Projects

Any recipe is now a search away, and an AI will write you a new one in seconds. What a home kitchen still lacks is a companion: something that knows what your family likes, gets you through the cook with wet hands, and remembers how you made it last time. Most cooking apps answer with more scrolling, more pop-ups, and more screens to manage.

Sous is my attempt at the opposite: the ultimate home cook companion, and a calm one.

The prototype on our counter

The prototype is a working app running on a Pixel Tablet sitting on our kitchen counter. Recipes come in by message: I send one to our kitchen agent, and it lands in the app's queue alongside our family's own recipe book. Before we start, Sous turns each recipe into a cook plan. It splits prep from cooking, pulls out anything that has to happen ahead of time, and lays out which steps run in parallel.

Most tools today put inputs first — chats, docs, calls — and treat shared knowledge as the residue that's left over. Abuzz inverts that. The walk-in surface is what we're making, organized as a bento of live outputs. Conversations, drafts, huddles, and agent contributions feed those artifacts and are surfaced as their current best state. The one-liner I keep coming back to: _knowledge is the home; the room is where it gets made._

The prototype is a single self-contained HTML file (no build, no deps — open it and it runs) covering three surfaces: Home (the output-first walk-in), a Space called Memory design (a bound unit of work where a huddle is live, an artifact is being built, and three teammates plus a Cartographer agent are in the room), and Ask (⌘K synthesizes answers with citations to the primary source — voice call, doc, canvas note — not links). Agents (Cartographer, Researcher, Scribe) live inline with humans, distinct only by dashed circles and mono uppercase labels. No "✨ Ask AI" buttons, no segregated AI zone — they're teammates, not features.

The design tenets: Calm over dashboard — aim for Tuesday morning office arrival, not command center; desaturate and remove motion before adding. Output-first — inputs are modalities, not the center of any view. Presence is grouped, not floating — compact avatar stacks, no Zoom-style bubble swarm. Live modalities are first-class but ephemeral — they can be opened, joined, archived, retrieved, and they sit beside the artifact layer rather than in front of it. Most "I should add X" instincts have already been considered and rejected; the prototype is supposed to feel quiet.

The bet underneath the mockup: five-to-fifteen-person remote teams already have voice, video, docs, chat, and a growing pile of AI features bolted onto each. Abuzz is testing whether you can collapse those into one surface where the agents are teammates and the artifact is the room. If it works, the daily experience of small remote teams becomes less bureaucratic, more alive, and more like sharing an office — which is the actual job of a workplace, and the thing we lost when we went remote.

For six days, the job that syncs my messaging threads reported "ok" every 30 minutes while syncing nothing. The messaging service had started rejecting the old client at the handshake, and the sync tool exited cleanly on total failure. It logged 285 runs, every one "ok" and every one a failure. I found it by luck, during a routine upgrade.

The job belongs to my OpenClaw agent. OpenClaw is an open-source agent you host yourself, and mine runs on a small cloud machine around the clock. I talk to it on Telegram, and it's locked to my account. It runs on Claude, with access to my email, read-only access to my messaging threads, and a notes repository it commits to on its own. A dozen or so scheduled jobs do the routine work: watching for deadlines, tracking returns, syncing messages. Our kitchen agent lives there too. It sends recipes to Sous, our cooking app, and keeps our family recipe book.

That failure was typical. An always-on agent is an operations problem before it's an AI problem. The model was rarely what broke. The plumbing broke, and it broke quietly. Earlier in the summer, a missing flag made the same sync hang the agent's half-hourly check-in, and after that the sync quietly stopped running. Nothing came in for a month, and when I fixed it, 14,902 messages arrived at once. The check-ins had a failure of their own. Over a month and roughly 1,500 of them, the agent replied that everything was fine and never once ran the checklist it was meant to run.

Some failures were worse than silence. The returns tracker once warned me about a deadline on an order number that didn't exist anywhere in my email. The model had made it up. The rebuild changed one rule: nothing gets written unless it can point to the source message it came from.

A Gemini Deep Research run on one question: who says AGI is close, and what do they stand to gain if they're right? It catalogs 33 named figures across AI labs, academia, investors, skeptics and policymakers, each with their public AGI timeline, their view on how fast takeoff goes, and the equity or fund exposure behind it. The aim is to read each argument against the speaker's incentives, not to call anyone dishonest.

The report's last sections net out each speaker's financial book and ask what's left to learn from the loudest voices. It's embedded below.

Each capability in Daclaude is a plain-text `SKILL.md` file — the prompt is the codebase, an Obsidian vault is the persistent state, and ~30 skills handle email triage, voice capture, quests, medical reimbursements, and financial modeling. On a morning walk I can send a voice memo on Telegram; the bridge transcribes it, classifies it against a routing map (`ROUTING.md`), and files anything above 90% confidence straight into my rolling journal, a project folder, or my task list without me opening a laptop. When a skill falls short, I edit its markdown instructions mid-session: `/handoff` captures what broke, an adversarial verifier checks the fix, and learnings compound across sessions.

For a while, the system's biggest problem was that it wrote me too much. By September there were five bot-authored documents in my inbox every week: a content digest, an attention list, a deadline watch, a weekly review, and a weekly journal template. Each made sense when I added it; together they competed for the same few minutes, and some I wasn't opening at all. The weekly review proposed email filters for me to approve by ticking a box — in five weeks I ticked zero, and its list grew to twelve items nobody read.

Getting it right was mostly deleting. A daily prep note and a weekly recap went in June; a health table, a handwriting-to-journal pipeline, and a reading-review queue followed. What survived are two quiet documents: a Reads Issue on Tuesday and Thursday mornings — newsletters, feeds, and bookmarks synthesized into a single twenty-minute sit-down read — and a Weekly Digest on Fridays centered on one rule in the system's own instructions: orientation is presence, not productivity. There are no activity logs or streaks to keep; the opening prompt simply asks, "what do i WANT to do this week? and why?"

The issues are also how I talk back to the system. Any inline reply I leave under an item gets processed on a twice-daily pass and receipted in place. If I write `"do it"` under an item — with an instruction or without one — a background Claude session spins up on its own branch, does the work, and stops at ready-for-review. Anything urgent enough to surface outside that rhythm gets a single line at the top of my notes home page, and deleting the line dismisses it.

Most personal finance apps stop at showing you what you already spent. This skill connects Claude to live account data via MCP and routes across five working modes: current financial picture, multi-decade scenario projection ("what if we buy a house or take a sabbatical?"), an investment thesis workshop, advisor meeting prep, and tax planning.

The centerpiece is an auto-generated HTML dashboard with Chart.js visualizations. Key metrics up top, then a scenario explorer where you toggle between income, business growth, and market return assumptions and watch 10-year net worth trajectories shift in real time. FI progress with Coast FI milestones, forward-looking goal feasibility, and portfolio allocation tracking. A collapsible "Go deeper" section holds portfolio perspectives (five AI personas synthesize investment recommendations from actual data) and an interactive scenario builder.

The thesis workshop mode is the most unusual piece — five personas (Macro Strategist, Market Mechanic, Fundamental Analyst, Wealth Steward, Challenger) each analyze an investment thesis in their own voice, then synthesize where they agree and disagree. The Challenger persona evolved from a generic Contrarian — instead of reflexive disagreement, it proposes falsification criteria for the thesis and names specific conditions that would prove you wrong.

What we learned building it

Started with an autonomous research phase: built a backtesting engine over 18 months of multi-venue perpetual futures funding rate data and ran a 12-round "Ralph Loop" that tested 100+ strategy variants across entry timing, venue selection, concentration, and exit rules without human intervention. Single-venue concentration beat multi-venue complexity across every regime, and adversarial verification caught five critical backtester bugs before capital was touched.

Phase two shifted from pure backtesting to ML-driven prediction and live deployment. Built a LightGBM quantile regression model achieving 86% direction accuracy across 46 features, wrapped in a composable strategy shell that separated signal generation from entry, sizing, and exit decisions — testing 21 variants and proving that data-based exits beat prediction-based exits by 40+ percentage points. Deployed a FastAPI + Vercel cron pipeline for live signal generation alongside a TUI simulator, uncovering and fixing four ML pipeline bugs where the trained model was silently bypassed.

Skills are reusable instruction sets that tell Claude how to handle specific recurring workflows. Built these from my own daily work running a startup — radical-candor coaching, email triage, reality-checking plans, and prompt sharpening — then packaged them for other operators. The coaching skill alone replaced hours of solo "let me think through this" time.

At Recall the same pattern became a shared team repository (`recall-skills`): `/commit` for structured commits with a short reflection, and `/learn` for routing what a session discovered to the right docs, so what one person's agent learned reached everyone's. I also used the suite as the base for Operator Superpowers, an experiment in whether a five-agent AI executive team (a CEO plus marketing, sales, content and ops) could run a small real business while the founder focused on product.

A Gemini Deep Research run that built a longitudinal view of corporate ethics failures across big tech — antitrust, privacy, labor, safety, content, and AI-specific violations from 2000 through 2024. Each incident was scored by impact (severity × scope × duration × precedent) and plotted on a per-company timeline, with the bubble size scaled to the impact score.

The headline pattern: a small number of companies carry most of the cumulative impact, and the distribution is not what most narratives assume.

Meta's score is far above everyone else's. OpenAI's 26 comes from less than three years (2022–2024), which puts it level with companies that have 20+ year histories.

Embedded below.

Atlas started as a way for our team at Recall to ask questions about company context without digging through docs and chat. By December it was doing the reverse: comparing each day's commits, threads, and docs against our stated plans and telling us when our daily work had quietly drifted away from our own strategy.

The first version in August 2025 was a small MCP server. It pulled daily activity from docs, project tracking, code, and chat into a daily summary, and served it from one backend behind both desktop coding agents and a team chat bot. Anyone asking a question in chat and an engineer working in their editor were querying the same memory. Alongside company knowledge and daily summaries, we shipped early content tools to draft social posts and blog pieces in the company voice.

September 2025: Semantic search and chaining

A month in, daily summaries and keyword lookup weren't enough once history accumulated. We added `pgvector` semantic retrieval across all company knowledge and past summaries, alongside live web research, social and blog RSS monitoring, partnership research, and PR review context.

As COO, I led go-to-market, product strategy and operations through the company's merger and its pivot to an open evals network for AI, which we ran as an AI-native operating experiment.

A decentralized data network where applications build on shared, user-controlled data streams. It replaced the 3Box.js approach with event-sourced streams anchored to verifiable timestamps, IPFS for content-addressed storage and a custom consensus mechanism for ordering.

As CEO of 3Box Labs, I led the company through Ceramic's whole life: protocol design, network launch, the developer ecosystem and, in 2022, ComposeDB, a GraphQL interface that made it usable for ordinary web developers. Hundreds of applications were built on it. Mainnet launched in 2021, and Ceramic worked with IPFS, Filecoin and The Graph.

The idea I'd point to is composable data models: shared schemas that let different applications read and write the same data without coordinating with each other.

An open-source JavaScript SDK that let decentralized applications store user-controlled data and identity profiles. Instead of every app building its own user system, 3Box.js was a shared layer where people owned their information and could carry it between apps.

More than 100 early projects adopted it, among them Uniswap, Gitcoin and Aragon. It covered encrypted private data, public profiles, messaging threads and sharing across apps, all tied to a user's cryptographic account and stored on IPFS and OrbitDB, not a central server.

It proved people wanted this, and it showed where IPFS fell short for production: performance, reliability and queries all needed a rethink. That rethink became Ceramic.

The core problem wasn't gamifying arithmetic — it was mapping the new Common Core fluency standards into a progression that made mathematical sense instead of reading like a checklist. As PM at Zearn, I led the creation of proprietary curriculum IP around how the standards built on one another: which concepts were true prerequisites, where common student misconceptions lived, and how to sequence interactive visual models so kids built genuine number sense rather than memorizing procedures.

Number Gym became the first product suite to implement the new standards end to end, reaching millions of elementary students across the US through Zearn's school and district partnerships.

Each round adapted to where the student actually was: facts a kid missed came back on a spaced-repetition schedule, while mastered tables stepped out of the way so practice felt like a game rather than a drill. We tested the prototype directly with elementary classrooms in NYC public schools to tune the pacing and difficulty curve.