MLA 022 Vibe Coding: Codex vs Claude Code vs Antigravity vs Grok
Release Date: 02/09/2025
MLA 030 AI and Programming Jobs: What Happened and How to Position
Machine Learning Guide
The aggregate job market held, the entry-level door narrowed, and software postings sit a quarter below pre-pandemic. Why cheap implementation made specification, verification and domain scarce, how ML roles split five ways, and how to position. Links - stay healthy & sharp while you learn & code - this one has siblings, each on its own subject and produced the same way What coding agents did to programming and machine learning jobs by late 2026: the labor data, the mechanism behind it, how the ML career splintered into five roles, and a concrete positioning plan. Companion...
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MLA 029 OpenClaw and Personal Agents
Machine Learning Guide
OpenClaw as the worked example of the always-on personal agent: gateway, markdown memory, heartbeats, skills, coding agents from your phone, hosted vs local models, and the 2026 security record (exposed instances, two critical CVEs, ClawHavoc) with the posture that makes it survivable. Links - stay healthy & sharp while you learn & code - this one has siblings, each on its own subject and produced the same way Second and last episode of the agents pair. covered the theory: loops, tools, memory, protocols, SDKs, evaluation. This one takes a single category, the always-on...
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MLA 028 AI Agents: Loops, Tools, Memory, Protocols, and Evaluation
Machine Learning Guide
What an AI agent actually is, why coding agents got good first, how memory really works, what MCP and A2A standardize, which SDKs are alive, how to evaluate on trajectories, and where the products stand after browser agents contracted. Links - stay healthy & sharp while you learn & code - this one has siblings, each on its own subject and produced the same way First of two episodes on AI agents. This one is the architecture: the loop, tools and verifiable feedback, memory, the protocols (MCP, A2A, computer use), the SDK landscape, evaluation and observability, the product...
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MLA 027 The AI Media Pipeline: Voice, Music, ComfyUI, APIs, and Finishing
Machine Learning Guide
How to automate AI media end to end: clone your own voice on open TTS, pick music that's actually licensed, run ComfyUI graphs headless, design around fal, Replicate and provider queues, finish with ffmpeg, and stay inside licensing at every layer. Links - this one has siblings, each on its own subject and produced the same way Companion show. This episode is the overview of the media pipeline. For weekly, hands-on coverage of the video half, from a first usable clip to scenes that cut together, listen to on Gnothi. - stay healthy & sharp while you learn & code Pipeline,...
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MLA 026 AI Video Generation 2026: Veo, Gemini, Kling, Runway, MiniMax, Sora
Machine Learning Guide
Sora is shut down, Google runs two video models, Kling 3 does lip-synced dialogue, and open-weight MiniMax H3 is what you can actually fine-tune. What a usable clip costs, which models do native audio, how reference consistency works, and why the unit of work is the shot. Links Notes and resources at - this one has siblings, each on its own subject and produced the same way. Companion show: for weekly, hands-on coverage of the AI video pipeline, from a first usable clip to scenes that cut together, listen to . - stay healthy & sharp while you learn & code Second...
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MLA 025 AI Image Generation 2026: GPT Image, Nano Banana, Midjourney, Flux
Machine Learning Guide
Editing replaced generation as the core task. How GPT Image 2.5, Google's Nano Banana line, Midjourney V8.2 and Flux 2 differ, what open weights and LoRAs buy you, ControlNet vs instruction editing, and how licensing and C2PA provenance work now. Links - this one has siblings, each on its own subject and produced the same way Companion show. This episode is the overview. For weekly, hands-on coverage of the full image and video pipeline, from a first usable clip to scenes that cut together, listen to . - stay healthy & sharp while you learn & code First of three episodes on...
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MLG 036 Autoencoders
Machine Learning Guide
Auto encoders are neural networks that compress data into a smaller "code," enabling dimensionality reduction, data cleaning, and lossy compression by reconstructing original inputs from this code. Advanced auto encoder types, such as denoising, sparse, and variational auto encoders, extend these concepts for applications in generative modeling, interpretability, and synthetic data generation. Links Notes and resources at - stay healthy & sharp while you learn & code Build the future of multi-agent software with . Thanks to from for recording...
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MLG 035 Large Language Models 2
Machine Learning Guide
At inference, large language models use in-context learning with zero-, one-, or few-shot examples to perform new tasks without weight updates, and can be grounded with Retrieval Augmented Generation (RAG) by embedding documents into vector databases for real-time factual lookup using cosine similarity. LLM agents autonomously plan, act, and use external tools via orchestrated loops with persistent memory, while recent benchmarks like GPQA (STEM reasoning), SWE Bench (agentic coding), and MMMU (multimodal college-level tasks) test performance alongside prompt engineering techniques such as...
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MLG 034 Large Language Models 1
Machine Learning Guide
Explains language models (LLMs) advancements. Scaling laws - the relationships among model size, data size, and compute - and how emergent abilities such as in-context learning, multi-step reasoning, and instruction following arise once certain scaling thresholds are crossed. The evolution of the transformer architecture with Mixture of Experts (MoE), describes the three-phase training process culminating in Reinforcement Learning from Human Feedback (RLHF) for model alignment, and explores advanced reasoning techniques such as chain-of-thought prompting which significantly improve complex...
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MLA 024 Agentic Software Engineering: Specs, Verification, and the Review Loop
Machine Learning Guide
How working engineers ship with coding agents: issues an agent can verify, plan mode before code, a verification loop with a browser in it, agent review of agent code, worktrees and CI, cost discipline, and where agents still fail. Links - stay healthy & sharp while you learn & code - this one has siblings, each on its own subject and produced the same way Third and last episode of the vibe-coding sequence. picked an agent; explained the mechanics. This one is the practice: how working engineers ship real software with Claude Code, Codex, and Antigravity without...
info_outlineWhat a coding agent actually is, and how Claude Code, OpenAI Codex, Google Antigravity and xAI's Grok Build differ in philosophy rather than features. Plus open harnesses pi and OpenCode, the open-weight coders, and a decision rule by constraint.
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- Try a walking desk - stay healthy & sharp while you learn & code
- More OCDevel shows - this one has siblings, each on its own subject and produced the same way
First of three episodes on vibe coding: the field of coding agents (Claude Code, OpenAI Codex, Google Antigravity, Grok Build, pi, OpenCode) as of late 2026 and how to choose one. The next two cover the mechanics (Inside a Coding Agent) and the engineering practice (Agentic Software Engineering).
Vibe coding then and now
Andrej Karpathy's February 2025 post named "vibe coding": accept all, don't read the diffs, paste errors back in, and, the caveat everyone dropped, "not too bad for throwaway weekend projects." Collins made it Word of the Year 2025. The arc since is autocomplete, then chat, then agents that read, edit, run and iterate; the engineers who stayed with it moved from vibing to agentic engineering, which is the through line for the sequence.
Anatomy of a coding agent
The model and the harness are separate things, and most tool arguments are about one without noticing which. The harness is the loop around the model, and there are five parts worth naming: the model, the tools (read, write, shell, search, browser), the permission model and sandbox, the context strategy (what to keep, summarize, offload to files, or hand to a fresh subagent), and the surface (terminal, IDE, desktop, web, chat). Ask those five questions of any product; they matter more than the feature grid. The mechanics get their own episode.
Claude Code: terminal-first and composable
Anthropic's stated design is Unix-style composability: pipe logs into it, run it in CI, chain it with other tools. The terminal is the primary surface; the IDE extension, desktop app, web and mobile clients all connect to the same engine and share CLAUDE.md, settings and MCP servers, with handoffs between local, cloud and desktop. Headless -p mode, GitHub Actions and GitLab integrations, and the Claude Agent SDK make it embeddable. Subagents and worktrees are its answer to the context problem. Model tiers (Sonnet, Opus, Fable) sit behind one harness with 1M-context variants and an effort dial; usage is included in Claude subscriptions or billed via the API.
- Claude Code overview
- Model configuration
- Plans and costs
- Headless mode
- Subagents and worktrees
- Claude Agent SDK
- Claude Fable 5 and Mythos 5, Claude Opus 5
OpenAI Codex: cloud-first and parallel
Codex CLI and Codex cloud are one product under one ChatGPT login, with an IDE extension and a desktop app; codex cloud pushes local work into a sandboxed cloud environment and applies the result back. OpenAI's framing is isolated environments, parallel tasks, and comparing multiple solution attempts, with tasks launchable from the web, GitHub, GitLab, Linear or Slack. Codex-tuned models are trained for long-running agentic work, and the newest generation keeps notes across context windows instead of repeatedly compressing a session. The CLI is Apache-2.0 open source; the Agents API brings the same harness to your own cloud agents.
- Codex CLI docs
- Codex cloud docs
- Model lineup and plans and credits
- Codex CLI source
- Introducing the Codex app
- Introducing the Agents API
Google Antigravity: the agent manager
Antigravity still ships under that name, and it absorbed Gemini CLI: Google retired Gemini CLI into Antigravity CLI in June 2026, and Antigravity 2.0 spans IDE, desktop app, CLI and SDK. The philosophy is supervision: "manage higher-level tasks for the Agent, not individual tool calls," with agents working across editor, terminal and browser and producing artifacts you review. The model menu is multi-vendor on consumer tiers (several Gemini tiers plus Claude Sonnet and Opus and GPT-OSS); enterprise is Gemini only.
- Antigravity IDE
- Supported models and tiers
- Antigravity changelog
- Google I/O 2026 developer highlights
- Transitioning Gemini CLI to Antigravity CLI
Grok Build: the cost-first bet
xAI's coding story flipped in 2026 from a fast model in other people's harnesses (grok-code-fast-1, since deprecated from GitHub Copilot) to a first-party open-source terminal agent, Grok Build: interactive TUI, headless -p, Agent Client Protocol for editors, plan-review-approve, hooks, plugins, MCP and parallel subagents. xAI's own positioning for its coding model is fastest and cheapest for agentic coding; the premium model above it is the agent's default. Benchmark numbers and the subagent-count and price claims circulating in blogs are not on xAI's pages.
- Introducing Grok Build
- Grok Build 0.1 on the API
- Grok Build docs and source
- Grok Code Fast 1 deprecated in Copilot
Open harnesses: pi and OpenCode
Both are open source, model-agnostic and very active, and they're opposites in philosophy. pi is a toolkit: small core, unified API over 15+ providers, TUI, print/RPC/SDK modes, TypeScript extensions, branchable sessions, a package system, and deliberately no built-in permission system (containerize instead; the author ships the Gondolin micro-VM). OpenCode is a product: TUI, web, desktop and IDE surfaces, a ~50-entry provider list including local runtimes, permission config, and the optional Zen gateway of verified models. OpenCode's docs note Anthropic prohibits driving Claude subscription models through third-party harnesses and those plugins were removed. Also: Crush, Kilo Code, Cline, and DeepSeek's own harness.
Special mentions: open-weight coders and the second tier
Every lab with a model now ships a harness. Qwen has a coder line, an Apache-licensed open series and the Qwen Code CLI. Z.ai's GLM is pitched as the most capable open-weights model for coding, sold as a Coding Plan documented for Claude Code, Cline and OpenCode. DeepSeek serves 1M-context models with an Anthropic-format endpoint, so Claude-shaped harnesses can point at it. Moonshot's Kimi K3 is a very large open-weight multimodal model under a bespoke license, with the Kimi Code CLI. All coding claims are vendor-reported, now headlining SWE-bench Pro and Terminal Bench rather than SWE-bench Verified. Second tier: Copilot coding agent (cloud, inside Actions), Cursor (editor-first, adding cloud agents and a coordinator), Windsurf (folded into Devin), Cline (active), Aider (no commits since May 2026).
- Qwen3-Coder, Qwen Code
- GLM-5, GLM Coding Plan
- DeepSeek models and pricing
- Kimi K3, Kimi Code CLI
- Copilot coding agent, Cursor changelog, Cline, Aider
Decision rule by constraint
Already paying a lab: take that lab's agent (Claude Code, Codex, Antigravity) and stop shopping. Cloud vs local: Codex cloud or Copilot's agent for sandboxed parallel PRs; a harness plus a local or self-hosted model if everything has to stay behind your firewall. Cost: Grok Build or an open harness with a cheap open-weight model for mechanical work. Open weights: pi or OpenCode with Qwen, GLM, DeepSeek or Kimi. Test on a real bug you already understand, and budget for a primary agent plus a cheaper second one.
Related episodes
- MLA 23: Inside a Coding Agent
- MLA 24: Agentic Software Engineering
- MLA 28: AI Agents
- MLA 30: AI and Programming Jobs
Companion show: the Gnothi Claude Code series goes from your first terminal change to a repeatable delivery workflow.