Panagiotis Ganelis

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Panagiotis Ganelis

Panagiotis Ganelis

@pan__gan

Software Engineer | Blockchain | Basketball

Amsterdam Katılım Mayıs 2019
326 Takip Edilen72 Takipçiler
Panagiotis Ganelis
Panagiotis Ganelis@pan__gan·
👀
Justin Drake@drakefjustin

Today is a monumentous day for quantum computing and cryptography. Two breakthrough papers just landed (links in next tweet). Both papers improve Shor's algorithm, infamous for cracking RSA and elliptic curve cryptography. The two results compound, optimising separate layers of the quantum stack. The results are shocking. I expect a narrative shift and a further R&D boost toward post-quantum cryptography. The first paper is by Google Quantum AI. They tackle the (logical) Shor algorithm, tailoring it to crack Bitcoin and Ethereum signatures. The algorithm runs on ~1K logical qubits for the 256-bit elliptic curve secp256k1. Due to the low circuit depth, a fast superconducting computer would recover private keys in minutes. I'm grateful to have joined as a late paper co-author, in large part for the chance to interact with experts and the alpha gleaned from internal discussions. The second paper is by a stealthy startup called Oratomic, with ex-Google and prominent Caltech faculty. Their starting point is Google's improvements to the logical quantum circuit. They then apply improvements at the physical layer, with tricks specific to neutral atom quantum computers. The result estimates that 26,000 atomic qubits are sufficient to break 256-bit elliptic curve signatures. This would be roughly a 40x improvement in physical qubit count over previous state-of-the-art. On the flip side, a single Shor run would take ~10 days due to the relatively slow speed of neutral atoms. Below are my key takeaways. As a disclaimer, I am not a quantum expert. Time is needed for the results to be properly vetted. Based on my interactions with the team, I have faith the Google Quantum AI results are conservative. The Oratomic paper is much harder for me to assess, especially because of the use of more exotic qLDPC codes. I will take it with a grain of salt until the dust settles. → q-day: My confidence in q-day by 2032 has shot up significantly. IMO there's at least a 10% chance that by 2032 a quantum computer recovers a secp256k1 ECDSA private key from an exposed public key. While a cryptographically-relevant quantum computer (CRQC) before 2030 still feels unlikely, now is undoubtedly the time to start preparing. → censorship: The Google paper uses a zero-knowledge (ZK) proof to demonstrate the algorithm's existence without leaking actual optimisations. From now on, assume state-of-the-art algorithms will be censored. There may be self-censorship for moral or commercial reasons, or because of government pressure. A blackout in academic publications would be a tell-tale sign. → cracking time: A superconducting quantum computer, the type Google is building, could crack keys in minutes. This is because the optimised quantum circuit is just 100M Toffoli gates, which is surprisingly shallow. (Toffoli gates are hard because they require production of so-called "magic states".) Toffoli gates would consume ~10 microseconds on a superconducting platform, totalling ~1,000 sec of Shor runtime. → latency optimisations: Two latency optimisations bring key cracking time to single-digit minutes. The first parallelises computation across quantum devices. The second involves feeding the pubkey to the quantum computer mid-flight, after a generic setup phase. → fast- and slow-clock: At first approximation there are two families of quantum computers. The fast-clock flavour, which includes superconducting and photonic architectures, runs at roughly 100 kHz. The slow-clock flavour, which includes trapped ion and neutral atom architectures, runs roughly 1,000x slower (~100 Hz, or ~1 week to crack a single key). → qubit count: The size-optimised variant of the algorithm runs on 1,200 logical qubits. On a superconducting computer with surface code error correction that's roughly 500K physical qubits, a 400:1 physical-to-logical ratio. The surface code is conservative, assuming only four-way nearest-neighbour grid connectivity. It was demonstrated last year by Google on a real quantum computer. → future gains: Low-hanging fruit is still being picked, with at least one of the Google optimisations resulting from a surprisingly simple observation. Interestingly, AI was not (yet!) tasked to find optimisations. This was also the first time authors such as Craig Gidney attacked elliptic curves (as opposed to RSA). Shor logical qubit count could plausibly go under 1K soonish. → error correction: The physical-to-logical ratio for superconducting computers could go under 100:1. For superconducting computers that would be mean ~100K physical qubits for a CRQC, two orders of magnitude away from state of the art. Neutral atoms quantum computers are amenable to error correcting codes other than the surface code. While much slower to run, they can bring down the physical to logical qubit ratio closer to 10:1. → Bitcoin PoW: Commercially-viable Bitcoin PoW via Grover's algorithm is not happening any time soon. We're talking decades, possibly centuries away. This observation should help focus the discussion on ECDSA and Schnorr. (Side note: as unofficial Bitcoin security researcher, I still believe Bitcoin PoW is cooked due to the dwindling security budget.) → team quality: The folks at Google Quantum AI are the real deal. Craig Gidney (@CraigGidney) is arguably the world's top quantum circuit optimisooor. Just last year he squeezed 10x out of Shor for RSA, bringing the physical qubit count down from 10M to 1M. Special thanks to the Google team for patiently answering all my newb questions with detailed, fact-based answers. I was expecting some hype, but found none.

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Zama
Zama@zama·
BREAKING Zama becomes the native confidentiality layer for T-REX Ledger, the RWA infrastructure backed by Apex Group (servicing $3.5T in assets) and targeting $100B in tokenized assets by June 2027.
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Zama
Zama@zama·
$ZAMA is going live. Years in the making. Today is another foundational step toward Zama becoming the HTTPS layer for blockchain.
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Suraj Sharma
Suraj Sharma@suraj_sharma14·
If I Had to Start Web3 Again in 2026, I’d Do This Not more tutorials. Not more chains. Not more tools. I’d optimize for leverage + signal + compounding from Day 1. Here’s the exact path 👇 1.) I’d start by reading how systems fail (not how they work) Most builders learn happy paths. Real learning comes from failures. What I’d use instead: - Protocol post-mortems - Incident analyses - Design write-ups after things broke Hidden gems: 1. Paradigm research write-ups → paradigm.xyz/writing 2. Flashbots research → collective.flashbots.net 3. L2Beat risk analyses → l2beat.com/scaling/risk Why this matters: You start thinking in assumptions, incentives and edge cases early. That mindset compounds. 2.) I’d pick ONE narrow problem, not an ecosystem Instead of: “I’m learning Ethereum / Solana / zk / AI” I’d pick: - Indexing pain - Wallet UX - Governance tooling - Developer experience gaps - Then live there for months. Underused places to spot problems: - GitHub issues of infra projects - Forum threads in protocol governance - Open RFCs that never shipped This is where real project ideas come from. 3.) I’d read protocol code for architecture not syntax You don’t need to understand every line. You need to understand: - What’s modular - What’s intentionally hard-coded - Where flexibility was sacrificed Repos I’d read slowly: 1. Uniswap v4 hooks → github.com/Uniswap/v4-core 2. Compound governance contracts → github.com/compound-finan… 3. ERC-4337 reference implementation → github.com/eth-infinitism… Why this matters: You learn design trade-offs not just Solidity. 4.) I’d build “boring” infra before flashy apps Infra teaches you: - Constraints - Performance limits - Real user behavior Examples of underrated starter builds: - A small custom indexer (even if subgraphs exist) - A transaction simulator - A governance proposal analyzer - A gas + execution cost explorer Most devs skip this. That’s why it’s valuable. 5.) I’d learn to explain systems in plain English If you can’t explain: - Why something exists - What problem it solves - What trade-offs it makes - You don’t understand it yet. What helped me most: - Writing short public notes - Diagrams instead of code snippets - Explaining failures not wins Builders who write clearly get: - Faster feedback - Better collaborators - More trust 6.) I’d join ecosystems before applying to anything Not applications first. Presence first. What that actually means: - Commenting on proposals - Reviewing docs PRs - Sharing small experiments - Helping others debug Most fellowships, residencies and grants favor: “I’ve seen this person around” Over: “Great resume, zero context” 7.) I’d measure progress by signal not hype Bad metrics: - Number of tools learned - Chains touched - Tweets posted Good metrics: - One repo people actually use - One write-up people reference - One problem people DM you about That’s how careers compound quietly. >> The biggest mindset shift Web3 rewards: - Patience over speed - Depth over breadth - Systems over syntax If I were starting again, I’d stop trying to look “early” & start trying to look useful. Save this. Come back to it later.
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Georgios Konstantopoulos
Georgios Konstantopoulos@gakonst·
still on the lookout for more engineers for @tempo -- need killers at rust & databases to build the future of web payments...please apply in the ashby below
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terminally onλine εngineer
*man with no idea how to build software nor experience in building software nor experience in maintaining software* wow building software is so easy
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Panagiotis Ganelis
Panagiotis Ganelis@pan__gan·
@SumitM_X First release a version with deprecation warning. Then remove functionality with an error in next major release.
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SumitM
SumitM@SumitM_X·
You are planning to introduce breaking changes to a public-facing microservice API that is being used by multiple clients. How would you manage these changes to ensure existing clients are not disrupted?
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SumitM
SumitM@SumitM_X·
Two microservices, A and B, share the same database for some entities due to legacy design. Microservice A makes a change to the entity, but Microservice B does not reflect the change immediately, leading to inconsistent behavior. How would you refactor the system to solve the shared database issue without creating downtime?
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terminally onλine εngineer
Microsoft after hearing people were running self hosted github runners for free
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Artem Chystiakov
Artem Chystiakov@Arvolear·
What problem do L2s solve now?
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Parity Technologies
Parity Technologies@paritytech·
The numbers speak for themselves 👇 • 1,526,324 accounts migrated • 1.63B DOT moved • 53,407 stakers • 283 MB of data • All in 8h 39m The Asset Hub Migration is done. No forks. No downtime. Pure on-chain execution. @Polkadot just re-engineered itself mid-flight. Huge congratulations to the team - this was a colossal success built on months of hard work 👏
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trish
trish@_trish_xD·
Programming language you learned once but never touched again?
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Vitto Rivabella
Vitto Rivabella@VittoStack·
Shoutout to those building their own future: - No rich parents - No connections - No help - No excuses Just hard work, consistency, commitment, and focus.
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binji
binji@binji_x·
the eth core devs don’t tweet a lot about just how hard the work that they do is so let’s talk about it: 1. every line of code they merge can move more money than most banks process in a quarter. there is no staging server for that. 2. they swap consensus logic for a 400B + dollar economy without scheduling downtime. ever. 3. they coordinate hundreds of researchers, auditors, and client teams across time zones, cultures, and philosophies, yet ship like a single mind. 4. they do it all in public, with every decision dissected by the loudest peanut gallery on the internet, and still keep the vibe collaborative. 5. they design for attackers who have nine figure incentives and infinite patience. then they sleep anyway. 6. they keep six independent clients in perfect sync so the same block lives at the same height for every node in the world. 7. they turn bleeding edge research into production code while preserving backwards compatibility for machines that went online before defi even had a name. 8. they debug issues that only happen once a year on a single archive node because someone somewhere will rely on that edge case. 9. they write cryptography that must stay unbroken for decades while the math itself evolves beneath their feet. 10.when the upgrade lands smooth the outside world shrugs. inside ethereum we know it was a minor miracle. every successful fork proves that decentralized coordination can outperform the world’s best hierarchies and shows that open internet capital markets are now the default. thank you, truly. we owe you everything.
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John
John@ionleu·
As developers, how many pushups can you do?
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Mosh
Mosh@moshhamedani·
Unpopular opinion: Coding is therapeutic. Vibe coding is not.
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Hashlock
Hashlock@Hashlock_·
Energy runs on trust. @energywebx brings the ecosystem, while Hashlock brings audits, threat modeling, and monitoring. Hashlock has reviewed the core components of @energywebx that power solutions like Digital Spine, Green Proofs, and the EWX Marketplace. This helps utilities and enterprises move data and value with confidence. 🔐🌍 @EnergyWebx is a nonprofit building open-source digital infrastructure for the energy transition. With the Digital Spine and Green Proofs, they enable trusted identity, data exchange, and verifiable low-carbon attributes across markets. EWX and its marketplace application connects real-world energy workloads to decentralized execution. Securing the energy transition with @EnergyWebX, supported by rigorous audits of the chain’s foundation from Hashlock. 🔎 Check full audit: hashlock.com/audits/energy-… ⚡️ Explore EW: energyweb.org
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Energy Web
Energy Web@energywebx·
BIG NEWS: The 2025 Energy Web Upgrade is LIVE!🔥 One of the most important updates in Energy Web history: $EWT is now unified across chains, fully audited, and ready for the future! Learn what’s new in this thread🧵👇🏼
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