Video wird geladen...

Video konnte nicht geladen werden

Zur Startseite

🚀Announcing Qodo Aware 🚀 Your AI coding assistant is flying blind. It sees a few hops from your current file, but misses a LOT of context that matters. Qodo Aware is the first and ONLY deep research agent built for finding the right context across large, complex codebases: 10...

QodoAI's profile picture

Qodo

18,953 subscribers

19,416 Aufrufe • vor 9 Monaten •via X (Twitter)

Gesundheit & WellnessWissenschaft & TechnologieBildung
LIVE

Anya Rossi• Live Now

Private livecam show

0 Kommentare

Keine Kommentare verfügbar

Kommentare vom Original-Post werden hier angezeigt

Ähnliche Videos

This is the most insane application of MCP I've used. Context is king! Your coding agents thrive on high-quality context. Qodo Aware is a production-ready deep research agent that solves this. This is huge! Watch how I use it in Codex to retrieve context from large codebases.
6:45

This is the most insane application of MCP I've used. Context is king! Your coding agents thrive on high-quality context. Qodo Aware is a production-ready deep research agent that solves this. This is huge! Watch how I use it in Codex to retrieve context from large codebases.

elvis

80,225 Aufrufe • vor 9 Monaten

1. Augment Agent is the first AI coding assistant designed for professional software engineers working with large codebases. I built an Amazon clone using Augment Agent in just a few minutes.
0:36

1. Augment Agent is the first AI coding assistant designed for professional software engineers working with large codebases. I built an Amazon clone using Augment Agent in just a few minutes.

Poonam Soni

60,060 Aufrufe • vor 1 Jahr

Introducing Inspector's context-aware screenshot tool! Screenshots as context for your coding agent is a pain. I decided I was tired of it and shipped something today to help with that.
1:33

Introducing Inspector's context-aware screenshot tool! Screenshots as context for your coding agent is a pain. I decided I was tired of it and shipped something today to help with that.

mikeBuildsMore

21,932 Aufrufe • vor 7 Monaten

🧵 1/4 Code reviews slowing you down? 😩 They're very important but often painful: inconsistent, tedious, and disconnected from your team's standards. Meet Qodo Merge: The AI tool saving dev teams HOURS on code reviews. 🤖 It uses context-aware AI that actually understands your entire codebase. Here's how it transforms your PR process 👇
2:49

🧵 1/4 Code reviews slowing you down? 😩 They're very important but often painful: inconsistent, tedious, and disconnected from your team's standards. Meet Qodo Merge: The AI tool saving dev teams HOURS on code reviews. 🤖 It uses context-aware AI that actually understands your entire codebase. Here's how it transforms your PR process 👇

Rohit Ghumare

10,033 Aufrufe • vor 1 Jahr

Nia by @NozomioAI is an MCP that gives coding agents 10x more developer context. It indexes external repos and docs, so any coding agent always has the right context. Congrats on the launch, Arlan!
2:30

Nia by @NozomioAI is an MCP that gives coding agents 10x more developer context. It indexes external repos and docs, so any coding agent always has the right context. Congrats on the launch, Arlan!

Y Combinator

33,333 Aufrufe • vor 10 Monaten

your coding agent sucks because it lacks context. so I built nia to fix that. here is how you can 10x your coding agent (thread):
2:38

your coding agent sucks because it lacks context. so I built nia to fix that. here is how you can 10x your coding agent (thread):

Arlan

54,135 Aufrufe • vor 10 Monaten

Grok Build just got a /remember command. It stores notes with a side-by-side preview so your AI coding assistant actually retains context across sessions. The pattern is clear: Grok is being built as a developer that never forgets.
0:30

Grok Build just got a /remember command. It stores notes with a side-by-side preview so your AI coding assistant actually retains context across sessions. The pattern is clear: Grok is being built as a developer that never forgets.

Mario Nawfal

35,824 Aufrufe • vor 19 Tagen

o1-engineer is here! 🚀 A coding assistant built from the ground up to leverage o1 reasoning capabilities. It can create and edit multiple files or entire folders, plan complex projects, execute them, and write code reviews. All from your terminal 👨‍💻
1:09

o1-engineer is here! 🚀 A coding assistant built from the ground up to leverage o1 reasoning capabilities. It can create and edit multiple files or entire folders, plan complex projects, execute them, and write code reviews. All from your terminal 👨‍💻

Pietro Schirano

309,212 Aufrufe • vor 1 Jahr

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 🚀

30,799 Aufrufe • vor 1 Jahr

Announcing the launch of Hooks in Gemini CLI🚀🪝 Take full control and customize the agentic loop to your specific needs. Hooks allow you to add context, validate actions, enforce policies, loop the agent to continue to iterate, and a lot more! 👇Read the launch blog below
0:41

Announcing the launch of Hooks in Gemini CLI🚀🪝 Take full control and customize the agentic loop to your specific needs. Hooks allow you to add context, validate actions, enforce policies, loop the agent to continue to iterate, and a lot more! 👇Read the launch blog below

Gemini CLI

83,154 Aufrufe • vor 4 Monaten

I did it again. I built "Cursor for Research" 🔥 > deep research > context-aware > citations > fast (+ free)
2:20

I did it again. I built "Cursor for Research" 🔥 > deep research > context-aware > citations > fast (+ free)

Ray

35,639 Aufrufe • vor 1 Jahr

New course: Spec-Driven Development with Coding Agents, built in partnership with JetBrains, and taught by Paul Everitt | @pauleveritt@fosstodon.org. Vibe coding is fast, but often produces code that doesn't match what you asked for. This short course teaches you spec-driven development: write a detailed spec defining what to build, and work with your coding agent to implement it. Many of the best developers already build this way. A spec lets you control large code changes with a few words, preserve context across agent sessions, and stay in control as your project grows in complexity. Skills you'll gain: - Write a detailed specification to define your mission, tech stack, and roadmap, giving your agent the context it needs from the start - Plan, implement, and validate features in iterative loops using a spec as your agent's guide - Apply the same repeatable workflow to both new and legacy codebases - Package your workflow into a portable agent skill that works across agents and IDEs Join and write specs that keep your coding agent on track!
2:51

New course: Spec-Driven Development with Coding Agents, built in partnership with JetBrains, and taught by Paul Everitt | @[email protected]. Vibe coding is fast, but often produces code that doesn't match what you asked for. This short course teaches you spec-driven development: write a detailed spec defining what to build, and work with your coding agent to implement it. Many of the best developers already build this way. A spec lets you control large code changes with a few words, preserve context across agent sessions, and stay in control as your project grows in complexity. Skills you'll gain: - Write a detailed specification to define your mission, tech stack, and roadmap, giving your agent the context it needs from the start - Plan, implement, and validate features in iterative loops using a spec as your agent's guide - Apply the same repeatable workflow to both new and legacy codebases - Package your workflow into a portable agent skill that works across agents and IDEs Join and write specs that keep your coding agent on track!

Andrew Ng

455,074 Aufrufe • vor 2 Monaten

Context-aware, action-ready. ChatGPT agent understands the task, chooses the right tool, and gets to work. It uses connectors, context, and custom instructions to take smarter actions on your behalf.
2:35

Context-aware, action-ready. ChatGPT agent understands the task, chooses the right tool, and gets to work. It uses connectors, context, and custom instructions to take smarter actions on your behalf.

OpenAI

143,127 Aufrufe • vor 11 Monaten

Embedder (YC S25) just launched the world's first hardware-aware coding agent. By understanding and interacting directly with your hardware, it achieves state of the art performance in an embedded systems (C/C++) context. Embedder
1:40

Embedder (YC S25) just launched the world's first hardware-aware coding agent. By understanding and interacting directly with your hardware, it achieves state of the art performance in an embedded systems (C/C++) context. Embedder

Ethan Gibbs

708,808 Aufrufe • vor 10 Monaten

Embedder is the world's first hardware-aware coding agent. By understanding and interacting directly with your hardware, it achieves state of the art performance in an embedded systems (C++/Rust) context. Our latest update (v0.3.0) features a stunning new terminal UI, and our fastest, most capable firmware agent yet: Embedder
1:26

Embedder is the world's first hardware-aware coding agent. By understanding and interacting directly with your hardware, it achieves state of the art performance in an embedded systems (C++/Rust) context. Our latest update (v0.3.0) features a stunning new terminal UI, and our fastest, most capable firmware agent yet: Embedder

Ethan Gibbs

435,110 Aufrufe • vor 4 Monaten

AI agents are only as powerful as the content they can access. Anthropic announced Dispatch in Claude Cowork — assign tasks from your phone, let Claude navigate your computer, get complex work done autonomously. But without a file system built for AI, even the most capable agent is working blind. Connect Claude Cowork to Box and your files become the foundation it reasons from. Is your content infrastructure ready for AI agents?
0:52

AI agents are only as powerful as the content they can access. Anthropic announced Dispatch in Claude Cowork — assign tasks from your phone, let Claude navigate your computer, get complex work done autonomously. But without a file system built for AI, even the most capable agent is working blind. Connect Claude Cowork to Box and your files become the foundation it reasons from. Is your content infrastructure ready for AI agents?

Box

432,932 Aufrufe • vor 2 Monaten

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

10,936 Aufrufe • vor 19 Tagen

Meet Boxy, your new AI coding assistant 🤖 🧠 Has the full context of your project 🌱 Generates code in seconds 🔥 Refactors code from the UI 💬 Writes meaningful commit messages Read the full post or see the highlights below 👇
0:45

Meet Boxy, your new AI coding assistant 🤖 🧠 Has the full context of your project 🌱 Generates code in seconds 🔥 Refactors code from the UI 💬 Writes meaningful commit messages Read the full post or see the highlights below 👇

CodeSandbox

103,362 Aufrufe • vor 3 Jahren

🚨The world just started talking about AI Screen Context. We've been building something deeper — AirJelly can see your screen and remembers not just what you did, but who you are. No intent missed. No task overdue. 🪼Introducing AirJelly today, the world's first context-aware proactive agent, that lives on your desktop, grows with your context, and proactively get works done.
3:28

🚨The world just started talking about AI Screen Context. We've been building something deeper — AirJelly can see your screen and remembers not just what you did, but who you are. No intent missed. No task overdue. 🪼Introducing AirJelly today, the world's first context-aware proactive agent, that lives on your desktop, grows with your context, and proactively get works done.

AirJelly

1,212,557 Aufrufe • vor 1 Monat