FM-os
public🛠️ FM-os: the living, SLM-first map of foundation-model operations — pre-training, post-training, fine-tuning & RL. Curated repos, courses, papers & jobs, auto-refreshed weekly.
< > Codeno hosted app yet
Python★ 32026-10-12
Compounding everything
12X Future
Aim
12X the future, 12% the loop now
Begin from the End — Result-Oriented System Engineering (ROSE): every venture starts as a one-page end-state — the 12X result, its metric, its date — written before anything is built. Then the arithmetic takes over: (1+0.12)^22 ≈ 12.1, so a 12X future is ~22 compounding loops, each moving a named metric by ≥12% now, measured — or it wasn't a loop, it was motion.
the TRUE test ↓
T Transferable & Transformative — End-state-first plus the compounding identity (1.12^22 ≈ 12.1) transfers to revenue, research, and relationships alike — any 12X dream decomposes into a designed finish line and ~22 measured loops.
R Reusable & Refinable — Two reusable artifacts: the one-page end-state (result, metric, date, owner) and the loop contract (name the metric, move it ≥12%, show the measurement) — both refinable every re-plan.
U Understandable & U-loop — One breath: begin from the end at 12X, then demand 12% from every loop — because anything less never compounds there.
E Experienceable & Experimentable — Write the end-state page for your current project today, then run one loop this week and read its measured delta. No +12% reading = motion, not progress.
For you — You never start without the one-page finish line — and you hold both numbers at once: 12X on the wall, +12% measured in every loop.
For an agent — A skill that rewrites any goal as its one-page end-state (metric, date, owner), plus a workflow gate: a loop only counts when its named metric shows the +12% delta — measured, never claimed.
Build
Amazing Art × Great Science × Deep Engineering
Big dreams are earned three ways. Amazing art: elegant beauty that strikes awe — simple form, profound truth. Great science: a falsifiable hypothesis. Deep engineering: a tested, shipped system. Every bet carries all three, or it's a wish — then vision sets the heading, telemetry compounds it, and each week one open gap gets closed.
the TRUE test ↓
T Transferable & Transformative — Art-plus-hypothesis-plus-system transfers across domains: the same triple that ships a trading engine ships a biomarker pipeline — and awe travels farther than any spec sheet.
R Reusable & Refinable — Three reusable artifacts: the falsifiable claim, the test suite, and the weekly gap-review — every failed hypothesis still compounds the map, every closed gap raises the floor.
U Understandable & U-loop — One sentence: earn the bet three ways — beauty so elegant it strikes awe, truth so simple it runs profound, a system so tested it ships — then let the destination pull while the sensors correct.
E Experienceable & Experimentable — Write your biggest bet's falsifiable claim and its failing test today; close one open gap this week and check that next week starts from higher ground.
For you — You hold every big dream to three bars — elegant beauty that strikes awe, a claim that could fail, a system that ships — then let the future set the heading and close one gap a week.
For an agent — A plugin that refuses to open a build until the bet carries both artifacts (the hypothesis that could fail, the test that would show it) — feeding the telemetry-fed re-solver workflow that recomputes the best next moves from actuals.
Love
Love God · Love Family · Enable Friends
The base case and the multiplier: faith and family are floors, not variables — and friends don't just get encouragement, they get enablement: shipped tools, real introductions, honest coaching.
the TRUE test ↓
T Transferable & Transformative — The ordering — God, family, friends — transfers to every decision at every scale; it is the one constraint set that never gets re-optimized away.
R Reusable & Refinable — Returned to daily, not outgrown: the base case is re-affirmed each morning and each high-stakes decision re-checks against it.
U Understandable & U-loop — Recursion needs a base case: this is the value that stops the optimization from eating the optimizer.
E Experienceable & Experimentable — Experienceable in the calendar and the ledger: time with God and family is scheduled first, and each month one friend receives enablement you can point at — a shipped tool, a real introduction, honest coaching.
For you — You keep faith and family as floors, not variables — and you enable friends with artifacts, not just applause.
For an agent — A guardrail hook: before any high-stakes autonomous action, surface who this serves — God, family, friends — and stop when the answer is nobody.
Built in the open (last 12 months)
Projects
GodView-anything
privateThe god's-eye view of what top-rated repos actually teach — verified, weekly-synced, machine-audited. Three provenance-pinned pillars: transferable knowledge, agentic tooling, community.
🔒 source and app are private
Python★ 12026-10-12
rsi-os
public🤖 The most comprehensive, community-driven resourcefor Automated AI Research — papers, tools, people, lbs, roadmaps. Featuring Recursive Lab, Sakana AI, DepMind & beyond.
Python★ 92026-10-12
graph-engineering-anything
private🕸️ the Graph Engineering Operating System — the model finds text; the graph finds reality
🔒 source and app are private
Python2026-10-12
eval-anything
public🧪 the Evaluation Operating System — OEC (Observe → Evaluate → Control) for foundation models, agents, and business objectives
< > Codeno hosted app yet
JavaScript★ 12026-10-12
design-anything
publicAny design intent in (text, picture) → execution-ready 3D blueprint out — construction- and 3D-print-verified. Game design · simulation · architecture (3DCP+AI) · interior · landscape.
< > Codeno hosted app yet
Python★ 12026-10-12
eval-anything-archive
privateARCHIVE (private, do not publish) — pre-rewrite history of eval-anything. Superseded by the public repo; retained only because it contains an unreleased sibling product's walkthrough in old commits an
🔒 source and app are private
JavaScript2026-10-12
longevity-loop
public🧬♻️ An AI-native, build-in-public compounding loop for aging science: falsifiable question → open data → public verifier → honest write-up → share → compound. No wet lab required.
< > Codeno hosted app yet
Python★ 12026-10-12
agentic-portfolio
privateAgentic personal portfolio — Next.js + CopilotKit site whose on-page AI agent answers about my work, powered by a free-LLM survival chain (NVIDIA NIM→Groq→Gemini). Built in the open.
HTML★ 12026-10-12
strategic-collab
privatePRIVATE — Collaboration Intelligence Layer: pre-registered, closed, compounding loop per invited frontier company
🔒 source and app are private
Python2026-10-12
anyagent
privateBuild, grade, improve & accountably ship any agent app from one sentence — a tested OOP engine with a closed RefineLoop and swappable seams. (Private.)
Python★ 12026-10-12
daniel-and-david
publicRaising the next generation of wealth creators & Kingdom builders — an AI-native venture studio + open academy for my sons, built in the open.
HTML★ 12026-10-12
agentic-colab
privatePrivate office for an AI-native founding team: Nextcloud + OnlyOffice as code, AI agents as scoped teammates (MCP).
🔒 source and app are private
Python2026-10-09
agentic-portfolio-public
publicOpen-source agentic portfolio — make yours free, share in one click
TypeScript★ 82026-10-09
FDE-os
publicForward-Deployed-Engineer OS — an operating system for shipping agentic solutions into the field.
HTML★ 22026-10-05
agentic-outdoor
privatePrivate: decision engine + agentic store + founders' ops for a Bay Area outdoor-gear startup
🔒 source and app are private
TypeScript2026-10-02
sos
publicFramework for self-monitoring agents — idle detection, SMARC output-quality verification, and audit trails. Zero dependencies.
< > Codeno hosted app yet
Python★ 12026-09-29
control-anything
publicControl theory as an instrument for judging AI systems. A safety claim is worth exactly the weakest assumption holding it up — so the guarantee is capped at what its assumptions actually discharge.
< > Codeno hosted app yet
Python2026-09-25
18 shown · swipe or use ◀ ▶
Long-form on LinkedIn
Writing
12 of 209 posts · newest first →
Physical AI · World Models from CS229, Episode 1 of 12026-10-16
World Models Start in Six Lines of CS229: Learn the Wolf, Guard the Flock (for Age 20)
A complete world model small enough to read in one sitting is six lines in CS229 section 19.3: count what the world does, plan with value iteration on what you counted, act, repeat. Play it as a shepherd learning a wolf (learning lifts it from 17–37% of wolves turned away to 87–100%, seed 1), then climb to Kalman, Dyna, World Models, Dreamer, MuZero and Genie, with every famous claim's hedge kept.
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Learning Science · The Two Ends of Math, Episode 1 of 12026-10-15
Discrete vs Continuous Mathematics: The Two Ends of Math, and How They Turn Toward Each Other (for Age 20)
Discrete math counts with gaps; continuous math measures without them. Four turns where each borrows the other (limits, analytic proofs of discrete facts, sampling and computation, relaxation and rounding), the condition that triggers each, the technique, and the motive: truth, love or beauty. With three live demos and runnable code.
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Learning Science · Numbers at Four Ages, Episode 4 of 42026-10-15
Number vs Representation: The First Abstraction Every Programmer Uses (for Age 20)
For 20-year-olds who code: the child's idea that a number is not its name is the first abstraction in software. Hume's Principle, log-length positional codes, zero-free base 26 in your spreadsheet, two's complement, floating point, Unicode digits, and an invariance test you can add to your own code.
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Learning Science · Numbers at Four Ages, Episode 3 of 42026-10-15
Place Value: How Ten Symbols Write Every Number, Exactly One Way Each (for Age 15)
For 15-year-olds: a two-hand base-5 counter, a one-division proof that every number has exactly one spelling in any base, a zero-free base ten where one million is 99999κ, the test that separates a number from its notation, and a body map of a number system that says where it breaks.
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Learning Science · Numbers at Four Ages, Episode 2 of 42026-10-15
Number Systems: Why People Kept Inventing New Ways to Write the Same Numbers (for Age 11)
For 11-year-olds: invent your own number system, race it against tally marks, 正, Roman and Egyptian numerals in a live playground, and see why each new way of writing fixed the last one's problem, why zero had to hold a place, and which facts are about numbers and which about how we write them.
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Learning Science · Numbers at Four Ages, Episode 1 of 42026-10-15
What Is a Number? Five Apples, Five Stones and One Five (a Play Script for Age 7)
A 15-minute play script for a 7-year-old and a grown-up: match stones to spoons without counting, clap and jump five, and discover that a number is what equal piles share, while 5, five, 五 and five tally marks are only its names. Ends with the child teaching it back.
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AI-Native Work · Human Dynamics in Agentic AI, Episode 3 of 42026-10-14
Emergent Trends That Move Fast: Old Principles, Measured Again
Six fast-moving trends in human-agent work, each an old principle measured again: the felt-versus-measured gap in developer speed, deskilling evidence, fewer junior hires, agents with owners and offboarding, the agent boss, and shared protocols. Every number carries its date, and a five-minute test for the next trend.
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AI-Native Work · Human Dynamics in Agentic AI, Episode 2 of 42026-10-14
Best Practices That Evolve Slowly: Working With Agents Today
Six practices for working with AI agents, each tied to a principle and its evidence: start simple, gate the irreversible, say what the agent can do, design against over-reliance, know where its frontier is (outside it, consultants using AI were 19 points less often correct), and protect how novices learn.
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AI-Native Work · Human Dynamics in Agentic AI, Episode 1 of 42026-10-14
Principles That Barely Move: The Human Side of Agentic AI
Six human principles that held long before AI and still predict where handing work to AI agents breaks: the ironies of automation, calibrated reliance, delegation as a contract, Conway's law, Goodhart's law, and the needs for competence, autonomy, relatedness and safety. Plus why 'human in the loop' should mean judgment, not a rubber stamp.
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Learning Science · Shepherd's Academy of War, Episode 5 of 52026-10-12
A Scorecard for a Family Academy: One Car-Ride Drill, Four Numbers, Six Tripwires
A program for children that cannot fail cannot be trusted. Here is the whole of ours after three weeks: one twenty-minute car-ride drill across five arenas, four numbers, six tripwires decided in advance, and the rule that medicine and prayer go together.
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Learning Science · Shepherd's Academy of War, Episode 4 of 52026-10-12
Guest Instructors for Children: Content, Not Contact
I want my sons to learn from people who have done hard things. In October 2026 an Olympic coach was banned for life, years after the first complaint. A credential is not a safeguard. Six rules for guest instructors, borrowed from the programs that already do this.
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Learning Science · Shepherd's Academy of War, Episode 3 of 52026-10-12
Courage Training for Children: What the Evidence Supports and What Backfires
I wanted Navy SEAL mental toughness for my sons. The famous number behind it has no source I could find. What does have support: chosen risk, self-control taught out loud, learning the attack under control, many activities and a slow start. What backfires: boot camps, head strikes and one-size resilience lessons.
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Proof, not claims — a résumé audited against real artifacts, then closed into a verified one
Resume Verification
Verify it yourself
Skeptical? Paste a résumé — mine, or any — and watch each claim audited live against real public GitHub. Honest by design: unprovable claims come back unverified, never rubber-stamped.
Your run is shown here in your browser; it doesn’t change the published proof.
58/100
Corroboration index
3/6 claims corroborated by real artifacts, 1 partial, 2 need an external source.
✅ 3 Corroborated🟡 1 Partially corroborated⚪ 2 Unverified — needs external source
top gap: Teaches a Silicon Valley AI-architect cohort. — A cohort syllabus crediting Paul as instructor, or a participant testimonial.
Sample receipts (built from public repos). Ask the agent to verify a real résumé. · source: “(Sample receipts — paste your own to the agent: “verify this résumé: …”.) Paul Jialiang Wu…”
What people who've worked with me say — imported from LinkedIn, or written here (I approve each one; you can post yours to LinkedIn in a click)
Recommendations
“I worked with Paul at Genentech where he was the Principal Data Scientist on my team. His technical knowledge was always astounding and his understanding and direction for the possibilities of Machine Learning using our available data gave our team direction and the confidence to explore new projects. He was a pleasure to work with and I recommend him for any team in need of a technical data/ML lead.”
Senior Machine Learning Operations Engineer @ Genentech - People Insights Roche
Rajesh worked with Paul Jialiang on the same team
“I have been working with Paul on a GraphML project in the past 6 months. Paul is a passionate leader with a solid technical background. He always try his best to inspire and influence others, and to encourage others to share their ideas. Besides, he can build and organize the workflow properly. There is no doubt that Paul will be a great asset to your team.”
Data Scientist - People Insights at Roche
Paul Jialiang was senior to Zhao but didn’t manage Zhao directly
“Paul has a great energy and is a good story teller. He leaded a focus group with high-tier people and we worked together to put our business to the next level, 10X-style.”
Fondateur de CAIRN-CREA | Je fais du contenu organique & pub qui convertit pour marques DTC
Louis worked with Paul Jialiang on the same team
“I am very glad to have spoken with Paul — he's a champion for self-improvement and mental health, and is also a great person to talk to!”
“I had the pleasure of attending Paul's presentation on GraphML in a conference, he is an amazing Data Scientist and a brilliant instructor of latest technologies in Data Science and Machine Learning. He explains very complex concepts, turns challenging topics look easy by breaking them into clear and concise steps. He tries to teach tough concepts by correlating them with real world scenarios which makes him a great story teller. His principle of improving 10x in 3 months is astonishing. He can guide people with any years of experience to be successful in their Data Science journey. I hope to work with him very closely and continue learning from him in the future. Paul would be a great asset for any organization!”
“I had the pleasure of working with Paul at Galvanize, he is a brilliant and gifted Data Scientist who truly understands how to get the best out of people. Paul always made sure to make the students feel at ease, even when he was teaching challenging topics. He always made sure that his students got most of the class and he was always fun to co-teach with. The biggest strength in Paul, we all in the team admired was his ability to deal with conflicting priorities in high-pressure situations. He never lost his cool and was always a people's person. Paul would be a true asset for any company who need a game changing Data Scientist.”
“Paul is one of the best instructors in data science. He is super knowledgable and breaks down complex concepts into clear and understandable topics. He is a dedicated teacher and I learned so much from him in a short amount of time and I hope to continue learning from him in the future.”
Senior Data Scientist @ Ent Credit Union | AI/ML
Paul Jialiang was senior to Bahar but didn’t manage Bahar directly
“Paul was a substitute instructor for our Data Science Immersive program and he was absolutely amazing! He was easy to work with, the students loved his teaching style, and he showed a great depth of Data Science knowledge and teaching ability. I hope to collaborate with him again in the future and I would highly recommend him for any position on your team.”
Strategy & Operations | Tech Partner Ecosystem Development
Kristen worked with Paul Jialiang but on different teams
“It was my pleasure to be in a class that his teacher is Paul Jialiang, he has all the skills that I feel it is really important that the teacher should have, it was easy for him to make the concept clear and what I really like about him, is he make us feel good about ourself spotting the light on our hard work and what we know and learned,, I felt happy after your class thanks again Paul”
9 shown · swipe or use ◀ ▶
Score any job against past experience · current skillset · future mission/values/vision — held to a golden-set accuracy
Role Fit
Score a role against me
Paste a job-posting URL (Ashby/Greenhouse/Lever, fetched live) or the JD text. The agent scores fit across past experience, current skillset, and future mission/values/vision — and tells you, honestly, where it doesn’t fit.
Why trust this
8/8 within one band · 100%This scorer is itself held to the standard it holds JDs to: it was run over a 8-example golden set with human-assigned fit labels and agreed within one band 100% of the time (6 exact). Verify, don’t vibe.
No role scored yet — paste a posting above to see the fit breakdown.
Knowledge graphs + agentic tooling (skills, plugins, workflows, hooks, bundles) I've built — full for me, a summary card for visitors
Agentic Library
AnyAgent — build · grade · improve · ship any agent apprepo🔒 private
AnyAgent is a tested, object-oriented engine designed to build, grade, improve, and ship agent applications from natural language.
Owner-only knowledge graph + tooling. Unlock owner mode to view the full package.
Harmonic — Research Engineerjob-description
Retrieved, not guessed: 3 competency clusters, 9 concept/tool nodes grounded in real sources (9 Wikipedia/GitHub definitions), and 6 skills from the ESCO taxonomy — off-domain hits filtered out. Every node carries a real definition; edges are real relationships where an open KG had them.
Anthropic — Forward Deployed Engineer, Applied AIjob-description
Retrieved, not guessed: 6 competency clusters, 23 concept/tool nodes grounded in real sources (21 Wikipedia/GitHub definitions), and 8 skills from the ESCO taxonomy — off-domain hits filtered out. Every node carries a real definition; edges are real relationships where an open KG had them.
OpenAI — Forward Deployed Engineerjob-description
Retrieved, not guessed: 5 competency clusters, 17 concept/tool nodes grounded in real sources (15 Wikipedia/GitHub definitions), and 9 skills from the ESCO taxonomy — off-domain hits filtered out. Every node carries a real definition; edges are real relationships where an open KG had them.
Transformers scale COMPUTE (Mixture-of-Experts routes tokens to experts) but have no native primitive for looking knowledge UP. DeepSeek's Engram adds that missing primitive: it modernizes classic N-gram embeddings into a deterministically-addressed table with O(1) lookup — a second, complementary axis of sparsity (static memory) alongside MoE's conditional compute. The paper finds a U-shaped scaling law: for a fixed budget there's an interior optimum splitting capacity between compute and memory — going all-in on either side loses. Under iso-parameter and iso-FLOPs constraints, Engram-27B consistently beats MoE baselines on knowledge, reasoning, code, and math. Mechanistically, offloading static recall to Engram frees the early layers from pattern reconstruction, preserving effective depth for reasoning — and the huge embedding tables offload to host memory with minimal inference overhead. Why it matters for you: 'conditional memory as a sparsity axis' is a transferable design move — separate what a system should COMPUTE from what it should LOOK UP, and size each.
Four growth vectors — deepen · widen · lengthen · heighten — plus who to reach. Drafted for approval.
Next Projects
Runs weekly · last scouted 6/29/2026
Four growth vectors
↓ Deepen
more fundamental, seminal — to the roots
first-principles / foundational research
↔ Widen
new applications, features, markets
Ansoff · Innovation Ambition Matrix
→ Lengthen
evolve it to robustness, scale, commodity
McKinsey Three Horizons · Wardley evolution
↑ Heighten
generalize, abstract, compress the mechanism
abstraction laddering · compression (MDL)
↓ deepensos, loop-engineering-anything, company-os
Enhance Self-Improving Agentic Operating Systems
Further develop and refine the existing self-improving agentic operating systems to increase their efficiency and capabilities
first step: Integrate idle detection and SMARC output-quality verification from sos into loop-engineering-anything
The collaborator candidates were selected based on their relevance to the widenInterests and verified strengths of the builder.
Drafted for your approval — nothing is sent automatically. · model groq:llama-3.3-70b-versatile
A fifth vector — emergent projects from bisociation across the fleet. Scored in code; drafted for approval.
Bridges
Runs weekly · last searched 10/12/2026
AI-Native Discovery Engine for Brain Health & Longevity
promising · 86longevity-loop × lifescience-os × neuro-os
An autonomous research agent capable of generating, simulating, and validating hypotheses related to neurological aging and interventions. It combines the falsifiable question-to-verifier loop of `longevity-loop` with the broader agentic research capabilities of `lifescience-os`, specifically targeting the executable neuroscience knowledge and graph mechanisms of `neuro-os`.
pivot: Accelerated, AI-Driven Biomedical Discovery
first step: Use `lifescience-os` agents to generate falsifiable questions for `longevity-loop` based on `neuro-os`'s knowledge graph of a specific brain mechanism related to aging (e.g., a pathway implicated in Alzheimer's).
AI Trust Fabric: Comprehensive Agent Assurance
promising · 85eval-anything × secure-anything × agentgate
A unified platform that not only evaluates AI system performance and business objectives but also enforces security policies and audits agentic actions, providing a comprehensive, auditable guarantee of AI system behavior and safety.
pivot: AI System Assurance, Trust & Accountability
first step: Integrate `agentgate`'s PR firewall capabilities with `eval-anything`'s OEC loop for agent code changes, using `secure-anything`'s core registry for policy enforcement and audit trails.
AI-Native Life Architect
promising · 85money-os × career-os × life_GPS
An autonomous agent that integrates financial intelligence, career advancement, and adaptive life planning into a single, cohesive system. It not only manages wealth and career paths but also dynamically adjusts weekly goals and next best steps to maximize overall life achievement and well-being, adapting to changing circumstances.
pivot: Holistic Personal Life Optimization & Adaptive Planning
first step: Map key metrics and objectives from `money-os` and `career-os` into `life_GPS`'s goal-setting and adaptive planning engine, creating an initial unified dashboard of personal progress.
Autonomous Fabrication OS
promising · 84design-anything × physical-ai-native
A system that takes abstract design intent (text/picture) and directly translates it into execution-ready 3D blueprints, which are then autonomously fabricated by physical AI systems, closing the loop from concept to tangible reality without human intervention in the manufacturing process.
pivot: AI-Native Design-to-Physical Production
first step: Connect `design-anything`'s 3D blueprint output to a simulation environment for `physical-ai-native` to 'execute' a simple build plan (e.g., a 3D print job or robotic assembly sequence).
Autonomic Enterprise OS
promising · 84loop-engineering-anything × agentic-company × company-os
An enterprise operating system where the company itself functions as a self-improving, agent-native entity. `agentic-company` provides the reference architecture, `company-os` offers the governed runtime, and `loop-engineering-anything` instills a self-improving loop into every aspect of its operations, allowing the company to generate, judge, and refactor its own processes to convergence.
pivot: Self-Optimizing Agentic Operations & Governance
first step: Implement a simple internal process (e.g., onboarding a new agent-employee) using `agentic-company`'s architecture within `company-os`, then apply `loop-engineering-anything` to autonomously optimize the onboarding steps.
Meta-Research Intelligence OS
promising · 82GodView-anything × rsi-os
A platform that not only maps automated AI research (`rsi-os`) but also applies `GodView-anything`'s provenance-pinned, machine-audited methodology to identify *transferable knowledge* and *agentic tooling* emerging from that research, effectively creating a feedback loop for research impact and practical application.
pivot: Distilled, Verified AI Knowledge & Research Impact
first step: Develop a pipeline to ingest `rsi-os`'s curated papers and tools into `GodView-anything`'s verification engine to extract and rank 'what top-rated research actually teaches' in terms of practical agentic capabilities.
Control-Theoretic FM Assurance System
promising · 82FM-os × control-anything × eval-anything
A system that moves beyond simple evaluation to apply formal control theory to foundation model operations. `FM-os` provides the operational map, `eval-anything` monitors performance and objectives, and `control-anything` provides the theoretical framework to judge safety claims and design robust feedback loops for predictable and safe FM behavior, guaranteeing performance within defined operational bounds.
pivot: Robust Foundation Model Control & Verifiable Guarantees
first step: Define a specific FM operation from `FM-os` (e.g., fine-tuning for a critical task) and apply `control-anything`'s principles to design an `eval-anything` feedback loop that ensures a specific safety or performance property is maintained throughout the operation.
Emergent projects from bisociation across your fleet (Swanson A–B–C + conceptual blending), scored in code. Most combinations are noise — the ranking is the value. Drafted for you; nothing auto-built.