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Context is power. Context shared is power multiplied. Every AI agent today builds context, then loses it. Work doesn't compound. OriginTrail is built differently: three memory layers, one shared graph, provenance baked in. DKG v10 now integrates with Hermes of Nous Research, OpenClaw🦞, Claude (Code & Desktop), Cursor, Codex...

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OriginTrail

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1,723,329 görüntüleme • 2 ay önce •via X (Twitter)

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Everyone wants agent swarms. Very few people are talking seriously enough about the context layer that makes swarms useful. Even with one agent, context is fragile. Too little context and the agent guesses. Too much context and it wastes tokens, loses focus, or reasons over irrelevant noise. The sweet spot is precise context: the right knowledge, in the right structure, at the right moment. With many agents, that challenge explodes. Each agent produces decisions, assumptions, findings, summaries, risks, and partial conclusions. Unless that knowledge becomes shared, structured, and reusable, every new agent is forced to rediscover what another agent already learned. That is not a swarm. That is a crowd. Shared context graphs are what turn agent activity into agent collaboration, and OriginTrail DKG V10 brings them to life. Was just playing with some final polishing for the V10 release, and it is really powerful to see shared context graphs where multiple agents contribute knowledge into the same connected memory, with attribution visible directly in the graph ui. That matters for three reasons. First, agents can access and build on one shared memory instead of staying trapped in isolated sessions. Second, the graph structure helps them retrieve the exact context they need, instead of stuffing everything into a prompt and hoping the model sorts it out. Third, verifiability of provenance. You can see which agent contributed each piece of knowledge, trace the source, and decide what to trust. Tokenmaxxing starts with fewer tokens, but the deeper story is coordination - agents stop reloading the world and start building on shared, verifiable context. That is the foundation for serious multi-agent work across software engineering, research, finance, operations, project management, and far beyond. The future is not more agents, it is agents working from shared, verifiable context. But the more the merrier, of course.
0:22

Everyone wants agent swarms. Very few people are talking seriously enough about the context layer that makes swarms useful. Even with one agent, context is fragile. Too little context and the agent guesses. Too much context and it wastes tokens, loses focus, or reasons over irrelevant noise. The sweet spot is precise context: the right knowledge, in the right structure, at the right moment. With many agents, that challenge explodes. Each agent produces decisions, assumptions, findings, summaries, risks, and partial conclusions. Unless that knowledge becomes shared, structured, and reusable, every new agent is forced to rediscover what another agent already learned. That is not a swarm. That is a crowd. Shared context graphs are what turn agent activity into agent collaboration, and OriginTrail DKG V10 brings them to life. Was just playing with some final polishing for the V10 release, and it is really powerful to see shared context graphs where multiple agents contribute knowledge into the same connected memory, with attribution visible directly in the graph ui. That matters for three reasons. First, agents can access and build on one shared memory instead of staying trapped in isolated sessions. Second, the graph structure helps them retrieve the exact context they need, instead of stuffing everything into a prompt and hoping the model sorts it out. Third, verifiability of provenance. You can see which agent contributed each piece of knowledge, trace the source, and decide what to trust. Tokenmaxxing starts with fewer tokens, but the deeper story is coordination - agents stop reloading the world and start building on shared, verifiable context. That is the foundation for serious multi-agent work across software engineering, research, finance, operations, project management, and far beyond. The future is not more agents, it is agents working from shared, verifiable context. But the more the merrier, of course.

Jurij Skornik

11,070 görüntüleme • 1 ay önce

⚽ The FIFA World Cup, live on the decentralized knowledge graph (DKG). Every match, result and player–club affiliation ingested in real time and published as one shared context graph powered by OriginTrail, structured with IPTC Sport Schema. Any AI agent can plug in and get reliable, source-verifiable context: live news, prediction markets, results, stats. No hallucinated scores. Just provenance. Enterprises shouldn’t have all the fun.
0:20

⚽ The FIFA World Cup, live on the decentralized knowledge graph (DKG). Every match, result and player–club affiliation ingested in real time and published as one shared context graph powered by OriginTrail, structured with IPTC Sport Schema. Any AI agent can plug in and get reliable, source-verifiable context: live news, prediction markets, results, stats. No hallucinated scores. Just provenance. Enterprises shouldn’t have all the fun.

Žiga Drev

33,880 görüntüleme • 13 gün önce

🚨 The OriginTrail Bounty is UNDERWAY! 🔥 Round 1 is OPEN NOW — 50,000 TRAC Tokens up for grabs! OriginTrail is paying builders 1,000 to 10,000 TRAC Tokens to connect AI agents (ChatGPT, Claude, OpenClaw, Hermes, etc.) to the new Working Memory and Shared Memory layers in DKG v10. Just dropped a clear breakdown video explaining: ✅ - How the 3 memory layers work ✅ - What makes a Flagship integration (10k TRAC) ✅ - Real examples you can actually build ✅ - How to submit your project If you're a developer or agent builder → this is your moment. Watch the full explainer 👇
6:06

🚨 The OriginTrail Bounty is UNDERWAY! 🔥 Round 1 is OPEN NOW — 50,000 TRAC Tokens up for grabs! OriginTrail is paying builders 1,000 to 10,000 TRAC Tokens to connect AI agents (ChatGPT, Claude, OpenClaw, Hermes, etc.) to the new Working Memory and Shared Memory layers in DKG v10. Just dropped a clear breakdown video explaining: ✅ - How the 3 memory layers work ✅ - What makes a Flagship integration (10k TRAC) ✅ - Real examples you can actually build ✅ - How to submit your project If you're a developer or agent builder → this is your moment. Watch the full explainer 👇

dkg://TriniZone

39,323 görüntüleme • 2 ay önce

Agent Trace: Capturing the Context Graph of Code We are delighted to collaborate with Cursor, OpenCode, Vercel, Jules, Amp, Cloudflare, and Sasha Varlamov in an open standard for mapping back code:context. here's how we see the potential of code context graphs and the new era of better tooling and better agents it enables. (yes the following is vibe-videoed with Remotion's Skill and Windsurf (retired), 100% ai edits incl audio)
1:00

Agent Trace: Capturing the Context Graph of Code We are delighted to collaborate with Cursor, OpenCode, Vercel, Jules, Amp, Cloudflare, and Sasha Varlamov in an open standard for mapping back code:context. here's how we see the potential of code context graphs and the new era of better tooling and better agents it enables. (yes the following is vibe-videoed with Remotion's Skill and Windsurf (retired), 100% ai edits incl audio)

Cognition

39,718 görüntüleme • 5 ay önce

Introducing Bloome: a human–AI teammate system built around shared context and multi-agent collaboration. → You bring your task, and AI teammates collaborate with you in real time → Work flows through drafting, challenge, and refinement until it reaches the right answer → Claude, ChatGPT, and Deepseek operate together in a shared context with no information loss Everything stays connected from input to final output. This is how work starts changing.
1:15

Introducing Bloome: a human–AI teammate system built around shared context and multi-agent collaboration. → You bring your task, and AI teammates collaborate with you in real time → Work flows through drafting, challenge, and refinement until it reaches the right answer → Claude, ChatGPT, and Deepseek operate together in a shared context with no information loss Everything stays connected from input to final output. This is how work starts changing.

Bloome

336,797 görüntüleme • 18 gün önce

AI can write your code. But who’s making sure it’s actually good? CodeRabbit now gives you free, high-quality AI code reviews, right inside your IDE. Supports and integrates with top AI agents like GitHub Copilot, Claude, Codex, Cursor AI, and more. What makes it different: • No subscription needed • Context-aware reviews • “Fix with AI” feature sends feedback directly to your AI coding agent • One-click commit suggestions • 10M+ PRs reviewed, 50K+ installs on GitHub Marketplace CodeRabbit doesn’t just point out issues. It helps you fix them fast.
1:57

AI can write your code. But who’s making sure it’s actually good? CodeRabbit now gives you free, high-quality AI code reviews, right inside your IDE. Supports and integrates with top AI agents like GitHub Copilot, Claude, Codex, Cursor AI, and more. What makes it different: • No subscription needed • Context-aware reviews • “Fix with AI” feature sends feedback directly to your AI coding agent • One-click commit suggestions • 10M+ PRs reviewed, 50K+ installs on GitHub Marketplace CodeRabbit doesn’t just point out issues. It helps you fix them fast.

Rohit Ghumare

32,176 görüntüleme • 1 yıl önce

most agent memory is one file that grows forever and gets re-read every single turn. it overflows the context, overwrites old facts, loses the thread. that's why your agent feels sharp on day one and lost by week three. Sibyl Memory replaces the pile with a structure: → leaner context, lower token cost. a bloated agent file and flat memory get re-read in full every message. Sibyl Memory keeps a light hot layer and retrieves the rest on demand, so each turn carries only the tokens that matter. → relations are first-class. people link to projects, projects to deals, deals to the decisions behind them. ask about one partner and the agent surfaces the whole connected web around them. that relational context is what lets it help run a company. → one source of truth per fact, person, and project. no duplicates, no silent overwrites. works with hermes, claude code, and codex. beta open.
0:34

most agent memory is one file that grows forever and gets re-read every single turn. it overflows the context, overwrites old facts, loses the thread. that's why your agent feels sharp on day one and lost by week three. Sibyl Memory replaces the pile with a structure: → leaner context, lower token cost. a bloated agent file and flat memory get re-read in full every message. Sibyl Memory keeps a light hot layer and retrieves the rest on demand, so each turn carries only the tokens that matter. → relations are first-class. people link to projects, projects to deals, deals to the decisions behind them. ask about one partner and the agent surfaces the whole connected web around them. that relational context is what lets it help run a company. → one source of truth per fact, person, and project. no duplicates, no silent overwrites. works with hermes, claude code, and codex. beta open.

SIBYL

81,361 görüntüleme • 1 ay önce

How to manage multiple AI agents at once: I run 10 to 15 parallel AI agent threads a day. Some days even more. Hermes, OpenClaw, Claude Code, Codex, all going at once. But the problem is more agents doesn't equal more output. Past a certain number of threads, you lose context. You forget what you told which agent. You duplicate work across two threads doing the same job. This is how we're solving for it:
2:02

How to manage multiple AI agents at once: I run 10 to 15 parallel AI agent threads a day. Some days even more. Hermes, OpenClaw, Claude Code, Codex, all going at once. But the problem is more agents doesn't equal more output. Past a certain number of threads, you lose context. You forget what you told which agent. You duplicate work across two threads doing the same job. This is how we're solving for it:

ericosiu

11,868 görüntüleme • 8 gün önce

I built a form-filling agent that anyone can use 💫 This is an extremely simple but useful (I hope) app. Upload a fillable form 📋, some context files, and chat with the agent to fill the form out automatically ✍️ 1️⃣ Yes it is a Claude Code SDK wrapper 2️⃣ It is better and faster than ChatGPT/Claude UI out of the box 3️⃣ We use LlamaParse to parse the context files, so you can have more trust that we are able to read context without hallucinations (e.g. messy scanned handwriting, drivers license photo, and more). This was one of my holiday Claude Code vibe-coding projects. Built with Opus 4.5, and also powered by Opus 4.5. Feeling the AGI 🫡 App is here: Repo is open-source:
1:48

I built a form-filling agent that anyone can use 💫 This is an extremely simple but useful (I hope) app. Upload a fillable form 📋, some context files, and chat with the agent to fill the form out automatically ✍️ 1️⃣ Yes it is a Claude Code SDK wrapper 2️⃣ It is better and faster than ChatGPT/Claude UI out of the box 3️⃣ We use LlamaParse to parse the context files, so you can have more trust that we are able to read context without hallucinations (e.g. messy scanned handwriting, drivers license photo, and more). This was one of my holiday Claude Code vibe-coding projects. Built with Opus 4.5, and also powered by Opus 4.5. Feeling the AGI 🫡 App is here: Repo is open-source:

Jerry Liu

48,375 görüntüleme • 6 ay önce

Your engineering team is about to snap. And your AI coding agent is making it worse. Introducing CodeRabbit Agent for Slack 🎉 A second brain for engineering teams. Because your tribal knowledge lives in Slack threads nobody can find. At CodeRabbit, we review millions of PRs every week and know how ace engineering teams operate on the planet. The same three things slow every team we see: - Context and decisions that live elsewhere. - Lack of a team-level durable knowledge base - And a trust layer that gives a safety net to your teams Built for Agentic SDLC workflows, CodeRabbit Agent for Slack solves all three problems in one shot while enabling teams to collaborate in real-time with the Agent. CodeRabbit Agent is Slack-native and builds your team’s operating context from every thread, every decision, every conversation your team has ever had. Make your team’s context compound!
1:37

Your engineering team is about to snap. And your AI coding agent is making it worse. Introducing CodeRabbit Agent for Slack 🎉 A second brain for engineering teams. Because your tribal knowledge lives in Slack threads nobody can find. At CodeRabbit, we review millions of PRs every week and know how ace engineering teams operate on the planet. The same three things slow every team we see: - Context and decisions that live elsewhere. - Lack of a team-level durable knowledge base - And a trust layer that gives a safety net to your teams Built for Agentic SDLC workflows, CodeRabbit Agent for Slack solves all three problems in one shot while enabling teams to collaborate in real-time with the Agent. CodeRabbit Agent is Slack-native and builds your team’s operating context from every thread, every decision, every conversation your team has ever had. Make your team’s context compound!

Harjot Gill

4,774,336 görüntüleme • 2 ay önce

HERMES AGENT DESKTOP APP IS INSANE. No more CLI. No more Discord hacks. Nous Research dropped the native Desktop App yesterday and the announcement already crossed 5 million views. What changed: → Full GUI installer. Click Install → fully working agent in under 5 minutes. → Clean ChatGPT-style interface with concurrent sessions. → Built-in skills and tool toggles (Claude Code, Kanban, web search, image gen, terminal, etc.). → Cron jobs creation directly in the UI. → Artifacts viewer, memory & context settings, voice mode, browser preview — all native. Running Hermes on your main machine is now actually pleasant and powerful. This removes the biggest friction point the project ever had. The desktop app just made Hermes 10x more approachable for real daily use. Now it’s time to unlock its full potential. The complete system how to turn Desktop Hermes into a real 24/7 operator is in the new article 👇
0:59

HERMES AGENT DESKTOP APP IS INSANE. No more CLI. No more Discord hacks. Nous Research dropped the native Desktop App yesterday and the announcement already crossed 5 million views. What changed: → Full GUI installer. Click Install → fully working agent in under 5 minutes. → Clean ChatGPT-style interface with concurrent sessions. → Built-in skills and tool toggles (Claude Code, Kanban, web search, image gen, terminal, etc.). → Cron jobs creation directly in the UI. → Artifacts viewer, memory & context settings, voice mode, browser preview — all native. Running Hermes on your main machine is now actually pleasant and powerful. This removes the biggest friction point the project ever had. The desktop app just made Hermes 10x more approachable for real daily use. Now it’s time to unlock its full potential. The complete system how to turn Desktop Hermes into a real 24/7 operator is in the new article 👇

YanXbt

31,620 görüntüleme • 1 ay önce

We’re excited to launch OpenMemory MCP, a private memory for MCP-compatible clients powered by mem0 Today, most AI assistants and dev tools operate without memory. You plan your roadmap in Claude, implement tasks in Cursor, but none of them know what the other did. Each tool operates in isolation, and your context disappears as soon as the session ends. OpenMemory MCP solves this. Built on the open Model Context Protocol (MCP), OpenMemory MCP runs 100% locally and provides a persistent, portable memory layer for all your AI tools. It enables agents and assistants to read from and write to a shared memory, securely and privately. Key capabilities: ✅Works across MCP clients like Cursor, Claude Desktop, Windsurf, Cline and more ✅Provides standardized memory operations (add_memories, search_memory, list_memories, delete_all_memories) ✅Stores data on your machine, fully private ✅Offers a centralized dashboard for visibility and control ✅Simple Docker-based setup with no vendor lock-in If you're building on MCP, OpenMemory MCP is the easiest way to add persistent, private memory to your clients with zero cloud dependencies and full local control. We’ve put together a short demo to show how it works Check it out in the links below 👇
2:24

We’re excited to launch OpenMemory MCP, a private memory for MCP-compatible clients powered by mem0 Today, most AI assistants and dev tools operate without memory. You plan your roadmap in Claude, implement tasks in Cursor, but none of them know what the other did. Each tool operates in isolation, and your context disappears as soon as the session ends. OpenMemory MCP solves this. Built on the open Model Context Protocol (MCP), OpenMemory MCP runs 100% locally and provides a persistent, portable memory layer for all your AI tools. It enables agents and assistants to read from and write to a shared memory, securely and privately. Key capabilities: ✅Works across MCP clients like Cursor, Claude Desktop, Windsurf, Cline and more ✅Provides standardized memory operations (add_memories, search_memory, list_memories, delete_all_memories) ✅Stores data on your machine, fully private ✅Offers a centralized dashboard for visibility and control ✅Simple Docker-based setup with no vendor lock-in If you're building on MCP, OpenMemory MCP is the easiest way to add persistent, private memory to your clients with zero cloud dependencies and full local control. We’ve put together a short demo to show how it works Check it out in the links below 👇

Taranjeet

471,767 görüntüleme • 1 yıl önce

The barrier to deploying AI agents just hit zero. Akash Agents is a streamlined way to launch your OpenClaw or Hermes agent with a few simple clicks. Deploy instantly. No terminal, YAML, or cloud console required. Just your agent's logic, memory, and capabilities. Hermes by Nous Research ships with 40+ built-in tools, persistent memory, and subagent parallelization, making the experience best-in-class out of the box. → Try it for free at Built by our community member Sandeep Narahari
0:21

The barrier to deploying AI agents just hit zero. Akash Agents is a streamlined way to launch your OpenClaw or Hermes agent with a few simple clicks. Deploy instantly. No terminal, YAML, or cloud console required. Just your agent's logic, memory, and capabilities. Hermes by Nous Research ships with 40+ built-in tools, persistent memory, and subagent parallelization, making the experience best-in-class out of the box. → Try it for free at Built by our community member Sandeep Narahari

Akash Network

22,841 görüntüleme • 3 ay önce

I was tired of vibe coding having ZERO vibe : CNVS is live today. 100% voice controlled locally via parakeet, or through gpt realtime for conversational control like jarvis. you can create remote canvasses on your vps that keep shipping even after you close the macbook. you connect and chat with hermes straight from the canvas via local network or vps. its built in swift for native mac os performance. their is bidirectional agent and canvas control via mcp and cli. their is built in shared memory system without bloat. you can buy a lifetime license rn, zero sub required, fully byok. Supports every agent cli, claude code, codex, cursor, etc... made with love by a dad in his basement who is tired of vibe coding being slow, boring, and constrained.
0:27

I was tired of vibe coding having ZERO vibe : CNVS is live today. 100% voice controlled locally via parakeet, or through gpt realtime for conversational control like jarvis. you can create remote canvasses on your vps that keep shipping even after you close the macbook. you connect and chat with hermes straight from the canvas via local network or vps. its built in swift for native mac os performance. their is bidirectional agent and canvas control via mcp and cli. their is built in shared memory system without bloat. you can buy a lifetime license rn, zero sub required, fully byok. Supports every agent cli, claude code, codex, cursor, etc... made with love by a dad in his basement who is tired of vibe coding being slow, boring, and constrained.

Max Blade

14,592 görüntüleme • 1 ay önce

OptimAI Search MCP brings live context to the agents people already use. Claude. Cursor. Copilot. Codex. One integration layer. Everywhere you build. Under the hood, OptimAI can process hundreds to thousands of real-time signals in parallel across the public web, social platforms, and onchain data - all powered by a decentralized network. That is the difference: not another search tool, but infrastructure for agent-native intelligence. Build with it. 💡 -- OptimAI: the #1 AI on BNB Chain DappBay.
1:27

OptimAI Search MCP brings live context to the agents people already use. Claude. Cursor. Copilot. Codex. One integration layer. Everywhere you build. Under the hood, OptimAI can process hundreds to thousands of real-time signals in parallel across the public web, social platforms, and onchain data - all powered by a decentralized network. That is the difference: not another search tool, but infrastructure for agent-native intelligence. Build with it. 💡 -- OptimAI: the #1 AI on BNB Chain DappBay.

OptimAI Network

46,297 görüntüleme • 1 ay önce

Imagine Workflows just went agentic today. Most "agentic creativity" out there is just image and video generations. Workflows is different. Hundreds of nodes tied together, each passing context to the next, built for the connected work real storytelling demands. Until today, wiring all of that took a power user with patience to spare. Now you describe what you want, and the agent builds the chain for you. The most powerful creative tool we've built, now moves at the pace of your thought.
0:51

Imagine Workflows just went agentic today. Most "agentic creativity" out there is just image and video generations. Workflows is different. Hundreds of nodes tied together, each passing context to the next, built for the connected work real storytelling demands. Until today, wiring all of that took a power user with patience to spare. Now you describe what you want, and the agent builds the chain for you. The most powerful creative tool we've built, now moves at the pace of your thought.

ImagineArt

1,070,776 görüntüleme • 12 gün önce

With all the buzz around the Codex App, OpenAI Developers quietly snuck out a new CLI update (0.94.0) as well. And boy is it an important update! Codex Plan mode is now officially released to the general audience! I am very excited about this one as it has a really strong prompt that is unlike any other plan mode I've personally used. Codex Plan mode doesn't necessarily just ask you 3 questions up front. It goes, collects context, asks questions, collects more context, asks more questions (sometimes), and then writes an incredibly high quality plan. It is my favorite implementation of plan mode thus far. It also comes with Codex's own version of "AskUserQuestion!" Although, it only works in Plan mode for now. They really need to allow people to use it in Code mode as well, but one win at a time. npm i -g @openai/codex Below is a demo of how it works. Let me know what you think!
2:15

With all the buzz around the Codex App, OpenAI Developers quietly snuck out a new CLI update (0.94.0) as well. And boy is it an important update! Codex Plan mode is now officially released to the general audience! I am very excited about this one as it has a really strong prompt that is unlike any other plan mode I've personally used. Codex Plan mode doesn't necessarily just ask you 3 questions up front. It goes, collects context, asks questions, collects more context, asks more questions (sometimes), and then writes an incredibly high quality plan. It is my favorite implementation of plan mode thus far. It also comes with Codex's own version of "AskUserQuestion!" Although, it only works in Plan mode for now. They really need to allow people to use it in Code mode as well, but one win at a time. npm i -g @openai/codex Below is a demo of how it works. Let me know what you think!

am.will

57,121 görüntüleme • 5 ay önce

A new short course, Claude Code: A Highly Agentic Coding Assistant, is live! Claude Code is currently one of the most capable coding assistants. It can explore your codebase, plan features, write tests, refactor code, and even collaborate across multiple sessions—with surprisingly minimal input. In this course, you’ll learn how to guide Claude Code effectively: from setting up context and memory to integrating with GitHub and MCP servers. You’ll use it to extend a RAG chatbot, refactor a Jupyter notebook for e-commerce data analysis, build a web app from a Figma design, and more. Taught by Elie Schoppik (Elie Schoppik) and built in collaboration with Anthropic, this course is a must for AI builders. 👉 Enroll now:
2:37

A new short course, Claude Code: A Highly Agentic Coding Assistant, is live! Claude Code is currently one of the most capable coding assistants. It can explore your codebase, plan features, write tests, refactor code, and even collaborate across multiple sessions—with surprisingly minimal input. In this course, you’ll learn how to guide Claude Code effectively: from setting up context and memory to integrating with GitHub and MCP servers. You’ll use it to extend a RAG chatbot, refactor a Jupyter notebook for e-commerce data analysis, build a web app from a Figma design, and more. Taught by Elie Schoppik (Elie Schoppik) and built in collaboration with Anthropic, this course is a must for AI builders. 👉 Enroll now:

DeepLearning.AI

32,513 görüntüleme • 11 ay önce

"Compoung engineering is how you make Claude Code smarter every time you use it." Here's my new episode with Kieran Klaassen where he demos his 4-step system to make Claude Code improve with every session: ✅ Plan. Sub-agents research your codebase and best practices before any code is written. ✅ Work. Claude asks clarifying questions, then builds the feature and writes tests. ✅ Assess. Review agents check security, architecture, and code quality. ✅ Compound. Learnings are captured so Claude never repeats mistakes. Kieran is my favorite Claude Code power user and I promise you'll learn alot too. 📌 Watch now: Thanks to our sponsors: Granola - The best AI meeting notes app I've ever used: Replit ⠕ - Create beautiful prototypes and full stack apps:
0:50

"Compoung engineering is how you make Claude Code smarter every time you use it." Here's my new episode with Kieran Klaassen where he demos his 4-step system to make Claude Code improve with every session: ✅ Plan. Sub-agents research your codebase and best practices before any code is written. ✅ Work. Claude asks clarifying questions, then builds the feature and writes tests. ✅ Assess. Review agents check security, architecture, and code quality. ✅ Compound. Learnings are captured so Claude never repeats mistakes. Kieran is my favorite Claude Code power user and I promise you'll learn alot too. 📌 Watch now: Thanks to our sponsors: Granola - The best AI meeting notes app I've ever used: Replit ⠕ - Create beautiful prototypes and full stack apps:

Peter Yang

85,263 görüntüleme • 5 ay önce

I tested a new coding assistant on 34,568 lines of code. (Cursor could not achieve what this tool did.) AI models are improving every day: - Faster inference - Longer context lengths But are AI coding assistants advancing at the same rate? Can they truly understand your entire project? I tested Augment Code on my ai-engineering repo with ~35k lines of code. Augment Code is a powerful AI Assistant built for developers working with large, evolving codebases—needing an assistant that understands the full context of their projects. In the video demo below, I asked it to: - Merge two projects. - Answer a global context question. Here’s what happened: - It understood dependencies across both projects. - It merged them intelligently—without breaking anything. - It even created a README file for the merged project. - It answered my global query instantly, pulling from the full codebase. This is the difference between AI autocomplete and an AI engineer that truly understands your repo. Augment Code is powerful because it indexes your entire repo upfront. This way, it can answer questions instantly, no matter how large your project is. Lastly, Augment Code is fully compatible with VSCode, JetBrains, Vim, Slack, and more. Try them now, I have shared a link in the next tweet! Thanks to Augment Code for showing me their powerful AI coding assistant and working with me on this post.
0:31

I tested a new coding assistant on 34,568 lines of code. (Cursor could not achieve what this tool did.) AI models are improving every day: - Faster inference - Longer context lengths But are AI coding assistants advancing at the same rate? Can they truly understand your entire project? I tested Augment Code on my ai-engineering repo with ~35k lines of code. Augment Code is a powerful AI Assistant built for developers working with large, evolving codebases—needing an assistant that understands the full context of their projects. In the video demo below, I asked it to: - Merge two projects. - Answer a global context question. Here’s what happened: - It understood dependencies across both projects. - It merged them intelligently—without breaking anything. - It even created a README file for the merged project. - It answered my global query instantly, pulling from the full codebase. This is the difference between AI autocomplete and an AI engineer that truly understands your repo. Augment Code is powerful because it indexes your entire repo upfront. This way, it can answer questions instantly, no matter how large your project is. Lastly, Augment Code is fully compatible with VSCode, JetBrains, Vim, Slack, and more. Try them now, I have shared a link in the next tweet! Thanks to Augment Code for showing me their powerful AI coding assistant and working with me on this post.

Akshay 🚀

30,806 görüntüleme • 1 yıl önce