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i tested a fully local ai coding setup: gemma4 from Google DeepMind running through LM Studio, paired with pi which is a minimal open source coding harness this is part 1 of a two part series. here we cover the why and the complete hands-on setup 0:00 - Running...

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Nidhi Singh

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16,289 views • 1 month ago •via X (Twitter)

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Amp — Research Preview

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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 views • 3 months ago

The top question I got was does Halo support local LLM running on a Mac. Here's an example of the app using gemma-4-26b-a4b-qat via LM Studio running on a base M5/32GB. It's giving an average of 40 tok/s and in my brief testing is able to execute all tool calls correctly. Very usable with this app.
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The top question I got was does Halo support local LLM running on a Mac. Here's an example of the app using gemma-4-26b-a4b-qat via LM Studio running on a base M5/32GB. It's giving an average of 40 tok/s and in my brief testing is able to execute all tool calls correctly. Very usable with this app.

Tanmay

11,682 views • 1 month ago

Pi Agent vs OpenCode token usage A lot of people recommended Pi Agent so I decided to check Pi Agent took 1.1k tokens in first turn OpenCode took 11.5k Setup: 1) Trimmed OpenCode (from usual 30k first turn to 11.5k) - 0 MCPs - 2 lightweight plugins (opencode-env-protect and openslimedit) - 8k char AGENTS.md - 11585 deepseek-v4-flash input tokens - for $0.0016 2) Vanilla Pi - 0 MCPs - 0 system prompt - 8k char AGENTS.md - 1114 kimi-k2.6 input tokens - for $0.0008 I think using better models with capped tokens per turn can keep usage nearly the same as uncontrolled DeepSeek V4 Flash? The challenge now is finding the sweet spot. Can't cap tokens if quality drops. We'll see. Video below - OpenCode vs Pi side by side "say hi back" first turns test, with OpenCode Go usage for each
0:21

Pi Agent vs OpenCode token usage A lot of people recommended Pi Agent so I decided to check Pi Agent took 1.1k tokens in first turn OpenCode took 11.5k Setup: 1) Trimmed OpenCode (from usual 30k first turn to 11.5k) - 0 MCPs - 2 lightweight plugins (opencode-env-protect and openslimedit) - 8k char AGENTS.md - 11585 deepseek-v4-flash input tokens - for $0.0016 2) Vanilla Pi - 0 MCPs - 0 system prompt - 8k char AGENTS.md - 1114 kimi-k2.6 input tokens - for $0.0008 I think using better models with capped tokens per turn can keep usage nearly the same as uncontrolled DeepSeek V4 Flash? The challenge now is finding the sweet spot. Can't cap tokens if quality drops. We'll see. Video below - OpenCode vs Pi side by side "say hi back" first turns test, with OpenCode Go usage for each

raymel 👋

201,405 views • 2 months ago

HERMES AGENT + QWEN 3.6 IS CURRENTLY THE STRONGEST LOCAL SETUP MOST PEOPLE CAN RUN. Even if frontier models stopped improving tomorrow, this stack would still cover the majority of real work. Qwen 3.6 (72B and 235B) is officially supported in Hermes and brings: → Strong coding and complex refactoring → Reliable tool calling and multi-step execution → Excellent long context (128k–1M tokens) → Good structured reasoning with /goal workflows → Solid memory and session continuity Paired with Hermes memory, skills, Kanban, cron and tools — you get a fully private, self-improving agent that codes, automates and runs 24/7 without sending data anywhere. You don’t need the latest closed model to build something powerful. This is one of the strongest fully local stacks available right now — and it runs on real hardware.
0:25

HERMES AGENT + QWEN 3.6 IS CURRENTLY THE STRONGEST LOCAL SETUP MOST PEOPLE CAN RUN. Even if frontier models stopped improving tomorrow, this stack would still cover the majority of real work. Qwen 3.6 (72B and 235B) is officially supported in Hermes and brings: → Strong coding and complex refactoring → Reliable tool calling and multi-step execution → Excellent long context (128k–1M tokens) → Good structured reasoning with /goal workflows → Solid memory and session continuity Paired with Hermes memory, skills, Kanban, cron and tools — you get a fully private, self-improving agent that codes, automates and runs 24/7 without sending data anywhere. You don’t need the latest closed model to build something powerful. This is one of the strongest fully local stacks available right now — and it runs on real hardware.

YanXbt

53,423 views • 1 month ago

I loved talking to martin_casado on Uncapped. We talked about where venture capital is heading, his focus area of infrastructure, AI coding and open source, and more. I learned a ton from this one. Timestamps: (0:00) Intro (0:27) Importance of media in VC (3:50) Evolution of a16z (7:00) The case for specialization (10:32) Value of distribution (13:16) Why infrastructure is so valuable (19:49) The dynamics around conflicts (26:32) State of play in AI (30:48) The future of coding (34:58) Significance of open source (39:48) Marc Andreessen’s leadership (44:02) The only sin in VC (48:37) Scaling a lot of board seats
52:10

I loved talking to martin_casado on Uncapped. We talked about where venture capital is heading, his focus area of infrastructure, AI coding and open source, and more. I learned a ton from this one. Timestamps: (0:00) Intro (0:27) Importance of media in VC (3:50) Evolution of a16z (7:00) The case for specialization (10:32) Value of distribution (13:16) Why infrastructure is so valuable (19:49) The dynamics around conflicts (26:32) State of play in AI (30:48) The future of coding (34:58) Significance of open source (39:48) Marc Andreessen’s leadership (44:02) The only sin in VC (48:37) Scaling a lot of board seats

Jack Altman

124,413 views • 11 months ago

A lot of people are calling Hermes Agent the end of OpenClaw. BRUH! It's not... Nous Research trains actual models and they built an agent around that expertise. The local model routing is solid, but the part that matters for your business is that your conversations become fine-tuning data. You can train a model on how you actually work. 00:00 The Problem with Local AI Models 00:25 Introduction to Nous Research 01:04 Cross-Platform Agent Capabilities 01:44 Deep Local Model Integration 02:30 Routing Tasks to Different Models 03:06 Conversations as Training Data 03:50 Hermes Agent vs. OpenClaw 04:15 Future Plans and Series Overview
4:45

A lot of people are calling Hermes Agent the end of OpenClaw. BRUH! It's not... Nous Research trains actual models and they built an agent around that expertise. The local model routing is solid, but the part that matters for your business is that your conversations become fine-tuning data. You can train a model on how you actually work. 00:00 The Problem with Local AI Models 00:25 Introduction to Nous Research 01:04 Cross-Platform Agent Capabilities 01:44 Deep Local Model Integration 02:30 Routing Tasks to Different Models 03:06 Conversations as Training Data 03:50 Hermes Agent vs. OpenClaw 04:15 Future Plans and Series Overview

Ray Fernando

42,398 views • 4 months ago

Sonnet 4.5 & the AI Plateau Myth — epic conversation with Sholto Douglas of Anthropic 0:00 - Intro 1:09 - What's Behind The Rapid Pace of AI Releases at Anthropic 2:49 - Opus, Sonnet, and Haiku Model Tiers 4:14 - Sholto's Story: From Australian Fencer to AI Researcher 12:01 - The YouTube Effect: Mastery Through Observation 16:16 - Breaking Into AI Research Without Traditional Academic Signals 18:29 - DeepMind, Gemini, and Building Inference Stacks 23:05 - Why Anthropic? Culture and Mission Differences Amongst AI Research Labs 25:08 - What Is "Taste" in AI Research? 31:46 - This Week's Big Launch: Sonnet 4.5, Best Coding Model in the World 36:40 - From 7 Hours to 30 Hours: The Long-Running AI Agent Breakthrough 38:41 - How AI Agents Self-Correct and Maintain Coherence 43:13 - The Role of Memory in Extended Coding Sessions 47:42 - Pre-Training vs. RL: Textbooks vs. Worked Problems 52:11 - Test-Time Compute & Reinforcement Learning 55:55 - Why RL Finally Started Working on LLMs in 2024 59:38 - The Path to AGI 1:02:05 - Are We Hitting a Plateau in AI? So Many Low Hanging Fruits 1:03:41 - Beyond Coding: GDPVal & Impact Economic Sectors 1:05:47 - Preparing for 10-100x Individual Leverage & The Upcoming Robotics Explosion
1:10:03

Sonnet 4.5 & the AI Plateau Myth — epic conversation with Sholto Douglas of Anthropic 0:00 - Intro 1:09 - What's Behind The Rapid Pace of AI Releases at Anthropic 2:49 - Opus, Sonnet, and Haiku Model Tiers 4:14 - Sholto's Story: From Australian Fencer to AI Researcher 12:01 - The YouTube Effect: Mastery Through Observation 16:16 - Breaking Into AI Research Without Traditional Academic Signals 18:29 - DeepMind, Gemini, and Building Inference Stacks 23:05 - Why Anthropic? Culture and Mission Differences Amongst AI Research Labs 25:08 - What Is "Taste" in AI Research? 31:46 - This Week's Big Launch: Sonnet 4.5, Best Coding Model in the World 36:40 - From 7 Hours to 30 Hours: The Long-Running AI Agent Breakthrough 38:41 - How AI Agents Self-Correct and Maintain Coherence 43:13 - The Role of Memory in Extended Coding Sessions 47:42 - Pre-Training vs. RL: Textbooks vs. Worked Problems 52:11 - Test-Time Compute & Reinforcement Learning 55:55 - Why RL Finally Started Working on LLMs in 2024 59:38 - The Path to AGI 1:02:05 - Are We Hitting a Plateau in AI? So Many Low Hanging Fruits 1:03:41 - Beyond Coding: GDPVal & Impact Economic Sectors 1:05:47 - Preparing for 10-100x Individual Leverage & The Upcoming Robotics Explosion

Matt Turck

93,678 views • 10 months ago

Cursor Complete Guide for AI Coding... 1. The Basics, Composer, Cursor 2.0, Why use Cursor? 2. Multiple Agent Testing, Adding Database, Deploying to Vercel 3. Comparing the big 4: v0, Replit, Lovable, Cursor And more... with Senior Software Engineer Kehan Zhang TIME STAMPS --------------- 1. BASICS: 00:00 Introduction 01:01 Overview of Cursor and Its Features 01:47 Getting Started with Cursor 02:39 Understanding IDE and Vibe Coding 06:00 Cursor For Mobile Apps 10:26 Downloading and Installing Cursor 11:17 Creating and Managing Projects in Cursor 15:14 Building a Simple Game with Cursor 19:10 Advanced Features and Customization 40:28 Fixing Styling Rules 40:53 Redesigning the App 42:17 Exploring Cursor 2.0 Features 43:22 Setting Up the Project Structure 44:17 Adding and Testing Meme Templates 46:08 Debugging Text Issues 2. ADVANCED 49:46 Using Multiple Agents 01:10:40 Creating Custom Commands 01:14:15 Creating Commands in Settings Tab 01:15:11 Introduction to Instant DB 01:16:04 Setting Up Instant DB in Your Project 01:18:24 Building a Full Stack Application 01:19:04 Using the Agent to Plan and Build 01:26:06 Testing and Debugging the Application 01:53:02 Deploying the Application with Vercel 01:55:35 Setting Up the CLI 01:56:15 Understanding Command Line Interfaces (CLI) 01:57:32 Deploying Code to Vercel 01:58:07 Handling Environment Variables 01:58:44 Interacting with the Vercel Deployment 02:00:34 Exploring Cursor's Capabilities 3. COMPARING VIBE CODING TOOLS 02:09:48 Comparing Vibe Coding Tools 02:31:04 Final Thoughts and Recommendations
2:34:13

Cursor Complete Guide for AI Coding... 1. The Basics, Composer, Cursor 2.0, Why use Cursor? 2. Multiple Agent Testing, Adding Database, Deploying to Vercel 3. Comparing the big 4: v0, Replit, Lovable, Cursor And more... with Senior Software Engineer Kehan Zhang TIME STAMPS --------------- 1. BASICS: 00:00 Introduction 01:01 Overview of Cursor and Its Features 01:47 Getting Started with Cursor 02:39 Understanding IDE and Vibe Coding 06:00 Cursor For Mobile Apps 10:26 Downloading and Installing Cursor 11:17 Creating and Managing Projects in Cursor 15:14 Building a Simple Game with Cursor 19:10 Advanced Features and Customization 40:28 Fixing Styling Rules 40:53 Redesigning the App 42:17 Exploring Cursor 2.0 Features 43:22 Setting Up the Project Structure 44:17 Adding and Testing Meme Templates 46:08 Debugging Text Issues 2. ADVANCED 49:46 Using Multiple Agents 01:10:40 Creating Custom Commands 01:14:15 Creating Commands in Settings Tab 01:15:11 Introduction to Instant DB 01:16:04 Setting Up Instant DB in Your Project 01:18:24 Building a Full Stack Application 01:19:04 Using the Agent to Plan and Build 01:26:06 Testing and Debugging the Application 01:53:02 Deploying the Application with Vercel 01:55:35 Setting Up the CLI 01:56:15 Understanding Command Line Interfaces (CLI) 01:57:32 Deploying Code to Vercel 01:58:07 Handling Environment Variables 01:58:44 Interacting with the Vercel Deployment 02:00:34 Exploring Cursor's Capabilities 3. COMPARING VIBE CODING TOOLS 02:09:48 Comparing Vibe Coding Tools 02:31:04 Final Thoughts and Recommendations

Riley Brown

65,392 views • 8 months ago

8 rules to improve your AI coding agent. All of these rules work with Claude Code, Cursor, VS Code, and with most programming languages. Automating these rules will 10x the code quality and security produced by your AI coding agents. 1. Dependency checks - Prevent your agent from suggesting insecure libraries based on outdated training data. 2. Secret exposure - Auto-fix the use of hardcoded credentials introduced by your coding agent. 3. File and function size - Automatically refactor any files or functions that exceed a reasonable length. 4. Complexity and parameter limits - Simplify overly complex code written by the agent. 5. SQL Injection - Auto-fix all database interactions with unsanitized user input. 6. Unused variables and imports - Detect and remove dead code. 7. Detect invisible unicode characters in AI rules files - Remove zero-width spaces, direction overrides, and other invisible characters that can hide malicious behavior. 8. Insecure OpenAI API usage - Enforce use of secure OpenAI endpoints, proper authentication, and context isolation Here is how you can automate this: Install the Codacy extension. This will give you access to a CLI for local scanning and an MCP server for agent communication. From here on out, every time you need to generate some code: 1. Your agent will write the code 2. It will then call Codacy's CLI to check it 3. It will find any issues in real time 4. Your coding agent will fix the issues 5. When the code passes all checks, you are done Level of effort on your side: literally zero! Code quality and security because of this: 100x better! Here is the link to download the extension for your IDE: Thanks to the Codacy team for collaborating with me on this post.
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8 rules to improve your AI coding agent. All of these rules work with Claude Code, Cursor, VS Code, and with most programming languages. Automating these rules will 10x the code quality and security produced by your AI coding agents. 1. Dependency checks - Prevent your agent from suggesting insecure libraries based on outdated training data. 2. Secret exposure - Auto-fix the use of hardcoded credentials introduced by your coding agent. 3. File and function size - Automatically refactor any files or functions that exceed a reasonable length. 4. Complexity and parameter limits - Simplify overly complex code written by the agent. 5. SQL Injection - Auto-fix all database interactions with unsanitized user input. 6. Unused variables and imports - Detect and remove dead code. 7. Detect invisible unicode characters in AI rules files - Remove zero-width spaces, direction overrides, and other invisible characters that can hide malicious behavior. 8. Insecure OpenAI API usage - Enforce use of secure OpenAI endpoints, proper authentication, and context isolation Here is how you can automate this: Install the Codacy extension. This will give you access to a CLI for local scanning and an MCP server for agent communication. From here on out, every time you need to generate some code: 1. Your agent will write the code 2. It will then call Codacy's CLI to check it 3. It will find any issues in real time 4. Your coding agent will fix the issues 5. When the code passes all checks, you are done Level of effort on your side: literally zero! Code quality and security because of this: 100x better! Here is the link to download the extension for your IDE: Thanks to the Codacy team for collaborating with me on this post.

Santiago

49,331 views • 9 months ago

Full podcast episode with Guillermo Rauch, Max Hodak, and Blake Scholl 🛫. 40 minutes of unreleased material. The AI Industrial Revolution Part 1: Waste Tokens, Save Time 0:00 Three Frontier Founders 1:27 AI Software Factories 4:15 Waste Tokens, Save Time 5:47 Models Instructing Humans 9:29 Is Pure Software Dead? 12:03 You Don't Get Stuck Anymore Part 2: Vibe Coding Hardware 14:39 Vibe Coding a Turbine Blade 18:07 Open Source Compounds China's Advantage 20:15 You Always Want the Smartest Model 22:44 Software Still Needs Hands 24:43 Humans Are Becoming Verifiers Part 3: The Regulatory Frontier 27:53 The Regulatory Red Queen Race 32:32 Why There's No Innovation in Healthcare 36:49 We Need a True 50-State Experiment 40:31 China's FDA Is Beating Ours 43:37 Healthcare Is a Communist Society Inside Capitalism 45:57 Sid's Story: N-of-1 Medicine Part 4: The Autonomous Company 47:49 Autonomous Infrastructure 51:25 Your Job Is to Train the Agent 54:54 The Next Lord of the Rings 59:08 What's Your Definition of Art? 1:05:00 Can AI Have New Ideas? 1:07:03 A Large Number of Small Teams
1:10:01

Full podcast episode with Guillermo Rauch, Max Hodak, and Blake Scholl 🛫. 40 minutes of unreleased material. The AI Industrial Revolution Part 1: Waste Tokens, Save Time 0:00 Three Frontier Founders 1:27 AI Software Factories 4:15 Waste Tokens, Save Time 5:47 Models Instructing Humans 9:29 Is Pure Software Dead? 12:03 You Don't Get Stuck Anymore Part 2: Vibe Coding Hardware 14:39 Vibe Coding a Turbine Blade 18:07 Open Source Compounds China's Advantage 20:15 You Always Want the Smartest Model 22:44 Software Still Needs Hands 24:43 Humans Are Becoming Verifiers Part 3: The Regulatory Frontier 27:53 The Regulatory Red Queen Race 32:32 Why There's No Innovation in Healthcare 36:49 We Need a True 50-State Experiment 40:31 China's FDA Is Beating Ours 43:37 Healthcare Is a Communist Society Inside Capitalism 45:57 Sid's Story: N-of-1 Medicine Part 4: The Autonomous Company 47:49 Autonomous Infrastructure 51:25 Your Job Is to Train the Agent 54:54 The Next Lord of the Rings 59:08 What's Your Definition of Art? 1:05:00 Can AI Have New Ideas? 1:07:03 A Large Number of Small Teams

Naval

285,338 views • 2 months ago