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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...

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8090

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102,280 görüntüleme • 4 ay önce •via X (Twitter)

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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.
1:29

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 görüntüleme • 3 ay önce

Got early access to from Thomas Dohmke... Git rebuilt for AI agents If you use Claude Code, Codex or Cursor, the gap is clear, software is no longer just code and deploy, it is also context and knowledge Entire keeps it in the repo... sessions, prompts, decisions stored next to the code Connected a few repos already, sync is one step and basically instant
0:33

Got early access to from Thomas Dohmke... Git rebuilt for AI agents If you use Claude Code, Codex or Cursor, the gap is clear, software is no longer just code and deploy, it is also context and knowledge Entire keeps it in the repo... sessions, prompts, decisions stored next to the code Connected a few repos already, sync is one step and basically instant

Daniel San

12,432 görüntüleme • 1 ay önce

Introducing Deep Graph MCP for Claude Code 🤩 Claude Code by Anthropic is excellent and arguably one of the best tools for working with code today but... It has one major limitation, its native code search capabilities don't scale well to large repositories. By adding Deep Graph MCP, Claude's ability to explore large codebases improves dramatically. In this thread 🧵 I’ll show you how easy it is to connect this MCP to Claude Code.
1:12

Introducing Deep Graph MCP for Claude Code 🤩 Claude Code by Anthropic is excellent and arguably one of the best tools for working with code today but... It has one major limitation, its native code search capabilities don't scale well to large repositories. By adding Deep Graph MCP, Claude's ability to explore large codebases improves dramatically. In this thread 🧵 I’ll show you how easy it is to connect this MCP to Claude Code.

Daniel San

31,974 görüntüleme • 1 yıl önce

$60 BILLION FROM 4 MIT STUDENTS In 2022, four MIT classmates started Cursor. Four years later, SpaceX just completed a $60 BILLION acquisition of their company. This might be one of the craziest startup speedruns ever. - Cursor reached ~$2.6B in annualized enterprise revenue - It went from a VS Code fork to one of the biggest AI coding tools - SpaceX gets Cursor's talent, developer distribution and coding ecosystem - Cursor gets massive SpaceXAI compute and is already helping build Grok 4 people. 4 years. $60 billion. AI is creating giants faster than we've ever seen before.
1:56

$60 BILLION FROM 4 MIT STUDENTS In 2022, four MIT classmates started Cursor. Four years later, SpaceX just completed a $60 BILLION acquisition of their company. This might be one of the craziest startup speedruns ever. - Cursor reached ~$2.6B in annualized enterprise revenue - It went from a VS Code fork to one of the biggest AI coding tools - SpaceX gets Cursor's talent, developer distribution and coding ecosystem - Cursor gets massive SpaceXAI compute and is already helping build Grok 4 people. 4 years. $60 billion. AI is creating giants faster than we've ever seen before.

BuBBliK

46,105 görüntüleme • 1 ay önce

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 module but can be accessed and run from any module. Try it today:
1:11

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 module but can be accessed and run from any module. Try it today:

8090

136,424 görüntüleme • 3 ay önce

Cat Wu, head of product for Claude Code at Anthropic: "you can build almost anything with Claude from a single prompt now" one prompt gets you a working screen, not YOUR product Claude Code has never seen your design system, so it guesses the look fresh every time. how to hand Claude Code that system is in the article. better than the $300 design course in your tabs, and this one's free.
12:14

Cat Wu, head of product for Claude Code at Anthropic: "you can build almost anything with Claude from a single prompt now" one prompt gets you a working screen, not YOUR product Claude Code has never seen your design system, so it guesses the look fresh every time. how to hand Claude Code that system is in the article. better than the $300 design course in your tabs, and this one's free.

Mnimiy

113,663 görüntüleme • 3 ay önce

Codex is the new "super app" of AI tools. Here's the simple breakdown: Vibe coding + knowledge work in one place: - Cursor: code only. Can't make a doc you just view. - Claude Code: code only. Docs live in Cowork (separate, restricted). - Codex: both, same interface. Codex on complex tasks: - Better at infrastructure - Better at hard, multi-step problems - We tested it across our team The proof: - One-shotted a Replit clone. - Sandboxes. Mobile vibe coding. - 1 hour 20 minutes on 5.4. "The fastest way to do the most amount of things." That's why Codex is winning.
2:34

Codex is the new "super app" of AI tools. Here's the simple breakdown: Vibe coding + knowledge work in one place: - Cursor: code only. Can't make a doc you just view. - Claude Code: code only. Docs live in Cowork (separate, restricted). - Codex: both, same interface. Codex on complex tasks: - Better at infrastructure - Better at hard, multi-step problems - We tested it across our team The proof: - One-shotted a Replit clone. - Sandboxes. Mobile vibe coding. - 1 hour 20 minutes on 5.4. "The fastest way to do the most amount of things." That's why Codex is winning.

The Startup Ideas Podcast (SIP) 🧃

22,803 görüntüleme • 4 ay önce

In 2026, multi-player AI will eat single-player AI. Right now, most AI tools are built for one human + one model in a private workspace. ChatGPT, Cursor, Claude. Incredibly powerful, but currently optimized for individuals. The impact is massive: drafts, code, specs, campaigns, workflows. But almost none of it is shared, aligned, or contextualized across a team. We've seen this movie before. Cloud 1.0 was just "the same software, but online". The real breakthrough was collaboration. Google Docs beat Word. Figma beat Sketch. Notion beat Evernote. Every single-player tool lost to its multi-player counterpart. AI is about to go through the same transformation. AI 1.0 gave individuals superpowers. AI 2.0 will give teams superpowers. And just like cloud, single-player tools will go multi-player or get replaced. If you're building here, I'd love to talk.
2:20

In 2026, multi-player AI will eat single-player AI. Right now, most AI tools are built for one human + one model in a private workspace. ChatGPT, Cursor, Claude. Incredibly powerful, but currently optimized for individuals. The impact is massive: drafts, code, specs, campaigns, workflows. But almost none of it is shared, aligned, or contextualized across a team. We've seen this movie before. Cloud 1.0 was just "the same software, but online". The real breakthrough was collaboration. Google Docs beat Word. Figma beat Sketch. Notion beat Evernote. Every single-player tool lost to its multi-player counterpart. AI is about to go through the same transformation. AI 1.0 gave individuals superpowers. AI 2.0 will give teams superpowers. And just like cloud, single-player tools will go multi-player or get replaced. If you're building here, I'd love to talk.

Fareed Mosavat

55,496 görüntüleme • 9 ay önce

Sculptor: the missing UI for Claude Code 🎨 Imagine running 5 Claudes in parallel, safely in containers, while you stay in flow. Then bring their work straight into your IDE to test/edit together. This is how one developer ships like a team. Try it with Sonnet 4.5!
1:46

Sculptor: the missing UI for Claude Code 🎨 Imagine running 5 Claudes in parallel, safely in containers, while you stay in flow. Then bring their work straight into your IDE to test/edit together. This is how one developer ships like a team. Try it with Sonnet 4.5!

Kanjun 🐙

518,893 görüntüleme • 11 ay önce

A DEVELOPER CONNECTED CLAUDE CODE TO OBSIDIAN SO HIS AI AGENT WOULD STOP FORGETTING THE PROJECT EVERY MORNING. Every coding session used to start the same way. Claude would understand the repo, fix the bug, explain the architecture, and then the moment the session ended, all of that context disappeared. Same codebase. Same decisions. Same architecture. Same mistakes repeated again. So he added a memory layer. Instead of treating Claude Code like a smart terminal, he connected it to a local Obsidian vault through MCP. Now Claude can read the repo, open the vault, create notes, link concepts, and write important decisions back into the system. When it studies the codebase, it does not just answer once and forget. It creates notes for the major services, maps how the architecture works, links auth to the database, connects APIs to storage, and records why certain migrations or design choices exist. Obsidian becomes the project graph. Now when he asks why something was built a certain way, Claude does not guess from the current prompt. It reads the decision notes. When he starts a new branch, Claude checks the active context file. When the work is done, it updates what changed, what is blocked, and what the next agent needs to know before touching the repo. That is the real loop: read context, write code, capture decisions, update memory. Most people are still using AI coding tools like disposable chat windows. Ask, patch, close, forget. This setup turns Claude Code into infrastructure. The repo gets a memory layer that survives every session, and multiple AI agents can work from the same project map without stepping on each other. The unlock is not better prompting. The unlock is giving the agent somewhere to remember what it already learned.
4:19

A DEVELOPER CONNECTED CLAUDE CODE TO OBSIDIAN SO HIS AI AGENT WOULD STOP FORGETTING THE PROJECT EVERY MORNING. Every coding session used to start the same way. Claude would understand the repo, fix the bug, explain the architecture, and then the moment the session ended, all of that context disappeared. Same codebase. Same decisions. Same architecture. Same mistakes repeated again. So he added a memory layer. Instead of treating Claude Code like a smart terminal, he connected it to a local Obsidian vault through MCP. Now Claude can read the repo, open the vault, create notes, link concepts, and write important decisions back into the system. When it studies the codebase, it does not just answer once and forget. It creates notes for the major services, maps how the architecture works, links auth to the database, connects APIs to storage, and records why certain migrations or design choices exist. Obsidian becomes the project graph. Now when he asks why something was built a certain way, Claude does not guess from the current prompt. It reads the decision notes. When he starts a new branch, Claude checks the active context file. When the work is done, it updates what changed, what is blocked, and what the next agent needs to know before touching the repo. That is the real loop: read context, write code, capture decisions, update memory. Most people are still using AI coding tools like disposable chat windows. Ask, patch, close, forget. This setup turns Claude Code into infrastructure. The repo gets a memory layer that survives every session, and multiple AI agents can work from the same project map without stepping on each other. The unlock is not better prompting. The unlock is giving the agent somewhere to remember what it already learned.

DegenCalls

20,124 görüntüleme • 2 ay önce

well thats fucking it - anthropic has officially replaced software engineers. claude is now a 24 hr autonomous coding agent. claude can now operate your entire computer and CLAUDE CODE = end-to-end software engineering: - claude writes the code for you - then literally opens the app it coded - clicks through the entire app and find bugs - then fixes the bugs and improves the app in hours. previously claude generated code, you run it and give claude feedback. thats completely gone now. all in a continuous loop without leaving your terminal 😂 we're barely through monday. well done lol
0:45

well thats fucking it - anthropic has officially replaced software engineers. claude is now a 24 hr autonomous coding agent. claude can now operate your entire computer and CLAUDE CODE = end-to-end software engineering: - claude writes the code for you - then literally opens the app it coded - clicks through the entire app and find bugs - then fixes the bugs and improves the app in hours. previously claude generated code, you run it and give claude feedback. thats completely gone now. all in a continuous loop without leaving your terminal 😂 we're barely through monday. well done lol

Ejaaz

1,157,027 görüntüleme • 5 ay önce

FIVE LAYERS OF AGENT ENGINEERING, EACH ONE WRAPS THE ONE BELOW IT. IF YOU SKIP LAYER 2, YOUR LAYER 5 WILL LOOK BROKEN WHEN IT IS ACTUALLY JUST STANDING ON NOTHING. for weeks i debated harness vs loop vs graph like they were competing choices. then a stack diagram made the shape obvious. they are not choices. they are floors. 01 | prompt engineering. the message. unit of work: one input. inputs are role, instructions, examples, format. output is a single raw response. 02 | context engineering. the memory. unit of work: what stays in the window. a curator selects, compresses, and drops from query, docs, memory, prior turns, and tool outputs before the prompt runs. 03 | harness engineering. the machine. unit of work: the machine itself. gather (context + prompt) → LLM → tools or sub-agents → verifier → final response. the article calls this the operating environment. 04 | loop engineering. the system. unit of work: the run. goal + success criteria + max iterations + budget + completion check wrap around one harness pass. failed pass appends results to context and retries. 05 | graph engineering. the topology. unit of work: the graph run. goal + nodes + edges + state schema. graph routes to agent nodes, tool nodes, or human approval. a reviewer node with a different model and fresh context checks the final answer. the wrapping is the whole point. layer 5 assumes layer 4 works. layer 4 assumes layer 3 works. skip layer 2 and layer 3's verifier keeps failing without a clear reason. this is why swapping the model is a one-day project and swapping the stack is a quarter. the model is the commodity. the five layers around it are the engineering. full three-layer breakdown of the top of the stack (harness, loop, graph) in the post below.
0:20

FIVE LAYERS OF AGENT ENGINEERING, EACH ONE WRAPS THE ONE BELOW IT. IF YOU SKIP LAYER 2, YOUR LAYER 5 WILL LOOK BROKEN WHEN IT IS ACTUALLY JUST STANDING ON NOTHING. for weeks i debated harness vs loop vs graph like they were competing choices. then a stack diagram made the shape obvious. they are not choices. they are floors. 01 | prompt engineering. the message. unit of work: one input. inputs are role, instructions, examples, format. output is a single raw response. 02 | context engineering. the memory. unit of work: what stays in the window. a curator selects, compresses, and drops from query, docs, memory, prior turns, and tool outputs before the prompt runs. 03 | harness engineering. the machine. unit of work: the machine itself. gather (context + prompt) → LLM → tools or sub-agents → verifier → final response. the article calls this the operating environment. 04 | loop engineering. the system. unit of work: the run. goal + success criteria + max iterations + budget + completion check wrap around one harness pass. failed pass appends results to context and retries. 05 | graph engineering. the topology. unit of work: the graph run. goal + nodes + edges + state schema. graph routes to agent nodes, tool nodes, or human approval. a reviewer node with a different model and fresh context checks the final answer. the wrapping is the whole point. layer 5 assumes layer 4 works. layer 4 assumes layer 3 works. skip layer 2 and layer 3's verifier keeps failing without a clear reason. this is why swapping the model is a one-day project and swapping the stack is a quarter. the model is the commodity. the five layers around it are the engineering. full three-layer breakdown of the top of the stack (harness, loop, graph) in the post below.

kocer

30,675 görüntüleme • 16 gün önce

SOMEONE BUILT AN ENTIRE COMPANY BRAIN INSIDE CLAUDE CODE IN 7 DAYS Not Obsidian. Fully custom. A living map of every employee, every agent, every SOP on one screen. Click any node and it opens up: - what department it's in - what SOPs are attached - what it can actually access that permission layer is the whole game an employee opens the chat and the AI already knows their access level, agents, SOPs and tools surface in the conversation like you tagged them by hand Obsidian cannot do this... no dev team. no six month build. no enterprise budget. just Claude Code and one week has everything to build one full guide on building your own team below
1:20

SOMEONE BUILT AN ENTIRE COMPANY BRAIN INSIDE CLAUDE CODE IN 7 DAYS Not Obsidian. Fully custom. A living map of every employee, every agent, every SOP on one screen. Click any node and it opens up: - what department it's in - what SOPs are attached - what it can actually access that permission layer is the whole game an employee opens the chat and the AI already knows their access level, agents, SOPs and tools surface in the conversation like you tagged them by hand Obsidian cannot do this... no dev team. no six month build. no enterprise budget. just Claude Code and one week has everything to build one full guide on building your own team below

Madni Aghadi

397,670 görüntüleme • 2 ay önce

Anthropic just paid millions to hire Andrej Karpathy. He gave you the same knowledge for $0 the same week. Co-founder of OpenAI. Former head of AI at Tesla. The man who coined vibe coding. No recruitment fee. No exclusive access. Just a link and 29 minutes. LLMs are ghosts not animals. Vibe coding is dead. Software 3.0 is here. Watch it. Then read this. Because Karpathy tells you what Software 3.0 is. This shows you how to build one - a software factory with Claude Code that ships features while you sleep. The full build guide is below.
29:43

Anthropic just paid millions to hire Andrej Karpathy. He gave you the same knowledge for $0 the same week. Co-founder of OpenAI. Former head of AI at Tesla. The man who coined vibe coding. No recruitment fee. No exclusive access. Just a link and 29 minutes. LLMs are ghosts not animals. Vibe coding is dead. Software 3.0 is here. Watch it. Then read this. Because Karpathy tells you what Software 3.0 is. This shows you how to build one - a software factory with Claude Code that ships features while you sleep. The full build guide is below.

Rahul

1,205,226 görüntüleme • 3 ay önce

In every tech wave, the leaders of one era often aren't the leaders of the next. Inside the AI wave, software is going through a shift. Coatue Sector Head Max Cook (Max Cook) calls it the 'software handoff' and explains what marks a 'handoff' and how to spot one. More in our C:\Take below. ⬇️
1:21

In every tech wave, the leaders of one era often aren't the leaders of the next. Inside the AI wave, software is going through a shift. Coatue Sector Head Max Cook (Max Cook) calls it the 'software handoff' and explains what marks a 'handoff' and how to spot one. More in our C:\Take below. ⬇️

COATUE

18,197 görüntüleme • 2 ay önce

Re-introducing 8090... Two ways to work with us: → [BUILD] Software Factory: the platform for consulting and technology businesses building software with AI agents. The documentation stays alive because the system keeps it alive (no one ever updates the PRD on their own). → [BUY] 8090 Enterprise: AI-native custom software we design, build, and host tailored to your exact business needs. Give us a problem and we go execute. What part of your current SDLC is the most overdue for a rebuild?
3:40

Re-introducing 8090... Two ways to work with us: → [BUILD] Software Factory: the platform for consulting and technology businesses building software with AI agents. The documentation stays alive because the system keeps it alive (no one ever updates the PRD on their own). → [BUY] 8090 Enterprise: AI-native custom software we design, build, and host tailored to your exact business needs. Give us a problem and we go execute. What part of your current SDLC is the most overdue for a rebuild?

8090

307,870 görüntüleme • 4 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 ↓
0:58

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

THIS META ENGINEER SPENT 6 MONTHS IN CLAUDE CODE... AND HIS BIGGEST BOTTLENECK ISN'T WRITING CODE ANYMORE. It's reviewing AI-generated code. Key takeaways: • Start every feature in **Plan Mode**, not Edit Mode. • A clean `CLAUDE.md` is your AI's operating manual. Keep it concise. • Validation loops > better prompts. Give Claude ways to verify and fix its own work. • Fresh context beats bloated context every time. • Run multiple Claude instances in parallel instead of waiting on one. • Let Claude manage its own rules, skills, commands, and workflows. • MCPs are powerful, but only install what you actually need. • The best AI engineers aren't faster typists. They're better context engineers. The biggest shift: You're no longer programming the computer. You're programming the AI that writes the software. If you're serious about AI coding in 2026, this is one of the most practical breakdowns you'll watch. It goes far beyond prompts and shows how elite engineers actually structure their workflow. Video cred: John Kim (Youtube)
46:12

THIS META ENGINEER SPENT 6 MONTHS IN CLAUDE CODE... AND HIS BIGGEST BOTTLENECK ISN'T WRITING CODE ANYMORE. It's reviewing AI-generated code. Key takeaways: • Start every feature in **Plan Mode**, not Edit Mode. • A clean `CLAUDE.md` is your AI's operating manual. Keep it concise. • Validation loops > better prompts. Give Claude ways to verify and fix its own work. • Fresh context beats bloated context every time. • Run multiple Claude instances in parallel instead of waiting on one. • Let Claude manage its own rules, skills, commands, and workflows. • MCPs are powerful, but only install what you actually need. • The best AI engineers aren't faster typists. They're better context engineers. The biggest shift: You're no longer programming the computer. You're programming the AI that writes the software. If you're serious about AI coding in 2026, this is one of the most practical breakdowns you'll watch. It goes far beyond prompts and shows how elite engineers actually structure their workflow. Video cred: John Kim (Youtube)

Dami-Defi

11,779 görüntüleme • 2 ay önce