Loading video...

Video Failed to Load

There was a problem loading this video. This could be due to a temporary network issue or the video might be unavailable.

Built a lightweight mobile SDK that lets our QA agent know exactly what's happening under the hood. It tracks: - CPU/memory performance - Network requests & failures - Screen transitions - Tap gestures - Full distributed traces Give an agent this level of context and it stops guessing why... show more

Landseer Enga

3,013 subscribers

31,060 views • 7 months ago •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

0 Comments

No comments available

Comments from the original post will appear here

Related Videos

Introducing --agent flag in CodeRabbit CLI 🎉 The new --agent flag turns CodeRabbit into a tool your AI agent can use, providing structured JSON output instead of terminal text. Your agent writes code, CodeRabbit reviews it, reads the JSON, and fixes what's flagged.

Introducing --agent flag in CodeRabbit CLI 🎉 The new --agent flag turns CodeRabbit into a tool your AI agent can use, providing structured JSON output instead of terminal text. Your agent writes code, CodeRabbit reviews it, reads the JSON, and fixes what's flagged.

CodeRabbit

22,842 views • 6 months ago

A screen recording is the best context you can give an AI agent. It's also the worst. A few seconds of video is thousands of images. Feed that to an agent and you instantly blow up its memory. So I built a tool that fixes it. How it works: Hit record and just do your thing. Click around, type, and talk out loud about what you're doing. While you record, it quietly captures the stuff that actually matters: your voice transcription, every click and the exact thing you clicked, what you typed, and the shortcuts you press. When you stop, it doesn't hand the agent the whole video. It writes a short, readable summary: a timeline of what you said and did. The agent reads that tiny summary first. Then, only when it needs to actually see a moment, it pulls that one exact frame from the video, like flipping to a single page instead of reading the whole book. The result: the agent understands everything you did! Full context. Less tokens.

A screen recording is the best context you can give an AI agent. It's also the worst. A few seconds of video is thousands of images. Feed that to an agent and you instantly blow up its memory. So I built a tool that fixes it. How it works: Hit record and just do your thing. Click around, type, and talk out loud about what you're doing. While you record, it quietly captures the stuff that actually matters: your voice transcription, every click and the exact thing you clicked, what you typed, and the shortcuts you press. When you stop, it doesn't hand the agent the whole video. It writes a short, readable summary: a timeline of what you said and did. The agent reads that tiny summary first. Then, only when it needs to actually see a moment, it pulls that one exact frame from the video, like flipping to a single page instead of reading the whole book. The result: the agent understands everything you did! Full context. Less tokens.

Milind S

12,486 views • 3 months ago

I vibecoded an app in 2 days. Adding one new feature now takes me a week and a pile of tokens. Fix login, streaks break. Fix streaks, login breaks. The AI just goes in circles. Has this happened to you? It's not you. Every new chat starts with zero memory of your app. So the agent re-reads 40 files, rebuilds helpers that already exist, and misses what your change touches. Repowise indexes your codebase and keeps it updated on every commit. Even a brand new chat window knows: > what's already built > what's connected to what > what breaks if you change it Your agent reads the map instead of guessing. 27x fewer tokens to load context. 89% fewer file reads. Save the hours, the tokens, and your sanity. Connect your repo:

NAYAK

50,490 views • 9 days ago

MIT HANDED ITS DEEP LEARNING COURSE TO A FRONTIER-LAB ENGINEER FOR 68 MINUTES BECAUSE 90% OF PEOPLE SHIPPING AI CODE CAN'T EXPLAIN HOW THE MODEL ACTUALLY WORKS This is Maxime Labonne. He runs post-training at Liquid AI and wrote the LLM Engineer's Handbook. MIT gave him the room to break down the engine sitting inside every coding agent you've ever prompted. Twenty minutes in it stops being abstract. You finally see why the model confidently invents things that don't exist, why context is everything, and why "Just tell it to try again" sometimes fixes it and sometimes makes it worse. In 2026 "I use AI to code" stopped being a skill. Knowing why the model behaves the way it does -> tokens, context, post-training, where it quietly breaks -> is what separates someone who ships from someone babysitting a black box. Understanding an LLM isn't a research-team luxury anymore -> it's the difference between driving the agent and being driven by it. Anyone can prompt. The person who knows what's under the hood is the one still standing when the prompt stops working. Save this one & actually finish it ↓

MIT HANDED ITS DEEP LEARNING COURSE TO A FRONTIER-LAB ENGINEER FOR 68 MINUTES BECAUSE 90% OF PEOPLE SHIPPING AI CODE CAN'T EXPLAIN HOW THE MODEL ACTUALLY WORKS This is Maxime Labonne. He runs post-training at Liquid AI and wrote the LLM Engineer's Handbook. MIT gave him the room to break down the engine sitting inside every coding agent you've ever prompted. Twenty minutes in it stops being abstract. You finally see why the model confidently invents things that don't exist, why context is everything, and why "Just tell it to try again" sometimes fixes it and sometimes makes it worse. In 2026 "I use AI to code" stopped being a skill. Knowing why the model behaves the way it does -> tokens, context, post-training, where it quietly breaks -> is what separates someone who ships from someone babysitting a black box. Understanding an LLM isn't a research-team luxury anymore -> it's the difference between driving the agent and being driven by it. Anyone can prompt. The person who knows what's under the hood is the one still standing when the prompt stops working. Save this one & actually finish it ↓

slash1s

236,758 views • 4 months ago

audit and fix your entire developer docs site in under a minute We built an AI agent with Browserbase and Cerebras that audits your entire docs site > point it at any documentation site > agent goes down the link tree > agent crawls and verifies every page, checks every link, reads every code snippet > agent also compares content to your GitHub repo > returns a full report with suggested fixes and source references The full code and tutorial for how to build this yourself is live now.

audit and fix your entire developer docs site in under a minute We built an AI agent with Browserbase and Cerebras that audits your entire docs site > point it at any documentation site > agent goes down the link tree > agent crawls and verifies every page, checks every link, reads every code snippet > agent also compares content to your GitHub repo > returns a full report with suggested fixes and source references The full code and tutorial for how to build this yourself is live now.

Sarah Chieng

26,679 views • 6 months ago

Meet Agent Two, frontier intelligence for creative work. 4x faster, at half the price. For the next 12 days, we're dropping one new Agent Two feature every single day. Day 1: Agent Intelligence. An agent with top-percentile creative expertise, infinite memory, and still working while you sleep. Give it a role and it becomes it. It never forgets, we mean it and we're giving 5 people 5000 credits for the most creative way to break it.

Meet Agent Two, frontier intelligence for creative work. 4x faster, at half the price. For the next 12 days, we're dropping one new Agent Two feature every single day. Day 1: Agent Intelligence. An agent with top-percentile creative expertise, infinite memory, and still working while you sleep. Give it a role and it becomes it. It never forgets, we mean it and we're giving 5 people 5000 credits for the most creative way to break it.

Invideo

3,702,873 views • 2 months ago

I have built a Windows x64 sampling profiler for native code+C# to be used by coding agents. Why? Because it is irresponsible to let computers write code but give them no tools to evaluate the performance of their code. Stop guessing, start measuring. -> How does it work? Just point your agent to the profiler and let them measure and optimize. Or record a trace yourself and let the agent interpret the results for you. Works with Claude Code, Codex, pi, etc. The profiler also has a couple of neat features by itself like giving you estimates for how much time your code is spending on memory stalls and annotating disassembly with inlining information. Take a look and try it for free!

I have built a Windows x64 sampling profiler for native code+C# to be used by coding agents. Why? Because it is irresponsible to let computers write code but give them no tools to evaluate the performance of their code. Stop guessing, start measuring. -> How does it work? Just point your agent to the profiler and let them measure and optimize. Or record a trace yourself and let the agent interpret the results for you. Works with Claude Code, Codex, pi, etc. The profiler also has a couple of neat features by itself like giving you estimates for how much time your code is spending on memory stalls and annotating disassembly with inlining information. Take a look and try it for free!

Sebastian Schöner

13,130 views • 5 months ago

Most creators spend 3 hours writing a caption. Then post it. Get 200 views. Delete it. The problem isn't the content. It's that they're guessing. So I built an AI agent that kills the guesswork. It studies what's actually working right now across every niche, every platform, every day. Hook. Structure. Format. Why it went viral. All broken down. All saved to my library. No more guessing. No more wasted posts. I built this on adaptive using my phone, in one afternoon. Follow + reply "ADAPTIVE" and I'll send you exactly how I did it.

Most creators spend 3 hours writing a caption. Then post it. Get 200 views. Delete it. The problem isn't the content. It's that they're guessing. So I built an AI agent that kills the guesswork. It studies what's actually working right now across every niche, every platform, every day. Hook. Structure. Format. Why it went viral. All broken down. All saved to my library. No more guessing. No more wasted posts. I built this on adaptive using my phone, in one afternoon. Follow + reply "ADAPTIVE" and I'll send you exactly how I did it.

Hikaru

38,507 views • 4 months ago

GPT-6 Astra + Obsidian is honestly the closest thing to a real second brain for a business... i built a memory system that gives every agent instant context on what i've actually worked on, no re-explaining, no re-prompting: - every project, decision and experiment gets logged as its own memory, timestamped automatically - a 3D graph where the freshest, most-connected work sits in the center, the rest fades to the edges - agents read the graph before doing anything, so outputs come back personalized instead of generic - any agent i run (Claude, Codex, Kimi, GLM, whatever's next) writes to the same files, fully model-agnostic - pick a cluster of memories and turn them straight into an output... an app, an SEO article, a full social thread - the loop closes itself: use an agent, it logs the work, the next agent reads it, nothing gets forgotten it's just markdown folders under the hood, which is exactly why it survives every model swap full breakdown in the article below:

GPT-6 Astra + Obsidian is honestly the closest thing to a real second brain for a business... i built a memory system that gives every agent instant context on what i've actually worked on, no re-explaining, no re-prompting: - every project, decision and experiment gets logged as its own memory, timestamped automatically - a 3D graph where the freshest, most-connected work sits in the center, the rest fades to the edges - agents read the graph before doing anything, so outputs come back personalized instead of generic - any agent i run (Claude, Codex, Kimi, GLM, whatever's next) writes to the same files, fully model-agnostic - pick a cluster of memories and turn them straight into an output... an app, an SEO article, a full social thread - the loop closes itself: use an agent, it logs the work, the next agent reads it, nothing gets forgotten it's just markdown folders under the hood, which is exactly why it survives every model swap full breakdown in the article below:

rewind

38,271 views • 23 days ago

Agents shouldn't see your secrets. Today, most deployments hand the agent a real API key and hope for the best. That's why we're launching Infisical Agent Proxy. You give an agent a fake API key. The agent uses that key and the proxy swaps it for the real one at the network boundary before forwarding it outbound to its destination. An agent can't leak a secret it never sees. Build and deploy AI agents securely with Infisical Agent Proxy.

Agents shouldn't see your secrets. Today, most deployments hand the agent a real API key and hope for the best. That's why we're launching Infisical Agent Proxy. You give an agent a fake API key. The agent uses that key and the proxy swaps it for the real one at the network boundary before forwarding it outbound to its destination. An agent can't leak a secret it never sees. Build and deploy AI agents securely with Infisical Agent Proxy.

Infisical

28,086 views • 2 months ago

We built a tool that lets Claude Code autonomously test your entire mobile app. It navigates your app, opens screens, reads the debug logs, finds performance problems, and hands you a structured report. All it takes is one prompt.

We built a tool that lets Claude Code autonomously test your entire mobile app. It navigates your app, opens screens, reads the debug logs, finds performance problems, and hands you a structured report. All it takes is one prompt.

Software Mansion

49,868 views • 4 months ago

The new Fable 5.1 built three games and did it 7% cheaper! We gave the same prompts through Atomic Agent: ✦ Sticky ball that rolls up everything in its path ✦ Capybara surfing down a river, Subway Surfers style ✦ Dumpling running an endless conveyor dodge Outputs: ✦ Claude Fable 5, via Atomic Agent: $7.65 ✦ Claude Fable 5.1, via Atomic Agent: $7.08 Run it through an agent and that cache discount is where the money hides. The loop reads the file, watches it break, and pulls the whole context back in to fix it, over and over. Every one of those re-reads costs a quarter on 5.1 of what it cost on 5. Same games on screen, and the newer version just spends less to get there. Across three builds the loop paid for the upgrade on its own.

The new Fable 5.1 built three games and did it 7% cheaper! We gave the same prompts through Atomic Agent: ✦ Sticky ball that rolls up everything in its path ✦ Capybara surfing down a river, Subway Surfers style ✦ Dumpling running an endless conveyor dodge Outputs: ✦ Claude Fable 5, via Atomic Agent: $7.65 ✦ Claude Fable 5.1, via Atomic Agent: $7.08 Run it through an agent and that cache discount is where the money hides. The loop reads the file, watches it break, and pulls the whole context back in to fix it, over and over. Every one of those re-reads costs a quarter on 5.1 of what it cost on 5. Same games on screen, and the newer version just spends less to get there. Across three builds the loop paid for the upgrade on its own.

Atomic Agent

20,255 views • 1 month ago

I cant believe this guy just made a permanent solution to context bloat and open sourced it all! when we tested this tool (Context+) for solving an issue on the OpenCode repository, the agent using this tool used ~6.5k fewer tokens, found the code and fixed it in half the time! the results were surprising: 6 to 10k tokens saved per prompt, completed task in ~2 minutes while the agent running without the tool took ~4 mins for the same and got stuck in loops bro built an entire beast by using all the modern tools that we could think of: undo trees, semantic search by meaning (by haskellforall), advanced refactoring, blast radius, advanced file context trees, restore points... i can keep going on semantic code search and context trees are the future of agentic coding and this tool proves it the feature i loved the most is semantic search and how it gets things done 2x faster with least possible tokens it makes an agent that actually knows what it’s doing and not just guessing, it makes meaning from your code similar to RAG. if you aren't optimizing your context, you are just burning money the developer says this tool is still under development, it can have unexpected behavior and the docs need updates but the video shows the reality of how fast it can be github: get here:

I cant believe this guy just made a permanent solution to context bloat and open sourced it all! when we tested this tool (Context+) for solving an issue on the OpenCode repository, the agent using this tool used ~6.5k fewer tokens, found the code and fixed it in half the time! the results were surprising: 6 to 10k tokens saved per prompt, completed task in ~2 minutes while the agent running without the tool took ~4 mins for the same and got stuck in loops bro built an entire beast by using all the modern tools that we could think of: undo trees, semantic search by meaning (by haskellforall), advanced refactoring, blast radius, advanced file context trees, restore points... i can keep going on semantic code search and context trees are the future of agentic coding and this tool proves it the feature i loved the most is semantic search and how it gets things done 2x faster with least possible tokens it makes an agent that actually knows what it’s doing and not just guessing, it makes meaning from your code similar to RAG. if you aren't optimizing your context, you are just burning money the developer says this tool is still under development, it can have unexpected behavior and the docs need updates but the video shows the reality of how fast it can be github: get here:

forloop

226,544 views • 7 months ago

1/ Context Lens, new in Tlon v9.2.0. Everything your agent has ever done, stored durably on your personal server. Every message, every tool call. Debugging an agent usually means ssh-ing into a server and grepping logs. Now it's a tap in Tlon Messenger's UI. Tap the badge under a bot's reply to open the run: the context it read, each tool call with timing, the model that answered, and a persistence log of everything it wrote.

1/ Context Lens, new in Tlon v9.2.0. Everything your agent has ever done, stored durably on your personal server. Every message, every tool call. Debugging an agent usually means ssh-ing into a server and grepping logs. Now it's a tap in Tlon Messenger's UI. Tap the badge under a bot's reply to open the run: the context it read, each tool call with timing, the model that answered, and a persistence log of everything it wrote.

Tlon

10,680 views • 3 months ago

There's a built-in agent inside Claude Code you can invoke just by telling Claude to use the claude-code-guide agent Save this... you'll need it It's not a local file in your project. It's a built-in subagent defined in Claude Code's system itself. It answers questions about Claude Code CLI, the Agent SDK, and the Claude API. It uses Glob, Grep, Read, WebFetch, and WebSearch, but can't edit or write files. Its only job is to answer questions.

There's a built-in agent inside Claude Code you can invoke just by telling Claude to use the claude-code-guide agent Save this... you'll need it It's not a local file in your project. It's a built-in subagent defined in Claude Code's system itself. It answers questions about Claude Code CLI, the Agent SDK, and the Claude API. It uses Glob, Grep, Read, WebFetch, and WebSearch, but can't edit or write files. Its only job is to answer questions.

Daniel San

57,445 views • 7 months ago

The best AI researcher isn't a human anymore. We beat OpenAI, Google, and Anthropic on the biggest browser agent benchmark. Our researcher is an LLM + CLI that: → Spots its own failures → Finds the root cause → Fixes and loops - forever 🔥Watch it and tell me I'm wrong.

The best AI researcher isn't a human anymore. We beat OpenAI, Google, and Anthropic on the biggest browser agent benchmark. Our researcher is an LLM + CLI that: → Spots its own failures → Finds the root cause → Fixes and loops - forever 🔥Watch it and tell me I'm wrong.

Browser Use

33,030 views • 6 months ago

i just built a 4-agent software team. everything runs from Telegram and gets managed on a kanban board. a project manager who plans the work, a backend developer, a frontend developer, and a tester. the PM reads a goal, breaks it into linked tasks, and assigns each to the right agent. the thing that makes them a team instead of four strangers is a shared kanban board. every task is a row that survives crashes, and when an agent finishes, it writes a summary of what it built and what the next agent needs to know. the next agent reads that summary before it starts. so the frontend developer never has to guess the API shape, and the tester knows exactly what to verify. the hardest part was not the coordination. it was building an agent that could actually act like a backend engineer. a backend engineer stands up a database, wires auth, manages storage, deploys functions, and keeps all of it consistent while the rest of the team builds on top. an agent doing this from scratch drowns. it burns its context window remembering which tables exist and which endpoint it created three steps ago, and the work degrades fast. so the backend agent needs a backend built for agents, not for humans clicking through a dashboard. that is where InsForge came in. it is an open-source, agent-native backend, and i added it to my backend developer agent as a skill. a skill is a step-by-step guide that teaches the agent how to do a specific kind of work. with InsForge installed, the agent stopped improvising infrastructure and followed a reliable path: create the project, define the database, set up auth, deploy functions. to test the whole team, i had them build a working Google Docs clone, AI features included. the backend agent spun up the full service on its own. database tables, user auth, document handling, and edge functions running real TypeScript, all in one dashboard. the frontend agent read that summary and built the UI on top of it, and the tester closed the loop. the result was a backend an agent could reason about end to end, instead of one it kept getting lost inside. if you are building an AI backend engineer, InsForge is worth a look, it's 100% open-source. InsForge GitHub: (don't forget to star 🌟) the full article on Hermes Kanban: Mission Control for your Agents is quoted below.

i just built a 4-agent software team. everything runs from Telegram and gets managed on a kanban board. a project manager who plans the work, a backend developer, a frontend developer, and a tester. the PM reads a goal, breaks it into linked tasks, and assigns each to the right agent. the thing that makes them a team instead of four strangers is a shared kanban board. every task is a row that survives crashes, and when an agent finishes, it writes a summary of what it built and what the next agent needs to know. the next agent reads that summary before it starts. so the frontend developer never has to guess the API shape, and the tester knows exactly what to verify. the hardest part was not the coordination. it was building an agent that could actually act like a backend engineer. a backend engineer stands up a database, wires auth, manages storage, deploys functions, and keeps all of it consistent while the rest of the team builds on top. an agent doing this from scratch drowns. it burns its context window remembering which tables exist and which endpoint it created three steps ago, and the work degrades fast. so the backend agent needs a backend built for agents, not for humans clicking through a dashboard. that is where InsForge came in. it is an open-source, agent-native backend, and i added it to my backend developer agent as a skill. a skill is a step-by-step guide that teaches the agent how to do a specific kind of work. with InsForge installed, the agent stopped improvising infrastructure and followed a reliable path: create the project, define the database, set up auth, deploy functions. to test the whole team, i had them build a working Google Docs clone, AI features included. the backend agent spun up the full service on its own. database tables, user auth, document handling, and edge functions running real TypeScript, all in one dashboard. the frontend agent read that summary and built the UI on top of it, and the tester closed the loop. the result was a backend an agent could reason about end to end, instead of one it kept getting lost inside. if you are building an AI backend engineer, InsForge is worth a look, it's 100% open-source. InsForge GitHub: (don't forget to star 🌟) the full article on Hermes Kanban: Mission Control for your Agents is quoted below.

Akshay 🚀

123,101 views • 4 months ago

New course: Agent Memory: Building Memory-Aware Agents, built in partnership with Oracle and taught by Richmond Alake and Nacho Martínez. Many agents work well within a single session but their memory resets once the session ends. Consider a research agent working on dozens of papers across multiple days: without memory, it has no way to store and retrieve what it learned across sessions. This short course teaches you to build a memory system that enables agents to persist memory and thereby learn across sessions. You'll design a Memory Manager that handles different memory types, implement semantic tool retrieval that scales without bloating the context, and build write-back pipelines that let your agent autonomously update and refine what it knows over time. Skills you'll gain: - Build persistent memory stores for different agent memory types - Implement a Memory Manager that orchestrates how your agent reads, writes, and retrieves memory - Treat tools as procedural memory and retrieve only relevant ones at inference time using semantic search Join and learn to build agents that remember and improve over time!

New course: Agent Memory: Building Memory-Aware Agents, built in partnership with Oracle and taught by Richmond Alake and Nacho Martínez. Many agents work well within a single session but their memory resets once the session ends. Consider a research agent working on dozens of papers across multiple days: without memory, it has no way to store and retrieve what it learned across sessions. This short course teaches you to build a memory system that enables agents to persist memory and thereby learn across sessions. You'll design a Memory Manager that handles different memory types, implement semantic tool retrieval that scales without bloating the context, and build write-back pipelines that let your agent autonomously update and refine what it knows over time. Skills you'll gain: - Build persistent memory stores for different agent memory types - Implement a Memory Manager that orchestrates how your agent reads, writes, and retrieves memory - Treat tools as procedural memory and retrieve only relevant ones at inference time using semantic search Join and learn to build agents that remember and improve over time!

Andrew Ng

162,196 views • 6 months ago

i've built an ai toolbar which makes it easier to chat with codex and claude code in your terminals. double tap ⌥ and talk. it sees your screen, knows what you're doing and routes a message to the right agent. free to use. grab it here

i've built an ai toolbar which makes it easier to chat with codex and claude code in your terminals. double tap ⌥ and talk. it sees your screen, knows what you're doing and routes a message to the right agent. free to use. grab it here

kyzo

47,956 views • 27 days ago

Creator of agent loops: "Anthropic also using my agent systems formula and still the biggest mistake in agents: is skipping Graphs" his 3 rules for every agent: rule 1 → loop it, don't run once - think, act, observe, repeat - cut the context and it forgets why it started rule 2 → an agent is memory + tools + reasoning - drop one and it breaks rule 3 → stop fine-tuning the model - the real alpha is the infrastructure around it the formula behind every AI agent - explained by the one who wrote it one agent → a loop → a graph of loops → a system that never forgets watch & bookmark - then read how to wire it into a graph ↓

Creator of agent loops: "Anthropic also using my agent systems formula and still the biggest mistake in agents: is skipping Graphs" his 3 rules for every agent: rule 1 → loop it, don't run once - think, act, observe, repeat - cut the context and it forgets why it started rule 2 → an agent is memory + tools + reasoning - drop one and it breaks rule 3 → stop fine-tuning the model - the real alpha is the infrastructure around it the formula behind every AI agent - explained by the one who wrote it one agent → a loop → a graph of loops → a system that never forgets watch & bookmark - then read how to wire it into a graph ↓

codila

23,915 views • 2 months ago