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Dhairya Sindhwani@dhairyasindhwani70666
3
Events attended
2
Submissions made
India
1 year of experience
About me
I'm an incoming Computing student at Imperial College London who enjoys turning ideas into working products. My interests span AI, software engineering, and entrepreneurship, and I've worked on projects ranging from research and hackathons to building AI-powered applications. I'm always excited to learn new technologies, collaborate with others, and create software that people genuinely find useful.
🤝 Top Collaborators
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🤓 Latest Submissions
Synthetix Alpha
Synthetix Alpha is an autonomous options, equity and crypto trading agent with one hard rule: LLM agents research, a deterministic runtime trades. Agents read arXiv quant-finance papers and write strategies as declarative JSON specs: option legs by delta, DTE window, signal gates, exits, sizing. A backtester replays real option chains day by day with fills at mid plus half-spread, a same-day volume floor, split adjustment and commissions. Candidates are scored on worst year and drawdown, re-tested on an unseen vendor and underlying and swept for fragility. Kaggle chains, DoltHub volatility surfaces 2019-2026, FRED, Yahoo Finance earnings, FOMC dates and Finnhub news feed it, and WRDS validates spreads and factors. It caught its own error: on minute bars from the price a market order actually gets, the gap fade's edge was an opening-print artefact, and we say so. On a Hostinger VPS a scheduler fires at 09:31, 09:45, 15:50 and 15:56 ET and runs three sleeves: put credit verticals (sell 20-delta, buy 10-delta, 65 DTE, exit at 65% of credit, 2x stop or 21 DTE), an equity gap fade (ten most negative volatility-scaled gaps, open to close) and crypto dislocations (24h move below -3.5 sigma, 48h hold). The options screen spans 1,500 names: IV/RV 1.25-2, $20M ADV, chain spread under 10%, no earnings within 30 days. Every order clears governance.yaml gates: positive NAV, 20% total and 5% daily drawdown halts, 1x leverage, 10% per name, 12 slots, defined-risk only, 3% of equity at risk, and a crossing check (bid/ask credit at least 60% of mid). Multi-leg limit orders go through the Alpaca CLI with client order ids hashed from legs and date and deduplicated at the broker; a paper-only assertion refuses live keys. A dashboard (FastAPI on Render, Next.js on Vercel) shows the screen, positions and execution ledger; a Featherless-hosted LLM critic reviews each candidate as a risk officer; every approval re-runs the deterministic gate before a paper order is placed.
4 Sep 2026
ClipContext
ClipContext takes a short creator video and turns it into platform-ready metadata: ten candidate titles, ten candidate descriptions, and ten candidate hashtag sets are generated per video, each pool independently ranked by an AI discriminator, with the top 5 of each pool surfaced to the creator — plus, optionally, a direct upload of the analysed video to the creator's own YouTube channel with whichever candidates they picked. Every candidate is written in a style pulled from real trending videos — not a generic "make it punchy" instruction. ClipContext always analyzes worldwide YouTube trends in the video's own niche; if the creator gives their channel handle, it uses that specific creator's own top-performing videos instead. Local-first preprocessing: Video validation, audio extraction, 1 FPS frame scanning, visual-quality scoring, and perceptual-diversity frame selection all run locally before any paid AI call. Evidence-grounded generation: Titles/descriptions/hashtags are required to trace back to the video's actual transcript and visuals — not free-associated from a topic string. Genuine diversity, not ten rewordings. Each of the 10 candidates per pool is generated against a distinct strategy (question, bold claim, curiosity gap, number-led, story, technical, emotional, SEO-minimal, creator-voice, and more). Two trend sources: ClipContext always mines worldwide YouTube trends in the video's own niche for a real, data-derived style profile (typical title/description/hashtag structure, SEO vocabulary, tone) — and, if a creator hands over their channel, it swaps in that specific creator's own top-performing videos instead, so the output sounds like them, not a generic trending-video template. Independent AI ranking: A second model scores and ranks each candidate pool against the video's ground truth and real trend benchmarks, with a stated reason per score — the top 5 per pool are what reach the results page.
13 Jul 2026
