Flavio Espinoza

A commissioned production trading platform I designed and built end-to-end for a paying client -- signal engine, backtester, autotrader, live dashboard, and a real-time backend, with trade execution and P&L read directly from the Solana blockchain.

1. A.Built the entire platform solo by directing a strict hierarchy of AI agents, running every change through a control-vs-variant loop -- promote a variant only when it beats the current control on the same data.
2. B.Built a deterministic TypeScript backtest engine that replays full historic candle data, cross-validated against TradingView (Pine Script) and QuantConnect (Python) so backtest and live behavior match exactly.
3. C.Built Sol Signal -- a proprietary trend-flip algorithm that ingests any market's interval candles and emits long/short signals, in both historical and live modes.
4. D.Built the Next.js / React / D3.js dashboard on Vercel: a custom TradingView-style chart plotting candles, trend-flip markers, and live positions, updating in real time from on-chain data.
5. E.Built a WebSocket-first 24/7 backend -- persistent price, candle, and on-chain wallet feeds with RPC fallback, MongoDB as system of record, and PM2 on Railway with a health watchdog that auto-restarts stale feeds.
6. F.Building the automated trading bot + Signal Emitter (v2): event-driven, with a position state machine, self-signing wallets, and a custody model where each wallet can only return funds to the client's master wallet.

Model-agnostic AI voice interface: one audio pipe where the AI at the seam is swappable. I use Gemini for research, Claude for coding, ChatGPT for brainstorming -- none of them had hands-free voice I could actually use, so I built one platform that gives me all three.

1. A.Built a model-agnostic AI voice platform -- one audio pipeline (Silero VAD, Deepgram STT, streaming TTS) with the AI at the seam deliberately swappable; v1 runs the Claude Agent SDK (full CLI toolset), Phase 2 plugs in Gemini for research and ChatGPT for brainstorming behind the same pipe.
2. B.Killed my own first design. v1 was Python/FastAPI middleware that quietly neutered Claude -- three tools, a system prompt it ignored, hallucinated timestamps with no shell. Deleted the middleware and ran the Claude Agent SDK directly, archiving the Python backend as a tombstone. Control -> variant -> ship the winner.
3. C.Owned the full stack solo -- React/Vite/TypeScript front end (AudioWorklet 16kHz PCM capture, streamed reply bubbles, live karaoke highlight) and a Node/TypeScript WebSocket backend; same React/TS/Node + Anthropic Claude API stack production teams ship on.
4. D.Built a real-time voice loop -- server-side Silero VAD (energy fallback + pre-roll so the first word is never clipped), Deepgram nova-3 streaming STT with keyterm prompting, and a streaming Aura-2 TTS pipeline that synthesizes per sentence, strips code from the spoken path, paces paragraphs, and resumes from interruptions.
5. E.Instrumented its own spend -- live context-token indicator and a model-fallback cost alert that fires the moment the SDK returns a cheaper model than requested; dual-format (JSON + Markdown) chat persistence with full-text search.

AI training platform connecting domain experts to leading AI companies to evaluate and improve advanced models through human expertise.

1. A.Run coding tasks and A/B model-testing on production codebases using AI coding agents.
2. B.Wrote rubrics for AI assessments; the quality earned an invitation to serve as a reviewer.
3. C.My most recent A/B model-test assignment scored 5/5 (Pass). Reviewer comments: "Expert has created a strong and complex task that meets the quality standards. Strengths: Prompt is sufficiently complex and challenging; all files and links are validated (the GitHub repo size is more than 200 files); task has 5+ meaningful runs and 1+ hours per model; use case is realistic and developer facing; conversations of both models are deeply analysed; behavioral issue types are correctly categorized; 3+ meaningful behavioral issues are identified; ratings are aligned; Model A has clear preference and is justified; feedback is comparative between models, not descriptive. Weaknesses: none. Perfect task!"

Bless Network (formerly Blockless) -- seed-funded (~$8M: a $3M pre-seed and a $5M seed), ~12-person decentralized-compute startup. The world's first shared computer, where everyday devices contribute idle browser compute for token rewards. Growth was the product; I own the user-facing growth surface end to end, with no spec, just conversations with our founders and CTO.

1. A.My Chief Technology Officer directed me to use AI to start coding -- my first time. I used ChatGPT to figure everything out (May 2024), and it is where I went from never having used AI to running it as daily core infrastructure.
2. B.Replaced the old email/password/verify signup with a one-click Web3Auth single sign-on (OIDC) -- Google, GitHub, and passwordless email, each deriving a self-custodial Solana wallet and authenticating by challenge-response signature instead of a password. The marketing lead, tracking MailChimp signups against the prior six months, reported completed signups up ~70% and abandonment down ~45% (team-reported, not instrumented).
3. C.Compounded an underperforming referral program (a 10% bonus already in place) by building stacked, OAuth-verified social and quiz boosts on top -- X +5%, Discord +5%, quiz +5% -- summed into one live "Total Boost" to drive virality. The founders reported engagement up ~45% (team-reported, not instrumented).
4. D.Fought the X/Twitter OAuth integration to completion against an API mid-rename from twitter.com to x.com with badly out-of-date docs -- a moving-target integration with no clean answer, shipped by iterating until it worked.

Overclock Labs (Akash Network) -- a seed- and token-funded decentralized cloud marketplace on Cosmos, ~20 people. On a three-person, fully-remote team I proposed the stack and owned the front end, the Node (Fastify mutual-TLS) proxy, the in-browser wallet and certificate crypto, and the four-stage CI/CD pipeline behind the Akash Console -- turning a nine-step command-line deploy into a web app a developer could actually finish.

1. A.Turned a nine-step blockchain deploy protocol into a guided, finishable flow -- from template to running workload -- hiding wallet signing, on-chain certificates, bid selection, and lease creation behind a usable UI.
2. B.Proposed the stack and built the multi-step (5-step) SDL deployment wizard (React + create-react-app, Formik validation) with an 18-plus template marketplace of pre-validated node configs pulled via the GitHub API.
3. C.Designed the Fastify mutual-TLS proxy and WebSocket real-time monitoring -- streaming logs, Kubernetes events, lease status, and an xterm shell back from 100-plus providers, with throttled rendering tuned for continuous streams.
4. D.Built the four-stage GitHub Actions CI/CD pipeline -- auto-versioning, multi-arch Docker builds, staging, and approval-gated production.

WebShield -- a seed-stage healthcare-identity startup (~7 people; seed led by New Enterprise Associates, with a milestone-based convertible note up to $10M) building a person-centric healthcare authorization and consent network. As the sole front-end engineer, I owned the authentication layer and the Node API server behind Exemplar, the app that demonstrated the product to customers and investors.

1. A.Built a production single sign-on (OpenID Connect) layer from scratch, implemented directly from the OAuth 2.0 / OIDC spec on a Koa/Node backend -- the authorization-code flow, JWT signature verification, and state/nonce replay/CSRF protection by hand -- because no off-the-shelf module existed for our stack.
2. B.Led the team's pivot off a stalled enterprise-federation effort (PingFederate) to Okta, clearing the configuration bottleneck and freeing the CTO to focus on the core network.
3. C.Designed a contract-first "mock network" -- a GraphQL resolved_person + trust_summary projection served as fixtures -- that let the entire product ship and demo months ahead of the Go backend; going live to production was a single endpoint-URL swap.
4. D.Owned both directions of the network API: publishing new people and organizations into it (ingest) and querying resolved identities back out (discovery + trust query), all behind that one swappable contract.

A professional crypto trading terminal where the chart IS the interface -- chart-as-order-entry, Fibonacci batch ordering, and a from-scratch indicator library. Same client as eScanner.

1. A.Built the chart itself into the order-entry surface -- a custom D3.js v4 candlestick engine (reworked from react-stockcharts) where open orders are draggable lines on the price chart.
2. B.Built drag-to-modify orders -- dragging an order line triggers an atomic, fully async cancel-and-replace: cancel, wait for escrowed funds to release, recalculate the amount while preserving total cost, then place the new limit order.
3. C.Built Fibonacci batch ordering with curve-based "paradigm" distributions -- draw a fib range and deploy 10+ weighted limit orders in one action, front-loading the deepest rungs to cover a spread.
4. D.Built the real-time Node.js + Express + Socket.IO backend on CCXT's unified exchange layer, with live WebSocket order/balance updates and a D3.js depth chart; shipped in versions (DAX → Bittrex + HitBTC → HitBTC). This project later won me the Sol Engine commission.

A real-time cryptocurrency market scanner I built solo for a private trading client -- live-streaming an entire exchange's markets, aggregating them in Elasticsearch, and surfacing the moves worth trading through fast, filterable candlestick views. Same client as Street Fighter.

1. A.Built a real-time crypto market scanner end to end (sole developer) -- live exchange data over Socket.IO, Elasticsearch time-series aggregation, and interactive D3.js / React-Stockcharts candlesticks.
2. B.Koa.js API on an 8-instance PM2 cluster behind Nginx with SSL and ip_hash sticky-session load balancing across 5 Socket.IO upstreams.
3. C.Streamed HitBTC market data via CCXT into OHLCV candle aggregations with configurable intervals and percent-change / volume filters, watchlists, and ignore-lists.
4. D.React / Redux SPA (50+ components) with JWT / Passport auth, Stripe subscriptions, and SendGrid email.

Swim AI (now Nstream) -- a seed-stage edge-computing startup building a real-time streaming and IoT (Internet of Things) analytics platform for device tracking and monitoring; a tight core R&D team of fewer than 15 in San Jose during my tenure. It went on to raise a $10M Series B (ARM, Cambridge Innovation Capital) after I left.

1. A.Designed and developed a web map application on the FLUX pattern with unidirectional IoT data flow.
2. B.Augmented human decision-making with the most accurate, relevant real-time and contextual data -- turning live device data into operational decisions.
3. C.Delivered projects for the San Francisco Transit Authority (live bus tracking), Palo Alto (traffic-light monitoring), the City of Chicago (transformer and meter locations and power consumption), and Boston (automated street-light control). Built with React, the Google Maps API, and Swim APIs; deployed on AWS.

SolarCity -- a NASDAQ-listed (SCTY) residential-solar company of ~15,000–19,000 employees, financed by corporate debt and tax-equity rather than VC rounds, and acquired by Tesla in 2016 (~$2.6B). I was a Lead Full-Stack Engineer on the internal software team that built the real-time installation-analytics platform.

1. A.Led development of a real-time data analytics platform (Python, Django, Angular) for high-volume solar-installation metrics.
2. B.Architected a Django REST API serving real-time analytics to an Angular front end with D3.js visualizations.
3. C.Integrated Elasticsearch for full-text search and analytics across installation records and customer data.
4. D.Optimized MS SQL Server queries, cutting dashboard load times from 40+ seconds to under 3.

Vivint Solar -- A Blackstone private-equity-backed residential solar company founded in 2011 as a Vivint Inc. spin-off (bypassing traditional VC series rounds). I joined as the 6th engineer on a lean core development team that rapidly scaled to 45 engineers over 14 months. In October 2014, the company launched its IPO on the NYSE (VSLR) at a baseline market capitalization of $1.5 billion ($16.00/share). Vivint Solar was later acquired in October 2020 for $3.2 billion by Sunrun, which trades on the NASDAQ (RUN).

1. A.Led a 3-person engineering team building Mercury, Vivint Solar's real-time sales-and-installation analytics platform -- a key source of the sales-and-revenue analytics and presentations used in the company's 2014 NYSE IPO due diligence.
2. B.Engineered an Angular pivot dashboard to replace static HTML readouts -- letting stakeholders instantly slice-and-dice installation metrics, sun-hour distributions, and sales-rep performance in real time.
3. C.Supported macro-filtering (state, system size, module count), account-level drill-downs, and instant CSV export of 100K-plus rows without interface degradation.
4. D.Built the authentication layer and the Node API layer that hooked into a new near-real-time Elasticsearch indexed data store -- our backend team stood up that store, ingesting millions of rows from relational SQL databases and Salesforce via the Elasticsearch JDBC River, replacing a legacy daily reporting job that took over an hour. Our APIs were what tied into it and powered the slice-and-dice.