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Vikas gupta

@vicky_grok • 10,970 subscribers

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🚨 YOUR GPU IS PROBABLY WASTING MORE THAN YOU THINK. vLLM is built to squeeze far more useful work out of your GPU when serving LLMs. Running an LLM at scale isn’t just about having a powerful GPU. The real problem is how efficiently you use its memory and compute. That’s where vLLM comes in. → High-throughput LLM inference and serving → PagedAttention for smarter KV-cache memory management → Continuous batching to keep GPUs busy → Prefix caching + chunked prefill → OpenAI-compatible API out of the box → Supports a huge range of modern LLM architectures → Quantization support for running models more efficiently And the crazy part? You can start an OpenAI-compatible inference server with: `vllm serve ` So your application can talk to your own model almost like it’s talking to OpenAI. The bigger idea: Don’t just buy more GPUs. Make the GPUs you already have work harder. That’s why vLLM has become such a major project in LLM inference. 🔥 #vLLM #AI #LLM #Inference #GPU #MachineLearning #AIInfrastructure #OpenSource #AIAgents #Developers

🚨 YOUR GPU IS PROBABLY WASTING MORE THAN YOU THINK. vLLM is built to squeeze far more useful work out of your GPU when serving LLMs. Running an LLM at scale isn’t just about having a powerful GPU. The real problem is how efficiently you use its memory and compute. That’s where vLLM comes in. → High-throughput LLM inference and serving → PagedAttention for smarter KV-cache memory management → Continuous batching to keep GPUs busy → Prefix caching + chunked prefill → OpenAI-compatible API out of the box → Supports a huge range of modern LLM architectures → Quantization support for running models more efficiently And the crazy part? You can start an OpenAI-compatible inference server with: `vllm serve ` So your application can talk to your own model almost like it’s talking to OpenAI. The bigger idea: Don’t just buy more GPUs. Make the GPUs you already have work harder. That’s why vLLM has become such a major project in LLM inference. 🔥 #vLLM #AI #LLM #Inference #GPU #MachineLearning #AIInfrastructure #OpenSource #AIAgents #Developers

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🚨 THIS IS SO WILD Microsoft just open sourced a free tool that records you doing a task ONCE — and your AI agent repeats it forever. the GitHub repo already has 3,400+ stars. It lets you: → record a real work session: screen, clicks, windows, pages, even your narration → have Copilot reconstruct it as one intent plus an ordered step list → turn that one run into a Skill the agent runs on demand → or an Automation on a schedule — same procedure, no hands → generalize from one example: teach it one form, it submits all of them no replaying your clicks like a macro. it prefers the agent's native tools and learns the general case. this is where one-person businesses are headed. save this for later.

🚨 THIS IS SO WILD Microsoft just open sourced a free tool that records you doing a task ONCE — and your AI agent repeats it forever. the GitHub repo already has 3,400+ stars. It lets you: → record a real work session: screen, clicks, windows, pages, even your narration → have Copilot reconstruct it as one intent plus an ordered step list → turn that one run into a Skill the agent runs on demand → or an Automation on a schedule — same procedure, no hands → generalize from one example: teach it one form, it submits all of them no replaying your clicks like a macro. it prefers the agent's native tools and learns the general case. this is where one-person businesses are headed. save this for later.

161,875 просмотров • 1 месяц назад

🚨 THIS IS ACTUALLY INSANE Python developers are still installing five different tools to manage one project. `pip` for packages. `virtualenv` for environments. `pyenv` for Python versions. `pipx` for CLI tools. `poetry` for project management. uv replaces all of them with one tool. And it’s written in Rust. The headline number: 10–100× faster than pip. But speed isn't even the most interesting part. uv can: → Create and manage Python projects → Resolve dependencies with a universal lockfile → Create virtual environments → Install and switch between Python versions → Run standalone Python scripts with dependencies → Install CLI tools with `uvx` → Compile and sync requirements → Manage Cargo-style workspaces The workflow becomes ridiculously simple: `uv init` → create a project `uv add` → add dependencies `uv run` → run your code `uv lock` → lock dependencies `uv sync` → reproduce the environment And if you're already using pip? You don't have to throw your workflow away. The `uv pip` interface is designed as a drop-in replacement while giving you the speed boost. The other clever bit: uv uses a global cache to deduplicate dependencies and save disk space. So instead of assembling a Python toolchain piece by piece, you get one fast CLI that covers the whole workflow. And unlike a lot of newer developer tools, uv is already considered stable and production-ready. If you work with Python, this is one of those tools you should probably try once. You might not go back. #Python #DevTools #Rust #OpenSource #Programming #AIEngineering

🚨 THIS IS ACTUALLY INSANE Python developers are still installing five different tools to manage one project. `pip` for packages. `virtualenv` for environments. `pyenv` for Python versions. `pipx` for CLI tools. `poetry` for project management. uv replaces all of them with one tool. And it’s written in Rust. The headline number: 10–100× faster than pip. But speed isn't even the most interesting part. uv can: → Create and manage Python projects → Resolve dependencies with a universal lockfile → Create virtual environments → Install and switch between Python versions → Run standalone Python scripts with dependencies → Install CLI tools with `uvx` → Compile and sync requirements → Manage Cargo-style workspaces The workflow becomes ridiculously simple: `uv init` → create a project `uv add` → add dependencies `uv run` → run your code `uv lock` → lock dependencies `uv sync` → reproduce the environment And if you're already using pip? You don't have to throw your workflow away. The `uv pip` interface is designed as a drop-in replacement while giving you the speed boost. The other clever bit: uv uses a global cache to deduplicate dependencies and save disk space. So instead of assembling a Python toolchain piece by piece, you get one fast CLI that covers the whole workflow. And unlike a lot of newer developer tools, uv is already considered stable and production-ready. If you work with Python, this is one of those tools you should probably try once. You might not go back. #Python #DevTools #Rust #OpenSource #Programming #AIEngineering

39,971 просмотров • 12 дней назад

🚨 THIS IS ACTUALLY INSANE 𝗔𝗻𝗱𝗿𝗲𝘄 𝗡𝗴 just open sourced a free AI coworker that delivers FINISHED work, not chat. the GitHub repo already has 16,000+ stars. It lets you: → say the outcome: "prepare the customer brief", "untangle my calendar" → watch it break the task into steps and work across your desktop, files, and apps → keep control: writes, sends, and shell commands are approval-gated → connect 25+ tools — GitHub, Slack, Jira, Notion, Gmail, Calendar — plus anything over MCP → run it on a schedule: morning brief, weekly report, standing watch no subscriptions. no model lock-in. bring your own key — OpenAI, Anthropic, Gemini, DeepSeek, Kimi, Grok — or run fully local with Ollama. your data leaves only through the model and integrations you choose. this is where one-person businesses are headed. save this for later.

🚨 THIS IS ACTUALLY INSANE 𝗔𝗻𝗱𝗿𝗲𝘄 𝗡𝗴 just open sourced a free AI coworker that delivers FINISHED work, not chat. the GitHub repo already has 16,000+ stars. It lets you: → say the outcome: "prepare the customer brief", "untangle my calendar" → watch it break the task into steps and work across your desktop, files, and apps → keep control: writes, sends, and shell commands are approval-gated → connect 25+ tools — GitHub, Slack, Jira, Notion, Gmail, Calendar — plus anything over MCP → run it on a schedule: morning brief, weekly report, standing watch no subscriptions. no model lock-in. bring your own key — OpenAI, Anthropic, Gemini, DeepSeek, Kimi, Grok — or run fully local with Ollama. your data leaves only through the model and integrations you choose. this is where one-person businesses are headed. save this for later.

94,085 просмотров • 1 месяц назад

🚨 GOOGLE IS BUILDING KUBERNETES FOR AI AGENTS. Google just open-sourced AX — a high-throughput orchestrator designed to run autonomous agent workloads at massive scale. Think: Kubernetes → containers AX → AI agents → Run agents inside isolated sandboxes → Pre-wire Git repos, MCP servers, and skills with Workspaces → Restrict network access with Gateways → Configure LLM providers with Models → ax watch to monitor agents live → ax ssh to look inside a running sandbox → Suspend + resume agents without losing their state → Declarative YAML for the entire workflow The crazy part? You can define the whole agent environment in YAML: Task + Workspace + Gateway + Model Then: ax apply -f task.yaml And AX handles the orchestration. Google says it's designed to run billions of autonomous agent workloads in a cluster. This feels less like another AI agent framework… and more like infrastructure for an entire agent fleet. #Google #AIAgents #AI #Kubernetes #DevOps #CloudNative #OpenSource #AgentInfrastructure #LLM #Developers

🚨 GOOGLE IS BUILDING KUBERNETES FOR AI AGENTS. Google just open-sourced AX — a high-throughput orchestrator designed to run autonomous agent workloads at massive scale. Think: Kubernetes → containers AX → AI agents → Run agents inside isolated sandboxes → Pre-wire Git repos, MCP servers, and skills with Workspaces → Restrict network access with Gateways → Configure LLM providers with Models → ax watch to monitor agents live → ax ssh to look inside a running sandbox → Suspend + resume agents without losing their state → Declarative YAML for the entire workflow The crazy part? You can define the whole agent environment in YAML: Task + Workspace + Gateway + Model Then: ax apply -f task.yaml And AX handles the orchestration. Google says it's designed to run billions of autonomous agent workloads in a cluster. This feels less like another AI agent framework… and more like infrastructure for an entire agent fleet. #Google #AIAgents #AI #Kubernetes #DevOps #CloudNative #OpenSource #AgentInfrastructure #LLM #Developers

21,130 просмотров • 18 дней назад

🚨 THIS IS INSANE A Claude Code skill just started REFUSING to build websites that look like everyone else’s. Sameness is literally a failed check. It’s called scrollcraft — a skill that builds premium scroll-driven sites and holds them to a real design standard. And it already crossed 900+ GitHub stars in 4 days. Here’s the wild part: 1. Eight page grammars, each forbidding what the others require 2. A fingerprint gate: differ from your past builds on 4 of 6 dimensions 3. Every site must invent one bespoke interaction that exists only there 4. A headless browser walks every scroll position hunting “dead scroll” 5. It measures contrast on the composited page, per line, at the brightest frame No shared skeleton. No default six sections. No AI-purple gradients. It will argue with you about your peak — on purpose. It also ships: → A refuse list (feature-card grids, scroll cues, gradient text, fake dashboards) → A type floor (2 families, 45–75ch measure) → A feeling curve written before any act exists → One engineered peak that gets the asset budget → A contact sheet at the end of every build And yes: the engine is never edited per project, so builds can’t converge

🚨 THIS IS INSANE A Claude Code skill just started REFUSING to build websites that look like everyone else’s. Sameness is literally a failed check. It’s called scrollcraft — a skill that builds premium scroll-driven sites and holds them to a real design standard. And it already crossed 900+ GitHub stars in 4 days. Here’s the wild part: 1. Eight page grammars, each forbidding what the others require 2. A fingerprint gate: differ from your past builds on 4 of 6 dimensions 3. Every site must invent one bespoke interaction that exists only there 4. A headless browser walks every scroll position hunting “dead scroll” 5. It measures contrast on the composited page, per line, at the brightest frame No shared skeleton. No default six sections. No AI-purple gradients. It will argue with you about your peak — on purpose. It also ships: → A refuse list (feature-card grids, scroll cues, gradient text, fake dashboards) → A type floor (2 families, 45–75ch measure) → A feeling curve written before any act exists → One engineered peak that gets the asset budget → A contact sheet at the end of every build And yes: the engine is never edited per project, so builds can’t converge

31,426 просмотров • 1 месяц назад

🚨 THIS IS ACTUALLY INSANE Your AI agent can have access to the web. But if it can't reliably read what’s actually on the page, that access is almost useless. We looked at Firecrawl as the web layer for AI agents and the numbers are hard to ignore. The setup is simple: Give it a URL, search query, or website. Firecrawl handles the ugly part — crawling, scraping, rendering, extracting, and turning web content into something an AI model can actually use. The headline numbers: → 173,000+ GitHub stars → Search, scrape and interact with the web at scale → Supports web pages, PDFs, DOCX and other content → Structured data extraction for AI workflows → MCP support for connecting it directly to AI agents The workflow looks like this: Search → Scrape → Crawl → Extract → Feed the agent Three things stand out: 1. Scraping becomes an infrastructure layer Instead of maintaining your own pile of HTTP clients, parsers, browser automation and retry logic, you can treat web access as an API. 2. Agents get more than raw HTML The goal isn't just downloading a webpage. It's turning messy web content into clean context that an LLM can reason over. 3. The same layer works across different agent workflows Research agents. RAG pipelines. AI search. Competitive intelligence. Web-data extraction. The interesting shift: AI agents don't just need better models. They need better access to the information those models are supposed to reason about. Firecrawl is building that layer. Save this repo.

🚨 THIS IS ACTUALLY INSANE Your AI agent can have access to the web. But if it can't reliably read what’s actually on the page, that access is almost useless. We looked at Firecrawl as the web layer for AI agents and the numbers are hard to ignore. The setup is simple: Give it a URL, search query, or website. Firecrawl handles the ugly part — crawling, scraping, rendering, extracting, and turning web content into something an AI model can actually use. The headline numbers: → 173,000+ GitHub stars → Search, scrape and interact with the web at scale → Supports web pages, PDFs, DOCX and other content → Structured data extraction for AI workflows → MCP support for connecting it directly to AI agents The workflow looks like this: Search → Scrape → Crawl → Extract → Feed the agent Three things stand out: 1. Scraping becomes an infrastructure layer Instead of maintaining your own pile of HTTP clients, parsers, browser automation and retry logic, you can treat web access as an API. 2. Agents get more than raw HTML The goal isn't just downloading a webpage. It's turning messy web content into clean context that an LLM can reason over. 3. The same layer works across different agent workflows Research agents. RAG pipelines. AI search. Competitive intelligence. Web-data extraction. The interesting shift: AI agents don't just need better models. They need better access to the information those models are supposed to reason about. Firecrawl is building that layer. Save this repo.

20,121 просмотров • 1 месяц назад

🚨 YOUR GPU IS PROBABLY WASTING MORE THAN YOU THINK. vLLM is built to squeeze far more useful work out of your GPU when serving LLMs. Running an LLM at scale isn’t just about having a powerful GPU. The real problem is how efficiently you use its memory and compute. That’s where vLLM comes in. → High-throughput LLM inference and serving → PagedAttention for smarter KV-cache memory management → Continuous batching to keep GPUs busy → Prefix caching + chunked prefill → OpenAI-compatible API out of the box → Supports a huge range of modern LLM architectures → Quantization support for running models more efficiently And the crazy part? You can start an OpenAI-compatible inference server with: `vllm serve ` So your application can talk to your own model almost like it’s talking to OpenAI. The bigger idea: Don’t just buy more GPUs. Make the GPUs you already have work harder. That’s why vLLM has become such a major project in LLM inference. 🔥 #vLLM #AI #LLM #Inference #GPU #MachineLearning #AIInfrastructure #OpenSource #AIAgents #Developers

🚨 YOUR GPU IS PROBABLY WASTING MORE THAN YOU THINK. vLLM is built to squeeze far more useful work out of your GPU when serving LLMs. Running an LLM at scale isn’t just about having a powerful GPU. The real problem is how efficiently you use its memory and compute. That’s where vLLM comes in. → High-throughput LLM inference and serving → PagedAttention for smarter KV-cache memory management → Continuous batching to keep GPUs busy → Prefix caching + chunked prefill → OpenAI-compatible API out of the box → Supports a huge range of modern LLM architectures → Quantization support for running models more efficiently And the crazy part? You can start an OpenAI-compatible inference server with: `vllm serve ` So your application can talk to your own model almost like it’s talking to OpenAI. The bigger idea: Don’t just buy more GPUs. Make the GPUs you already have work harder. That’s why vLLM has become such a major project in LLM inference. 🔥 #vLLM #AI #LLM #Inference #GPU #MachineLearning #AIInfrastructure #OpenSource #AIAgents #Developers

14,138 просмотров • 1 месяц назад

🚨 THIS IS ACTUALLY INSANE We benchmarked 4 different AI-agent architectures against the exact same 120-task suite. The winner wasn't the agent with the biggest step budget. It was the one that verified its work. The setup: 12 services. 24 people. 48 incidents. 2 traps deliberately wired into the environment. Same tasks. Same 16-call budget. Four different designs. Here’s what happened: → Single-shot plan: 83.3% success → Closed loop: 87.5% → Loop + semantic memory: 87.5% with 52.5% fewer tool calls → Loop + verification: 100% success Three findings completely changed how I think about agent architecture: 1. More budget ≠ more accuracy The blind planner stayed at 83.3% from 2 to 16 calls. Its problem wasn't the budget. Its ceiling was architectural. 2. Memory is basically free budget The semantic cache hit 68.7% and eliminated 134 of 255 tool calls. Zero drop in success. 3. Verification is cheap insurance Just 41 additional calls across the entire benchmark bought +12.5 percentage points in success. It rescued all 15 trap tasks. That's the part I find most interesting. We're often asking: “How many steps should we give the agent?” Maybe the better question is: “What should the agent spend those steps on?” Planning? Retrieval? Memory? Verification? The architecture matters more than the raw step budget. Save this if you're building AI agents. #AgenticAI #AIAgents #LLM #MachineLearning #AIEngineering

🚨 THIS IS ACTUALLY INSANE We benchmarked 4 different AI-agent architectures against the exact same 120-task suite. The winner wasn't the agent with the biggest step budget. It was the one that verified its work. The setup: 12 services. 24 people. 48 incidents. 2 traps deliberately wired into the environment. Same tasks. Same 16-call budget. Four different designs. Here’s what happened: → Single-shot plan: 83.3% success → Closed loop: 87.5% → Loop + semantic memory: 87.5% with 52.5% fewer tool calls → Loop + verification: 100% success Three findings completely changed how I think about agent architecture: 1. More budget ≠ more accuracy The blind planner stayed at 83.3% from 2 to 16 calls. Its problem wasn't the budget. Its ceiling was architectural. 2. Memory is basically free budget The semantic cache hit 68.7% and eliminated 134 of 255 tool calls. Zero drop in success. 3. Verification is cheap insurance Just 41 additional calls across the entire benchmark bought +12.5 percentage points in success. It rescued all 15 trap tasks. That's the part I find most interesting. We're often asking: “How many steps should we give the agent?” Maybe the better question is: “What should the agent spend those steps on?” Planning? Retrieval? Memory? Verification? The architecture matters more than the raw step budget. Save this if you're building AI agents. #AgenticAI #AIAgents #LLM #MachineLearning #AIEngineering

18,556 просмотров • 1 месяц назад

🚨 Karpathy was right. He warned that 90% of AI advice dies in 6 months spoiler: most tools will not even survive 90 days this guy is literally giving away the exact 2026 playbook for AI Agents. he covers what to learn, how to build, and when to skip 👀

🚨 Karpathy was right. He warned that 90% of AI advice dies in 6 months spoiler: most tools will not even survive 90 days this guy is literally giving away the exact 2026 playbook for AI Agents. he covers what to learn, how to build, and when to skip 👀

35,287 просмотров • 3 месяцев назад

🚨 YOUR NETWORK HAS BEEN LYING TO YOU. Trippy lets you see exactly what’s happening to your connection, hop by hop. When a site feels slow, most people just run ping and call it a day. But that only tells you something is wrong. Trippy helps you figure out where it’s wrong. → Combines ping + traceroute in one interactive terminal UI → Shows latency and packet-loss stats across every hop → Supports ICMP, UDP, TCP and QUIC tracing → Displays AS/ASN and GeoIP information for network hops → Visualizes ECMP / multipath routing → Includes an interactive map for traced hosts → Supports both IPv4 and IPv6 → Built in Rust and available across major desktop/server platforms And it gets even more interesting. Recent releases added DSCP/ECN visibility, better loss heuristics, privacy controls, improved TUI features, and a public API for building tools on top of Trippy’s tracing libraries. The command is ridiculously simple: trip Then you get a live view of the path between you and the destination. Instead of asking “why is my internet slow?” you can start asking “which hop is screwing it up?” That’s a much better network debugging tool. 🔥 #Networking #Rust #DevTools #Linux #SysAdmin #OpenSource #CyberSecurity #Developers

🚨 YOUR NETWORK HAS BEEN LYING TO YOU. Trippy lets you see exactly what’s happening to your connection, hop by hop. When a site feels slow, most people just run ping and call it a day. But that only tells you something is wrong. Trippy helps you figure out where it’s wrong. → Combines ping + traceroute in one interactive terminal UI → Shows latency and packet-loss stats across every hop → Supports ICMP, UDP, TCP and QUIC tracing → Displays AS/ASN and GeoIP information for network hops → Visualizes ECMP / multipath routing → Includes an interactive map for traced hosts → Supports both IPv4 and IPv6 → Built in Rust and available across major desktop/server platforms And it gets even more interesting. Recent releases added DSCP/ECN visibility, better loss heuristics, privacy controls, improved TUI features, and a public API for building tools on top of Trippy’s tracing libraries. The command is ridiculously simple: trip Then you get a live view of the path between you and the destination. Instead of asking “why is my internet slow?” you can start asking “which hop is screwing it up?” That’s a much better network debugging tool. 🔥 #Networking #Rust #DevTools #Linux #SysAdmin #OpenSource #CyberSecurity #Developers

14,279 просмотров • 1 месяц назад

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