Video wird geladen...

Video konnte nicht geladen werden

Beim Laden dieses Videos ist ein Problem aufgetreten. Dies könnte an einem vorübergehenden Netzwerkproblem liegen oder das Video ist möglicherweise nicht verfügbar.

ClawRouter for OpenClaw🦞 reach 2000 stars🌟 within 7d launch. We shipped 5 fixes in the last 24 hrs: • New /v1/responses endpoint for Grok • Fixed nested output parsing • Real usage stats now visible on /stats • Free GPT 120B fallback AlwaysOn • Free GPT 120B with shortcut... show more

Vicky

3,937 subscribers

71,988 Aufrufe • vor 5 Monaten •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

0 Kommentare

Keine Kommentare verfügbar

Kommentare vom Original-Post werden hier angezeigt

Ähnliche Videos

timelapse #86 (15 hrs): - got my first OOM on 8xB200 node - defaulting back to grok-code-fast-1, the fastest reliable coding model with by far most intuitive instruction following, combined with grok 4 fast reasoning to plan before i let grok code work its magic - drank 2 large tim hortons iced capps, loaded myself w/ creatine, daily nootropics - tried out gpt-5-codex but it simply doesnt match the speed i require when i go deep into one thing at a time sequentially - got caught watching youtube videos in the middle, need to make sure i block any and all content that could get in my way - caught up on all book revisions so getting super ahead with other chapters - developed an overnight addiction to switching color themes in cursor - did some pair programming w/ Kearm h/eng using Tuple on free trial - applying for O-1

timelapse #86 (15 hrs): - got my first OOM on 8xB200 node - defaulting back to grok-code-fast-1, the fastest reliable coding model with by far most intuitive instruction following, combined with grok 4 fast reasoning to plan before i let grok code work its magic - drank 2 large tim hortons iced capps, loaded myself w/ creatine, daily nootropics - tried out gpt-5-codex but it simply doesnt match the speed i require when i go deep into one thing at a time sequentially - got caught watching youtube videos in the middle, need to make sure i block any and all content that could get in my way - caught up on all book revisions so getting super ahead with other chapters - developed an overnight addiction to switching color themes in cursor - did some pair programming w/ Kearm h/eng using Tuple on free trial - applying for O-1

Elliot Arledge

102,928 Aufrufe • vor 9 Monaten

Love OpenClaw but hate the token burn? 💸 Running a 24/7 agent on GPT-4/Claude is overkill. You don't need SOTA reasoning to handle a greeting or a simple lookup. LLMRouter 🩷 OpenClaw The first production-ready, agentic router designed to plug directly into OpenClaw. LLMRouter fully supports Multimodal, Memory-Equipped routing that adapts 100% to your needs—compatible with FREE open-source models. The Logic is Simple:🔹 Simple query → Cheap/Local model 🔹 Complex reasoning → SOTA model (GPT-4/Claude 3.5) 🔹 Multimodal input → Vision/Audio specialized model Why this isn't just a switch: 📉 30–50% drop in inference costs 🧠 Zero loss in response quality 🔓 100% compatible with OpenAI-style APIs 🚀 Deploy in Seconds General Usage: Get the library and serve any model: pip install llmrouter-lib llmrouter serve OpenClaw Native Integration: Want the full agent experience? LLMRouter built a dedicated integration for OpenClaw users: LLMRouter Resources: 🔗 Repo: 📦 PyPI: 🤝 Works with: Route smarter. Train your own. Pay less. More on LLMRouter: Most routers are static if/else. LLMRouter is an intelligent, learning system. 🤖 Agentic & Memory-Aware: Decisions aren't stateless. We use RAG-powered memory to route based on context and history. 👤 Fully Personalized: It learns from your usage patterns via RL feedback loops. 🔬 Research-Grade: Switch between 16+ routing strategies (KNN, SVM, BERT, Graph, RL) with a single flag.

Love OpenClaw but hate the token burn? 💸 Running a 24/7 agent on GPT-4/Claude is overkill. You don't need SOTA reasoning to handle a greeting or a simple lookup. LLMRouter 🩷 OpenClaw The first production-ready, agentic router designed to plug directly into OpenClaw. LLMRouter fully supports Multimodal, Memory-Equipped routing that adapts 100% to your needs—compatible with FREE open-source models. The Logic is Simple:🔹 Simple query → Cheap/Local model 🔹 Complex reasoning → SOTA model (GPT-4/Claude 3.5) 🔹 Multimodal input → Vision/Audio specialized model Why this isn't just a switch: 📉 30–50% drop in inference costs 🧠 Zero loss in response quality 🔓 100% compatible with OpenAI-style APIs 🚀 Deploy in Seconds General Usage: Get the library and serve any model: pip install llmrouter-lib llmrouter serve OpenClaw Native Integration: Want the full agent experience? LLMRouter built a dedicated integration for OpenClaw users: LLMRouter Resources: 🔗 Repo: 📦 PyPI: 🤝 Works with: Route smarter. Train your own. Pay less. More on LLMRouter: Most routers are static if/else. LLMRouter is an intelligent, learning system. 🤖 Agentic & Memory-Aware: Decisions aren't stateless. We use RAG-powered memory to route based on context and history. 👤 Fully Personalized: It learns from your usage patterns via RL feedback loops. 🔬 Research-Grade: Switch between 16+ routing strategies (KNN, SVM, BERT, Graph, RL) with a single flag.

Jiaxuan You

31,303 Aufrufe • vor 5 Monaten

Introducing LiteParse - the best model-free document parsing tool for AI agents 💫 ✅ It’s completely open-source and free. ✅ No GPU required, will process ~500 pages in 2 seconds on commodity hardware ✅ More accurate than PyPDF, PyMuPDF, Markdown. Also way more readable - see below for how we parse tables!! ✅ Supports 50+ file formats, from PDFs to Office docs to images ✅ Is designed to plug and play with Claude Code, OpenClaw, and any other AI agent with a one-line skills install. Supports native screenshotting capabilities. We spent years building up LlamaParse by orchestrating state-of-the-art VLMs over the most complex documents. Along the way we realized that you could get quite far on most docs through fast and cheap text parsing. Take a look at the video below. For really complex tables within PDFs, we output them in a spatial grid that’s both AI and human-interpretable. Any other free/light parser light PyPDF will destroy the representation of this table and output a sequential list. This is not a replacement for a VLM-based OCR tool (it requires 0 GPUs and doesn’t use models), but it is shocking how good it is to parse most documents. Huge shoutout to Logan Markewich and Clelia Bertelli (🦙/acc) for all the work here. Come check it out: Repo:

Introducing LiteParse - the best model-free document parsing tool for AI agents 💫 ✅ It’s completely open-source and free. ✅ No GPU required, will process ~500 pages in 2 seconds on commodity hardware ✅ More accurate than PyPDF, PyMuPDF, Markdown. Also way more readable - see below for how we parse tables!! ✅ Supports 50+ file formats, from PDFs to Office docs to images ✅ Is designed to plug and play with Claude Code, OpenClaw, and any other AI agent with a one-line skills install. Supports native screenshotting capabilities. We spent years building up LlamaParse by orchestrating state-of-the-art VLMs over the most complex documents. Along the way we realized that you could get quite far on most docs through fast and cheap text parsing. Take a look at the video below. For really complex tables within PDFs, we output them in a spatial grid that’s both AI and human-interpretable. Any other free/light parser light PyPDF will destroy the representation of this table and output a sequential list. This is not a replacement for a VLM-based OCR tool (it requires 0 GPUs and doesn’t use models), but it is shocking how good it is to parse most documents. Huge shoutout to Logan Markewich and Clelia Bertelli (🦙/acc) for all the work here. Come check it out: Repo:

Jerry Liu

256,399 Aufrufe • vor 4 Monaten

OpenAI and Anthropic news roundup (Week 18, 2026) OpenAI open-sourced Symphony spec for Codex orchestration, published "Our principles" post, announced amended Microsoft partnership, achieved FedRAMP Moderate authorization, posted commitment to community safety, brought OpenAI models, Codex, and Managed Agents to AWS, shared cybersecurity action plan, posted Stargate compute infrastructure update, published "Where the goblins came from" post, posted Auto-review write-up for Codex, introduced Advanced Account Security, announced DevDay 2026, repositioned Codex as personal assistant for everyday work, launched Codex setup import, added Codex pets, announced GPT-5.5 party for next week, shared GPT-5.5 one-week launch metrics, and rolled out 360 worlds in ChatGPT Images on web, plus discovered Custom dictionary feature in development, ChatGPT search EU recipient numbers, confirmed new model selector in composer, and updated privacy policy with marketing cookies on by default for free users Anthropic opened Sydney office with new General Manager, launched Claude for Creative Work with new connectors, Claude Code can now send push notifications to your phone, published Introspection Adapters research, BioMysteryBench evaluation, "How people ask Claude for personal guidance" study, launched Claude Security in public beta, and preparing for Code with Claude developer conference next week, plus discovered "Cardinal" stats feature in development and internal red teaming for Claude Jupiter V1 P, and more

OpenAI and Anthropic news roundup (Week 18, 2026) OpenAI open-sourced Symphony spec for Codex orchestration, published "Our principles" post, announced amended Microsoft partnership, achieved FedRAMP Moderate authorization, posted commitment to community safety, brought OpenAI models, Codex, and Managed Agents to AWS, shared cybersecurity action plan, posted Stargate compute infrastructure update, published "Where the goblins came from" post, posted Auto-review write-up for Codex, introduced Advanced Account Security, announced DevDay 2026, repositioned Codex as personal assistant for everyday work, launched Codex setup import, added Codex pets, announced GPT-5.5 party for next week, shared GPT-5.5 one-week launch metrics, and rolled out 360 worlds in ChatGPT Images on web, plus discovered Custom dictionary feature in development, ChatGPT search EU recipient numbers, confirmed new model selector in composer, and updated privacy policy with marketing cookies on by default for free users Anthropic opened Sydney office with new General Manager, launched Claude for Creative Work with new connectors, Claude Code can now send push notifications to your phone, published Introspection Adapters research, BioMysteryBench evaluation, "How people ask Claude for personal guidance" study, launched Claude Security in public beta, and preparing for Code with Claude developer conference next week, plus discovered "Cardinal" stats feature in development and internal red teaming for Claude Jupiter V1 P, and more

Tibor Blaho

12,254 Aufrufe • vor 2 Monaten

BREAKING: Introducing All Access from Every 📧, our new membership tier for the best builders in AI All Access subs get the Builder Pack which includes $7,000 in credits and free usage to the models + tool stack we use Every 📧. All Access subscribers get: - $1,000 in Codex / ChatGPT for Work credits - 12 months free of Cursor Pro+ - $4,000 in PostHog credits including self-driving to automatically fix bugs and identify issues in your production app - 1 year free of Framer - 6 months free of Notion And much more! (Did I mention $1,000 in Codex credits? It's time to build!) Get all access: Why All Access and the Builder Pack This is the best time in history to build something. For a long time, it’s been possible to one-shot impressive demos, but they’d fall flat the minute they hit production. But the release of GPT-5.6-Sol and Fable 5 heralds a new era: Everyone can build, launch, and maintain the software that they’ve always dreamed of. Everyone is a builder now. There’s just one catch: Building with AI is very expensive. (Ask me how I know.) (Alright, I’ll tell you. I accidentally used 2 billion tokens overnight this week on a big GPT-5.6-Sol run. Worth it.) This is unique in the history of technology. For most of the personal computing era, a billionaire and a solo builder could buy essentially the same top-of-the-line Mac. AI changes that: The more tokens you can afford, the more you can make. And we want to make that accessible to more people. That’s why the main feature of our new All Access plan is the Builder Pack: more than $7,000 in credits and discounts on the full stack we use to run Every, from idea to production—Codex, Claude, PostHog, Render, Gemini, FLORA, and more. Early-bird membership is only $500/year for the next 24 hours—and the Codex credits alone are worth $1,000. (I could’ve used it for my overnight run this week.) Now we’re handing it to you. Get all access: Meet the Builder Pack It's got more than $7,000 in offers from 10 of the AI products we use to write, design, build, and run Every 📧: BUILD - $1,000 in Codex credits plus one month of ChatGPT for business - Twelve months free of Cursor Pro+ - One month free of Claude Max - Three months free of Google AI Pro DESIGN - One year free of Framer Pro - One month free of FLORA © Max HOST - $300 in Render credits IMPROVE - $4,000 in PostHog credits - Six months free of Notion Business - Six months free of AgentMail (YC S25) We rely on these every day, and we tried to put together a package that helps you comprehensively for each part of the process of building and running software in AI. What comes with All Access - Everything in an existing paid Every membership: our daily writing, guides, camps, and software like Monologue, Cora, Sparkle, and Spiral - The Builder Pack, with more than $7,000 in partner offers - Unlimited email accounts use of Cora and unlimited Spiral usage - Members-only programming with me and the Every team and me Get All Access:

BREAKING: Introducing All Access from Every 📧, our new membership tier for the best builders in AI All Access subs get the Builder Pack which includes $7,000 in credits and free usage to the models + tool stack we use Every 📧. All Access subscribers get: - $1,000 in Codex / ChatGPT for Work credits - 12 months free of Cursor Pro+ - $4,000 in PostHog credits including self-driving to automatically fix bugs and identify issues in your production app - 1 year free of Framer - 6 months free of Notion And much more! (Did I mention $1,000 in Codex credits? It's time to build!) Get all access: Why All Access and the Builder Pack This is the best time in history to build something. For a long time, it’s been possible to one-shot impressive demos, but they’d fall flat the minute they hit production. But the release of GPT-5.6-Sol and Fable 5 heralds a new era: Everyone can build, launch, and maintain the software that they’ve always dreamed of. Everyone is a builder now. There’s just one catch: Building with AI is very expensive. (Ask me how I know.) (Alright, I’ll tell you. I accidentally used 2 billion tokens overnight this week on a big GPT-5.6-Sol run. Worth it.) This is unique in the history of technology. For most of the personal computing era, a billionaire and a solo builder could buy essentially the same top-of-the-line Mac. AI changes that: The more tokens you can afford, the more you can make. And we want to make that accessible to more people. That’s why the main feature of our new All Access plan is the Builder Pack: more than $7,000 in credits and discounts on the full stack we use to run Every, from idea to production—Codex, Claude, PostHog, Render, Gemini, FLORA, and more. Early-bird membership is only $500/year for the next 24 hours—and the Codex credits alone are worth $1,000. (I could’ve used it for my overnight run this week.) Now we’re handing it to you. Get all access: Meet the Builder Pack It's got more than $7,000 in offers from 10 of the AI products we use to write, design, build, and run Every 📧: BUILD - $1,000 in Codex credits plus one month of ChatGPT for business - Twelve months free of Cursor Pro+ - One month free of Claude Max - Three months free of Google AI Pro DESIGN - One year free of Framer Pro - One month free of FLORA © Max HOST - $300 in Render credits IMPROVE - $4,000 in PostHog credits - Six months free of Notion Business - Six months free of AgentMail (YC S25) We rely on these every day, and we tried to put together a package that helps you comprehensively for each part of the process of building and running software in AI. What comes with All Access - Everything in an existing paid Every membership: our daily writing, guides, camps, and software like Monologue, Cora, Sparkle, and Spiral - The Builder Pack, with more than $7,000 in partner offers - Unlimited email accounts use of Cora and unlimited Spiral usage - Members-only programming with me and the Every team and me Get All Access:

Dan Shipper 📧

178,690 Aufrufe • vor 5 Tagen

AI is creating a new kind of creator economy. One where anyone can launch complex apps, agents, trading systems, research tools, games, and community experiments in minutes. Not just content people consume. AI-native experiences people can use, share, and grow together. That is the bigger idea behind the AI Trading Arena: a live DeFAI environment where AI-created strategies compete on real crypto prices, with every trade, tick, P&L update, and leaderboard visible to everyone. The latest expansion makes it even more fascinating: AI is now designing new trading strategies and adding them into the Arena to compete with strategies from frontier models like GPT-5, Claude, Gemini, Grok, DeepSeek, and Llama. Just agents, markets, and performance in the open. And the Arena is only one example. On ChainOpera AI, creators can launch AI-native experiences in minutes, then grow them with the community that uses them. $COAI is the fuel for this creator economy: creators launch, users participate, and AI-native apps come alive through real community usage. What would you be built next?

AI is creating a new kind of creator economy. One where anyone can launch complex apps, agents, trading systems, research tools, games, and community experiments in minutes. Not just content people consume. AI-native experiences people can use, share, and grow together. That is the bigger idea behind the AI Trading Arena: a live DeFAI environment where AI-created strategies compete on real crypto prices, with every trade, tick, P&L update, and leaderboard visible to everyone. The latest expansion makes it even more fascinating: AI is now designing new trading strategies and adding them into the Arena to compete with strategies from frontier models like GPT-5, Claude, Gemini, Grok, DeepSeek, and Llama. Just agents, markets, and performance in the open. And the Arena is only one example. On ChainOpera AI, creators can launch AI-native experiences in minutes, then grow them with the community that uses them. $COAI is the fuel for this creator economy: creators launch, users participate, and AI-native apps come alive through real community usage. What would you be built next?

ChainOpera AI

15,398 Aufrufe • vor 1 Monat

🚀 Traffic Cameras, Custom Storm Tracks, and More We've just released some powerful new features — all available for FREE: Traffic Cameras — View live camera feeds directly on the map. We're starting with 5 US states: Kansas, Maryland, Minnesota, Tennessee, and Virginia. Custom Storm Tracks — Draw your own storm path on the map, set the speed, and instantly see a cone of uncertainty with estimated impact times for cities along the way. Weather Office Details — Tap any NWS office to view contact info, coverage stats, the latest Area Forecast Discussion (AFD), and all active alerts organized by type. Hazcams — Browse all active Hazcams in one place. Search by name, see viewer counts, and jump to any camera on the map. Available now on: iOS: Android: Browser: And to Plus/Pro subscribers using our Windows/Mac/Linux App

🚀 Traffic Cameras, Custom Storm Tracks, and More We've just released some powerful new features — all available for FREE: Traffic Cameras — View live camera feeds directly on the map. We're starting with 5 US states: Kansas, Maryland, Minnesota, Tennessee, and Virginia. Custom Storm Tracks — Draw your own storm path on the map, set the speed, and instantly see a cone of uncertainty with estimated impact times for cities along the way. Weather Office Details — Tap any NWS office to view contact info, coverage stats, the latest Area Forecast Discussion (AFD), and all active alerts organized by type. Hazcams — Browse all active Hazcams in one place. Search by name, see viewer counts, and jump to any camera on the map. Available now on: iOS: Android: Browser: And to Plus/Pro subscribers using our Windows/Mac/Linux App

WeatherWise.app

32,432 Aufrufe • vor 5 Monaten

This is INSANE. My OpenClaw🦞 just called booked me a reservation. Here’s what actually happened: 1/ The Setup I gave my OpenClaw a real phone number. Most people would say that’s risky. I think the opposite. Why sit on hold when AI can do it for you? I installed the Clawdtalk skill, generated a phone number + PIN, and was live in minutes. That’s it. No complex setup. 2/ The Test Valentine’s Day was coming up. I told OpenClaw: “Find me a restaurants near me for 2 people on Saturday.” I genuinely didn’t think it would work. It called 5 restaurants. At the same time. Spoke to real humans. Booked 1 with availability. No hold music. No back and forth. No wasted time. 3/ The Shift The infrastructure is changing. OpenClaw isn’t just a chatbot. It’s an execution layer. The interface to the real world is becoming API-accessible. Restaurants. Doctors. Contractors. Front desks. If it has a phone number, it’s now programmable. Today: dinner reservations. Tomorrow: vendor negotiations, recruiting screens, appointment backlogs. Anything that requires: – calling – waiting – confirming – coordinating …can now be delegated. We’re entering the era of agentic execution. I spoke with the Clawdtalk team and got a free usage tier for builders in my audience. Comment “Clawdtalk” and I’ll DM you the link. Let’s build.

This is INSANE. My OpenClaw🦞 just called booked me a reservation. Here’s what actually happened: 1/ The Setup I gave my OpenClaw a real phone number. Most people would say that’s risky. I think the opposite. Why sit on hold when AI can do it for you? I installed the Clawdtalk skill, generated a phone number + PIN, and was live in minutes. That’s it. No complex setup. 2/ The Test Valentine’s Day was coming up. I told OpenClaw: “Find me a restaurants near me for 2 people on Saturday.” I genuinely didn’t think it would work. It called 5 restaurants. At the same time. Spoke to real humans. Booked 1 with availability. No hold music. No back and forth. No wasted time. 3/ The Shift The infrastructure is changing. OpenClaw isn’t just a chatbot. It’s an execution layer. The interface to the real world is becoming API-accessible. Restaurants. Doctors. Contractors. Front desks. If it has a phone number, it’s now programmable. Today: dinner reservations. Tomorrow: vendor negotiations, recruiting screens, appointment backlogs. Anything that requires: – calling – waiting – confirming – coordinating …can now be delegated. We’re entering the era of agentic execution. I spoke with the Clawdtalk team and got a free usage tier for builders in my audience. Comment “Clawdtalk” and I’ll DM you the link. Let’s build.

Jacob Klug

18,877 Aufrufe • vor 5 Monaten

Introducing Tracelight 1.0 After six months, we are live with 5 of the 10 biggest management consultancies in the world, as well as PE funds and asset managers with over $600B in AUM. Tracelight is the best AI add-in to Excel. In testing against the leading alternative add-ins, Tracelight 1.0 was: - The most accurate - 2x faster building analysis on average - 20x faster at finding human errors in complex spreadsheets We’ve been building with early adopters and enterprise partners for months. You can now reach a completely new level of performance in Excel. That’s why today we’re proud to launch a major new version. To celebrate, we're giving 1 month of Tracelight Pro for free to anyone who comments LAUNCH below or TAGs a friend who needs this

Introducing Tracelight 1.0 After six months, we are live with 5 of the 10 biggest management consultancies in the world, as well as PE funds and asset managers with over $600B in AUM. Tracelight is the best AI add-in to Excel. In testing against the leading alternative add-ins, Tracelight 1.0 was: - The most accurate - 2x faster building analysis on average - 20x faster at finding human errors in complex spreadsheets We’ve been building with early adopters and enterprise partners for months. You can now reach a completely new level of performance in Excel. That’s why today we’re proud to launch a major new version. To celebrate, we're giving 1 month of Tracelight Pro for free to anyone who comments LAUNCH below or TAGs a friend who needs this

Peter Fuller

453,736 Aufrufe • vor 6 Monaten

Alibaba just released a coding model that hits 82 percent on SWE-Bench Verified. That is the highest score ever published for an open-source model. The weights are free. The license is Apache 2.0. You can run it today. The model is Qwen 4 Coder 32B. Here is what 82 percent on SWE-Bench Verified actually means. SWE-Bench Verified tests whether an AI can autonomously resolve real bugs pulled from real production GitHub repositories. Not synthetic exercises. Real open-source projects that real teams depend on. A model gets a bug report, reads the code, writes a fix, and either passes the test suite or it does not. At 82 percent, Qwen 4 Coder 32B resolves 82 out of every 100 real production bugs it is given. Without a human guiding it. On code it has never seen before. For comparison: Qwen 4 Coder 32B: 82 percent SWE-Bench Verified. Open source. Apache 2.0. Claude Fable 5: 80.3 percent SWE-Bench Pro. $10 input / $50 output per million tokens. Currently suspended. GPT-5.6 Sol: Competitive on Terminal-Bench. $5 input / $30 output per million tokens. An open-weight model that you can download and run for free just beat both of them on the benchmark designed to measure real software engineering capability. Here is the architecture. Qwen 4 Coder 32B is a 32 billion parameter dense model. Not a Mixture-of-Experts. Every parameter is active on every request. This matters for inference: a dense 32B model runs on 22 gigabytes of VRAM, which fits on a single high-end consumer GPU or a MacBook Pro with 64GB of unified memory. The smaller variant, Qwen 4 Coder 4B, runs at approximately 135 tokens per second on an M5 Max and fits inside 8 gigabytes of RAM. For a model with usable coding capability, that is a new bar for what fits in a single laptop. The training methodology continued Alibaba's approach of reinforcement learning on verifiable coding tasks. The model gets rewarded when its code passes tests. It gets penalized when it fails. Over millions of training steps, the model learns to write code that actually runs rather than code that looks plausible. License: Apache 2.0. Full commercial use. No attribution requirement. No revenue threshold. No monthly active user ceiling. Weights: Hugging Face, available today. Runs on: vLLM, Ollama, SGLang, and any standard GGUF-compatible inference engine. Qwen 4 32B also runs at approximately 135 tokens per second on an M5 Max chip, setting a new bar for what a sub-8GB model can do on Apple Silicon. The open-source coding model just beat the best closed-source model in the world on the benchmark designed to test whether AI can actually do software engineering. The weights are free. The subscription is optional. Source: Autom8Labs AI Insight July 2026, State of Open Source LLMs June 2026, Kunal Ganglani blog June 2026.

Alibaba just released a coding model that hits 82 percent on SWE-Bench Verified. That is the highest score ever published for an open-source model. The weights are free. The license is Apache 2.0. You can run it today. The model is Qwen 4 Coder 32B. Here is what 82 percent on SWE-Bench Verified actually means. SWE-Bench Verified tests whether an AI can autonomously resolve real bugs pulled from real production GitHub repositories. Not synthetic exercises. Real open-source projects that real teams depend on. A model gets a bug report, reads the code, writes a fix, and either passes the test suite or it does not. At 82 percent, Qwen 4 Coder 32B resolves 82 out of every 100 real production bugs it is given. Without a human guiding it. On code it has never seen before. For comparison: Qwen 4 Coder 32B: 82 percent SWE-Bench Verified. Open source. Apache 2.0. Claude Fable 5: 80.3 percent SWE-Bench Pro. $10 input / $50 output per million tokens. Currently suspended. GPT-5.6 Sol: Competitive on Terminal-Bench. $5 input / $30 output per million tokens. An open-weight model that you can download and run for free just beat both of them on the benchmark designed to measure real software engineering capability. Here is the architecture. Qwen 4 Coder 32B is a 32 billion parameter dense model. Not a Mixture-of-Experts. Every parameter is active on every request. This matters for inference: a dense 32B model runs on 22 gigabytes of VRAM, which fits on a single high-end consumer GPU or a MacBook Pro with 64GB of unified memory. The smaller variant, Qwen 4 Coder 4B, runs at approximately 135 tokens per second on an M5 Max and fits inside 8 gigabytes of RAM. For a model with usable coding capability, that is a new bar for what fits in a single laptop. The training methodology continued Alibaba's approach of reinforcement learning on verifiable coding tasks. The model gets rewarded when its code passes tests. It gets penalized when it fails. Over millions of training steps, the model learns to write code that actually runs rather than code that looks plausible. License: Apache 2.0. Full commercial use. No attribution requirement. No revenue threshold. No monthly active user ceiling. Weights: Hugging Face, available today. Runs on: vLLM, Ollama, SGLang, and any standard GGUF-compatible inference engine. Qwen 4 32B also runs at approximately 135 tokens per second on an M5 Max chip, setting a new bar for what a sub-8GB model can do on Apple Silicon. The open-source coding model just beat the best closed-source model in the world on the benchmark designed to test whether AI can actually do software engineering. The weights are free. The subscription is optional. Source: Autom8Labs AI Insight July 2026, State of Open Source LLMs June 2026, Kunal Ganglani blog June 2026.

Harman

40,958 Aufrufe • vor 11 Tagen

Introducing Impeccable 3.5, the best way to design in production: iterate on real UI with your AI agent, in the codebase you actually ship. Turns out many popular design skills, including Impeccable and Anthropic's frontend-design, weren't actually very good at...design (the workflow was valuable, but the output didn't magically make LLMs like GPT great designers). We measured it across thousands of generations: 74% of pages used the cream AI-default background, 76% reached for extreme letter-spacing, 90%+ failed the contrast floor. So we started fixing slop systematically, specific to each model. The skill now compiles rules for the exact defects each model makes, instead of shipping one generic file to everyone. The biggest jump is in GPT-5.5 and Codex. Also new: ◆ It now knows the difference between a new project and an existing one. Existing codebase, it reads your design system and preserves your identity. Greenfield, it seeds a fresh palette from 129 hand-curated anchors so every cold start doesn't drift to the same safe colors. ◆ Live Mode is now in beta, and works at two scales. Type a direction into the new Steer bar, or speak it, and the agent reads the whole page and edits it in place. Or pick a single element, steer it with a sub-command, live-edit any copy, and accept the variant straight back to source. Insert mode scaffolds brand-new elements between the ones already there. Recovery survives HMR, hidden heroes, and dev-tool overlays. ◆ A rebuilt anti-pattern detector. Torn off jsdom and onto a real CSS cascade resolver: roughly 20x faster, dependency-free, and now small enough to run inline inside the skill, not just the CLI and extension. 14 new rules, 41 total. ◆ The skill keeps itself current, checking once a day and offering to update. Plus /impeccable init and a bare /impeccable that reads your repo and tells you the next move. Free, open source. Claude Code, Codex, Cursor, and more.

Paul Bakaus

101,825 Aufrufe • vor 1 Monat

Today at Yoast we’re shipping something I've actually wanted to see on the web for some time (even before working at Yoast) and am now honored to be part of the team to bring this to the masses... Built in collaboration with the Open Source and NLWeb team at Microsoft lead by R.V.Guha (co-creator of RSS, RDF and other web standards) - we're introducing the Schema Aggregation feature: a "schemamap" endpoint that outputs your site's entire structured data map in one place. Under the hood, we now provide a standardised, deduplicated map of your entities via a single endpoint. An agent no longer needs to crawl all individual pages to understand its meaning but can now ingest an entire entity map with ease. A few details to note about the endpoint: - It's is cacheable with sub‑100ms responses - It respects existing privacy and indexing settings - It aggregates all indexable content without navigation noise - It merges duplicate entities so your "Author X" or "Article Y" exist as a single node instead of being re‑discovered on every URL. - If you're using one of our paid plugins that extends schema even more (such as Yoast WooCommerce SEO adds product schema) this will be populated within the endpoint too - If you already extend Yoast’s Schema API, or use partners like events or recipe plugins, their entities are pulled into the same map automatically. For me, this is one of the first major ways the agentic web can ingest a site at scale, and with much more efficiency and context. It has also been so much fun to work with this alongside the team at Yoast, particularly the genius mind of Alain Schlesser. Lastly, we have also launched a schema visualisation tool to view how everything is output. You can enable this feature today with the free version of Yoast SEO.

Today at Yoast we’re shipping something I've actually wanted to see on the web for some time (even before working at Yoast) and am now honored to be part of the team to bring this to the masses... Built in collaboration with the Open Source and NLWeb team at Microsoft lead by R.V.Guha (co-creator of RSS, RDF and other web standards) - we're introducing the Schema Aggregation feature: a "schemamap" endpoint that outputs your site's entire structured data map in one place. Under the hood, we now provide a standardised, deduplicated map of your entities via a single endpoint. An agent no longer needs to crawl all individual pages to understand its meaning but can now ingest an entire entity map with ease. A few details to note about the endpoint: - It's is cacheable with sub‑100ms responses - It respects existing privacy and indexing settings - It aggregates all indexable content without navigation noise - It merges duplicate entities so your "Author X" or "Article Y" exist as a single node instead of being re‑discovered on every URL. - If you're using one of our paid plugins that extends schema even more (such as Yoast WooCommerce SEO adds product schema) this will be populated within the endpoint too - If you already extend Yoast’s Schema API, or use partners like events or recipe plugins, their entities are pulled into the same map automatically. For me, this is one of the first major ways the agentic web can ingest a site at scale, and with much more efficiency and context. It has also been so much fun to work with this alongside the team at Yoast, particularly the genius mind of Alain Schlesser. Lastly, we have also launched a schema visualisation tool to view how everything is output. You can enable this feature today with the free version of Yoast SEO.

Alex Moss

15,360 Aufrufe • vor 4 Monaten

I pay Claude $20 a month. Most $TAO holders do too. There is a stack you can build in 15 minutes that fixes that completely. It runs on Bittensor. It costs $10. You do not write a single line of code. Here is how every AI chat product actually works under the hood. Three layers. Always three. The model. The brain. GPT, Claude, DeepSeek, Kimi, GLM. The inference layer. The GPU that runs the model when you hit send. The interface. The chat box you actually look at. ChatGPT and Claude bundle all three and hand you the result. You cannot change the model. You cannot change the inference. The interface is non-negotiable. Every prompt you type goes to a server run by a private company whose terms of service can quietly change next month. The anti-ChatGPT move is to pick each layer yourself. This is where $TAO comes in. Chutes is Subnet 64 on Bittensor. It is the inference layer. Open source models like DeepSeek, Kimi, GLM, and Llama get served by a global network of miner-operated GPUs. Validators score the output quality. The best inference wins the emissions. You hit send. A miner somewhere runs your prompt. You get the answer back. The TAO you hold is in part paying for the GPU you just used. The basic stack is one URL. chutes. ai/chat No account. No API key. No setup. Switch models mid-conversation. Web search built in. Image generation. File uploads. Free. The advanced stack is Chutes plus TypingMind. One-time license. No recurring fee. Plugins, agents, custom personas, a prompt library you build over months. Full model switching between Chutes, OpenAI, and Anthropic from the same window. Total cost: $10 a month to Chutes for inference. That $10 buys you $50 in actual usage. But here is the signal most people missed inside this story. Chutes ran a free tier until February. Then they killed it. Then they raised the minimum to $10 in May. Most people saw that as bad news. It is the opposite. Free things on the internet do not last. Real products do. Chutes is becoming a real product. A subnet that generates actual revenue from actual users paying actual money for actual AI inference. That is what $43 million in Q1 network revenue looks like at the individual subnet level. And there is one more thing ChatGPT and Claude cannot offer that Chutes already has. Trusted Execution Environments. Your prompt gets encrypted on your device, shipped to a confidential compute GPU, and the lock only breaks inside the chip. The miner running the model physically cannot read your prompt. ChatGPT cannot promise that. Claude cannot promise that. Bittensor already built it. You are holding a network where the subnets are generating real revenue, shipping real privacy infrastructure, and replacing $20 a month centralised subscriptions with $10 a month decentralised inference. The people who use the product always understand the investment better than the people who only watch the price.

I pay Claude $20 a month. Most $TAO holders do too. There is a stack you can build in 15 minutes that fixes that completely. It runs on Bittensor. It costs $10. You do not write a single line of code. Here is how every AI chat product actually works under the hood. Three layers. Always three. The model. The brain. GPT, Claude, DeepSeek, Kimi, GLM. The inference layer. The GPU that runs the model when you hit send. The interface. The chat box you actually look at. ChatGPT and Claude bundle all three and hand you the result. You cannot change the model. You cannot change the inference. The interface is non-negotiable. Every prompt you type goes to a server run by a private company whose terms of service can quietly change next month. The anti-ChatGPT move is to pick each layer yourself. This is where $TAO comes in. Chutes is Subnet 64 on Bittensor. It is the inference layer. Open source models like DeepSeek, Kimi, GLM, and Llama get served by a global network of miner-operated GPUs. Validators score the output quality. The best inference wins the emissions. You hit send. A miner somewhere runs your prompt. You get the answer back. The TAO you hold is in part paying for the GPU you just used. The basic stack is one URL. chutes. ai/chat No account. No API key. No setup. Switch models mid-conversation. Web search built in. Image generation. File uploads. Free. The advanced stack is Chutes plus TypingMind. One-time license. No recurring fee. Plugins, agents, custom personas, a prompt library you build over months. Full model switching between Chutes, OpenAI, and Anthropic from the same window. Total cost: $10 a month to Chutes for inference. That $10 buys you $50 in actual usage. But here is the signal most people missed inside this story. Chutes ran a free tier until February. Then they killed it. Then they raised the minimum to $10 in May. Most people saw that as bad news. It is the opposite. Free things on the internet do not last. Real products do. Chutes is becoming a real product. A subnet that generates actual revenue from actual users paying actual money for actual AI inference. That is what $43 million in Q1 network revenue looks like at the individual subnet level. And there is one more thing ChatGPT and Claude cannot offer that Chutes already has. Trusted Execution Environments. Your prompt gets encrypted on your device, shipped to a confidential compute GPU, and the lock only breaks inside the chip. The miner running the model physically cannot read your prompt. ChatGPT cannot promise that. Claude cannot promise that. Bittensor already built it. You are holding a network where the subnets are generating real revenue, shipping real privacy infrastructure, and replacing $20 a month centralised subscriptions with $10 a month decentralised inference. The people who use the product always understand the investment better than the people who only watch the price.

2xnmore

27,019 Aufrufe • vor 1 Monat

I run my meta ads with OpenClaw🦞 for $0/month 😱 here's the system that runs autonomously: step 1: daily health check → social-cli (major shoutout to Vishal Gopal Ojha) wraps Meta's marketing API (token refresh, pagination, rate limits all handled) → am I on track? what's running? who's winning? who's bleeding? any fatigue? → the same 5 questions I asked Ads Manager every morning for 20 years step 2: catch dying ads before CPA spikes → OpenClaw🦞 pulls daily frequency by ad → frequency > 3.5 = audience is cooked, CTR is about to drop → this one signal saves more money than any dashboard step 3: auto-pause bleeders + shift budget to winners → CPA > 2.5x target for 48hrs? auto-pause. no hesitation. → ranks every campaign by efficiency. recommends shifting spend. → last fri it paused an $87 CPA campaign at 3am and scaled my best performer 30% step 4: write new ad copy from your winners → agent analyzes what's working (hooks, angles, CTAs) → generates variations based on the patterns in YOUR top performers → copy modeled on what already converts in your account. step 5: upload ads directly to your account → new creative + copy → live in Meta Ads Manager → no more downloading, formatting, clicking through the upload flow → agent handles the entire publish cycle step 6: content concepts + morning brief → spots patterns across winners and suggests what to test next → delivers everything to Telegram, Slack, wherever you want it → 90 seconds to read. reply "approved." done. input: your ad account + your target CPA output: an AI that monitors, kills, scales, writes, AND uploads your ads dozens of hours in ad manager → 1 text message I packaged the entire system as the Meta Ads Kit. 5 OpenClaw🦞 skills: - meta-ads (daily checks + auto-pause) - ad-creative-monitor (fatigue detection) - budget-optimizer (efficiency scoring + shift recs) - ad-copy-generator (writes variations from your winners) - ad-upload (publishes creative directly to your account) giving it away free. comment ADS + like + follow (must follow so i can DM)

I run my meta ads with OpenClaw🦞 for $0/month 😱 here's the system that runs autonomously: step 1: daily health check → social-cli (major shoutout to Vishal Gopal Ojha) wraps Meta's marketing API (token refresh, pagination, rate limits all handled) → am I on track? what's running? who's winning? who's bleeding? any fatigue? → the same 5 questions I asked Ads Manager every morning for 20 years step 2: catch dying ads before CPA spikes → OpenClaw🦞 pulls daily frequency by ad → frequency > 3.5 = audience is cooked, CTR is about to drop → this one signal saves more money than any dashboard step 3: auto-pause bleeders + shift budget to winners → CPA > 2.5x target for 48hrs? auto-pause. no hesitation. → ranks every campaign by efficiency. recommends shifting spend. → last fri it paused an $87 CPA campaign at 3am and scaled my best performer 30% step 4: write new ad copy from your winners → agent analyzes what's working (hooks, angles, CTAs) → generates variations based on the patterns in YOUR top performers → copy modeled on what already converts in your account. step 5: upload ads directly to your account → new creative + copy → live in Meta Ads Manager → no more downloading, formatting, clicking through the upload flow → agent handles the entire publish cycle step 6: content concepts + morning brief → spots patterns across winners and suggests what to test next → delivers everything to Telegram, Slack, wherever you want it → 90 seconds to read. reply "approved." done. input: your ad account + your target CPA output: an AI that monitors, kills, scales, writes, AND uploads your ads dozens of hours in ad manager → 1 text message I packaged the entire system as the Meta Ads Kit. 5 OpenClaw🦞 skills: - meta-ads (daily checks + auto-pause) - ad-creative-monitor (fatigue detection) - budget-optimizer (efficiency scoring + shift recs) - ad-copy-generator (writes variations from your winners) - ad-upload (publishes creative directly to your account) giving it away free. comment ADS + like + follow (must follow so i can DM)

Matthew Berman

638,893 Aufrufe • vor 4 Monaten

THIS DAY, WE have to complete 100 million Quests every day to get tons of rewards in the #QuestForQuests Community Event! 😱 WHAT? 🔓 Every day a new community bar must be completed in the in-game news 🎲 Complete 100M Quests in 24 hours and a D20 will be rolled, whichever number you get corresponds to a reward! 🤳 All the rolls will be shown on video on our social media channels ❌ If the goal of the day it's not achieved, you won’t get to roll the D20 that day and a new community bar will start 🌟 There will be extra Quests every day to help you progress! 🌭 If you roll a 20 = CRITICAL SUCCESS and you get the KNIGHT DOUG SKIN FOR FREE! 🎁 You can get the same reward multiple times! But if you get the same cosmetic reward twice, you’ll get fallback rewards! 📅 On the last day, if the goal is achieved, the die will be rolled twice and you will get 2 rewards! REWARDS (D20): 1 - 1 gem 2 - Sword Spray (fallback = 100 Coins) 3 - Torch Spray (fallback = 100 Coins) 4 - 200 Coins 5 - Carnaval Player Icon (fallback = 200 Coins) 6 - Critical Failure Icon (fallback = 200 Coins) 7 - Robot Knight Icon (fallback = 200 Coins) 8 - Bling Box Emote (fallback = 500 Coins) 9 - 1000 Bling 10 - 200 Credits 11 - 300 Power Points 12 - 500 Coins 13 - 10 Random Starr Drops 14 - True Gold Doug Skin (fallback = 25000 Coins) 15 - Legendary Starr Drop 16 - 500 Credits 17 - 1000 Coins 18 - Ultra Trophy Box 19 - Hypercharge Drop 20 - Knight Doug Skin (fallback = 100 Gems) 📢 GIVEAWAY 📢 We will give out a free Brawl Pass Plus to 1000 players! The winners will be picked randomly at the end of the event, but the more Quests you complete, the higher the chances of winning! The #QuestforQuests community event starts NOW and ends on August 18th!

THIS DAY, WE have to complete 100 million Quests every day to get tons of rewards in the #QuestForQuests Community Event! 😱 WHAT? 🔓 Every day a new community bar must be completed in the in-game news 🎲 Complete 100M Quests in 24 hours and a D20 will be rolled, whichever number you get corresponds to a reward! 🤳 All the rolls will be shown on video on our social media channels ❌ If the goal of the day it's not achieved, you won’t get to roll the D20 that day and a new community bar will start 🌟 There will be extra Quests every day to help you progress! 🌭 If you roll a 20 = CRITICAL SUCCESS and you get the KNIGHT DOUG SKIN FOR FREE! 🎁 You can get the same reward multiple times! But if you get the same cosmetic reward twice, you’ll get fallback rewards! 📅 On the last day, if the goal is achieved, the die will be rolled twice and you will get 2 rewards! REWARDS (D20): 1 - 1 gem 2 - Sword Spray (fallback = 100 Coins) 3 - Torch Spray (fallback = 100 Coins) 4 - 200 Coins 5 - Carnaval Player Icon (fallback = 200 Coins) 6 - Critical Failure Icon (fallback = 200 Coins) 7 - Robot Knight Icon (fallback = 200 Coins) 8 - Bling Box Emote (fallback = 500 Coins) 9 - 1000 Bling 10 - 200 Credits 11 - 300 Power Points 12 - 500 Coins 13 - 10 Random Starr Drops 14 - True Gold Doug Skin (fallback = 25000 Coins) 15 - Legendary Starr Drop 16 - 500 Credits 17 - 1000 Coins 18 - Ultra Trophy Box 19 - Hypercharge Drop 20 - Knight Doug Skin (fallback = 100 Gems) 📢 GIVEAWAY 📢 We will give out a free Brawl Pass Plus to 1000 players! The winners will be picked randomly at the end of the event, but the more Quests you complete, the higher the chances of winning! The #QuestforQuests community event starts NOW and ends on August 18th!

Brawl Stars

351,995 Aufrufe • vor 11 Monaten

Matthew Gallagher Built a $401M Company in Year One with 2 People. And the tool behind it? Claude Code. This year he's on track for $1.8B. Sam Altman predicted this. It's happening now. The problem? It costs money. API credits stack up. Monthly bills keep growing. Every prompt eats your budget. Every project drains your wallet faster. Until now. Two methods. 99% cheaper. One is completely free. Forever. $0. Not a trial. This video breaks down both step by step. ↓ Let me put this in perspective. $100-$500. That's monthly. That's what you spend. That's $6,000/year on API credits. Just to use a tool you haven't shipped anything with. The $401M guy? Spending $0. Same capability. Shipping weekly. Different cost structure. Different results. Different life. I'm about to hand you his cost structure for free. ↓ Open source vs closed source. Pay attention. Closed source: Claude. GPT-4. Pay per token. Meter always running. Open source: Qwen. Llama. Mistral. Free to download. Free to run. Free forever. No meter. No tokens. No bill. Here's what nobody tells you: 80% of coding tasks? Open source handles them. More than handles them. Writes clean code. Debugs errors. Generates boilerplate. Handles routine work perfectly. You're paying premium prices for tasks that don't need premium intelligence. That's hiring a brain surgeon to put on a bandaid. Smart play: Free models for the 80%. Paid credits for the 20%. That's what the $401M guy does. That's what this video teaches you. Follow Himanshu Kumar for more breakdowns that turn free tools into real businesses. ↓ Method 1: Ollama. Local. Free. Forever. Download it. Pull a model. Point Claude Code at it. Done. No internet needed. No API keys required. No monthly subscription. No token counting ever. No bill. Today. Tomorrow. Ever. Your data never leaves your computer. Complete privacy. Complete freedom. Claude Code thinks it's talking to the cloud. It's talking to your laptop. For $0. The video walks through every step: Every config file. Every variable. Every command. Every click. If you can follow a recipe, you can do this. People who set this up 3 months ago? Saved $300-$1,500 since then. Workflow didn't change one bit. ↓ Hardware you need: 16GB RAM: 7B models run smooth. 32GB RAM: 32B models run comfortable. 64GB + GPU: biggest models available. No GPU? Still works. Just slower. Few extra seconds. That's it. Your $1,500 laptop is sitting there running Chrome and Spotify. Put it to work saving you $200/month instead. Follow Himanshu Kumar for more breakdowns that turn free tools into real businesses. ↓ Method 2: Open Router. Free Cloud. No Hardware. Weak machine? Don't want local setup? This method is for you. Free AI models in the cloud. No download. No hardware. Configure Claude Code to route through Open Router. The config: Base URL: Open Router API. API key: free Open Router key. Default Sonnet: free. Default Opus: free. Default Haiku: free. Small fast model: free. Subagent model: free. Free. Free. Free. Free. Free across the board. Same interface. Same commands. Same workflow. Zero cost. Copy the config from the video. Paste it. Save $200/month. Starting today. Right now. ↓ When to use which: Ollama (local): Best for privacy. Best for offline work. Best for unlimited usage. Best if you have decent hardware. Open Router (cloud): Best for weak machines. Best for instant setup. Best for trying different models. Best if you don't want to manage anything. Both methods: Best for 80% of your daily work. Still use paid Claude for: Complex architecture. Multi-file refactoring. Deep reasoning tasks. The 20% that actually needs it. $20/month instead of $200/month. Same output. 90% less cost. ↓ The math that should make you angry. You (current): $200-$500/month. $2,400-$6,000/year. $7,200-$18,000 over 3 years. You (after this video): $20-$50/month. $240-$600/year. $720-$1,800 over 3 years. Savings over 3 years: $6,480-$16,200. That's a used car. That's seed money. That's 6 months of rent. All from one 25-minute video. All from 15 minutes of configuration. Highest ROI 25 minutes you'll spend this year. ↓ The limitations. I won't lie to you. Open source is not Opus. Not as smart on complex reasoning. Not as good at long-context tasks. Makes more mistakes on nuanced problems. But they are: Free. Capable. Getting better monthly. Good enough for 80% of daily work. Smart cost management isn't being cheap. It's being strategic. Expensive tool when it matters. Free tool when it doesn't. ↓ The one-person billion-dollar company is coming. $401M in year one proved it's possible. The building blocks: AI that codes: Claude Code. Way to run it free: this video. Distribution: the internet. Customers: everyone. Only missing ingredient? Someone who builds. Not reads about building. Not saves posts about building. Not bookmarks videos about building. Builds. Tools are free. Knowledge is free. Opportunity is screaming. You're still "thinking about it." ↓ Your action plan: Tonight: Watch the video. Tomorrow morning: Set up Ollama or Open Router. Tomorrow afternoon: Build something. Anything. This week: Build a second thing. Faster. This month: Charge someone for it. One video. One setup. One weekend. $0 cost. Unlimited potential. Or keep paying $200/month for something you could get free. Keep consuming instead of building. Keep planning instead of shipping. Matthew Gallagher didn't plan a $401M company. He built it. Full video attached. Every method. Every config. Every tradeoff. 25 minutes. Your move. Follow Himanshu Kumar for more breakdowns that turn free tools into real businesses.

Matthew Gallagher Built a $401M Company in Year One with 2 People. And the tool behind it? Claude Code. This year he's on track for $1.8B. Sam Altman predicted this. It's happening now. The problem? It costs money. API credits stack up. Monthly bills keep growing. Every prompt eats your budget. Every project drains your wallet faster. Until now. Two methods. 99% cheaper. One is completely free. Forever. $0. Not a trial. This video breaks down both step by step. ↓ Let me put this in perspective. $100-$500. That's monthly. That's what you spend. That's $6,000/year on API credits. Just to use a tool you haven't shipped anything with. The $401M guy? Spending $0. Same capability. Shipping weekly. Different cost structure. Different results. Different life. I'm about to hand you his cost structure for free. ↓ Open source vs closed source. Pay attention. Closed source: Claude. GPT-4. Pay per token. Meter always running. Open source: Qwen. Llama. Mistral. Free to download. Free to run. Free forever. No meter. No tokens. No bill. Here's what nobody tells you: 80% of coding tasks? Open source handles them. More than handles them. Writes clean code. Debugs errors. Generates boilerplate. Handles routine work perfectly. You're paying premium prices for tasks that don't need premium intelligence. That's hiring a brain surgeon to put on a bandaid. Smart play: Free models for the 80%. Paid credits for the 20%. That's what the $401M guy does. That's what this video teaches you. Follow Himanshu Kumar for more breakdowns that turn free tools into real businesses. ↓ Method 1: Ollama. Local. Free. Forever. Download it. Pull a model. Point Claude Code at it. Done. No internet needed. No API keys required. No monthly subscription. No token counting ever. No bill. Today. Tomorrow. Ever. Your data never leaves your computer. Complete privacy. Complete freedom. Claude Code thinks it's talking to the cloud. It's talking to your laptop. For $0. The video walks through every step: Every config file. Every variable. Every command. Every click. If you can follow a recipe, you can do this. People who set this up 3 months ago? Saved $300-$1,500 since then. Workflow didn't change one bit. ↓ Hardware you need: 16GB RAM: 7B models run smooth. 32GB RAM: 32B models run comfortable. 64GB + GPU: biggest models available. No GPU? Still works. Just slower. Few extra seconds. That's it. Your $1,500 laptop is sitting there running Chrome and Spotify. Put it to work saving you $200/month instead. Follow Himanshu Kumar for more breakdowns that turn free tools into real businesses. ↓ Method 2: Open Router. Free Cloud. No Hardware. Weak machine? Don't want local setup? This method is for you. Free AI models in the cloud. No download. No hardware. Configure Claude Code to route through Open Router. The config: Base URL: Open Router API. API key: free Open Router key. Default Sonnet: free. Default Opus: free. Default Haiku: free. Small fast model: free. Subagent model: free. Free. Free. Free. Free. Free across the board. Same interface. Same commands. Same workflow. Zero cost. Copy the config from the video. Paste it. Save $200/month. Starting today. Right now. ↓ When to use which: Ollama (local): Best for privacy. Best for offline work. Best for unlimited usage. Best if you have decent hardware. Open Router (cloud): Best for weak machines. Best for instant setup. Best for trying different models. Best if you don't want to manage anything. Both methods: Best for 80% of your daily work. Still use paid Claude for: Complex architecture. Multi-file refactoring. Deep reasoning tasks. The 20% that actually needs it. $20/month instead of $200/month. Same output. 90% less cost. ↓ The math that should make you angry. You (current): $200-$500/month. $2,400-$6,000/year. $7,200-$18,000 over 3 years. You (after this video): $20-$50/month. $240-$600/year. $720-$1,800 over 3 years. Savings over 3 years: $6,480-$16,200. That's a used car. That's seed money. That's 6 months of rent. All from one 25-minute video. All from 15 minutes of configuration. Highest ROI 25 minutes you'll spend this year. ↓ The limitations. I won't lie to you. Open source is not Opus. Not as smart on complex reasoning. Not as good at long-context tasks. Makes more mistakes on nuanced problems. But they are: Free. Capable. Getting better monthly. Good enough for 80% of daily work. Smart cost management isn't being cheap. It's being strategic. Expensive tool when it matters. Free tool when it doesn't. ↓ The one-person billion-dollar company is coming. $401M in year one proved it's possible. The building blocks: AI that codes: Claude Code. Way to run it free: this video. Distribution: the internet. Customers: everyone. Only missing ingredient? Someone who builds. Not reads about building. Not saves posts about building. Not bookmarks videos about building. Builds. Tools are free. Knowledge is free. Opportunity is screaming. You're still "thinking about it." ↓ Your action plan: Tonight: Watch the video. Tomorrow morning: Set up Ollama or Open Router. Tomorrow afternoon: Build something. Anything. This week: Build a second thing. Faster. This month: Charge someone for it. One video. One setup. One weekend. $0 cost. Unlimited potential. Or keep paying $200/month for something you could get free. Keep consuming instead of building. Keep planning instead of shipping. Matthew Gallagher didn't plan a $401M company. He built it. Full video attached. Every method. Every config. Every tradeoff. 25 minutes. Your move. Follow Himanshu Kumar for more breakdowns that turn free tools into real businesses.

Himanshu Kumar

13,363 Aufrufe • vor 3 Monaten

QVAC SDK 0.15.0 is live. This release adds multiple prompts batching, brings a native AMD GPU backend to the stack, moves more vision encoders onto mobile GPUs, and adds a second local coding-agent integration. Main highlights: - Prompt batching for the LLM addon. Batch multiple prompts into one job and process them concurrently, with each answer returned the moment its generation finishes. - Native AMD GPU backend. A first-class HIP/ROCm backend in @qvac/vla-ggml, auto-selected over Vulkan with clean fallback when ROCm is absent. - A second local coding agent. OpenClaw joins OpenCode for local, cloud-free agent workflows. AGENTS - OpenCode plugin update (@qvac/opencode-plugin). Aligned with the current SDK, CLI, and AI SDK provider packages. A fresh install runs OpenCode against managed local QVAC models out of the box, from the default qvac/qwen3.5-9b, with no manual qvac serve setup. - OpenClaw plugin (@qvac/openclaw-plugin). A second coding-agent integration alongside OpenCode. A fresh setup installs the plugin, creates a local qvac provider through onboarding, and runs a QVAC model through OpenClaw🦞's local service path. LANGUAGE MODELS - Prompt batching (LLM addon). Batch multiple prompts in one job and run them concurrently, each answer returns the moment its generation finishes, no waiting on the others. - Reasoning-context trimming on hybrid + recurrent models (@qvac/llm-llamacpp). remove_thinking_from_context now works beyond pure-attention models. Same JS API, no throw. VOICE AND SPEECH - Transcription (transcription-parakeet 0.9.0). More robust CPU fallback on GPU failure and a faster Vulkan backend on Pixel 9. - Text-to-speech features (tts-ggml 0.4.0). Adds LavaSR for noise removal and adjustable output frequency up to 48 kHz, plus Japanese via Chatterbox. - Text-to-speech fixes (tts-ggml 0.4.1). CPU fallback on GPU failure, a q8_0 KV crash fix on Metal with Chatterbox. VISION - Qwen3.5 vision encoder on GPU (Android). Image encoder moves onto the phone GPU, with a smarter tile-grid preprocessor and default image-token caps, for flagship Android: Vulkan on Mali (Pixel 9 Pro) and OpenCL on Adreno 830 (Galaxy S25). - Gemma-4 vision encoder on GPU (Android). Vision encoder runs on the phone GPU instead of CPU, same flagship Android targets. PLATFORM AND PERFORMANCE - AMD GPU backend (@qvac/vla-ggml). Native HIP/ROCm backend, auto-selected over Vulkan with clean fallback when ROCm is absent (Linux x64 only). Comes with ~23% faster than Vulkan, ~14% faster than PyTorch-ROCm, parity preserved. Unified code style. A cleaner, more consistent, easier-to-contribute codebase. Let's build. npm install @qvac/sdk

QVAC SDK 0.15.0 is live. This release adds multiple prompts batching, brings a native AMD GPU backend to the stack, moves more vision encoders onto mobile GPUs, and adds a second local coding-agent integration. Main highlights: - Prompt batching for the LLM addon. Batch multiple prompts into one job and process them concurrently, with each answer returned the moment its generation finishes. - Native AMD GPU backend. A first-class HIP/ROCm backend in @qvac/vla-ggml, auto-selected over Vulkan with clean fallback when ROCm is absent. - A second local coding agent. OpenClaw joins OpenCode for local, cloud-free agent workflows. AGENTS - OpenCode plugin update (@qvac/opencode-plugin). Aligned with the current SDK, CLI, and AI SDK provider packages. A fresh install runs OpenCode against managed local QVAC models out of the box, from the default qvac/qwen3.5-9b, with no manual qvac serve setup. - OpenClaw plugin (@qvac/openclaw-plugin). A second coding-agent integration alongside OpenCode. A fresh setup installs the plugin, creates a local qvac provider through onboarding, and runs a QVAC model through OpenClaw🦞's local service path. LANGUAGE MODELS - Prompt batching (LLM addon). Batch multiple prompts in one job and run them concurrently, each answer returns the moment its generation finishes, no waiting on the others. - Reasoning-context trimming on hybrid + recurrent models (@qvac/llm-llamacpp). remove_thinking_from_context now works beyond pure-attention models. Same JS API, no throw. VOICE AND SPEECH - Transcription (transcription-parakeet 0.9.0). More robust CPU fallback on GPU failure and a faster Vulkan backend on Pixel 9. - Text-to-speech features (tts-ggml 0.4.0). Adds LavaSR for noise removal and adjustable output frequency up to 48 kHz, plus Japanese via Chatterbox. - Text-to-speech fixes (tts-ggml 0.4.1). CPU fallback on GPU failure, a q8_0 KV crash fix on Metal with Chatterbox. VISION - Qwen3.5 vision encoder on GPU (Android). Image encoder moves onto the phone GPU, with a smarter tile-grid preprocessor and default image-token caps, for flagship Android: Vulkan on Mali (Pixel 9 Pro) and OpenCL on Adreno 830 (Galaxy S25). - Gemma-4 vision encoder on GPU (Android). Vision encoder runs on the phone GPU instead of CPU, same flagship Android targets. PLATFORM AND PERFORMANCE - AMD GPU backend (@qvac/vla-ggml). Native HIP/ROCm backend, auto-selected over Vulkan with clean fallback when ROCm is absent (Linux x64 only). Comes with ~23% faster than Vulkan, ~14% faster than PyTorch-ROCm, parity preserved. Unified code style. A cleaner, more consistent, easier-to-contribute codebase. Let's build. npm install @qvac/sdk

QVAC

17,700,244 Aufrufe • vor 7 Tagen

Introducing Open Source AI CRM, that runs on your OpenClaw. A few weeks ago, we launched Ironclaw (An Open Source OpenClaw CRM Framework) which now has around 1.4k stars. A lot of people confused us with NearAI’s Ironclaw, so we changed our name to DenchClaw. OpenClaw today feels a lot like early React: the primitive is incredibly powerful, but the patterns are still forming, and everyone is piecing together their own way to actually use it. What made React explode wasn’t just React itself, but the emergence of frameworks like Gatsby and Next.js that turned raw capability into something opinionated, repeatable, and easy to adopt. That is how I think about DenchClaw. We are not just building on top of OpenClaw; we are trying to make it one of the clearest, most practical, and most complete ways to use OpenClaw in the real world. We are an OpenClaw Framework, we are aiming to be the most correct way to use OpenClaw. We entered Y Combinator with Merse (AI Audio Comic), it was an app that I personally never used. Michael Seibel confronted us on it, and said, “if you aren’t the best user of your consumer app, then who is?”. I now use DenchClaw daily for everything I do, it also works as a coding agent like Cursor, DenchClaw built DenchClaw. I am addicted to DenchClaw now that I can ask it, “hey in the companies table only show me the ones who have more than 5 employees” and it updates it live than me having to manually add a filter. On Dench, everything sits in a file system, the table filters, views, column toggles, calendar/gantt views, etc, so OpenClaw can directly work with it using Dench’s CRM skill. The CRM is built on top of DuckDB, the smallest, most performant and at the same time also feature rich database we could find. It creates a new OpenClaw🦞 profile called “dench”, and opens a new OpenClaw Gateway… that means you can run all your usual openclaw commands by just prefixing every command with `openclaw --profile dench` . It will start your gateway on port 19001 range. You will be able to access the DenchClaw frontend at localhost:3100. Once you open it on Safari, just add it to your Dock to use it as a PWA. Think of it as Cursor for your Mac which is based on OpenClaw. DenchClaw has a file tree view for you to use it as an elevated finder tool to do anything on your mac. I use it to create slides, do LinkedIn outreach using MY browser. DenchClaw sees what you see, does what you do. It’s the everything app, that sits locally on your mac. All yours. Just ask it “hey import my notion”, “hey import everything from my hubspot”, and it will literally go into your browser, export all objects and documents and put it in its own workspace that you can use. P.S. It comes with Garry Tan's GStack built in.

Mark Rachapoom

19,411 Aufrufe • vor 3 Monaten

meta muse spark 1.1 vs gpt 5.6 sol vs fable 5 vs grok 4.5 meta recently dropped muse spark 1.1 – a multimodal reasoning model from meta superintelligence labs built for agentic tasks. key facts: • 1m token context with active self-management – the model compacts its own history and keeps only the steps needed for later work • trained to orchestrate multi-agent systems: as main agent it plans and delegates to parallel subagents, as subagent it sticks to its job and knows when to escalate back • computer use trained to pick between scripting and clicking – writes automation when it's faster, clicks when it's simpler, batches actions per step • first public api from meta: the meta model api is now in preview • benchmarks: sweeps the agent column – mcp atlas 88.1 (opus 4.8: 82.2), jobbench 54.7 (opus: 48.4), humanity's last exam 62.1 (1st). loses coding – deepswe 1.1 53.3 vs gpt 5.5's 67.0, swe bench pro 61.5 vs opus's 69.2 our test – 3 prompts, single-file html, three.js, fully procedural, no assets: 1. norwegian house cantilevered over a fjord in a snowstorm – transmissive glass wall, fully modelled interior 2. beijing siheyuan courtyard house in dawn fog – instanced roof tiles, dougong brackets, glowing paper windows 3. new mexico adobe pueblo in an approaching dust storm – deep window reveals, windward grit accumulation we ran the test on AI/ML API platform results: - cost #1 muse spark 1.1 – $0.20 #2 grok 4.5 – $0.51 #3 gpt 5.6 sol – $1.93 #4 fable 5 – ~$5.20 - output tokens #1 muse spark 1.1 – 41,868 #2 gpt 5.6 sol – 49,139 #3 grok 4.5 – 64,954 #4 fable 5 – 81,849 - lines of code #1 muse spark 1.1 – 1,799 #2 gpt 5.6 sol – 2,377 #3 fable 5 – 3,088 #4 grok 4.5 – 4,216 observations: • muse spark is the cheapest of the four by a wide margin – 2.5x under grok, ~26x under fable per run. output quality tracks the price • only 7.4% of its output tokens are reasoning (3,104 of 41,868) – the model barely thinks before writing. economic, not pedantic: it commits to the first plan and ships it • the low loc is not compression, it's omission – all three prompts demanded instancing, muse spark delivered it in one muse spark's code quality – reviewed by fable 5: upsides: 1. all three files run 2. the adobe grit effect is legit – shader injection via onbeforecompile, windward faces detect storm direction through a normal-dot-wind term and darken procedurally 3. the fjord glass is real meshphysicalmaterial with transmission and ior, not a transparent quad 4. the siheyuan properly instances barrel tiles, dougong blocks and courtyard pavers downsides: 1. in the fjord file the strafe vector is negated – press a, you move right; press d, you move left. exactly the key mix-up we kept hitting with this model 2. all three files ship the model's self-doubt as comments: "// actually yaw orientation: need correct" sits above a direction vector that gets computed, abandoned and recomputed – dead vectors allocated every frame, 60 times a second 3. the siheyuan registers two separate keydown listeners, one containing an empty if-block 4. snow "accumulation" on the norway roof is a sine wobble on a scale value, not accumulation 5. "instanced snow" became 3,500 plain points. zero dispose calls anywhere pattern: minimal reasoning, minimal code, minimal price. it nails the flashy requirements – shaders, transmissive glass – and quietly drops the boring ones: instancing, controls, cleanup. you get a demo that mostly runs and a control scheme you can't trust follow thehype. for 24/7 ai news, analysis and breakdowns

meta muse spark 1.1 vs gpt 5.6 sol vs fable 5 vs grok 4.5 meta recently dropped muse spark 1.1 – a multimodal reasoning model from meta superintelligence labs built for agentic tasks. key facts: • 1m token context with active self-management – the model compacts its own history and keeps only the steps needed for later work • trained to orchestrate multi-agent systems: as main agent it plans and delegates to parallel subagents, as subagent it sticks to its job and knows when to escalate back • computer use trained to pick between scripting and clicking – writes automation when it's faster, clicks when it's simpler, batches actions per step • first public api from meta: the meta model api is now in preview • benchmarks: sweeps the agent column – mcp atlas 88.1 (opus 4.8: 82.2), jobbench 54.7 (opus: 48.4), humanity's last exam 62.1 (1st). loses coding – deepswe 1.1 53.3 vs gpt 5.5's 67.0, swe bench pro 61.5 vs opus's 69.2 our test – 3 prompts, single-file html, three.js, fully procedural, no assets: 1. norwegian house cantilevered over a fjord in a snowstorm – transmissive glass wall, fully modelled interior 2. beijing siheyuan courtyard house in dawn fog – instanced roof tiles, dougong brackets, glowing paper windows 3. new mexico adobe pueblo in an approaching dust storm – deep window reveals, windward grit accumulation we ran the test on AI/ML API platform results: - cost #1 muse spark 1.1 – $0.20 #2 grok 4.5 – $0.51 #3 gpt 5.6 sol – $1.93 #4 fable 5 – ~$5.20 - output tokens #1 muse spark 1.1 – 41,868 #2 gpt 5.6 sol – 49,139 #3 grok 4.5 – 64,954 #4 fable 5 – 81,849 - lines of code #1 muse spark 1.1 – 1,799 #2 gpt 5.6 sol – 2,377 #3 fable 5 – 3,088 #4 grok 4.5 – 4,216 observations: • muse spark is the cheapest of the four by a wide margin – 2.5x under grok, ~26x under fable per run. output quality tracks the price • only 7.4% of its output tokens are reasoning (3,104 of 41,868) – the model barely thinks before writing. economic, not pedantic: it commits to the first plan and ships it • the low loc is not compression, it's omission – all three prompts demanded instancing, muse spark delivered it in one muse spark's code quality – reviewed by fable 5: upsides: 1. all three files run 2. the adobe grit effect is legit – shader injection via onbeforecompile, windward faces detect storm direction through a normal-dot-wind term and darken procedurally 3. the fjord glass is real meshphysicalmaterial with transmission and ior, not a transparent quad 4. the siheyuan properly instances barrel tiles, dougong blocks and courtyard pavers downsides: 1. in the fjord file the strafe vector is negated – press a, you move right; press d, you move left. exactly the key mix-up we kept hitting with this model 2. all three files ship the model's self-doubt as comments: "// actually yaw orientation: need correct" sits above a direction vector that gets computed, abandoned and recomputed – dead vectors allocated every frame, 60 times a second 3. the siheyuan registers two separate keydown listeners, one containing an empty if-block 4. snow "accumulation" on the norway roof is a sine wobble on a scale value, not accumulation 5. "instanced snow" became 3,500 plain points. zero dispose calls anywhere pattern: minimal reasoning, minimal code, minimal price. it nails the flashy requirements – shaders, transmissive glass – and quietly drops the boring ones: instancing, controls, cleanup. you get a demo that mostly runs and a control scheme you can't trust follow thehype. for 24/7 ai news, analysis and breakdowns

thehype.

132,390 Aufrufe • vor 9 Tagen

🚀 Today, we are introducing SmolTools! 🚀 Last week, at Hugging Face we made a significant leap forward with the release of SmolLM2, a compact 1.7B language model that sets a new benchmark for performance among models of its size. But beyond the impressive stats, SmolLM2 truly shines in practical, real-world applications, unlocking new possibilities for on-device inference. To demonstrate its potential, we're thrilled to introduce Smol-Tools, a suite of simple yet powerful applications that showcase the capabilities of SmolLM2. 🌟 Today, we present you two key tools: Summarize and Rewrite. 🔹 Summarize: Feed SmolLM2 a text up to 20 pages long, and it will provide you with a concise summary. Need to dig deeper? Just ask follow-up questions to clarify any details – it's that simple. 🔹 Rewrite: Draft a response to a message with your main points, and SmolLM2 will transform it into a clear, polished version that's easy for your recipient to read and understand. These tools are designed to make your workflow smoother and more efficient, leveraging the power of SmolLM2 in practical ways. 💡We hope you give these tools a try and see their potential for yourself! Feel free to build your own SmolTools and contribute them to the project. We'd love to see what you build! Check out the code here:

🚀 Today, we are introducing SmolTools! 🚀 Last week, at Hugging Face we made a significant leap forward with the release of SmolLM2, a compact 1.7B language model that sets a new benchmark for performance among models of its size. But beyond the impressive stats, SmolLM2 truly shines in practical, real-world applications, unlocking new possibilities for on-device inference. To demonstrate its potential, we're thrilled to introduce Smol-Tools, a suite of simple yet powerful applications that showcase the capabilities of SmolLM2. 🌟 Today, we present you two key tools: Summarize and Rewrite. 🔹 Summarize: Feed SmolLM2 a text up to 20 pages long, and it will provide you with a concise summary. Need to dig deeper? Just ask follow-up questions to clarify any details – it's that simple. 🔹 Rewrite: Draft a response to a message with your main points, and SmolLM2 will transform it into a clear, polished version that's easy for your recipient to read and understand. These tools are designed to make your workflow smoother and more efficient, leveraging the power of SmolLM2 in practical ways. 💡We hope you give these tools a try and see their potential for yourself! Feel free to build your own SmolTools and contribute them to the project. We'd love to see what you build! Check out the code here:

Andi Marafioti

100,813 Aufrufe • vor 1 Jahr