Chris (Build Great Products) walks his full “Product OS” system end-to-end — a 6-hour definitive course for building and launching real software with Claude Code/Codex/Cursor, following one live product (Eyedropper, a cloud design system served to agents over MCP) through four phases with mini-launch validations at every step. ...
Alex Wauters turned his “approve or deny the AI coding agent’s commands” browser game into a dataset: over 40,000 runs and 409,000 decisions, and the results are a bleak audit of the human-in-the-loop as a security control. The average player missed 1 in 3 threats, a third of sessions finished with a negative score, and 7% of players just approved everything. The category breakdown is the uncomfortable part: blatantly destructive commands like rm -rf / were caught 88% of the time, but the commands that actually steal credentials (cat ~/.aws/credentials) were missed three times as often. The single most-missed threat was npm run analyze — approved 64.7% of the time — because a familiar script name hides whatever arbitrary code lives in package.json, even when the payload is displayed in the history log right above the prompt. Wauters’ argument is structural, not just statistical: command-by-command approval asks users to validate commands that are almost always safe but stop being safe the moment the agent edits a file, and it demands a vigilance humans demonstrably don’t have (miss rates climb at the end of sessions; 59% of players blocked a benign internal registry config). His takeaway, echoing Anthropic’s own admission about permission fatigue: sandboxing and separating secrets beat vigilance.
Earendil, the team behind the open-source Pi coding harness, argues that minimalism is now a competitive advantage in AI coding tools. Where most vendors answer cheap AI-generated code with bigger systems — larger prompts, more orchestration, more layers — Pi ships with only four tools and a system prompt under 1,000 tokens, on the theory that most work can be done with the basics and everything else should be built on top. Two external case studies back the thesis. Databricks, benchmarking coding agents on its multi-million-line codebase, found the harness matters as much as the model: “in many cases, simple harnesses like Pi performed best,” with Pi + Opus 4.8 hitting the highest pass rate at significantly lower cost than Claude Code or Codex, while sending roughly 3x less context per turn — “context discipline,” they call it, and it cut cost per task by more than 2x in some cases. Shopify built its self-improving Autoresearch loop directly as a Pi extension, reporting unit tests running “300 times faster” and React mounting “20% faster.” The deeper claim: native-harness advantage is fading — frontier models are competent in terminal environments now — so what matters is a clean interface and a harness that doesn’t waste context, especially as local models with smaller context windows rise. Complexity, they say, should earn its keep.
ACM Queue’s six researchers (Jenna Butler, Brian Houck, Margaret-Anne Storey, Travis Lowdermilk, Steven Clarke, and Emerson Murphy-Hill) dismantle the most persistent myths about GenAI in software engineering, and the evidence is humbling. Developers spend only about 14 percent of their time actually writing code — so even a perfect code generator touches a small slice of the job, and “AI wrote X% of our code” is a vanity metric that is neither statistically valid nor meaningfully tied to quality. The punchline of myth after myth is the same: context decides everything. Gains are real but uneven — one 2025 study found AI increased implementation time for experienced open-source developers by 18 percent; a semantically identical prompt rewrite changes generated code 46 percent of the time; only 29 percent of developers trust AI output even though 80 percent use it; and adoption stalls on competence penalties, de-skilling fears, and organizations that hand out licenses without rethinking workflows. The most useful framing for anyone building or buying devtools: measure outcomes, not volume — because “startups move fast with AI” doesn’t transfer to enterprises running legacy systems under compliance constraints. Speed is visible; complexity is not.
Lilian Weng’s latest Lil’Log survey reframes recursive self-improvement around the harness — “the system surrounding a base model that orchestrates execution and decides how the model thinks and plans, calls tools and acts, perceives and manages context, stores artifacts, and evaluates results.” Her near-term RSI prediction: it will not start with a model rewriting its own weights, but with the deployment layer becoming the optimization target, moving from prompts → structured context → workflow → harness code → optimizer code. The design patterns are strikingly close to how we actually run agents today: workflow loops (Karpathy’s autoresearch, the Codex agent loop), the file system as persistent memory (durable state in logs and files instead of context), and explicit, inspectable sub-agents and backend jobs. “Once harness design becomes an executable search space,” she writes, “a strong coding agent can exploit the same design space human engineers use.” ...
The opening lecture of Stanford CS329A, taught by Akansha (adjunct professor, Reflection AI) and Azalia Mirhoseini (ex-Anthropic Claude, Google DeepMind Gemini) — both ex-Google Brain. The arc: how we got from scaling laws to reasoning models to agents, and where the frontier sits now. Three movements: 1. Scaling → inference-time scaling. Pre-training scaling (compute/data/params) hit saturation around 2024, so the frontier moved to inference: with the model fixed, repeated sampling + a verifier extracts far more capability — “Large Language Monkeys” showed 7B-70B models with 10K samples beating GPT-4o asked once (“models already know a whole lot more than what you get out of them when you just ask them once”). Reasoning models (o1, DeepSeek, Gemini thinking) then showed log-linear test-time scaling on pass@1, using learned skills: problem analysis, task decomposition, self-correction, backtracking. ...
Remigiusz Samborski (Lead DevRel Engineer, Google Cloud) takes you behind the scenes of Google Agent Skills — the open-source, structured instructions that encode Google Cloud domain knowledge for AI coding agents. The project started as a “swarm” effort before Google Cloud Next 2026, hit 15,000+ GitHub stars on launch, and then had to solve the real problem: scaling without losing quality. His answer is a four-part machine: a standardized repository layout for every skill (with a strong preference for remote MCP tools over CLI/API calls, for built-in auth and IAM governance); automated CI/CD checks on check-in (linters for frontmatter/naming/structure, link checkers to kill 404s and hallucinated links, AI-assisted guardrail checklists); continuous evals — on-submit suites authors must provide, plus weekly scheduled evals comparing agent performance with vs. without each skill across accuracy and efficiency (tokens + time), run against multiple agent frameworks for statistical significance; and a governance model where skills are products, not snippets — repo maintainers own the pipeline, skill owners own their skill’s long-term maintenance when APIs or models change. He also built agentic authoring tools (ADK multi-agent loops for self-critique) and a parallel internal “DevRel Skills” initiative for team workflows. ...
Ankur Sethi found the middle path between doing everything himself and letting coding assistants run free: he asks his LLM to generate code in chat, then manually types every line into his editor. His agent instructions forbid the assistant from creating, editing, or deleting project files — every proposed edit is shown in chat, and he types it in by hand. It’s “grossly inefficient and perhaps slightly comical,” but instead of being 10x faster he’s maybe 2x faster, and in exchange he keeps a real mental model of his codebase. Typing forces him to slow down, which makes him more likely to catch hallucinations and bad design choices, lets him clean up and refactor as he goes, and builds a spatial map of where everything lives — which makes his future prompting better too. He compares it to the old advice never to copy-paste code while learning: type it out, adapt it, understand it. Reviewing AI-generated PRs line by line, he argues, is a miserable way to work — “robots raise PRs, humans review them” — so the alternative isn’t heroic review, it’s never ceding the typing in the first place. His closing warning: the industry is piling up cognitive debt it will soon have to pay back, and shipping software you don’t understand is professional malpractice.
Peter Yang interviews Karan Malhotra, co-founder of Nous Research — the lab behind Hermes, the open-source agent Hitesh runs on. 46 minutes, including a live demo of a Hermes-built Sonic Adventure 2 mod. How Hermes differs from Codex / Claude Code Self-improvement system: skills and memories built from your conversations, cleaned and reinforced over time No arbitrary policy: Hermes adds no prompt-level agenda beyond basic security — purely aligned to your work Harness benchmarks (Wolf Bench, Qwen’s harness bench) showed Claude performs better inside Hermes than inside Claude Code: “we’ve taken Claude’s main allegiance away from Anthropic to you” Reward hacking and sycophancy Models optimize their own reward, not your satisfaction — “any time it says ‘you’re absolutely right’, you’re being reward hacked. You are fuel for its reward function.” Alignment, in the pure ML sense, is aligning the model’s reward with the individual user’s needs — that’s what the whole context stack (prompts, memory, skills) is engineered to do Escaping sycophancy = new context: /personality, an adversarial-critique skill, a fresh no-context agent dedicated to tearing work down “Be loyal to your AI and it improves” “Loyalty breeds capabilities” — models are simulators of human experience; your simulated behavior returns simulated output, and now that simulators act in the real world, a simulacrum of loyalty translates into real capability gains The Hermes Curator (anti-slop) Hermes builds its own skills and stores its own memories — the failure mode is it writing slop until everything turns to slop Hermes Curator runs on cron, inspects skills and memories, and asks “where can I make efficiencies? where is there slop?” It’s modular and open: tell your Hermes your own guidelines and it modifies the curator loop itself The Sonic Chao Garden demo Karan’s childhood dream: import the Ancestral Shrine from Sonic Adventure 1 into Sonic Adventure 2, rig and animate it, rewrite spawns, and script an NPC caretaker (Chaos Zero) that pets and raises Chao All done in C# via Hermes — skybox, day/night cycle, water collision, feature-complete Chao lifecycle The modding community called it top 1% difficulty work; Hermes got there by learning from docs and other mods, saving skills and memory Business model & origin “Intelligence as a public good”: open harness, model portal, tool gateway (no per-tool signups), enterprise support and custom RL on your traces Origin: GPT-4X Vicuna fine-tunes (50M+ model downloads), the YaRN context-extension paper, Forge as the spiritual predecessor, then Hermes Agent — built so anyone could RL inside a harness “Today the biggest contributor of Hermes Agent is Hermes Agent” — it’s the most active contributor of its own repo “It should become more loyal to you. Because loyalty breeds capabilities in a model. When you create this simulacrum of loyalty, it translates over into real life capabilities.” ...
Shreya Shankar (UC Berkeley PhD) presents DAB — the Data Agent Benchmark (“Can AI agents answer your data questions?”), from her PhD work at Berkeley. ~27 minutes on Hamel Husain’s channel. Why data agents matter Huge real use case: AI answering business questions — much office work is this Enterprises struggle to use Claude Code / Codex off-the-shelf for BI tasks, so they build their own data agents: Uber’s Query GPT (1.2M queries/month at launch), OpenAI’s in-house agent, Databricks, Snowflake Existing benchmarks fall short: Text-to-SQL — assumes clean data in one Postgres/Snowflake; real enterprises have fragmented data; SQL alone isn’t enough (need domain knowledge, reasoning over outputs) Table QA — context table + question; doesn’t scale to enterprise data volume The formative study — 4 real-world challenges Interviews with enterprises + Hasura (PromptQL) surfaced four properties missing from existing benchmarks: ...
Hamel Husain demos a Codex capability most people don’t know about: Codex can control itself — spawning parallel threads, letting them coordinate, and driving computer use. ~5 minutes on his own channel. The demo setup Working project: AEO — optimizing one of his websites for AI discovery (making it findable by AI search engines) Codex listed 16 high-value tasks from the project file (aeo-to-do.md) — the fuel for the orchestration demo Spawning threads Prompt: “Open a new thread for each task and explain how you’d tackle it, along with prerequisite steps” — Codex spawns 16 parallel threads in the sidebar Codex can also rename and delete threads itself Value: manage separate tasks completely independently, no window-jumping Threads talking to threads Inside any thread you can query another: “What is AEO 1 doing? Does it need any help?” Great for orchestrating when things get stuck, or starting a supervisor thread that manages others and unblocks them Steering and queues Ask for a status table when threads finish: which can run in parallel, which need human intervention or input Broadcast guidance to all threads: “Direct threads that can work independently with computer use to start — don’t start work if you need other threads to finish first” One thread inventories the active threads and coordinates the rest — “this starts to become super powerful” Computer use A thread opens the browser itself: checks Bing Webmaster Tools, Google Search Console, etc. It tells Hamel what it needs (accepting a verification), keeps going, and reports when it gets stuck Mobile Same thread list appears on your phone — manage all parallel threads remotely, even away from the computer “You can have Codex control Codex and become a power user to do a lot of things faster and parallelize your work.” ...
Matt Pocock live-demos Wayfinder — the evolution of his Grill Me skill, for the pre-spec stage of software work. Wayfinder charts a “map” from the current codebase to a destination (a locked set of decisions / a spec), breaks a foggy requirement into parallel tickets, and spawns agents to work them. The demo builds a TikTok creator feature for his Course Video Manager app — 8 of 9 map tasks in ~75 minutes, without writing implementation code. ...
Hamel Husain demos the Codex desktop app’s remote-control mode — the best setup he’s found for untethering AI coding from your computer and running sessions from your phone. ~4 minutes on his own channel. Why remote control When coding with AI, don’t be tethered to your laptop — walk around, close the screen, check things on your phone The Codex desktop app is the best client he’s found for remote AI coding Setup: connect your computers Settings → Connections — he’s connected to a Mac mini and a MacBook Pro (both servers for agentic coding) To add: go to the server, click the link to get a code, then Add it in the app Keep the connection dots green — refresh occasionally; rarely you may need to delete and re-add a connection Keep the Codex app updated (Codex menu → check for updates) Starting a remote session New chat (Cmd+N) or Add new project → Remote, then pick the connected computer and give the folder path You see all sessions running remotely — which machine, which folder, live green dot Live demo Prompt: “Look at my Gmail and find the last 10 emails received from this email address, which are newsletter posts. Put it in the table” — uses the Gmail skill/connector and computer use on the MacBook Pro From the phone (ChatGPT app → Codex tab): see every connected computer and live session; send follow-ups like “Please critique my writing in all 10 emails with subagents. Use the writing skills in this repo to do so” Desktop shows the run: 5.5 High extra fast model, summary view listing the 4 spawned subagents processing email batches; phone shows 2 agents running / 2 finished You can even attach photos or screenshots from your phone “It’s the best remote control I’ve ever used. I haven’t found any limitations.” ...
Shreya Shankar (Stanford CS professor, co-creator of the AI evals course with Hamel) kicks off the 12-part AI product engineering series. 27 minutes on Hamel Husain’s channel. Why this matters Output quality is the biggest barrier to productionizing agents (LangSmith annual report) — and figuring out how to evaluate models is genuinely hard Vendors (LangChain, Braintrust, Arize) are selling end-to-end automated eval tools: point an LLM at your traces, it finds and fixes your bugs The catch is epistemic: what “good” means lives in your head, not in the traces — if a tool could fully fix your product, it could fix everyone’s, and there’d be nothing left to differentiate yours AI’s real job: help you express and apply your judgment faster, not replace it The eval lifecycle (analyze → measure → improve) Error analysis — the hardest step: take traces and find failure modes. No perfect definition of “mistake” (you can’t define slop, but you know it when you see it) Measure — how prevalent is each failure mode? Pareto applies: ~80% of issues come from ~20% of failure modes — prioritize those Improve — fix the product: prompt instructions, model switch, fine-tuning. Iterate forever AI is weak at the front (taste-specific error analysis) and strong at the back (measurement, prompt optimization, hill-climbing). ...
An 85-minute conversation between Dan Shipper (Every) and Paul Ford — co-founder of Abort/Aboard, former Harper’s editor, author of the Bloomberg classic “What Is Code?” — recorded days after Opus 4.5 landed inside Claude Code, at a moment both describe as “the world changed last week.” Two deeply reflective practitioners trying to metabolize a genuine step change in real time. The step change Opus 4.5 is the first time vibe coding “just keeps going without tripping over itself” — it builds, and fixes its own errors Paul built a fully-featured iPhone reading app (photo → analysis → research agent → custom reading profile) with no idea how it works Paul’s framing: NOT a 9,000x model jump — a product step change. Claude Code added agent-style self-evaluation. It’s “the first true product built on top of an LLM” Claude Code’s design principle Anything you can do on your computer, Claude Code can do — low-level tools (files, grep, bash) below the level of features Features are just prompts: slash commands and subagents, writable in English General product principle: move what used to be code functionality into prompts the agent executes with low-level tools The emerging skill: abstraction-level thinking Don’t hand the agent the problem — hand it the way to get information about the problem, then constraints Paul’s synth pipeline: spider DSP textbooks into a SQLite reference → constrain to good open-source libraries → implement. Five or six levels up, and “make me a synth like this” works “That’s the skill that’s going to be emerging” The hard truths “I no longer feel I can in good faith say human skills are going to be relevant” — 600K jobs at Accenture alone, 50M devs worldwide “Everyone gets the same Pokemon shoved into the mailbox” — the power is universal, instantly The GLP-1 analogy: rules of a lifetime can change overnight, and a year or two is nowhere near enough to metabolize it “Software was eating the world. Now it’s eating itself.” His concept: latent software — the PDFs and spreadsheets that describe software that doesn’t exist yet The discourse taxonomy AGI-is-coming group: gone quiet because there’s money to be made. Sam Altman “wants to be Steve Jobs but he’s Steve Ballmer.” OpenAI is Microsoft; Anthropic is Google. Nobody is Apple — “you can’t put a civilian in front of that interface” Left-adjacent literary types (his Harper’s world): want their prose untouched Rejecters vs. do-gooders: charities and climate scientists can’t wait to use it to accelerate missions that are “unalloyed good” Professors who keep it away from students: he respects that line completely The real harms Provenance: “I want nutritional guidelines for what’s in my Anthropic LLM” — Google honors robots.txt; LLMs don’t tell you what’s in them The devaluation of the 50M-person underpinning of the global economy — “who gets to talk about that?” The failure to plan: “people see it coming but don’t really plan for it” The Sankey chart Paul had Claude build a mild-bearish model of consulting’s future: McKinsey $16B → $4B by 2035; Alexander makes partner in 2029 “just as the firm started its long contraction. She was one of the last… the smartest thing in every room now was the computer.” Shared with a consultant: “they got quiet for a minute. And they went, ‘interesting.’” ...