description into actual CAD geometry you can keep editing. In this case, the model builds a radial mechanical part with a central hub, repeated ribs, an outer ring, and a full pattern of mounting holes, all inside the real Onshape document. There is no fake “3D-looking” render hiding the hard part. The geometry exists in the CAD workspace, can be rotated, inspected, modified, and used as the starting point for the next iteration. That changes the workflow from manually clicking through every feature to describing the structure, letting GPT build the first pass, then correcting what matters. Once MCP gets reliable enough, the boring part of CAD starts looking less like modeling and more like supervising.

Rina

16,772 görüntüleme • 24 gün önce

New short course: Build AI Apps with MCP Servers: Working with Box Files, built with Box and taught by Ben Kus , their CTO. Many AI applications require custom code for basic file operations. The Model Context Protocol (MCP) standardizes this by letting you offload file tasks to dedicated servers that provide tools an LLM can use directly. In this course, you'll process documents stored in a Box folder using the Box MCP server. Rather than writing custom integration code to connect to the Box API and download files, you'll design your application to use the tools provided via MCP. Skills you'll gain: - Build an LLM-powered document processing app, using the Box MCP server to access files - Design a multi-agent system using Google's Agent Development Kit (ADK), consisting of specialized agents for file operations - Coordinate the multi-agent workflow through an orchestrator that uses the Agent2Agent (A2A) protocol to connect to the agents You'll start with a local file-processing app, refactor it to work with Box's MCP server, then evolve it into a multi-agent system. Sign up here:

New short course: Build AI Apps with MCP Servers: Working with Box Files, built with Box and taught by Ben Kus , their CTO. Many AI applications require custom code for basic file operations. The Model Context Protocol (MCP) standardizes this by letting you offload file tasks to dedicated servers that provide tools an LLM can use directly. In this course, you'll process documents stored in a Box folder using the Box MCP server. Rather than writing custom integration code to connect to the Box API and download files, you'll design your application to use the tools provided via MCP. Skills you'll gain: - Build an LLM-powered document processing app, using the Box MCP server to access files - Design a multi-agent system using Google's Agent Development Kit (ADK), consisting of specialized agents for file operations - Coordinate the multi-agent workflow through an orchestrator that uses the Agent2Agent (A2A) protocol to connect to the agents You'll start with a local file-processing app, refactor it to work with Box's MCP server, then evolve it into a multi-agent system. Sign up here:

Andrew Ng

81,983 görüntüleme • 11 ay önce

HERMES AGENT WITHOUT TOOLS IS A CHATBOT. WITH THEM IT BUILDS 3D TOWERS IN BLENDER, CHECKS STOCK PRICES, AND DRIVES VS CODE. tonbi JUST DROPPED THE FULL GUIDE. module 6 of his 10-part Hermes masterclass. best breakdown of the tool layer anyone has published. what you need to know: TOOLS vs SKILLS vs MCP skills = instructions (markdown, loaded into context) tools = callable functions (Python, agent emits call, Hermes executes) MCP = adapters to external systems (Blender, Stripe, Linear, Notion) every tool has three parts: → the function (does the real work) → the schema (what the model sees to decide when to call it) → the registry (makes the tool exist in the agent) the model never runs the function directly. it emits a structured request (tool name + JSON args). Hermes executes and returns the result. if the tool fails, the error goes back as JSON. the agent recovers instead of crashing. TOOL SETS CONTROL THE SURFACE hermes chat --tool-sets web # only web tools loaded. no files, no terminal. hermes chat --tool-sets safe # read-only: web search, vision, image gen. # no file writes. no terminal. no code execution. mid-session: /tools enable video /tools disable terminal or toggle in the dashboard: hermes dashboard → Skills → Tool Sets MCP SERVERS two transport types: STDIO (local subprocess) or HTTP (remote endpoint). add a server: hermes mcp # or ask: "add the MCP server for Blender" filter tools with include/exclude per server. keep only what you trust. security: → OAuth 2.1 PKCE (no long-lived tokens in config) → package scanning via api.osv. dev before launch → all MCP calls go through approval gates HERMES AS MCP SERVER hermes mcp serve exposes 10 tools via FastMCP. connect VS Code Copilot or Cursor to your running Hermes instance. BUILD YOUR OWN TOOL He built a stock price tool live: → Python function calling Finnhub API → schema with name, description, parameters → registered in tool_sets.py → agent calls it automatically when relevant any repeating API call in your workflow can become a native tool. full Hermes architecture deep-dive in the article 👇

HERMES AGENT WITHOUT TOOLS IS A CHATBOT. WITH THEM IT BUILDS 3D TOWERS IN BLENDER, CHECKS STOCK PRICES, AND DRIVES VS CODE. tonbi JUST DROPPED THE FULL GUIDE. module 6 of his 10-part Hermes masterclass. best breakdown of the tool layer anyone has published. what you need to know: TOOLS vs SKILLS vs MCP skills = instructions (markdown, loaded into context) tools = callable functions (Python, agent emits call, Hermes executes) MCP = adapters to external systems (Blender, Stripe, Linear, Notion) every tool has three parts: → the function (does the real work) → the schema (what the model sees to decide when to call it) → the registry (makes the tool exist in the agent) the model never runs the function directly. it emits a structured request (tool name + JSON args). Hermes executes and returns the result. if the tool fails, the error goes back as JSON. the agent recovers instead of crashing. TOOL SETS CONTROL THE SURFACE hermes chat --tool-sets web # only web tools loaded. no files, no terminal. hermes chat --tool-sets safe # read-only: web search, vision, image gen. # no file writes. no terminal. no code execution. mid-session: /tools enable video /tools disable terminal or toggle in the dashboard: hermes dashboard → Skills → Tool Sets MCP SERVERS two transport types: STDIO (local subprocess) or HTTP (remote endpoint). add a server: hermes mcp # or ask: "add the MCP server for Blender" filter tools with include/exclude per server. keep only what you trust. security: → OAuth 2.1 PKCE (no long-lived tokens in config) → package scanning via api.osv. dev before launch → all MCP calls go through approval gates HERMES AS MCP SERVER hermes mcp serve exposes 10 tools via FastMCP. connect VS Code Copilot or Cursor to your running Hermes instance. BUILD YOUR OWN TOOL He built a stock price tool live: → Python function calling Finnhub API → schema with name, description, parameters → registered in tool_sets.py → agent calls it automatically when relevant any repeating API call in your workflow can become a native tool. full Hermes architecture deep-dive in the article 👇

YanXbt

32,680 görüntüleme • 3 ay önce

Kimi K3 dropped 3 days ago and i already used it to build my own PUBG the model that just took the number one coding spot on earth and it runs on a free tier no team no engine i just fed it free open models and assets that are already public and told it what i wanted it built the entire game itself the shooting the recoil the hit detection all of it came straight out of the model i barely wrote a line what youre watching here is the deathmatch mode and its already live battle royale is next im deep in it and the graphics get reworked right after i know it looks raw right now i wont pretend otherwise but the endgame is a build that runs perfect on phones and the moment its on mobile you drop ads in and it earns on its own the part that still gets me is my whole cost for this was a single subscription

Kimi K3 dropped 3 days ago and i already used it to build my own PUBG the model that just took the number one coding spot on earth and it runs on a free tier no team no engine i just fed it free open models and assets that are already public and told it what i wanted it built the entire game itself the shooting the recoil the hit detection all of it came straight out of the model i barely wrote a line what youre watching here is the deathmatch mode and its already live battle royale is next im deep in it and the graphics get reworked right after i know it looks raw right now i wont pretend otherwise but the endgame is a build that runs perfect on phones and the moment its on mobile you drop ads in and it earns on its own the part that still gets me is my whole cost for this was a single subscription

savip.

68,984 görüntüleme • 1 ay önce

THIS GUY CONNECTED HIS AI AGENTS TO HIS OBSIDIAN AND BUILT A BRAIN THAT LEARNS ON ITS OWN. HERE'S HOW TO BUILD IT Obsidian is just markdown files sitting in a folder. That turns out to be the perfect memory for an AI agent, because an agent can read and write those files directly. He wired his agents into the vault so they pull context from it, do the work, and write what they learned back. The notes aren't the point. The loop is, and it gets sharper every cycle How to build it: 1. Point an agent at your vault. The fastest way, no plugins, no API keys: open a terminal and run npx obsidian-mcp /path/to/your/vault. That exposes your Obsidian folder to Claude as a tool it can read, search, and write to. Add it to your Claude Code or Cowork config and restart 2. Confirm it can see the brain. Ask it: "list the notes in my vault and summarize what's in them." If it reads them back, the connection is live. Now it starts every task with everything the vault already holds instead of from zero 3. Give each agent one job and a write-back rule. Tell it: "research this, then save what you found as a new note in /brain with links to related notes." One agent researches, one summarizes, one plans. Each writes its output back into the vault 4. Close the loop. Add one line to every agent's instructions: "read /brain before starting, write your result back when done." Now each task leaves the vault richer, and the next run reads that before it works. It compounds instead of resetting 5. You only steer. Review what the brain produces, point it at the next thing. The agents handle the reading, writing, and connecting The edge isn't better notes. It's a brain that feeds itself, so the work gets sharper every cycle instead of starting over Bookmark this

THIS GUY CONNECTED HIS AI AGENTS TO HIS OBSIDIAN AND BUILT A BRAIN THAT LEARNS ON ITS OWN. HERE'S HOW TO BUILD IT Obsidian is just markdown files sitting in a folder. That turns out to be the perfect memory for an AI agent, because an agent can read and write those files directly. He wired his agents into the vault so they pull context from it, do the work, and write what they learned back. The notes aren't the point. The loop is, and it gets sharper every cycle How to build it: 1. Point an agent at your vault. The fastest way, no plugins, no API keys: open a terminal and run npx obsidian-mcp /path/to/your/vault. That exposes your Obsidian folder to Claude as a tool it can read, search, and write to. Add it to your Claude Code or Cowork config and restart 2. Confirm it can see the brain. Ask it: "list the notes in my vault and summarize what's in them." If it reads them back, the connection is live. Now it starts every task with everything the vault already holds instead of from zero 3. Give each agent one job and a write-back rule. Tell it: "research this, then save what you found as a new note in /brain with links to related notes." One agent researches, one summarizes, one plans. Each writes its output back into the vault 4. Close the loop. Add one line to every agent's instructions: "read /brain before starting, write your result back when done." Now each task leaves the vault richer, and the next run reads that before it works. It compounds instead of resetting 5. You only steer. Review what the brain produces, point it at the next thing. The agents handle the reading, writing, and connecting The edge isn't better notes. It's a brain that feeds itself, so the work gets sharper every cycle instead of starting over Bookmark this

Yarchi

58,549 görüntüleme • 3 ay önce

iit's wild that this still isn't the default way people use AI. Andrew Ng, a co-founder of Google Brain, gave a talk that crossed 2 million views: stop feeding your AI a folder of notes, build it a graph it can actually think with. his point is simple, stop making your AI read the whole vault to answer one thing. give it a graph, a router that points, an index that scores, edges that link, and it walks straight to the answer. it compounds, cheaper and faster the more you add. here's the whole thing: > point Claude at your notes and have it write a tiny router, your CLAUDE.md > build an index, one line per note, so it always knows where an answer lives > keep each note to one idea, and link the ones that depend on each other > make retrieval run as code, so it scores and opens the right files before the model thinks > tell it to prove the graph beats a flat folder on speed and cost, then keep the index fresh on every write set it up once and your AI stops guessing. it already knows your world before you type a word. full 12-step guide below. bookmark this.

iit's wild that this still isn't the default way people use AI. Andrew Ng, a co-founder of Google Brain, gave a talk that crossed 2 million views: stop feeding your AI a folder of notes, build it a graph it can actually think with. his point is simple, stop making your AI read the whole vault to answer one thing. give it a graph, a router that points, an index that scores, edges that link, and it walks straight to the answer. it compounds, cheaper and faster the more you add. here's the whole thing: > point Claude at your notes and have it write a tiny router, your CLAUDE.md > build an index, one line per note, so it always knows where an answer lives > keep each note to one idea, and link the ones that depend on each other > make retrieval run as code, so it scores and opens the right files before the model thinks > tell it to prove the graph beats a flat folder on speed and cost, then keep the index fresh on every write set it up once and your AI stops guessing. it already knows your world before you type a word. full 12-step guide below. bookmark this.

KingWilliam

15,099 görüntüleme • 1 ay önce

Today we're launching Amplitude MCP MCP was made for data analytics. Ask a vague question and have an AI model iteratively query Amplitude to find you insights. Amplitude MCP exposes all of Amplitude's functionality so an agent can interact with it directly. It allows you to use Amplitude without having to know anything about Amplitude's user interface or your data taxonomy. It uses the intelligence of off the shelf AI agents, including Claude, Gemini, and Cursor, to query Amplitude data you already have to find answers. It leverages all of your existing Amplitude data and how it's used to navigate through large complex behavioral datasets. You can have conversations about your product data directly inside any AI model with MCP. It can create Amplitude dashboards and notebooks with what it finds and even make recommendations. This is the best MCP use case I've ever seen. Watching an AI agent think, reason, query Amplitude, and then repeat that process iteratively is magical. See it in action:

Today we're launching Amplitude MCP MCP was made for data analytics. Ask a vague question and have an AI model iteratively query Amplitude to find you insights. Amplitude MCP exposes all of Amplitude's functionality so an agent can interact with it directly. It allows you to use Amplitude without having to know anything about Amplitude's user interface or your data taxonomy. It uses the intelligence of off the shelf AI agents, including Claude, Gemini, and Cursor, to query Amplitude data you already have to find answers. It leverages all of your existing Amplitude data and how it's used to navigate through large complex behavioral datasets. You can have conversations about your product data directly inside any AI model with MCP. It can create Amplitude dashboards and notebooks with what it finds and even make recommendations. This is the best MCP use case I've ever seen. Watching an AI agent think, reason, query Amplitude, and then repeat that process iteratively is magical. See it in action:

Spenser Skates

32,332 görüntüleme • 10 ay önce

CLAUDE DROVE BLENDER THROUGH MCP TO BUILD THIS PRODUCT SHOT. THE EXPLODED ASSEMBLY IS THE TELL. parts floating in, snapping together, the cap folding shut. that choreography is code. offsets and timing curves. exactly what a model driving Blender is good at now. what it didn't make is the hard geometry. the faceted cap, the perforated ring, the embossed seam. precision meshes, modeled once by a human or bought. no prompt gives you panel lines that read at studio sharpness. and the finish is still taste. deep red gloss, brushed gold, matte black under soft light. the model sets the shader values. whether it looks expensive or looks like a render is a human eye. the assembly got cheap. the parts and the finish didn't.

CLAUDE DROVE BLENDER THROUGH MCP TO BUILD THIS PRODUCT SHOT. THE EXPLODED ASSEMBLY IS THE TELL. parts floating in, snapping together, the cap folding shut. that choreography is code. offsets and timing curves. exactly what a model driving Blender is good at now. what it didn't make is the hard geometry. the faceted cap, the perforated ring, the embossed seam. precision meshes, modeled once by a human or bought. no prompt gives you panel lines that read at studio sharpness. and the finish is still taste. deep red gloss, brushed gold, matte black under soft light. the model sets the shader values. whether it looks expensive or looks like a render is a human eye. the assembly got cheap. the parts and the finish didn't.

explos1ve

17,012 görüntüleme • 1 ay önce

I plugged ChatGPT Luna 5.6 and Claude Opus 5 into Blender through MCP, and one of them is one-shotting entire 3D scenes now. Top is Luna 5.6 in fast mode modeling ship cannons with a firing animation. Watching the scene grow mesh by mesh in real time is a separate kind of dopamine. Bottom is Opus 5 building a full carriage scene with terrain, rocks and lighting. This one is simply built for 3D -- the whole thing from a single prompt with zero retries and zero corrections. The entire bridge is one open-source repo, no Python and no modeling skills required: > The repo is with 16.9k stars under MIT license. > It works with any model -- Claude, GPT, Cursor, any MCP client. > The model sees your scene through a live socket and edits it in real time. A year ago this workflow didn't exist, and now the gap between an idea and a rendered 3D scene is one prompt. Save this one -- you will come back for the repo ↓

I plugged ChatGPT Luna 5.6 and Claude Opus 5 into Blender through MCP, and one of them is one-shotting entire 3D scenes now. Top is Luna 5.6 in fast mode modeling ship cannons with a firing animation. Watching the scene grow mesh by mesh in real time is a separate kind of dopamine. Bottom is Opus 5 building a full carriage scene with terrain, rocks and lighting. This one is simply built for 3D -- the whole thing from a single prompt with zero retries and zero corrections. The entire bridge is one open-source repo, no Python and no modeling skills required: > The repo is with 16.9k stars under MIT license. > It works with any model -- Claude, GPT, Cursor, any MCP client. > The model sees your scene through a live socket and edits it in real time. A year ago this workflow didn't exist, and now the gap between an idea and a rendered 3D scene is one prompt. Save this one -- you will come back for the repo ↓

slash1s

150,102 görüntüleme • 1 ay önce

Anthropic engineer just built a second brain that reads 3 files instead of 5,000, and cut the token bill 40% no orchestrator, no plugin, just a router file, an index, and code that scores before the model ever opens anything point claude at a flat folder of notes and every question reads all of them, that’s the default and it’s expensive this breakdown turns that exact pattern into a system anyone can run CLAUDE.md → index.md → nodes → edges → retrieval as code → memory → ship it twelve steps, one shared rule, evidence lives in the index, the model reads three files while the notes folder still holds thousands the stale index test, one line drifts and the whole graph quietly turns back into the pile it was built to escape, no exceptions save it and watch, then read the article below

Anthropic engineer just built a second brain that reads 3 files instead of 5,000, and cut the token bill 40% no orchestrator, no plugin, just a router file, an index, and code that scores before the model ever opens anything point claude at a flat folder of notes and every question reads all of them, that’s the default and it’s expensive this breakdown turns that exact pattern into a system anyone can run CLAUDE.md → index.md → nodes → edges → retrieval as code → memory → ship it twelve steps, one shared rule, evidence lives in the index, the model reads three files while the notes folder still holds thousands the stale index test, one line drifts and the whole graph quietly turns back into the pile it was built to escape, no exceptions save it and watch, then read the article below

leopardracer

37,304 görüntüleme • 1 ay önce

$Google open-sourced MCP Toolbox for Databases. I gave it access to everything else. For context, Google's MCP Toolbox for Databases is an open-source server that lets AI agents securely query structured databases like PostgreSQL and MySQL through the MCP protocol However, most enterprise knowledge doesn't actually live in databases. It's scattered across emails, Slack threads, GitHub repos, Salesforce records, customer reviews, and internal docs. So Agents can't see any of it, which means they're working with a fraction of the context they need. I fixed that using MindsDB. It acts as a universal SQL layer that sits on top of all your data sources: structured, semi-structured, and unstructured. This means you can query Salesforce, Gmail, GitHub, S3 files, Jira, and 200+ more sources using SQL syntax. The clever part is how it connects to the MCP Toolbox. MindsDB exposes everything through MySQL, so from the Agent's perspective, it's just running SQL and getting context back. It doesn't know or care that the data came from five different sources behind the scenes. This setup unlocks some powerful capabilities: → One SQL interface for dozens of enterprise sources → Cross-datasource joins (combine GitHub and CRM data in a single query) → Built-in ML capabilities for working with unstructured data → Simple MCP tools that now have massively expanded reach In the video below, the Agent queries GitHub data and a customer review database in one SQL query. So what used to require ETL pipelines and weeks of engineering effort now happens instantly. At the end of the day, AI agents are only as useful as the data they can access. This gives them a lot more to work with. I have shared the GitHub repo in the replies, where you can find more details about this.$

Google open-sourced MCP Toolbox for Databases. I gave it access to everything else. For context, Google's MCP Toolbox for Databases is an open-source server that lets AI agents securely query structured databases like PostgreSQL and MySQL through the MCP protocol However, most enterprise knowledge doesn't actually live in databases. It's scattered across emails, Slack threads, GitHub repos, Salesforce records, customer reviews, and internal docs. So Agents can't see any of it, which means they're working with a fraction of the context they need. I fixed that using MindsDB. It acts as a universal SQL layer that sits on top of all your data sources: structured, semi-structured, and unstructured. This means you can query Salesforce, Gmail, GitHub, S3 files, Jira, and 200+ more sources using SQL syntax. The clever part is how it connects to the MCP Toolbox. MindsDB exposes everything through MySQL, so from the Agent's perspective, it's just running SQL and getting context back. It doesn't know or care that the data came from five different sources behind the scenes. This setup unlocks some powerful capabilities: → One SQL interface for dozens of enterprise sources → Cross-datasource joins (combine GitHub and CRM data in a single query) → Built-in ML capabilities for working with unstructured data → Simple MCP tools that now have massively expanded reach In the video below, the Agent queries GitHub data and a customer review database in one SQL query. So what used to require ETL pipelines and weeks of engineering effort now happens instantly. At the end of the day, AI agents are only as useful as the data they can access. This gives them a lot more to work with. I have shared the GitHub repo in the replies, where you can find more details about this.

Akshay 🚀

40,562 görüntüleme • 6 ay önce