Viking from Jomsborg. The DAO investor. Applied math. Geometric quantization. Building reasoning layer.
jomsborg.eth
Based in Poland
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I think there is another layer emerging:
Invariant Engineering
Graphs describe the topology of an AI workflow: states, transitions, dpnd.
But correctness doesn't come from the graph itself. It comes from the properties that must hold regardless of which path execution takes.
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Storage is a $1.88B sector.
Everyone stores data. Almost nobody moves it.
The Layers 👇
→ Permanent storage : $AR
→ Oracle network : $LINK
→ Knowledge graph : $TRAC
→ Peer to peer : $BTT
→ File storage : $FIL
→ Data availability : $EIGEN
→ Blockchain indexing : $GRT
→ Web scraping : $GRASS
→ Data marketplace : $OCEAN
→ Video compute : $LPT
Storage is the smallest part of data.
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Consider a number that consists of the decimal digits of pi, in reverse order. A portion of "backwards pi" is show in the figure. It has the same digits as pi, but they go forever to the left instead of the right.
→ Is "backwards pi" a real number?
(Thanks to @ztwiig for providing this idea.)
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19 year old Japanese student built a trading bot with Claude Code in 2 days.
Used his iPad as a second monitor.
First night: $6,732 profit.
Starting capital: $68.
Total profit so far: $750,000.
[𝐍𝐨𝐭𝐞: 𝐅𝐨𝐥𝐥𝐨𝐰 𝐌𝐞 @zakiraicoder 𝐅𝐨𝐫 𝐢𝐧𝐬𝐭𝐚𝐧𝐭𝐥𝐲 𝐚𝐮𝐭𝐨 𝐃𝐌]
Here's how it works👇
The bot scans over 50 markets simultaneously.
Syncs live BTC data from Binance every second.
Spots price errors before humans even notice.
The edge is pure speed + pattern recognition.
While traders stare at charts trying to predict the next move, his bot is already executing on mispricing across dozens of markets.
No guessing.
No emotions.
No hesitation.
Just Claude Code logic finding gaps that close in seconds.
He built the entire system in 48 hours:
→ Claude Code handles the trading logic
→ Binance API feeds real-time BTC data
→ iPad displays multi-market monitoring
→ Executes trades when arbitrage windows open
The system runs 24/7.
Every price dislocation = profit opportunity.
Most people are still trading manually, refreshing charts, second-guessing entries.
Meanwhile this 19 year old engineering student turned $68 into $750K by letting Claude Code do what humans can't: process 50 markets instantly and execute without fear.
Why are people still trading manually?
I'm giving away the exact Claude Code setup for free.
24 hours only.
To get it 👇
1→ Comment " Join "
2→ Like and Repost
3→ Follow @zakiraicoder (so I can send it via DM)
( First 2,000 people will get it! Like, comment, and follow quickly )
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Ce gars à regroupé tous les projets avec JEV qu'il a vus passer et les à rassemblés dans un seul site !
Pas mal pour voir les use cases et prendre un peu d inspiration ! Le site c est jevable.com 👇
Looking for inspiration on what you are able to do with Jev (by @typefaceai) this weekend?
Built a website called Jevable (jevable.com) which showcases all the fun demos on X - filterable by a few categories.
Feel free to add your own project by using the + button!
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new solidity compiler dropped
firefly.social/post/x/210344…
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I built an AI trading bot with Jev + Opus 5.5, and it's actually insane.
It's a high-frequency algo trading framework (trading $HYPE below).
I break down everything in my latest video.
Watch now 👉 piped.video/Pk7W7BKMwqo?si=dbr4…
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Jacek (Jomsborg.eth) retweeted
There are many suggestions to model nuclei as knots of quark strings as topological vortices e.g. nature.com/articles/s41567-0…
Their simulations getting good agreements: zenodo.org/records/22942341
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Jacek (Jomsborg.eth) retweeted
Vitalik: Ethereum is Becoming More Than a Blockchain
Speaking at the 2026 Shanghai Blockchain International Week, Ethereum co-founder Vitalik Buterin shared a broader vision for where Ethereum is heading.
His main point: Ethereum is evolving from a blockchain that executes everything into a global system focused on computation, privacy and verification.
Here are the key ideas:
1/ ZK is becoming the foundation → Instead of every node doing the same computation, users or other systems can do the work and generate a proof. Ethereum only needs to verify that proof.
2/ Privacy becomes programmable → Vitalik said blockchain is moving beyond simply answering “Who can send what?” The next question is “Who can see what?” This means applications could control which information is visible without exposing everything publicly.
3/ Scaling through proofs → Complex computations can be split, processed in parallel and compressed into proofs before being verified on Ethereum. This could allow much more computation without putting all of it directly on-chain.
4/ Ethereum becomes a full pipeline → Vitalik described a future involving user devices, private transaction pools, multiple participants, block construction, L2s and finally Ethereum for verification. Ethereum does not need to perform every step itself.
5/ AI changes development → AI can help developers write, test and formally verify increasingly complex cryptographic systems. Vitalik believes this can make advanced Ethereum infrastructure both faster to build and more secure.
6/ Quantum resistance matters → Ethereum is also moving toward quantum-safe cryptography, with STARK-based systems playing an important role in the longer-term roadmap.
Ethereum's future may not simply be about making the L1 faster.
It is about building a trust layer where computation can happen anywhere, privacy can be programmed, and complex results can be mathematically proven and verified on-chain.
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Jacek (Jomsborg.eth) retweeted
Why do I feel on track to my $5K EoY zcash:native prediction?
More and more whales asking me: Why price surge?
Me: No fucking clue.
What's your explanation?
Not investment advice, DYOR.
Putting money where Mouth:
I predicted that ZEC > $1200 by Sept. 25.
Result: I was right 😎
Now, time for EoY prediction: ZEC > $5000.
Let's see if I'm right again.
(I'm not in it for the 27X reward, I just like being right)
Not investment advice, DYOR.
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Securitize $SECZ pumping to $2.4B in anticipation of the incoming onchain RWA narrative
And nobody knows there's a project of similar caliber trading at just 0.5% its mcap
The re-valuation will not be gentle
🤫 $IXS
As the bull market kicks in people are searching for an underrated gem in a solid narrative
My bet is on $IXS for the RWA sector
At a mere $11M fdv right now, it's a no-brainer
>Elite backers
>Professional team, all doxxed
>Fully licensed (international)
>Massive TAM as RWA infrastructure
>Less than 1% the mcap of comparable entities
>IXS token is fully circulating, no unlocks left
The next big sector boom will occur in RWAs, and there's no token with better risk-reward than IXS
DYOR and judge for yourself
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I think you do underestimate @Securitize $SECZ infrastructure
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There is a mysterious professor from Poland mentioned in the interview :) My comment a la Anderson vs. Rubin:
@soubhikdeb:
Do you know how to help with better.codes?
nasqret:
No. I have no technical ability. And I know nothing about attack bounds.
Soubhik:
You must know something.
nasqret:
Well, I know what I like and what I don’t like. And I’m decisive about what I like and what I don’t like.
Soubhik:
So what are you being paid for?
nasqret:
The confidence that I have in my taste and my ability to express what I feel has proven helpful for cryptographers.
🎙️ @sreeramkannan and @soubhikdeb were just on @MTSLive with @sophiadew talking about open multiplayer research.
What we keep coming back to is who gets to do science after AGI. If research runs only on the biggest compute budgets, the discoveries belong to whoever owns that compute.
@YukonResearch is how we run that playbook on EVERY HARD PROBLEM.
Full conversation:
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Founder of @Zcash and Chief Product Officer at Shielded Labs, @zooko, will be at Quantum & Privacy Day.
Zcash launched in 2016 and brought zero-knowledge cryptography to production.
October 8 | Tower Club, Singapore
Join us in Singapore:
luma.com/jtof4o9b
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🚨 Jensen Huang’s Latest Stock Calls
Last time he called out:
$NBIS (Nebius) — at $18
$INTC (Intel) — at $40
$MU (Micron) — at $56
Now he’s calling:
$OKLO (Oklo) — at $39
$BE (Bloom Energy) — at $277
$UUUU (Energy Fuels) — at $11.68
“Nuclear is a wonderful source of energy”
💡 Don’t miss out again. Save this list and turn on notifications so you catch the next alert.
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Jacek (Jomsborg.eth) retweeted
WOW!! Zeta(5) is irrational! Here's a Lean formalization: github.com/mo271/zeta5 Amazing what we'll learn (with AI help)
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Jev Founder, Diogo Amogo, just released 12-page PDF on building a Jev Harness for production agents
This is a 10-step blueprint for making agent decisions up to 200x faster and 400x cheaper without giving a model authority over your system:
step 1 → meet the decision layer: the LLM creates, Jev judges, code enforces and the harness decides what happens next
step 2 → expose every hidden decision: routing, evidence checks, retries, approvals, escalation and stopping should be visible parts of the agent graph
step 3 → stop dumping the entire transcript into every call: build a compact state packet with only the current goal, facts, evidence, constraints and available actions
step 4 → turn prompts into contracts: declare the possible outcomes, describe what each one means, add an escape route and version the decision like code
step 5 → route by confidence and consequence: automate high-confidence internal work, collect evidence in the middle and send dangerous actions to a human
step 6 → ask independent questions together: choose the next worker, estimate urgency and check approval against one immutable state snapshot
step 7 → build menus from live state: Jev should only see tools, workers, files and browser controls that actually exist and are allowed right now
step 8 → make retrieval decision-aware: deterministic filters remove impossible candidates, Jev keeps the evidence that matters and the LLM receives the smallest useful context
step 9 → create a receipt for every decision: preserve the state, contract version, full probability distribution, threshold, selected route and resulting action
step 10 → deploy in shadow mode: compare Jev with the existing agent, measure calibration and automate the safest branch before expanding the boundary
TypeSafe reports roughly 70–500ms for Jev-shaped decisions and pricing of $0.042 per million input tokens with no metered output-token cost
The real unlock is bigger than one benchmark:
Your most expensive model stops wasting time on decisions that never required another sentence
Send this PDF and the article below to your Claude Code or Codex instance and start rebuilding your agent around typed judgment ↓
Most AI agents waste tokens on decisions that never needed text
Jev turns routing, scoring, and verification into a fast decision layer
I broke down the architecture most agent builders are still missing ↓
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R.I.P. data scientists.
Andrej Karpathy just open-sourced what is replacing the traditional data scientist...
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In mean time
I just hit a major breakthrough in my research.
While analyzing OpenAI’s Navier–Stokes proof, I found evidence that part of its repeated mathematical machinery may not belong to the underlying problem at all. It may be a consequence of the representation used to solve it.
This post explains what I found and the new method that came out of it: Structural Path Compression.
[Paper in the first reply]
Imagine you're lost in a crazy-hard maze. You finally find the exit, but then you realize something wild-the map you used to navigate actually created some of the twists and turns. That's the possibility I found hiding inside part of OpenAI's Navier-Stokes proof.
OpenAI says roughly 10,000 concurrent AI agents helped produce the result, but here's the thing: some of the repeated work in Section 9 appears to come from how the proof represents and organizes the problem, rather than from new mathematical structure being added each time. Think of it like trying to do calculus with Roman numerals-every step is valid, but it's a nightmare. Switch to decimals and suddenly it's easier.
The breakthrough came when someone stopped looking at the proof as a timeline (step 1, step 2, etc.) and asked what actually depends on what at the same level of math. The answer was surprising: most of the repetition wasn't part of the math itself, but how it was represented.
The core idea is called Structural Path Compression (SPC). It's like pressing an elevator button instead of proving you can climb each stair individually. Once certain conditions are met, you can skip straight to your desired accuracy without all the middle steps.
This isn't just about one math problem-it's about how we frame problems in general. The same information can be a puzzle or a breeze depending on how you look at it.
Sometimes, what looks like a "mathematical problem" is really just bad framing.
Change that, and some problems vanish while the truth remains.
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Another challenge
Opus 5.5 - moim zdaniem najlepsza realizacja zadania jaką widziałem do tej pory. Perfekcyjne.
Total cost: $19.86
Total duration (API): 52m 6s
Total duration (wall): 4h 21m 14s (zostawiłem na 3h bezczynnego czekania - byłem w mieście)
Total code changes: 2004 lines added, 0 lines removed
Usage by model:
- claude-haiku-4-5: 899 input, 13 output, 0 cache read, 0 cache write ($0.0010)
- claude-opus-5-5: 324 input, 296.8k output, 35.7m cache read, 846.6k cache write ($19.86)
Prompt cache (main): 114 requests · 98% of input tokens from cache · 1 miss (last 3h 33m 19s ago — likely cause: prompt unchanged — likely server-side, 387.4k tokens re-cached) · cold — idle 3h 16m 9s, next turn re-caches ~487.3k tokens
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