Rivus Project Priorities

Generated: 2026-03-09T16:52 PT  |  Source: PRIORITIES.md  |  Hash: eed3b9f4

Scoring Rubric (1-5 each, max 50)

Dimension135
PayoffNice to haveUseful, saves timeGame-changing, compounds
AmplificationOne-off, manualReusable for several tasksRuns autonomously at scale
ShowabilityInternal tooling onlyDemoable to collaboratorsJaw-dropping wow, differentiating
FeasibilityUnclear how, high riskKnown approach, some riskClear path, low risk
EffortMonths of focused workWeeksDays to working v1
MomentumCold start, nothing builtSome pieces existAlready partially working
DefensibilityEasily replicatedSome proprietary data/edgeUnique data, arch, or workflow moat
VC narrativeIrrelevant to pitchSupporting evidenceCore to the story, demo "aha"
MonetizationPure internal toolCould charge eventuallyClear revenue path / customer pull
ExcitementDread / burned outNeutral, would do itCan't stop thinking about it

Rankings

#ProjectPayAmpShowFeasEffMomDefVCMonExcTotalNotes
Tier 1 — Active Focus (score 39+)
1 Intel + VC intel 5454344544 42 Core deliverable. Companies + people + VC = one system. Pitch with your own tool analyzing VCs = ultimate "aha"
2 Vario (multi-model platform) 4545453335 41 Foundation for everything. Excitement keeps it near top
3 World inference + investing 5534334354 39 Macro reasoning via ng → investable insight. No burnout
3b Market signal intelligence 5543324454 39 Ingest news + world events → assess stock/sector impact. Real-time edge
Tier 2 — High Potential (score 34-38)
4 Ideas (idea eval + landscape) 4444453324 37 Upgrades Draft novelty, finance thesis eval, semnet synergy
4b Skillz feasibility spec 4344523435 37 Moonshot worth a feasibility pass. Days to identify paths
5 Skillz 5532225325 34 Highest ceiling + deepest moat. Excitement pulls it up
5 Supply chain / bottleneck 4343324443? 34 Unique vertical, clear enterprise ROI, defensible
5 Video analyzer 4344333334 34 Earnings calls + media consumption
Tier 3 — Backburner (score <34)
8 Learning / recall + gyms 4433334224? 32 Symbolic recall + gyms for self-improvement
9 Draft (doc analysis + writing) 4344342223 31 Useful but crowded. Ideas module is its upgrade path
10 SuperReader + SuperFeed 4443212234 29 Cold start. Shared pipeline, single initiative
11 Brainstormer 3334432114 28 Retrieval + human thought UI
12 Semantic net / inference 4432223214 27 Cool research play. Excitement keeps it from bottom

Active Tracks

#1 Intel + VC Intel

13,732 companies • 4,340 VC firms • 4,890 people • Founder scoring 3/7 dims

#2 Vario ng

4 blocks (produce, score, revise, reduce) • 11 recipes • 221 tests • CLI + Python API
Missing: enrich, execute, decompose, steer, plan blocks. No benchmarks.
DayWhatUnlocks
1Build enrich block (web fetch + search + RAG)VC, supply chain, world inference
2Build execute block (run code, call APIs, tool use)Verification, data processing, agentic tasks
3Port benchmark.py, run MATH on 11 recipesNumbers: which recipe wins
4Run ng on real Draft doc + wire gyms to ngProof ng is useful. Gym baselines
5Build decompose block (sub-task breakdown)Complex multi-step reasoning
6TenOneTen through ng: enrich → score → dossierKiller demo powered by ng
7World inference: model_debate on macro questionsInvestable insight
Detailed next steps
  • Build enrich block — web fetch + search context injection. 4-8h
  • Build execute block — run code, call APIs, verify claims. 4-8h
  • Build decompose block — break hard problems into sub-queries. 4-8h
  • Port benchmark.py to ng, run MATH/MMLU-Pro on all 11 recipes
  • Run ng on real Draft doc (best_of_n, refine_until_converged)
  • Wire gyms to run through ng recipes
  • TenOneTen through ng (after enrich)
  • World inference: 5-10 macro questions through model_debate
  • Wire ng recipes into Studio tab. 4-8h
  • Persist ng RunLog to experiments DB. 2-3h

#3 Learning + Gyms

Goal: demonstrate product performance self-improves through gyms

#4 World inference + investing

The non-burnout path to finance. Macro reasoning via ng → investable insight.
ng model_debate "What happens to semi demand if AI capex peaks in 2027?"
ng confirm "Is TSMC pricing power sustainable given Intel foundry?"
ng weighted_vote "Top 3 supply chain bottlenecks for 2026-2027?"

#4b Market signal intelligence NEW

Ingest news + world events → assess stock/sector impact in minutes. Reactive, not proactive.
news source → ingest → entity extraction → impact assessment (variong) → signal

Defensible because: combines intel company graph (who supplies whom), supply chain bottleneck data (which disruptions cascade), and multi-model reasoning (variong) into a response pipeline no retail investor has.

Data sources (tiered)
  • Phase 1: RSS feeds (Reuters, Bloomberg, TechCrunch), SEC EDGAR filings, WARN notices
  • Phase 2: Twitter/X financial accounts, earnings whisper channels, Finnhub news API
  • Phase 3: Satellite imagery proxies, alternative data APIs

#5 Jobs running (ongoing)

Autonomous job clusters keeping data pipelines flowing

TenOneTen Demo Sprint

Cold start — nothing in DB yet. Target: TenOneTen Ventures (LA, data science founders)
  1. Discover firm — scrape tenoneten.com, get partners, bios, thesis
  2. Map portfolio — scrape portfolio page, get ~30-50 companies
  3. Ingest to registry — add firm, partners, portfolio companies
  4. Score founders — run 3-dim scoring on portfolio founders
  5. Reachability — Tim's 2-hop path to partners
  6. Generate dossier — thesis, portfolio heat map, partner profiles
  7. Polish UI — make it look good in kb.localhost/intel/people

Target VC Firms (20)

SV Tier 1
1. a16z — largest tech fund
2. Sequoia Capital — AI/infra thesis
3. Benchmark — small, data-driven
4. Greylock — enterprise/AI
5. Lightspeed — growth + early
6. Accel — global, data/AI
SV Tech/AI-Focused
7. Khosla Ventures — deep tech, AI-forward
8. Founders Fund — contrarian, Thiel
9. NEA — large, broad tech
10. Index Ventures — dev tools/infra
11. Coatue — crossover, data-driven
12. General Catalyst — tech + health
13. Radical Ventures — pure AI (Hinton)
LA-Based / LA-Present
14. Upfront Ventures — LA flagship
15. Greycroft — LA + NYC
16. M13 — tech-enabled consumer
17. Wonder Ventures — LA early stage
18. TenOneTen — data science founders
19. Bonfire Ventures — LA enterprise B2B
20. Fika Ventures — enterprise AI

Dollar Value Estimates

ProjectDifficultySize (person-mo)Payoff Range
Intel (companies + people)Medium3-6$50K-500K/yr
Vario (multi-model platform)Medium4-8$0 (infra) or $100K-1M
VC intelMedium2-4$100K-1M/yr
SkillzHard6-18$1M-50M/yr
Skillz feasibility specEasy0.5-1Unlocks above
World inference + investingMedium2-4$100K-10M/yr
Market signal intelligenceMedium3-6$200K-5M/yr
Supply chain / bottleneckMedium4-8$200K-2M/yr
Video analyzerMedium2-4$50K-300K/yr
Learning / symbolic recallHard4-8$0 (internal) or $50K-500K
Draft (doc analysis + writing)Easy2-3$20K-200K/yr
SuperReader + SuperFeedMedium4-8$50K-500K/yr
Semantic net / inferenceHard6-12$50K-1M/yr
BrainstormerEasy1-2$0 (feature of Vario)