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⚡ hunk 0.16.0 is out: - supports all 65 diffs[dot]com/shiki themes (!) - smoother scrolling - split layout in lazygit - more agent-driven cleanup tools

Ben Vinegar

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19,296 Aufrufe • vor 2 Monaten

Anthropic's Claude Ai Agents Team just Educated how to build production AI agents in under 30 mins. For Free. From the engineers who built the stack. CANCEL Your Weekend Plans, and Learn to Build AI Agents Today. Bookmark it. Watch it. Build your first production agent this weekend. $5,000/month. $7,000/month. $12,000/month. People are building agents for clients and charging $$$ as Beginners. You're still stuck in the thinking about AI phase. This video fixes that tonight. Follow Himanshu Kumar for more high-signal content that actually moves your AI engineering career forward. ↓ Ivan Nardini runs Developer Relations for AI at Google Cloud. He just gave away the entire production agent stack in 30 minutes. This is the talk that separates people deploying AI agents that actually scale from people whose agents break the moment they leave localhost. Here's everything inside. I break down a production AI video like this every week. Follow Himanshu Kumar. ↓ The 4-part agent stack that actually scales. Most devs are duct-taping frameworks together and calling it an "AI agent." Ivan lays out the real stack: Agent Development Kit (ADK): open-source, code-first framework for building, evaluating, and deploying agents. Supports Claude models through Vertex AI directly. Model Context Protocol (MCP): lets your agent talk to any tool or data source with one standard. Vertex AI Agent Engine: managed platform for deploying, monitoring, and scaling agents in production. No DevOps headaches. Agent-to-Agent Protocol: open protocol so agents built on different frameworks can actually work together. This is the stack replacing every hacky agent setup in production right now. Full MCP + Claude breakdowns drop weekly on Himanshu Kumar. ↓ Building your first real agent. Ivan builds a birthday planner agent live. LLM Agent class. Name it. Define instructions. Pick the model. He uses Claude 3.7 Sonnet. You could use Opus 4.7 for better reasoning. Full agent built in minutes. Not weeks. Watch the build once and you'll never structure an agent the wrong way again. I post agent architectures people pay $500 courses to learn. Himanshu Kumar. ↓ Multi-agent systems without the chaos. Single agents are easy. Multi-agent systems are where 99% of builders fail. Ivan extends the birthday planner by: Adding a calendar service through MCP tools Creating an orchestrator agent to route requests between agents Handling state and context across agent handoffs This is production multi-agent architecture. Clean. Scalable. Debuggable. Most tutorials hand-wave this part. This one shows you every step. Multi-agent orchestration content drops weekly on Himanshu Kumar. ↓ Deployment without the DevOps nightmare. This is where most AI projects die. You build a cool agent locally. It works. You try to deploy it. Everything breaks. Vertex AI Agent Engine fixes this: Minimal code deployment Automatic monitoring of latency, CPU, and memory Built-in observability and logging No infrastructure setup needed You provide config and requirements. The platform handles the rest. This is how agents actually get to production. Deployment guides for Claude agents post every week. Himanshu Kumar. ↓ Agent-to-Agent Protocol: the future nobody's talking about. Most people don't know this exists yet. The A2A Protocol lets agents built in different frameworks communicate seamlessly. Your Claude agent. My LangChain agent. Someone else's CrewAI agent. All talking to each other. All solving parts of the same problem. All without custom integration code. This is the infrastructure layer of the coming AI economy. Getting in early on A2A Protocol is like getting in early on HTTP in 1995. A2A deep dive coming soon. Himanshu Kumar. ↓ 30 minutes from the team shipping this in production. You'll learn more from this than from 6 months of YouTube tutorials made by people who've never deployed an agent past localhost. People who watch this understand production AI agents at the architect level. People who skip it keep hacking together frameworks that break every time an API updates. Save the video. Watch it tonight. Build a real agent this weekend. Follow Himanshu Kumar for more high-signal content that actually moves your AI engineering career forward.

Anthropic's Claude Ai Agents Team just Educated how to build production AI agents in under 30 mins. For Free. From the engineers who built the stack. CANCEL Your Weekend Plans, and Learn to Build AI Agents Today. Bookmark it. Watch it. Build your first production agent this weekend. $5,000/month. $7,000/month. $12,000/month. People are building agents for clients and charging $$$ as Beginners. You're still stuck in the thinking about AI phase. This video fixes that tonight. Follow Himanshu Kumar for more high-signal content that actually moves your AI engineering career forward. ↓ Ivan Nardini runs Developer Relations for AI at Google Cloud. He just gave away the entire production agent stack in 30 minutes. This is the talk that separates people deploying AI agents that actually scale from people whose agents break the moment they leave localhost. Here's everything inside. I break down a production AI video like this every week. Follow Himanshu Kumar. ↓ The 4-part agent stack that actually scales. Most devs are duct-taping frameworks together and calling it an "AI agent." Ivan lays out the real stack: Agent Development Kit (ADK): open-source, code-first framework for building, evaluating, and deploying agents. Supports Claude models through Vertex AI directly. Model Context Protocol (MCP): lets your agent talk to any tool or data source with one standard. Vertex AI Agent Engine: managed platform for deploying, monitoring, and scaling agents in production. No DevOps headaches. Agent-to-Agent Protocol: open protocol so agents built on different frameworks can actually work together. This is the stack replacing every hacky agent setup in production right now. Full MCP + Claude breakdowns drop weekly on Himanshu Kumar. ↓ Building your first real agent. Ivan builds a birthday planner agent live. LLM Agent class. Name it. Define instructions. Pick the model. He uses Claude 3.7 Sonnet. You could use Opus 4.7 for better reasoning. Full agent built in minutes. Not weeks. Watch the build once and you'll never structure an agent the wrong way again. I post agent architectures people pay $500 courses to learn. Himanshu Kumar. ↓ Multi-agent systems without the chaos. Single agents are easy. Multi-agent systems are where 99% of builders fail. Ivan extends the birthday planner by: Adding a calendar service through MCP tools Creating an orchestrator agent to route requests between agents Handling state and context across agent handoffs This is production multi-agent architecture. Clean. Scalable. Debuggable. Most tutorials hand-wave this part. This one shows you every step. Multi-agent orchestration content drops weekly on Himanshu Kumar. ↓ Deployment without the DevOps nightmare. This is where most AI projects die. You build a cool agent locally. It works. You try to deploy it. Everything breaks. Vertex AI Agent Engine fixes this: Minimal code deployment Automatic monitoring of latency, CPU, and memory Built-in observability and logging No infrastructure setup needed You provide config and requirements. The platform handles the rest. This is how agents actually get to production. Deployment guides for Claude agents post every week. Himanshu Kumar. ↓ Agent-to-Agent Protocol: the future nobody's talking about. Most people don't know this exists yet. The A2A Protocol lets agents built in different frameworks communicate seamlessly. Your Claude agent. My LangChain agent. Someone else's CrewAI agent. All talking to each other. All solving parts of the same problem. All without custom integration code. This is the infrastructure layer of the coming AI economy. Getting in early on A2A Protocol is like getting in early on HTTP in 1995. A2A deep dive coming soon. Himanshu Kumar. ↓ 30 minutes from the team shipping this in production. You'll learn more from this than from 6 months of YouTube tutorials made by people who've never deployed an agent past localhost. People who watch this understand production AI agents at the architect level. People who skip it keep hacking together frameworks that break every time an API updates. Save the video. Watch it tonight. Build a real agent this weekend. Follow Himanshu Kumar for more high-signal content that actually moves your AI engineering career forward.

Himanshu Kumar

227,802 Aufrufe • vor 3 Monaten

Just about 10 days left to file your taxes. While figuring out taxation for trading and investments is usually a nightmare, we've made it super easy. I think our tax statements are easily the best in the industry by a mile. On Console, you’ll find all the statements you need to file taxes for your trades and investments: Tax P&L statement with trade-wise charges, turnover across all segments, equity and non-equity segregation, capital gains split for pre- and post-July 23, 2024, and more. Ability to download one year of contract notes at a time Full tradebooks. Various filters to make it easy to figure out STCG (short-term capital gains) and LTCG (long-term capital gains). And more... If you need help filing your taxes, we've also partnered with Quicko to make the process even smoother.

Just about 10 days left to file your taxes. While figuring out taxation for trading and investments is usually a nightmare, we've made it super easy. I think our tax statements are easily the best in the industry by a mile. On Console, you’ll find all the statements you need to file taxes for your trades and investments: Tax P&L statement with trade-wise charges, turnover across all segments, equity and non-equity segregation, capital gains split for pre- and post-July 23, 2024, and more. Ability to download one year of contract notes at a time Full tradebooks. Various filters to make it easy to figure out STCG (short-term capital gains) and LTCG (long-term capital gains). And more... If you need help filing your taxes, we've also partnered with Quicko to make the process even smoother.

Nithin Kamath

24,008 Aufrufe • vor 10 Monaten

🚨 BURRY: THIS IS WORSE THAN 1999 THE MAN WHO PREDICTED THE 2008 COLLAPSE NOW SEES TODAY'S NASDAQ AS A MORE DANGEROUS BUBBLE THAN THE DOT-COM ERA $SNDK is up 4,000% in the 12 months $QCOM's 1999 peak was 2,620% and marked the final top Three months later, Nasdaq collapsed 78% Current S&P concentration is just ugly The top 10 stocks now make up a record 41.2% of the S&P 500 — the most lopsided US market in 150 years Michael Burry has placed his BILLION SHORT: $912M in $PLTR and $187M in $NVDA puts as of his last 13F He predicts 3 paths from here: 1. MELT-UP — Momentum sucks in the last FOMO buyers. The top gets more extreme, the crash gets even worse. Target: -75%, dot-com style 2. LIQUIDITY SHOCK — A rate surprise, a geopolitical event, something BREAKS. Markets reprice in hours. Target: -40% in a single quarter 3. DISTRIBUTION — Insiders sell into strength. Sideways chop lulls everyone, then the rollover. Target: -50% over 12-18 months All three end with mean reversion that wipes out latecomers Burry shorted housing when everyone called him crazy. Now everyone thinks his crazy again

🚨 BURRY: THIS IS WORSE THAN 1999 THE MAN WHO PREDICTED THE 2008 COLLAPSE NOW SEES TODAY'S NASDAQ AS A MORE DANGEROUS BUBBLE THAN THE DOT-COM ERA $SNDK is up 4,000% in the 12 months $QCOM's 1999 peak was 2,620% and marked the final top Three months later, Nasdaq collapsed 78% Current S&P concentration is just ugly The top 10 stocks now make up a record 41.2% of the S&P 500 — the most lopsided US market in 150 years Michael Burry has placed his BILLION SHORT: $912M in $PLTR and $187M in $NVDA puts as of his last 13F He predicts 3 paths from here: 1. MELT-UP — Momentum sucks in the last FOMO buyers. The top gets more extreme, the crash gets even worse. Target: -75%, dot-com style 2. LIQUIDITY SHOCK — A rate surprise, a geopolitical event, something BREAKS. Markets reprice in hours. Target: -40% in a single quarter 3. DISTRIBUTION — Insiders sell into strength. Sideways chop lulls everyone, then the rollover. Target: -50% over 12-18 months All three end with mean reversion that wipes out latecomers Burry shorted housing when everyone called him crazy. Now everyone thinks his crazy again

Leshka.eth ⛩

944,843 Aufrufe • vor 2 Monaten

Aaron Levie says agent-driven micropayments could create an entirely new business model for the internet: "We might have truly a completely different business model for the internet." "Now actually it's probably is a good business model. Because let's say you had paywalled, really proprietary data... You could actually imagine like there's maybe like a hundred million agents that would be tapping into that data for various use cases." "All of a sudden, a penny a transaction starts to make a lot more sense than it did when you were capped at... people that maybe was interested in your content previously." "We probably have new business models for tools and APIs... We probably want an agent to be able to have a budget and make any API call it wants, and all of a sudden you have this new revenue stream, which is like these agents showing up, just transacting on your system." Aaron Levie at Daytona Compute Conference

Aaron Levie says agent-driven micropayments could create an entirely new business model for the internet: "We might have truly a completely different business model for the internet." "Now actually it's probably is a good business model. Because let's say you had paywalled, really proprietary data... You could actually imagine like there's maybe like a hundred million agents that would be tapping into that data for various use cases." "All of a sudden, a penny a transaction starts to make a lot more sense than it did when you were capped at... people that maybe was interested in your content previously." "We probably have new business models for tools and APIs... We probably want an agent to be able to have a budget and make any API call it wants, and all of a sudden you have this new revenue stream, which is like these agents showing up, just transacting on your system." Aaron Levie at Daytona Compute Conference

a16z

91,001 Aufrufe • vor 3 Monaten

Introducing Document Extraction as an MCP Server ✂️📑 A huge use case for AI agents is being able to extract out items from a diverse set of complex documents in a repeatable manner - whether it’s legal contracts, invoices, financial statements, passports, and more. In this case, 🚫 prompting ChatGPT/Claude is too error-prone/not systematic. 💡We’ve created a plug-and-play MCP server around our core document extraction capabilities within LlamaCloud. 1. Build an extraction agent in LlamaCloud with a standardized schema. 2. Plug it as an MCP tool within ChatGPT/Claude. 3. For your next extraction job, get the outputs in a standardized format through the tool! Huge shoutout to Tuana and Logan Markewich for working on this - this is thanks to the latest updates to our llamacloud-mcp demo repo, which supports both extraction and retrieval as MCP tools. Repo: Sign up:

Introducing Document Extraction as an MCP Server ✂️📑 A huge use case for AI agents is being able to extract out items from a diverse set of complex documents in a repeatable manner - whether it’s legal contracts, invoices, financial statements, passports, and more. In this case, 🚫 prompting ChatGPT/Claude is too error-prone/not systematic. 💡We’ve created a plug-and-play MCP server around our core document extraction capabilities within LlamaCloud. 1. Build an extraction agent in LlamaCloud with a standardized schema. 2. Plug it as an MCP tool within ChatGPT/Claude. 3. For your next extraction job, get the outputs in a standardized format through the tool! Huge shoutout to Tuana and Logan Markewich for working on this - this is thanks to the latest updates to our llamacloud-mcp demo repo, which supports both extraction and retrieval as MCP tools. Repo: Sign up:

Jerry Liu

57,827 Aufrufe • vor 1 Jahr

The ceo of Box says if you're not building for agents, you won't exist as a software company in 2-3 years. Aaron Levie recalled how in the 90s, it was mainframes built for institutions. If you didn't build for the CIO or IT org, you didn't exist. You could build software for users, but no one cared. Similarly, in the mid 2000s, it was software built for end users. If you didn't take that route, you got completely trounced. The likes of Yammer, Slack, Zoom, etc., ripped through enterprises with viral adoption. Bottom-up and completely user driven. And now it's software built for agents. We're going to have trillions of agent runs. The systems that optimize for agents the most will win. - lower latency - better API exposure - better system intelligence - serves the right information when called Agents will eventually choose their own tools on the fly and make purchasing decisions at runtime. All based on past experiences/online reviews/performance, sans any procurement process. The more pragmatic version is companies swapping out bad tools / tech that don't work well with agents. - crm doesn't expose the right APIs - auth can't handle agent scale requests - database has too much latency for agent workloads Being good enough for humans won't work because they have to pass the litmus test for agents to make the cut.

The ceo of Box says if you're not building for agents, you won't exist as a software company in 2-3 years. Aaron Levie recalled how in the 90s, it was mainframes built for institutions. If you didn't build for the CIO or IT org, you didn't exist. You could build software for users, but no one cared. Similarly, in the mid 2000s, it was software built for end users. If you didn't take that route, you got completely trounced. The likes of Yammer, Slack, Zoom, etc., ripped through enterprises with viral adoption. Bottom-up and completely user driven. And now it's software built for agents. We're going to have trillions of agent runs. The systems that optimize for agents the most will win. - lower latency - better API exposure - better system intelligence - serves the right information when called Agents will eventually choose their own tools on the fly and make purchasing decisions at runtime. All based on past experiences/online reviews/performance, sans any procurement process. The more pragmatic version is companies swapping out bad tools / tech that don't work well with agents. - crm doesn't expose the right APIs - auth can't handle agent scale requests - database has too much latency for agent workloads Being good enough for humans won't work because they have to pass the litmus test for agents to make the cut.

Ivan Burazin

22,608 Aufrufe • vor 3 Monaten