#053 AI in the Terminal: Enhancing Coding with Warp

Nicolay here, Most AI coding conversations focus on which model to use. This one focuses on workflow - the specific commands, git strategies, and review processes that let one engineer ship production code with AI agents doing 80% of the work. Today I have the chance to talk to Kieran Klaassen, who built Cora (an AI email management tool) almost entirely solo using AI agents. His approach: treat AI agents like junior developers you manage, not tools you operate. The key insight centers on "compound engineering" - extracting reusable systems from every code review and interaction. Instead of just reviewing pull requests, Kieran records his review sessions with his colleague, transcribes them, and feeds the transcriptions to Claude to extract coding patterns and philosophical approaches into custom slash commands. In the podcast, we also touch on: Git worktrees for running multiple AI agents simultaneously The evolution from Cursor Composer to Claude Code and Friday Why pull request review is the real bottleneck, not code generation How to structure research phases to avoid AI going off the rails and more 💡 Core Concepts Compound Engineering: Extracting reusable systems, SOPs, and taste from every AI interaction - treating each code review or feature build as an opportunity to teach the AI your standards and decision-making patterns. Git Worktrees for AI Agents: Running multiple AI coding agents simultaneously by checking out different branches in separate file system directories, allowing parallel feature development without conflicts. Research-First AI Development: Starting every feature with a dedicated research phase where AI gathers context, explores multiple approaches, and creates detailed GitHub issues before any code is written. Tiered Code Review Systems: Implementing different review checklists and standards based on risk level (payments, migrations, etc.) with AI assistants handling initial passes before human review. 📶 Connect with Kieran: X / Twitter Cora 📶 Connect with Nicolay: Newsletter LinkedIn X / Twitter Bluesky Website My Agency Aisbach (for ai implementations / strategy) ⏱️ Important Moments The Sonnet 3.5 Breakthrough Moment: [09:30] Kieran describes vibe-coding a Swift app in one evening, realizing AI could support solo entrepreneurship for the first time. Building Cora's First Prototype: [12:45] One night to build a prototype that drafts email responses - the moment they knew there was something special about AI handling email. The Nice, France Experiment: [13:40] Testing automatic email archiving while walking around town, discovering the "calm feeling" that became Cora's core value proposition. Git Worktrees Discovery: [50:50] How Kieran discovered worktrees by asking AI for a solution to run multiple agents simultaneously, leading to his current parallel development workflow. Cursor 3.7 Breaking Point: [19:57] The moment Cursor became unusable after shipping too many changes at once, forcing the search for better agentic tools. Friday vs Claude Code Comparison: [22:23] Why Friday's "YOLO mode" and end-to-end pull request creation felt more like having a colleague than using a tool. Compound Engineering Philosophy: [33:18] Recording code review sessions and extracting engineering taste into reusable Claude commands for future development. The Research Phase Strategy: [04:48] Why starting with comprehensive GitHub issue research prevents AI agents from going off-rails during implementation. Pull Request Review Bottleneck: [28:44] How reviewing AI-generated code, not writing it, becomes the main constraint when scaling with agents. Multiple Agent Management: [48:14] Running Claude Code work trees in parallel terminals, treating each agent as a separate team member with distinct tasks. 🛠️ Tools & Tech Mentioned Claude Code Cursor Friday AI Git Worktrees Warp Terminal GitHub CLI Charlie AI (PR review bot) Context7 MCP Anthropic Prompt Improver

Nicolay here, while everyone races to cloud-scale LLMs, Pete Warden is solving AI problems by going completely offline. No network connectivity required. Today I have the chance to talk to Pete Warden, CEO of Useful Sensors and author of the TinyML book. His philosophy: if you can't explain to users exactly what happens to their data, your privacy model is broken. Key Insight: The Real World Action Gap LLMs excel at text-to-text transformations but fail catastrophically at connecting language to physical actions. There's nothing in the web corpus that teaches a model how "turn on the light" maps to sending a pin high on a microcontroller. This explains why every AI agent demo focuses on booking flights and API calls - those actions are documented in text. The moment you step off the web into real-world device control, even simple commands become impossible without custom training on action-to-outcome data. Pete's company builds speech-to-intent systems that skip text entirely, going directly from audio to device actions using embeddings trained on limited action sets. 💡 Core Concepts Speech-to-Intent: Direct audio-to-action mapping that bypasses text conversion, preserving ambiguity until final classification ML Sensors: Self-contained circuit boards processing sensitive data locally, outputting only simple signals without exposing raw video/audio Embedding-Based Action Matching: Vector representations mapping natural language variations to canonical device actions within constrained domains ⏱ Important Moments Real World Action Problem: [06:27] LLMs discuss turning on lights but lack training data connecting text commands to device control Apple Intelligence Challenges: [04:07] Design-led culture clashes with AI accuracy limitations Speech-to-Intent vs Speech-to-Text: [12:01] Breaking audio into text loses critical ambiguity information Limited Action Set Strategy: [15:30] Smart speakers succeed by constraining to ~3 functions rather than infinite commands 8-Bit Quantization: [33:12] Remains deployment sweet spot - processor instruction support matters more than compression On-Device Privacy: [47:00] Complete local processing provides explainable guarantees vs confusing hybrid systems 🛠 Tools & Tech Whisper: github.com/openai/whisper Moonshine: github.com/usefulsensors/moonshine TinyML Book: oreilly.com/library/view/tinyml/9781492052036 Stanford Edge ML: github.com/petewarden/stanford-edge-ml 📚 Resources Looking to Listen Paper: looking-to-listen.github.io Lottery Ticket Hypothesis: arxiv.org/abs/1803.03635 Connect: [email protected] | petewarden.com | usefulsensors.com Beta Opportunity: Moonshine browser implementation for client-side speech processing in JavaScript

Nicolay here,most AI conversations focus on training bigger models with more compute. This one explores the counterintuitive world where averaging weights from different models creates better performance than expensive post-training. Today I have the chance to talk to Maxime Labonne, who's a researcher at Liquid AI and the architect of some of the most popular open source models on Hugging Face. He went from researching neural networks for cybersecurity to building "Frankenstein models" through techniques that shouldn't work but consistently do. Key Insight: Model Merging as a Free LunchThe core breakthrough is deceptively simple: take two fine-tuned models, average their weights layer by layer, and often get better performance than either individual model. Maxime initially started writing an article to explain why this couldn't work, but his own experiments convinced him otherwise. The magic lies in knowledge compression and regularization. When you train a model multiple times on similar data, each run creates slightly different weight configurations due to training noise. Averaging these weights creates a smoother optimization path that avoids local minima. You can literally run model merging on a CPU - no GPUs required. In the podcast, we also touch on: Obliteration: removing safety refusal mechanisms without retraining Why synthetic data now comprises 90%+ of fine-tuning datasets The evaluation crisis and automated benchmarks missing real-world performance Chain of thought compression techniques for reasoning models 💡 Core Concepts Model Merging: Averaging weights across layers from multiple fine-tuned models to create improved performance without additional training Obliteration: Training-free method to remove refusal directions from models by computing activation differences Linear Merging: The least opinionated merging technique that simply averages weights with optional scaling factors Refusal Direction: The activation pattern that indicates when a model will output a safety refusal 📶 Connect with Maxime: X / Twitter: https://x.com/maximelabonne LinkedIn: https://www.linkedin.com/in/maxime-labonne/ Company: https://www.liquid.ai/ 📶 Connect with Nicolay: LinkedIn: https://www.linkedin.com/in/nicolay-gerold/ X / Twitter: https://x.com/nicolaygerold Website: https://www.nicolaygerold.com/ ⏱ Important Moments Model Merging Discovery Process: [00:00:30] Maxime explains how he started writing an article to debunk model merging Two Main Merging Use Cases: [11:04] Clear distinction between merging checkpoints versus combining different task-specific capabilities Linear Merging as Best Practice: [21:00] Why simple weight averaging consistently outperforms more complex techniques Layer Importance Hierarchy: [21:18] First and last layers have the most influence on model behavior Obliteration Technique Explained: [36:07] How to compute and subtract refusal directions from model activations Synthetic Data Dominance: [50:00] Modern fine-tuning uses 90%+ synthetic data 🛠 Tools & Tech Mentioned MergeKit: https://github.com/cg123/mergekit Transformer Lens: https://github.com/TransformerLensOrg/TransformerLens Hugging Face Transformers: https://github.com/huggingface/transformers PyTorch: https://pytorch.org/ 📚 Recommended Resources Maxime's Model Merging Articles: https://huggingface.co/blog/merge Model Soups Paper: https://arxiv.org/abs/2203.05482 Will Brown's Rubric Engineering: https://x.com/willccbb/status/1883611121577517092

Nicolay here,

Nicolay here, Today I have the chance to talk to Charity Majors, CEO and co-founder of Honeycomb, who recently has been writing about the cost crisis in observability. "Your source of truth is production, not your IDE - and if you can't understand your code there, you're flying blind." The key insight is architecturally simple but operationally transformative: replace your 10-20 observability tools with wide structured events that capture everything about a request in one place. Most teams store the same request data across metrics, logs, traces, APM, and error tracking - creating a 20X cost multiplier while making debugging nearly impossible because you're reconstructing stories from fragments. Charity's approach flips this: instrument once with rich context, derive everything else from that single source. This isn't just about cost - it's about giving engineers the connective tissue to understand distributed systems. When you can correlate "all requests failing from Android version X in region Y using language pack Z," you find problems in minutes instead of days. The second is putting developers on call for their own code. This creates the tight feedback loop that makes engineers write more reliable software - because nobody wants to get paged at 3am for their own bugs. In the podcast, we also touch on: Why deploy time is the foundational feedback loop (15 minutes vs 15 hours changes everything) The controversial "developers on call" stance and why ops people rarely found companies How microservices made everything trace-shaped and killed traditional metrics approaches The "normal engineer" philosophy - building for 4am debugging, not peak performance AI making "code of unknown quality" the new normal Progressive deployment strategies (kibble → dogfood → production) and more 💡 Core Concepts Wide Structured Events: Capturing all request context in one instrumentation event instead of scattered log lines - enables correlation analysis that's impossible with fragmented data. Observability 2.0: Moving from metrics-as-workhorse to structured-data-as-workhorse, where you instrument once and derive metrics/alerts/dashboards from the same rich dataset. SLO-based Alerting: Replacing symptom alerts (CPU, memory, disk) with customer-impact alerts that measure whether you're meeting promises to users. Progressive Deployment: Gradual rollout through staged environments (kibble → dogfood → production) that builds confidence without requiring 2X infrastructure. Trace-shaped Systems: Architecture pattern recognizing that distributed systems problems are fundamentally about correlating events across time and services, not isolated metrics. 📶 Connect with Charity: LinkedIn Bluesky Personal Blog Company 📶 Connect with Nicolay: LinkedIn X / Twitter Website ⏱️ Important Moments Gateway Drug to Engineering: [01:04] How IRC and bash tab completion sparked Charity's fascination with Unix command line possibilities ADHD and Incident Response: [01:54] Why high-pressure outages brought out her best work - getting "dead calm" when everything's broken Code vs. Production Reality: [02:56] Evolution from focusing on code beauty to understanding performance, behavior, and maintenance over time The Alexander's Horse Principle: [04:49] Auto-deployment as daily practice - if you grow up deploying constantly, it feels natural by the time you scale Production as Source of Truth: [06:32] Why your IDE output doesn't matter if you can't understand your code's intersection with infrastructure and users The Logging Evolution: [08:03] Moving from debugger-style spam logs to fewer, wider structured events oriented around units of work Bubble Up Anomaly Detection: [10:27] How correlating dimensions reveals that failures cluster around specific Android versions, regions, and feature combinations Everything is Trace-Shaped: [12:45] Why microservices complexity is about locating problems in distributed systems, not just identifying them AI as Acceleration of Automation: