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CHINA JUST DROPPED AN AI CODING MODEL WITH A 1M CONTEXT WINDOW. And I connected it to Claude Code to see what it could actually do. Meet GLM-X Preview On paper, a few things immediately stood out: → 1M context window → Agentic coding capabilities → Works inside Claude... Code → Designed for large-scale coding and reasoning workflows But specs don't matter much if the model can't deliver in practice. So I gave it a real-world task. THE TEST One prompt: > Build a modern AI lead generation dashboard using React and Tailwind CSS. Requirements: → Dark mode → Analytics dashboard → Lead table → Email outreach section → Responsive design → Production-ready component structure Instead of generating a few snippets, it planned the architecture, generated the dashboard components, created the Tailwind configuration, and walked through the implementation requirements. What impressed me most wasn't the code itself. It was how well it maintained context throughout the workflow. That's where a 1M context window starts becoming useful. Less time re-explaining requirements. Less context loss. More room for complex projects. The AI coding race is getting very interesting. And it's no longer just GPT, Claude, and Gemini competing for attention. Results from my test below 👇show more

Md Riyazuddin

112,844 subscribers

31,199 次观看 • 1 个月前 •via X (Twitter)

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I've been playing with MiniMax M3 recently. The combination of coding, agent workflows, 1M context, and multimodal understanding made me curious enough to try something real. So I gave it one task: "Build me a Skill Hub to manage all my Claude Code skills."👇

I've been playing with MiniMax M3 recently. The combination of coding, agent workflows, 1M context, and multimodal understanding made me curious enough to try something real. So I gave it one task: "Build me a Skill Hub to manage all my Claude Code skills."👇

ares. 🎧

49,239 次观看 • 2 个月前

$Everyone's sleeping on MiniMax. Again. They just shipped M3. The first open-weights model to combine frontier coding, 1M context, and native multimodality in one drop. I plugged it into Claude Code this morning. Pasted a design from Dribbble. Watched M3 write production-ready React code in one session. At the agency, I just replaced Opus 4.8 with M3 for 80% of our coding tasks. The output is the same and we are running everything at a fraction of the cost. Open infrastructure is the future.$

Everyone's sleeping on MiniMax. Again. They just shipped M3. The first open-weights model to combine frontier coding, 1M context, and native multimodality in one drop. I plugged it into Claude Code this morning. Pasted a design from Dribbble. Watched M3 write production-ready React code in one session. At the agency, I just replaced Opus 4.8 with M3 for 80% of our coding tasks. The output is the same and we are running everything at a fraction of the cost. Open infrastructure is the future.

Prajwal Tomar

12,904 次观看 • 2 个月前

Chinese labs are now competing with each other to build the strongest open-weight model. > DeepSeek started the domino effect > Qwen kept improving across coding, reasoning and long context > GLM, MiniMax and Moonshot joined the race > Kimi K3 pushed it to 2.8T parameters and a 1M context window > now Alibaba is answering with Qwen3.8 > 2.4T parameters > full open-weight release coming soon > Alibaba says it is second only to Fable 5 Qwen3.8 may be the model that brings open-weight AI closest to Claude Fable 5.

Chinese labs are now competing with each other to build the strongest open-weight model. > DeepSeek started the domino effect > Qwen kept improving across coding, reasoning and long context > GLM, MiniMax and Moonshot joined the race > Kimi K3 pushed it to 2.8T parameters and a 1M context window > now Alibaba is answering with Qwen3.8 > 2.4T parameters > full open-weight release coming soon > Alibaba says it is second only to Fable 5 Qwen3.8 may be the model that brings open-weight AI closest to Claude Fable 5.

Tornado guy

65,117 次观看 • 22 天前

Claude Code is a major (and accidental!) hit for Anthropic that surprised even its creator, Boris Cherny. Claude Code, an Agentic AI coding product that lives in the terminal. Most of the new code at Anthropic is created through it today. And in the last 5 months since it was launched publicly, Claude Code went from $0 to $400M in revenue run rate (as per The Information). 00:00 – Intro 01:15 – Did You Expect Claude Code’s Success? 04:22 – How Claude Code Works and Origins 08:05 – Command Line vs IDE: Why Start Claude Code in the Terminal? 11:31 – The Evolution of Programming: From Punch Cards to Agents 13:20 – Product Follows Model: Simple Interfaces and Fast Evolution 15:17 – Who Is Claude Code For? (Engineers, Designers, PMs & More) 17:46 – What Can Claude Code Actually Do? (Actions & Capabilities) 21:14 – Agentic Actions, Subagents, and Workflows 25:30 – Claude Code’s Awareness, Memory, and Knowledge Sharing 33:28 – Model Context Protocol (MCP) and Customization 35:30 – Safety, Human Oversight, and Enterprise Considerations 38:10 – UX/UI: Making Claude Code Useful and Enjoyable 40:44 – Pricing for Power Users and Subscription Models 43:36 – Real-World Use Cases: Debugging, Testing, and More 46:44 – How Does Claude Code Transform Onboarding? 49:36 – The Future of Coding: Agents, Teams, and Collaboration 54:11 – The AI Coding Wars: Competition & Ecosystem 57:27 – The Future of Coding as a Profession 58:41 – What’s Next for Claude Code

Claude Code is a major (and accidental!) hit for Anthropic that surprised even its creator, Boris Cherny. Claude Code, an Agentic AI coding product that lives in the terminal. Most of the new code at Anthropic is created through it today. And in the last 5 months since it was launched publicly, Claude Code went from $0 to $400M in revenue run rate (as per The Information). 00:00 – Intro 01:15 – Did You Expect Claude Code’s Success? 04:22 – How Claude Code Works and Origins 08:05 – Command Line vs IDE: Why Start Claude Code in the Terminal? 11:31 – The Evolution of Programming: From Punch Cards to Agents 13:20 – Product Follows Model: Simple Interfaces and Fast Evolution 15:17 – Who Is Claude Code For? (Engineers, Designers, PMs & More) 17:46 – What Can Claude Code Actually Do? (Actions & Capabilities) 21:14 – Agentic Actions, Subagents, and Workflows 25:30 – Claude Code’s Awareness, Memory, and Knowledge Sharing 33:28 – Model Context Protocol (MCP) and Customization 35:30 – Safety, Human Oversight, and Enterprise Considerations 38:10 – UX/UI: Making Claude Code Useful and Enjoyable 40:44 – Pricing for Power Users and Subscription Models 43:36 – Real-World Use Cases: Debugging, Testing, and More 46:44 – How Does Claude Code Transform Onboarding? 49:36 – The Future of Coding: Agents, Teams, and Collaboration 54:11 – The AI Coding Wars: Competition & Ecosystem 57:27 – The Future of Coding as a Profession 58:41 – What’s Next for Claude Code

Matt Turck

82,161 次观看 • 1 年前

🚨 Do you realize what just happened in the world of AI coding... This guy found a workaround that turns GLM 5.1 into a real Claude Code Killer, and now it’s completely UNLIMITED and FREE. While Claude chokes you with limits, pushes subscriptions, and sometimes just refuses complex tasks... GLM 5.1 absolutely destroys it.Why GLM 5.1 is better than Claude: > Much more powerful and accurate in complex coding and system architecture > Superior logical reasoning with almost zero hallucinations > Holds massive context and doesn’t “forget” your requirements > Delivers clean, production-ready code much faster > No limits, no payments, just pure unlimited power Most developers are still paying and struggling with Claude… and have no idea that the bypass already exists. If you’re still stuck with limits, the game just changed.

🚨 Do you realize what just happened in the world of AI coding... This guy found a workaround that turns GLM 5.1 into a real Claude Code Killer, and now it’s completely UNLIMITED and FREE. While Claude chokes you with limits, pushes subscriptions, and sometimes just refuses complex tasks... GLM 5.1 absolutely destroys it.Why GLM 5.1 is better than Claude: > Much more powerful and accurate in complex coding and system architecture > Superior logical reasoning with almost zero hallucinations > Holds massive context and doesn’t “forget” your requirements > Delivers clean, production-ready code much faster > No limits, no payments, just pure unlimited power Most developers are still paying and struggling with Claude… and have no idea that the bypass already exists. If you’re still stuck with limits, the game just changed.

Bober_smart

57,255 次观看 • 4 个月前

🚨 This Chinese model is INSANE !! I just tested Tencent's Hunyuan AI model to build a professional landing page & the output is mind blowing 🤯 Here's my exact prompt: "Create a modern landing page for a crypto related product with beautiful hero section, 3d elements, pricing table, and a whitelist form" What it generated: → Clean, responsive front-end code → Complex UI components with smooth interactions → Built-in debugging (it fixed its own errors) → Production-ready in minutes This 295B parameter model has 2 insane agents: → Coding Agent (handles the entire front-end) → Working Agent (task planning & multi-tool workflows) It also has a context window of 256k tokens !! Watch the video to see the full build 🔥 Try it: Tencent Hy

🚨 This Chinese model is INSANE !! I just tested Tencent's Hunyuan AI model to build a professional landing page & the output is mind blowing 🤯 Here's my exact prompt: "Create a modern landing page for a crypto related product with beautiful hero section, 3d elements, pricing table, and a whitelist form" What it generated: → Clean, responsive front-end code → Complex UI components with smooth interactions → Built-in debugging (it fixed its own errors) → Production-ready in minutes This 295B parameter model has 2 insane agents: → Coding Agent (handles the entire front-end) → Working Agent (task planning & multi-tool workflows) It also has a context window of 256k tokens !! Watch the video to see the full build 🔥 Try it: Tencent Hy

AGAFE

110,528 次观看 • 1 个月前

I tested a new coding assistant on 34,568 lines of code. (Cursor could not achieve what this tool did.) AI models are improving every day: - Faster inference - Longer context lengths But are AI coding assistants advancing at the same rate? Can they truly understand your entire project? I tested Augment Code on my ai-engineering repo with ~35k lines of code. Augment Code is a powerful AI Assistant built for developers working with large, evolving codebases—needing an assistant that understands the full context of their projects. In the video demo below, I asked it to: - Merge two projects. - Answer a global context question. Here’s what happened: - It understood dependencies across both projects. - It merged them intelligently—without breaking anything. - It even created a README file for the merged project. - It answered my global query instantly, pulling from the full codebase. This is the difference between AI autocomplete and an AI engineer that truly understands your repo. Augment Code is powerful because it indexes your entire repo upfront. This way, it can answer questions instantly, no matter how large your project is. Lastly, Augment Code is fully compatible with VSCode, JetBrains, Vim, Slack, and more. Try them now, I have shared a link in the next tweet! Thanks to Augment Code for showing me their powerful AI coding assistant and working with me on this post.

I tested a new coding assistant on 34,568 lines of code. (Cursor could not achieve what this tool did.) AI models are improving every day: - Faster inference - Longer context lengths But are AI coding assistants advancing at the same rate? Can they truly understand your entire project? I tested Augment Code on my ai-engineering repo with ~35k lines of code. Augment Code is a powerful AI Assistant built for developers working with large, evolving codebases—needing an assistant that understands the full context of their projects. In the video demo below, I asked it to: - Merge two projects. - Answer a global context question. Here’s what happened: - It understood dependencies across both projects. - It merged them intelligently—without breaking anything. - It even created a README file for the merged project. - It answered my global query instantly, pulling from the full codebase. This is the difference between AI autocomplete and an AI engineer that truly understands your repo. Augment Code is powerful because it indexes your entire repo upfront. This way, it can answer questions instantly, no matter how large your project is. Lastly, Augment Code is fully compatible with VSCode, JetBrains, Vim, Slack, and more. Try them now, I have shared a link in the next tweet! Thanks to Augment Code for showing me their powerful AI coding assistant and working with me on this post.

Akshay 🚀

30,856 次观看 • 1 年前

I have been testing DeepSeek-V4-Pro with the Pi coding agent. I am mindblown by how well it works out of the box. A few notes: I spent a few hours building an LLM wiki with an agent powered entirely by DeepSeek-V4-Pro on Fireworks inference. This is the first time I feel like there is an open-weight model that can reason at the level of Claude and Codex. And it does this in a cost-effective way with support for 1M context length. To be clear, I am using DeepSeek-V4-Pro inside of Pi without any special configuration. It works out of the box. It's exciting that there is a model that can just be plugged into a basic harness like Pi, and it just works. I've never seen that before. Most models require lots of configuration and setup. DeepSeek's DeepSeek-V4-Pro is clearly good at agentic coding (probably the best from the open-weight models), but the model is also great on knowledge-intensive tasks where reasoning matters. The agent pulled agentic engineering best practices from different company docs (Anthropic, OpenAI, Google, Stripe, Meta, Modal, DeepSeek, Mistral, Cohere), searched and digested Reddit and HN threads, summarized arxiv papers, and surfaced trending GitHub repos. Then it distilled everything into actionable tips across categories. I love the Wiki it built. The quality is really good. Here is a snapshot of what the wiki looks like: DeepSeek-V4-Pro handled the task without breaking stride. Multi-step research queries, code generation for scaffolding, context-heavy reasoning across disparate sources. For coding specifically, this is the first open-weight model that genuinely feels like a Codex or Claude Code experience. It compares in capability and actual multi-turn agentic work. What made the loop feel so responsive was Fireworks' inference speed (the fastest in the market) and the fact that they actually validate models at the systems level before shipping. No corrupted reasoning traces. Just fast, reliable iteration. The hybrid CSA and HCA attention design cuts KV cache to just 10% and inference FLOPs by nearly 4x at 1M-token context. This is what makes the agent loop actually fast and cheap enough to run in practice. For devs who've been watching open-weight models close the gap but haven't found one that actually delivers in practice, this is the closest I've seen. Try it here:

I have been testing DeepSeek-V4-Pro with the Pi coding agent. I am mindblown by how well it works out of the box. A few notes: I spent a few hours building an LLM wiki with an agent powered entirely by DeepSeek-V4-Pro on Fireworks inference. This is the first time I feel like there is an open-weight model that can reason at the level of Claude and Codex. And it does this in a cost-effective way with support for 1M context length. To be clear, I am using DeepSeek-V4-Pro inside of Pi without any special configuration. It works out of the box. It's exciting that there is a model that can just be plugged into a basic harness like Pi, and it just works. I've never seen that before. Most models require lots of configuration and setup. DeepSeek's DeepSeek-V4-Pro is clearly good at agentic coding (probably the best from the open-weight models), but the model is also great on knowledge-intensive tasks where reasoning matters. The agent pulled agentic engineering best practices from different company docs (Anthropic, OpenAI, Google, Stripe, Meta, Modal, DeepSeek, Mistral, Cohere), searched and digested Reddit and HN threads, summarized arxiv papers, and surfaced trending GitHub repos. Then it distilled everything into actionable tips across categories. I love the Wiki it built. The quality is really good. Here is a snapshot of what the wiki looks like: DeepSeek-V4-Pro handled the task without breaking stride. Multi-step research queries, code generation for scaffolding, context-heavy reasoning across disparate sources. For coding specifically, this is the first open-weight model that genuinely feels like a Codex or Claude Code experience. It compares in capability and actual multi-turn agentic work. What made the loop feel so responsive was Fireworks' inference speed (the fastest in the market) and the fact that they actually validate models at the systems level before shipping. No corrupted reasoning traces. Just fast, reliable iteration. The hybrid CSA and HCA attention design cuts KV cache to just 10% and inference FLOPs by nearly 4x at 1M-token context. This is what makes the agent loop actually fast and cheap enough to run in practice. For devs who've been watching open-weight models close the gap but haven't found one that actually delivers in practice, this is the closest I've seen. Try it here:

elvis

59,974 次观看 • 3 个月前

Most AI demos stop at text prompts. I wanted to see what Qwen3.7-Plus could do beyond chat, so I tested it through the API using an Airbnb interface screenshot. The model analyzed the UI, identified key components, explained the visual hierarchy, highlighted usability strengths, suggested UX improvements, and even generated a React + Tailwind structure to recreate the interface. A few things that stood out: 👁️ Multimodal understanding from a single screenshot 🧠 Strong reasoning around UX and product design decisions 💻 Able to bridge visual inputs and code generation ⚡ OpenAI-compatible API that was easy to integrate 💰 Impressive capability-to-cost ratio Qwen3.7-Plus isn't just a chatbot. It's a multimodal agent model designed for workflows that combine vision, reasoning, coding, and automation. Try Qwen3.7-Plus with limited time 20% off:

Most AI demos stop at text prompts. I wanted to see what Qwen3.7-Plus could do beyond chat, so I tested it through the API using an Airbnb interface screenshot. The model analyzed the UI, identified key components, explained the visual hierarchy, highlighted usability strengths, suggested UX improvements, and even generated a React + Tailwind structure to recreate the interface. A few things that stood out: 👁️ Multimodal understanding from a single screenshot 🧠 Strong reasoning around UX and product design decisions 💻 Able to bridge visual inputs and code generation ⚡ OpenAI-compatible API that was easy to integrate 💰 Impressive capability-to-cost ratio Qwen3.7-Plus isn't just a chatbot. It's a multimodal agent model designed for workflows that combine vision, reasoning, coding, and automation. Try Qwen3.7-Plus with limited time 20% off:

Kuria | AI

17,044 次观看 • 2 个月前

I've been using Claude Code for non-coding tasks, like sorting emails and analyzing my spending This was annoying to setup because Claude doesn’t have the right context - it can’t see my email or my credit card statements So we built a virtual filesystem that turns all of my data sources (gmail, notion, gdrive and more) into local folders on my computer I can just describe what I need (e.g. "invoices from the last week") and a folder appears on my computer with exactly those files This gives Claude context, but it also gives it memory - since it has a local filesystem that syncs in the background, it continuously gets access to my data The more things you expose as files, the more Claude can do for you

I've been using Claude Code for non-coding tasks, like sorting emails and analyzing my spending This was annoying to setup because Claude doesn’t have the right context - it can’t see my email or my credit card statements So we built a virtual filesystem that turns all of my data sources (gmail, notion, gdrive and more) into local folders on my computer I can just describe what I need (e.g. "invoices from the last week") and a folder appears on my computer with exactly those files This gives Claude context, but it also gives it memory - since it has a local filesystem that syncs in the background, it continuously gets access to my data The more things you expose as files, the more Claude can do for you

Eli Mernit

67,006 次观看 • 6 个月前

A DEVELOPER CONNECTED CLAUDE CODE TO OBSIDIAN SO HIS AI AGENT WOULD STOP FORGETTING THE PROJECT EVERY MORNING. Every coding session used to start the same way. Claude would understand the repo, fix the bug, explain the architecture, and then the moment the session ended, all of that context disappeared. Same codebase. Same decisions. Same architecture. Same mistakes repeated again. So he added a memory layer. Instead of treating Claude Code like a smart terminal, he connected it to a local Obsidian vault through MCP. Now Claude can read the repo, open the vault, create notes, link concepts, and write important decisions back into the system. When it studies the codebase, it does not just answer once and forget. It creates notes for the major services, maps how the architecture works, links auth to the database, connects APIs to storage, and records why certain migrations or design choices exist. Obsidian becomes the project graph. Now when he asks why something was built a certain way, Claude does not guess from the current prompt. It reads the decision notes. When he starts a new branch, Claude checks the active context file. When the work is done, it updates what changed, what is blocked, and what the next agent needs to know before touching the repo. That is the real loop: read context, write code, capture decisions, update memory. Most people are still using AI coding tools like disposable chat windows. Ask, patch, close, forget. This setup turns Claude Code into infrastructure. The repo gets a memory layer that survives every session, and multiple AI agents can work from the same project map without stepping on each other. The unlock is not better prompting. The unlock is giving the agent somewhere to remember what it already learned.

A DEVELOPER CONNECTED CLAUDE CODE TO OBSIDIAN SO HIS AI AGENT WOULD STOP FORGETTING THE PROJECT EVERY MORNING. Every coding session used to start the same way. Claude would understand the repo, fix the bug, explain the architecture, and then the moment the session ended, all of that context disappeared. Same codebase. Same decisions. Same architecture. Same mistakes repeated again. So he added a memory layer. Instead of treating Claude Code like a smart terminal, he connected it to a local Obsidian vault through MCP. Now Claude can read the repo, open the vault, create notes, link concepts, and write important decisions back into the system. When it studies the codebase, it does not just answer once and forget. It creates notes for the major services, maps how the architecture works, links auth to the database, connects APIs to storage, and records why certain migrations or design choices exist. Obsidian becomes the project graph. Now when he asks why something was built a certain way, Claude does not guess from the current prompt. It reads the decision notes. When he starts a new branch, Claude checks the active context file. When the work is done, it updates what changed, what is blocked, and what the next agent needs to know before touching the repo. That is the real loop: read context, write code, capture decisions, update memory. Most people are still using AI coding tools like disposable chat windows. Ask, patch, close, forget. This setup turns Claude Code into infrastructure. The repo gets a memory layer that survives every session, and multiple AI agents can work from the same project map without stepping on each other. The unlock is not better prompting. The unlock is giving the agent somewhere to remember what it already learned.

DegenCalls

20,124 次观看 • 1 个月前

Boris Cherny, the creator of Claude Code, just explained why the features most users never find are the ones that matter most in this podcast he breaks down not just how to use Claude, but what it all means for the next few years: > the right way to give Claude context before it starts working > why giving it too much information is just as bad as too little > how Claude Code is already changing how engineers build things > how the people who built Claude actually use it day to day based on this podcast and months of using Claude myself, I put together a guide on everything Claude can do that most people have no idea exists you can find it below

Boris Cherny, the creator of Claude Code, just explained why the features most users never find are the ones that matter most in this podcast he breaks down not just how to use Claude, but what it all means for the next few years: > the right way to give Claude context before it starts working > why giving it too much information is just as bad as too little > how Claude Code is already changing how engineers build things > how the people who built Claude actually use it day to day based on this podcast and months of using Claude myself, I put together a guide on everything Claude can do that most people have no idea exists you can find it below

Anatoli Kopadze

22,474 次观看 • 2 个月前

Most discussions around Claude Code focus on model quality or capability. But when you actually use it, the friction shows up earlier, more at the system level than the model itself. Token limits get the blame, but a lot of it comes from inefficient sessions: repeated context, extra tool calls, and small loops that quietly stack up. So it starts to feel less like “is the model good enough?” and more like “is the workflow slowing it down?” That’s where WozCode comes in. It helps streamline sessions, less repetition, smarter batching of tool calls, and tighter context usage so you can actually get more work done within the same limits. Woz just launched on Product Hunt. If Claude Code felt a bit restrictive before, it’s worth trying again with a cleaner workflow setup. Same model. Smoother execution. Upvote here:

Most discussions around Claude Code focus on model quality or capability. But when you actually use it, the friction shows up earlier, more at the system level than the model itself. Token limits get the blame, but a lot of it comes from inefficient sessions: repeated context, extra tool calls, and small loops that quietly stack up. So it starts to feel less like “is the model good enough?” and more like “is the workflow slowing it down?” That’s where WozCode comes in. It helps streamline sessions, less repetition, smarter batching of tool calls, and tighter context usage so you can actually get more work done within the same limits. Woz just launched on Product Hunt. If Claude Code felt a bit restrictive before, it’s worth trying again with a cleaner workflow setup. Same model. Smoother execution. Upvote here:

Parul Gautam

15,079 次观看 • 3 个月前

I just found a way to use Claude Code completely free. Claude Code is Anthropic’s terminal coding agent that usually needs a paid API key to run. But a viral GitHub project called Free Claude Code, now sitting at 37k stars, lets you skip the billing entirely. It works as a tiny proxy that sits between Claude Code and free model providers like NVIDIA NIM, OpenRouter, DeepSeek, and Gemini. Every request Claude Code would send to Anthropic gets quietly rerouted to a free model on the other side. The interface stays the same. What’s actually thinking behind it changes. Setup takes a couple of quick commands and a free API key from NVIDIA NIM or OpenRouter. Paste it into the local dashboard, pick a model, and you’re building. The catch is you’re not really using Claude anymore. But when the code ships, does the model badge matter? -- I have shared the Github Link in my free Whatsapp Community ↓

I just found a way to use Claude Code completely free. Claude Code is Anthropic’s terminal coding agent that usually needs a paid API key to run. But a viral GitHub project called Free Claude Code, now sitting at 37k stars, lets you skip the billing entirely. It works as a tiny proxy that sits between Claude Code and free model providers like NVIDIA NIM, OpenRouter, DeepSeek, and Gemini. Every request Claude Code would send to Anthropic gets quietly rerouted to a free model on the other side. The interface stays the same. What’s actually thinking behind it changes. Setup takes a couple of quick commands and a free API key from NVIDIA NIM or OpenRouter. Paste it into the local dashboard, pick a model, and you’re building. The catch is you’re not really using Claude anymore. But when the code ships, does the model badge matter? -- I have shared the Github Link in my free Whatsapp Community ↓

Vaibhav Sisinty

36,744 次观看 • 1 个月前

Introducing Hexclave, the platform for apps with users. Customer infrastructure is now one prompt. Build your frontend, backend, and database, and Hexclave takes care of the rest: - Analytics - Authentication - Emails - Payments - ...and more 👇 see the thread below! Hexclave is open-source and natively built for AI coding agents. Just ask Claude to send an email, restrict a user, or generate a dashboard, and it will happily comply. [1/6 👇👇]

Introducing Hexclave, the platform for apps with users. Customer infrastructure is now one prompt. Build your frontend, backend, and database, and Hexclave takes care of the rest: - Analytics - Authentication - Emails - Payments - ...and more 👇 see the thread below! Hexclave is open-source and natively built for AI coding agents. Just ask Claude to send an email, restrict a user, or generate a dashboard, and it will happily comply. [1/6 👇👇]

Konsti Wohlwend

26,952 次观看 • 1 个月前

I plugged MiniMax (official) M3 into Claude Code in under 5 minutes and gave it a real task. most models need hand-holding every few steps. M3 ran 147 iterations on its own. what it actually did: → took a broken CUDA kernel → ran it for 24 hours, autonomously → pushed hardware efficiency from 7.6% to 71.3% → shipped a 9.4x speedup I didn't touch it once across that whole window. the rest of the spec sheet: → 1M token context window → native multimodal from day one → fully open weights pricing: → $20/month for 1.7B tokens → $50/month for 5.1B tokens → text, image, speech, and music all share one pool usually with this kind of capability the bill catches up fast. M3 keeps both lines low at the same time. that's the part that surprised me. and if this is what M3 does at launch, the full rollout is going to be one to watch. what would you build with 1M context? #MiniMax #MiniMaxM3 #ClaudeCode #LLM

I plugged MiniMax (official) M3 into Claude Code in under 5 minutes and gave it a real task. most models need hand-holding every few steps. M3 ran 147 iterations on its own. what it actually did: → took a broken CUDA kernel → ran it for 24 hours, autonomously → pushed hardware efficiency from 7.6% to 71.3% → shipped a 9.4x speedup I didn't touch it once across that whole window. the rest of the spec sheet: → 1M token context window → native multimodal from day one → fully open weights pricing: → $20/month for 1.7B tokens → $50/month for 5.1B tokens → text, image, speech, and music all share one pool usually with this kind of capability the bill catches up fast. M3 keeps both lines low at the same time. that's the part that surprised me. and if this is what M3 does at launch, the full rollout is going to be one to watch. what would you build with 1M context? #MiniMax #MiniMaxM3 #ClaudeCode #LLM

Nelly;

17,576 次观看 • 2 个月前

someone just rebuilt claude code in pure python and made it ~230x cheaper to run it's called clawcodex. a full from-scratch port of the claude code agent loop. 230k+ lines of python, MIT licensed it keeps your request prefix byte-stable, so DeepSeek's prompt cache covers your entire system + tools + history on every turn cache-hit input then bills at ~$0.0435 per 1M tokens the same 1M tokens on Claude Fable 5 runs you $10 so the longer you code, the more the cache pays off what you actually get: → 25 LLM providers (Claude, GPT, DeepSeek, GLM, local ollama) → 1M token context window → real agent loop: tools, skills, REPL, session history → one-line install, mac/linux/wsl and it's sitting at 677 stars. basically nobody is looking yet bookmark this before it gone

someone just rebuilt claude code in pure python and made it ~230x cheaper to run it's called clawcodex. a full from-scratch port of the claude code agent loop. 230k+ lines of python, MIT licensed it keeps your request prefix byte-stable, so DeepSeek's prompt cache covers your entire system + tools + history on every turn cache-hit input then bills at ~$0.0435 per 1M tokens the same 1M tokens on Claude Fable 5 runs you $10 so the longer you code, the more the cache pays off what you actually get: → 25 LLM providers (Claude, GPT, DeepSeek, GLM, local ollama) → 1M token context window → real agent loop: tools, skills, REPL, session history → one-line install, mac/linux/wsl and it's sitting at 677 stars. basically nobody is looking yet bookmark this before it gone

Argona

51,369 次观看 • 1 个月前

I finally caved to the hype. Here's my attempt at #vibecoding with *zero* manual coding. I gave myself 30 minutes to build a 3D RPG with #threejs using Cursor + Claude 3.7-sonnet. I'm honestly impressed with what it was able to generate: - Infinite terrain generation/chunking - Trees, rocks and bushes - NPC manager - Skeletons and goblins - Health bars - Combat mechanics (this is where it broke down) It only ended up failing because Cursor got confused about which files to update and I ran out of time. The code it was generating still looked solid, so I'm guessing I was hitting the limits of the context window.

I finally caved to the hype. Here's my attempt at #vibecoding with zero manual coding. I gave myself 30 minutes to build a 3D RPG with #threejs using Cursor + Claude 3.7-sonnet. I'm honestly impressed with what it was able to generate: - Infinite terrain generation/chunking - Trees, rocks and bushes - NPC manager - Skeletons and goblins - Health bars - Combat mechanics (this is where it broke down) It only ended up failing because Cursor got confused about which files to update and I ran out of time. The code it was generating still looked solid, so I'm guessing I was hitting the limits of the context window.

Dan Greenheck

44,797 次观看 • 1 年前

A new short course, Claude Code: A Highly Agentic Coding Assistant, is live! Claude Code is currently one of the most capable coding assistants. It can explore your codebase, plan features, write tests, refactor code, and even collaborate across multiple sessions—with surprisingly minimal input. In this course, you’ll learn how to guide Claude Code effectively: from setting up context and memory to integrating with GitHub and MCP servers. You’ll use it to extend a RAG chatbot, refactor a Jupyter notebook for e-commerce data analysis, build a web app from a Figma design, and more. Taught by Elie Schoppik (Elie Schoppik) and built in collaboration with Anthropic, this course is a must for AI builders. 👉 Enroll now:

A new short course, Claude Code: A Highly Agentic Coding Assistant, is live! Claude Code is currently one of the most capable coding assistants. It can explore your codebase, plan features, write tests, refactor code, and even collaborate across multiple sessions—with surprisingly minimal input. In this course, you’ll learn how to guide Claude Code effectively: from setting up context and memory to integrating with GitHub and MCP servers. You’ll use it to extend a RAG chatbot, refactor a Jupyter notebook for e-commerce data analysis, build a web app from a Figma design, and more. Taught by Elie Schoppik (Elie Schoppik) and built in collaboration with Anthropic, this course is a must for AI builders. 👉 Enroll now:

DeepLearning.AI

32,513 次观看 • 1 年前

Most AI demos start with a prompt. I started with an entire startup. I gave MiniMax M3: • product docs • pricing • customer feedback • roadmap • competitor research Then dropped everything into Cursor and asked it to build the product. What impressed me wasn't the code. It was how well M3 connected information across all the documents and turned them into actual product decisions. Feels less like "AI coding" and more like working with a junior product + engineering team.

Most AI demos start with a prompt. I started with an entire startup. I gave MiniMax M3: • product docs • pricing • customer feedback • roadmap • competitor research Then dropped everything into Cursor and asked it to build the product. What impressed me wasn't the code. It was how well M3 connected information across all the documents and turned them into actual product decisions. Feels less like "AI coding" and more like working with a junior product + engineering team.

Elias

21,729 次观看 • 2 个月前