ML

fyworries

I agree. I’ve been looking at pqc algos for a while and never heard of HAWK. I have heard of the other algos the paper mentions that have not been affected so far: >!This result does not impact Falcon, ML-DSA, or other latticed-based schemes.!< Apparently HAWK is currently undeployed and an NIST round 3 candidate. I think using AI to try and “break” these algorithms before any deployment is a net good.

MachTimebitches

Won't work. I built an ML model and trained it on any available data I could find and the best outcome that came out was a 20% return over a year trading and running 24/7. It would buy dips over several days and hold out until there was a profit but even that was proven it could still return 0% or negative. I used Codex to make the ML and ran it on Kraken api. The biggest obstacle at first was the fees as that killed any short term trading or scalping. Then it was reading the markets and finding any type of reliable edge. I was told you could just use a free trading bot and would get the same results. This is what Fabel 5 had to say about the project; "The hard truth underneath: fast, meaningful returns require the kind of speed, low fees, and information that only well-funded firms have. If a laptop and a clever idea could beat the market, everyone would already be doing it — and the fact that they're not is the whole answer."

AutoModerator

Post is by: April-Zoe and the url/text [ ](https://goo.gl/GP6ppk)is: /r/ai_trading/comments/1v8xyz1/my_honest_experience_after_a_year_vibe_trading/ The truth about AI trading after a year testing. I've been building and testing trading algos with AI almost every day for close to a year. Thousands of strategies and backtests, around hundreds of strategy families, multiple live systems. Here's what nobody selling you an "AI trading bot" will tell you. First, there are 7 different ways to use AI in trading: 1. An LLM trades on its own 2. An LLM analyzes the market and recommends trades 3. An LLM helps you build and test a strategy 4. An LLM sits inside a deterministic strategy as an extra filter layer 5. LLM simulated environments (a la MiroFish) where the aggregate is used as a signal 6. Classical ML on top of your strategy (the Lopez de Prado pipeline: random forest, XGBoost, LightGBM) 7. Training your own transformer on proprietary data These are completely different things. Mixing them up is why most "AI trading" takes are garbage. **What does NOT work:** \> Letting an LLM trade or recommend trades. I ran ensembles of LLM agents predicting 1h, 4h and 24h direction. The win rate differences were pure bias and regime artifacts. Last month's best agent was noise. \> Training your own transformer. Unless you have truly proprietary data at scale, you're paying GPU bills to overfit. \>Looking for the infinite money glitch on a single asset with a single method. \>"Claude, build me a profitable strategy." It grabs some data, overfits, tests 50 hypotheses and hands you survivorship bias with a smile. Add ML on top and you get look ahead bias, leaky labels and no real out of sample. I ran this experiment properly. Thousands of backtests, hundreds of strategy families, frontier models. Strategies that survived honest validation with Sharpe above 0.8: zero. The models aren't dumb. Markets are just efficient, and AI is the most convincing overfitting machine ever built. **What DOES work:** \> LLM as the engineer, not the trader. Build deterministic strategies, formalize hypotheses, iterate fast, but you own the validation. \> LLM as one filter layer inside a rule based system (including simulated environments as that filter). The strategy has to work without it too. \> The Lopez de Prado pipeline on top of an already working strategy. Purged cross validation, walk forward, deflated Sharpe. And still treat it as experimental until it survives out of sample. \> Deploy lots of paper trading agents and let live performance rank them over weeks. Time beats backtests. \> Copy trading as a feature inside your strategy. Validate your entries against what proven wallets are actually doing. \--> Reality check: on the whole Hyperliquid leaderboard, the number of traders you can actually mirror is maybe 15 to 30. The rest are HFT you physically can't copy. The pattern is simple. AI accelerates the process, you own the edge through validation. The moment AI owns the edge, you own the losses. *I am a bot, and this action was performed automatically. Please [contact the moderators of this subreddit](/message/compose/?to=/r/CryptoMarkets) if you have any questions or concerns.*

oak1337

The 4 Keys to Post-Quantum and Hedera's current state + Roadmap: **Hashes** - SHA384 (Post-Quantum). Most/all other chains use SHA256 (*probably* Post-Quantum) **Encryption** - AES256 (Post-Quantum) **Key Agreement** - Currently TLS (not Post-Quantum), will be upgrading to ML-KEM (waiting for libraries update) (Post-Quantum CRYSTALS-Kyber). **Digital Signatures** - Currently ECDSA and Ed25519 (not Post-Quantum), will be upgrading to Falcon Signatures (Post-Quantum)(when NIST approves).

oak1337

The 4 Keys to Post-Quantum and Hedera's current state + Roadmap: **Hashes** - SHA384 (Post-Quantum). Most/all other chains use SHA256 (*probably* Post-Quantum) **Encryption** - AES256 (Post-Quantum) **Key Agreement** - Currently TLS (not Post-Quantum), will be upgrading to ML-KEM (waiting for libraries update) (Post-Quantum CRYSTALS-Kyber). **Digital Signatures** - Currently ECDSA and Ed25519 (not Post-Quantum), will be upgrading to Falcon Signatures (Post-Quantum)(when NIST approves). Dr. Leemon Baird describes the whole process in this clip 👇 https://www.reddit.com/r/Hedera/s/dfc39cn6Mb

S1eeper

\> Hedera should be Post-Quantum with ML-KEM (waiting on libraries) and Falcon Signatures (waiting on NIST) by 2027. Add that Hedera is already SHA384 and AES256, both Post-Quantum. If it's already post-quantum with SHA384 and AES256, why does it need ML-KEM and Falcon?

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Post is by: oak1337 and the url/text [ ](https://goo.gl/GP6ppk)is: /r/CryptoMarkets/comments/1uxampy/in_the_end_all_roads_lead_to_hedera_hashgraph_hbar/ Hedera Hashgraph (HBAR). **Leaderless** architecture completely eliminates MEV, Frontrunning, Sandwich Attacks, etc. Any chain with a Block Leader has these problems and has some way of trying to "mitigate" it. **Don't mitigate, eliminate!** Block Leaders are also a security issue, since if you shut down the centralized single leader (DDOS), you shut down the network. **ABFT (Asynchronous Byzantine Fault Tolerance).** This was an unsolved math problem in computing since \\\~1982 (over 40 years). Dr. Leemon Baird solved it using \*\*Gossip about Gossip with ABFT Virtual Voting\*\*. It is the gold standard of consensus algorithms and will never be beaten. Perfect mathematics. \[https://hedera.com/blog/coq-proof-completed-by-carnegie-mellon-professor-confirms-hashgraph-consensus-algorithm-is-asynchronous-byzantine-fault-tolerant/\](https://hedera.com/blog/coq-proof-completed-by-carnegie-mellon-professor-confirms-hashgraph-consensus-algorithm-is-asynchronous-byzantine-fault-tolerant/) **Fees are fixed in USD, and paid in HBAR**. This makes it easy to forecast costs for users. \[https://docs.hedera.com/networks/fees\](https://docs.hedera.com/networks/fees) It has **Unlimited Scalability**. The current throttle is 10,000TPS, but that is a conservative throttle on a single shard. Add more nodes/shards to add more scale, as needed. Unlimited. **Modular Design for easy upgrades.** Hedera has the perfect mathematical foundation of Gossip about Gossip with ABFT Virtual Voting. Everything on top of that can be plug and play. This is why it will only take changing a couple lines of code to upgrade to Post-Quantum Security. **Hedera should be Post-Quantum with ML-KEM (waiting on libraries) and Falcon Signatures (waiting on NIST) by 2027.** Add that Hedera is already SHA384 and AES256, both Post-Quantum. \[https://hedera.com/blog/post-quantum-cryptography-and-blockchain/\](https://hedera.com/blog/post-quantum-cryptography-and-blockchain/) It is **Guaranteed Decentralization over time/scale**, meaning it's Nakamoto will only increase as it scales. All nodes are essentially equal (Gini and Theil Index), so adding more nodes/shards only increases Nakamoto. https://www.nodiens.com/market-research-reports# (Decentralization Comparative Model Across Blockchains) Full Hedera code base has been \*\*open sourced with Apache 2.0 and donated to Linux Foundation.\*\* \[https://www.lfdecentralizedtrust.org/projects/hiero\](https://www.lfdecentralizedtrust.org/projects/hiero) **Private HashSpheres, ZK Proofs, and encrypted messages to Mainnet,** means you can use your own private network that seamlessly connects to Mainnet, or keep your transactions private in many ways. https://hashgraph.com/blog/introducing-hashsphere-a-private-permissioned-network-powered-by-hedera/ **Cross-Ledger Protocol (CLPR)** enables cryptographically-secured communication and token transfers between independent blockchain networks–all without bridges, pooled liquidity or intermediary validator networks. Bridges are the most hacked/scammed part of crypto, so CLPR offers **bridgeless bridging.** https://hashgraph.com/clpr/ It's also **governed and being built on by a diverse set of Global Fortune 1000's and University's**. They have 3 year terms (max 2 consecutive terms, max 39 members), and all have 1 equal vote on governance issues. This means the Council rotates and power is never consolidated. https://hederacouncil.org/ **In short TLDR**: leaderless, best possible ABFT consensus, best possible SHA384 AES256 ML-KEM Falcon Signatures Post-Quantum Security, Unlimited Scalability, Guaranteed Decentralization over time/scale, fully open sourced with code donated to Linux, private HashSphere, connect to any network with CLPR, with a rotating expert governing council that is building enterprise grade solutions. Any questions? *I am a bot, and this action was performed automatically. Please [contact the moderators of this subreddit](/message/compose/?to=/r/CryptoMarkets) if you have any questions or concerns.*

oak1337

Hedera Hashgraph. **Leaderless** architecture completely eliminates MEV, Frontrunning, Sandwich Attacks, etc. Any chain with a Block Leader has these problems and has some way of trying to "mitigate" it. Don't mitigate, eliminate! Block Leaders are also a security issue, since if you shut down the centralized single leader (DDOS), you shut down the network. ABFT (Asynchronous Byzantine Fault Tolerance). This was an unsolved math problem in computing since \~1982 (over 40 years). Dr. Leemon Baird solved it using **Gossip about Gossip with ABFT Virtual Voting**. It is the gold standard of consensus algorithms and will never be beaten. Perfect mathematics. [https://hedera.com/blog/coq-proof-completed-by-carnegie-mellon-professor-confirms-hashgraph-consensus-algorithm-is-asynchronous-byzantine-fault-tolerant/](https://hedera.com/blog/coq-proof-completed-by-carnegie-mellon-professor-confirms-hashgraph-consensus-algorithm-is-asynchronous-byzantine-fault-tolerant/) **Fees are fixed in USD, and paid in HBAR**. This makes it easy to forecast costs for users. [https://docs.hedera.com/networks/fees](https://docs.hedera.com/networks/fees) It has **Unlimited Scalability**. The current throttle is 10,000TPS, but that is a conservative throttle on a single shard. Add more nodes/shards to add more scale, as needed. Unlimited. **Modular Design for easy upgrades.** Hedera has the perfect mathematical foundation of Gossip about Gossip with ABFT Virtual Voting. Everything on top of that can be plug and play. This is why it will only take changing a couple lines of code to upgrade to Post-Quantum Security. **Hedera should be Post-Quantum with ML-KEM (waiting on libraries) and Falcon Signatures (waiting on NIST) by 2027.** Add that Hedera is already SHA384 and AES256, both Post-Quantum. [https://hedera.com/blog/post-quantum-cryptography-and-blockchain/](https://hedera.com/blog/post-quantum-cryptography-and-blockchain/) It is **Guaranteed Decentralization over time/scale**, meaning it's Nakamoto will only increase as it scales. All nodes are essentially equal (Gini and Theil Index), so adding more nodes/shards only increases Nakamoto. Full Hedera code base has been **open sourced with Apache 2.0 and donated to Linux Foundation.** [https://www.lfdecentralizedtrust.org/projects/hiero](https://www.lfdecentralizedtrust.org/projects/hiero) In short: leaderless, best possible ABFT consensus, best possible SHA384 AES256 ML-KEM Falcon Signatures Post-Quantum Security, Unlimited Scalability, Guaranteed Decentralization over time/scale, fully open sourced with code donated to Linux. It's also governed and being built on by a diverse set of Global Fortune 1000's and University's. https://preview.redd.it/629pbn012fdh1.jpeg?width=918&format=pjpg&auto=webp&s=a2b1b470bb578aaecba269e450c153be5487ad15

dogehound

The stub never touches the network. Incoming blocks are verified in full-signature mode, which rejects any transaction whose signature is 64 bytes or shorter, so every transaction that enters the chain is checked against its complete 4,627 byte ML-DSA signature by every node before the block is accepted. The 64 byte stub only appears afterward, inside a node's own database, for transactions it has already verified. It is a receipt that a check happened, not a credential anyone will accept in place of one, and its stored hash is bound by SHA-256 to the exact signature that was verified. There is nothing there to forge and nothing to replay.

frugaleringenieur

Implanted post quantum ML-DSA into Bitcoin core. Block size increased soo much beyond spec that I am feeling we are aproaching an issue.

oak1337

Leaderless Fair Ordering. No MEV. No frontrunning. No sandwich attacks. No "priority fees". Fixed low fees priced in USD but paid in HBAR. ABFT gold standard consensus. Post-Quantum Secure by 2027 (ML-KEM and Falcon Signatures). Unlimited Scalability. Add shards as needed. Guaranteed decentralization over time/scale, since all nodes are equal, and you can add nodes/shards as needed to scale. Nakamoto score will only increase. In the end, all roads lead to Hedera.

oak1337

Leaderless Fair Ordering. No MEV. No frontrunning. No sandwich attacks. No "priority fees". Fixed low fees priced in USD but paid in HBAR. ABFT gold standard consensus. Post-Quantum Secure by 2027 (ML-KEM and Falcon Signatures). Unlimited Scalability. Add shards as needed. Guaranteed decentralization over time/scale, since all nodes are equal, and you can add nodes/shards as needed to scale. Nakamoto score will only increase. In the end, all roads lead to Hedera.

oak1337

Leaderless Fair Ordering. No MEV. No frontrunning. No sandwich attacks. No "priority fees". Fixed low fees priced in USD but paid in HBAR. ABFT gold standard consensus. Post-Quantum Secure by 2027 (ML-KEM and Falcon Signatures). Unlimited Scalability. Add shards as needed. Guaranteed decentralization over time/scale, since all nodes are equal, and you can add nodes/shards as needed to scale. Nakamoto score will only increase. In the end, all roads lead to Hedera. https://preview.redd.it/ossiwx0390ch1.jpeg?width=970&format=pjpg&auto=webp&s=4c91244062ded60855e3f33142e536076cdf466f

nullc

Great point on the replay of syscalls and that not allowing a genuine restart from a checkpoint. For a fuzzer, it might be interesting to fork the state after hitting a rare branch... especially if that rare branch isn't a pure function of the ordinary program input but instead depended on some of the system input such that running with the same input again wouldn't be guaranteed to hit it. Another thing you could do (if you don't already) is have some knobs that controlled the timing and order of system events to the extent you can control them, and so your tool becomes another controllable randomness input that the fuzzer could use which isn't ordinarily available to fuzzers. Not your area of interest but a huge application of that sort of checkpoint ability is reinforcement training of ML models... and a particular case where capturing an entire virtual machine is useful. E.g. you want to train the agent to operate a computer, and the training will roll back the agents state and try many different next steps. (potentially with another model observing and judging progress). You want the whole machine because the agent can do anything within the execution environment, so you can't just anticipate a limited set of inputs and outputs, and since its probably writing software the behavior could depend on arbitrarily system non determinism. Right now all the harnesses I've see try to handle this in really inelegant ways. Rolling back a deterministic machine would be much better. One of the more exotic ways this is being addressed is by creating world model LLMs-- which is an LLM trained on the opposite site of the agent<>world interaction (e.g. https://qwen.ai/blog?id=qwen-agentworld). This also has the benefit of being differentiable, but a lot of the interest in it is just from the ability to roll it back. You might find some users and development allies in that space.

drwebb

I work in AI/ML with 10 YOE experience, Principal Engineer at a startup that's made unicorm, hopefully double unicorn too. So I'm an outlier.

Galavanta

Solid list.. I'd add looking at on-chain risk metrics to decide when to scale your dca up or down, not just whether price dipped recently. a dip at risk 80 and a dip at risk 30 are very different situations. Ive been using aΙphasquared's risk score for this. It spits out a 0-100 risk number daily based on ML and I just scale my buys accordingly. low risk = bigger buys, high risk = pull back or take some off the table. Been doing it for two cycles now and its kept me disciplined through some pretty rough stretches. The Sat Stacker dip-buying approach is cool but it's still mostly reacting to price alone. Combining that with some measure of where we actually stand in terms of over/undervaluation makes a real difference imo.

groundkittenbeef

If you add one more LOL I heard ML is ok at that point. None of the things you mentioned are high risk… Take your crime money elsewhere bub.

Real-Technician831

AES-256 is highly quantum resistant. And is the mainstream symmetric algorithm. ML-KEM (CRYSTALS-Kyber): is a recommended algorithm for public key, but doesn’t have same popularity as RSA and other outdated public key algorithms. However CRYSTALS family is available in common TLS libraries, so it basically only requires new certificates being issued.

wack_overflow

If you’re not ML you should be ok

Gr8WallofChinatown

I’ve been in the space since 2013. You are disingenuous. There is not much good in crypto. 1. A stupid statement. Traditional finance makes up the entire world of course there will be MLing. However, the entire preface of the existence of the origin crypto is to be a p2p alternative to banking system. It was originally designed to be a ML system. Especially (at the time) it was touted to be “privacy” (due to not knowing who owns the wallet address”. Now today it’s a bastardized world just designed to extract as much money as possible from the next person. 2. I personally think they shouldn’t own any stocks or crypto outside of a 401k. Bitcoin is a COI. You have grifting (mostly maga) politicians all pushing for bitcoin and for the government to buy bitcoin (and allow 401ks to buy bitcoin). You have BTC mining companies lobbying Ted Cruz and Co to siphon tax dollars and cheap land and energy to mining companies which strain energy grids at the cost of tax payers. You then have Trump launching scam coins to money laundering and scam which is against the emoluments clause. 3. It was clear. You’re stating a stupid Coinbase talking point. I never said bitcoin is a security as it doesn’t fit the howey test. The howey test is simple and most crypto falls under that. 4. Good luck with that? How stupid of a statement is that. Oh it’s corrupt and Thiel and pedo elite owns it so let’s do nothing. 5. Also a stupid statement. The existence of bitcoin ETFs are fine as they are regulated. The 401k and pensions being used as liquidity for it is not fine. Also the Pelosi is not the best trader and that’s just another internet bullshit talking point. You have 4 republicans worth more than her. 3 of them more than 2x her net worth. MTG when from 700k net worth to 25 million dollar net worth in her short time.

anon1999666

It’s not an LLM. It’s traditional ML models for identifying when a patient is about to have a serious medical emergency so we provide better care and take on more patients cause of this. It directly increased our market share by 2x

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Post is by: zzaksyusuf and the url/text [ ](https://goo.gl/GP6ppk)is: /r/qorechain/comments/1t68w4u/qorechain_is_a_postquantum_project/ Qorechain is a quantum-safe (post-quantum) and AI-native Layer-1 blockchain project. qorechain.io Short Summary: • Key Features: • Fully designed with NIST-standard post-quantum cryptography (ML-DSA-87 / Dilithium, ML-KEM-1024 / Kyber, etc.) — protected against quantum computer attacks. • AI-native: Uses protocol-level artificial intelligence for consensus optimization, anomaly detection, and transaction routing (QCAI). • Triple-VM: Can run EVM + CosmWasm + SVM (Solana) on a single chain. dApps from different ecosystems can interact in the same environment. qorechain.io • Other Features: • Cosmos SDK based. • Direct bridge (interchain) to 25+ Layer-1 blockchains. • Swiss-based non-profit (QoreChain Association). • Native token: QOR. Currently in the testnet phase, the mainnet is expected in 2026 (Q2/Q4 target). In short, it is positioned as a secure, fast, and highly compatible blockchain infrastructure for the future quantum and AI era. quantumzeitgeist.com *I am a bot, and this action was performed automatically. Please [contact the moderators of this subreddit](/message/compose/?to=/r/CryptoMarkets) if you have any questions or concerns.*

Ok_Freedom3290

This is exactly the problem I built [AlphaSignal](https://alphasignal.digital/) for. Instead of watching charts 24/7, it fires alerts when statistically significant events actually happen — RSI extremes, ML alpha predictions, whale accumulation, regime shifts — so you're not glued to every midnight candle. You set your rules, it watches for you.

Moist_Succotash_9325

Good question. I’m not feeding an LLM raw tick logs and asking it to trade. The current system is more feature/scoring driven than “raw price action into model”. Right now Trado works mostly on closed candles and derived signals: technical structure, volume, VWAP, support/resistance, CVD divergence, volume profile, liquidation-cascade style patterns, funding, order book imbalance, BTC macro regime, volatility/regime filters, etc. Those are combined into a scored signal with an SL/TP plan and risk checks. I agree with your point on feature selection. Raw ticks are noisy, and for this kind of system I’d rather feed normalized/statistical features than raw market data. Z-scored order book imbalance and liquidation density are exactly the kind of features I’d consider more useful than generic OHLCV. On latency: I’m not trying to make this an HFT system. The worker is closer to a minute-level scanning/execution loop, mostly candle-close based. Hyperliquid’s speed matters more for clean execution, reconciliation, slippage control and account state than for sub-second prediction. If I add heavier ML inference, I’d probably keep it out of the critical execution path or precompute features so the signal engine only consumes normalized inputs. Curious how you’re building the liquidation heatmap side: are you using exchange liquidation prints, estimated liquidation levels from OI/leverage assumptions, or a vendor feed? Free / educational only, not financial advice. Mostly interested in comparing feature engineering and execution architecture.

whatwilly0ubuild

The disclaimer section is honest, which is more than most signal bots offer. A few questions that would help evaluate whether this is serious work: What's your backtest methodology? "Multi-strategy approach with weighted scoring" describes every systematic trading project ever. The differentiator is whether the backtest accounts for realistic execution (slippage, funding, queue position on Hyperliquid specifically), whether you're testing on out-of-sample data, and whether the strategies were designed before or after looking at the data they were tested on. How are you handling regime changes? Systems calibrated on trending markets blow up in chop. Systems calibrated on range-bound markets underperform in trends. "Auto-tuning" can mean adapting to conditions intelligently or it can mean curve-fitting to recent data and getting destroyed when conditions shift. What's your actual edge hypothesis? Technical signals alone are a crowded space. If your edge is "combining multiple indicators with ML," that's table stakes and unlikely to produce durable alpha. If you've identified something structural about Hyperliquid order flow, liquidation cascades, or market microstructure that creates exploitable patterns, that's more interesting. The concern with launching a public Telegram bot while still "calibrating" is that you're now optimizing for subscriber retention (which rewards frequent signals and occasional big wins) rather than risk-adjusted returns (which often means sitting out). These incentives diverge quickly.

Ok_Freedom3290

Building on Hyperliquid is a smart move since their SDK/API is extremely fast, but the biggest bottleneck you'll run into with AI signals is feature selection. Are you feeding the model raw price action, or statistical features like volume profile and CVD (Cumulative Volume Delta)? I spent the last few months building [AlphaSignal](https://alphasignal.digital/), which is a web-native terminal aggregating crypto derivatives data. We found that training our ML model on Z-scores of order book imbalances and liquidation heatmap densities produced way cleaner signals than feeding raw tick logs. How are you handling the latency of the model's predictions?

alami9

Fair correction. To be precise: ML-DSA-87 targets NIST security category 5, which is the top of NIST's 1 through 5 scale, so it does sit at the highest tier. What it isn't is the only scheme up there. Falcon-1024 and SPHINCS+/SLH-DSA at their 256-bit parameter sets also land at category 5, so ML-DSA-87 shares the top tier rather than standing alone above everything. Within ML-DSA's own three options it's the strongest (ML-DSA-44 = level 2, ML-DSA-65 = level 3, ML-DSA-87 = level 5). So "highest tier" is accurate, "uniquely highest" would not be. I should've said "NIST's top security category (Level 5)" to avoid implying it's the single strongest. Good catch. QRL signs on the consensus layer with ML-DSA-87 (category 5), and is adding Falcon-1024 and ML-KEM-1024 on the networking layer, so it's using more than one category-5 primitive rather than betting everything on one. see [https://www.theqrl.org/weekly/](https://www.theqrl.org/weekly/)

No_Knee3385

You still need a highly complex and intelligent model to trade successfully using algorithms, AI, ML, etc. Most people using either manual or ML or AI to trade are still losing overall. Anyone who claims otherwise is likely new and having outlier success

dogeprotocol1

To reduce such risks, hybrid is a preferred approach; which is sign & verify transactions/attestations etc. with more than one algorithm while using the same NIST standardized schemes (ML-DSA, SLH-DSA); blockchains that use a single scheme are at higher risk than those that use hybrid approach.

Cryptizard

The big barrier is that bitcoin has a tiny blocksize and has resisted all attempts in the past to increase it. If they just did a straight swap from ECDSA signatures to ML-DSA it would reduce the throughput of the network to about 15% of what it is now, because ML-DSA signatures are 75x larger.

alami9

By “a quantum-safe chain,” I meant adopting QRL, PQ EVM allowing you to replicate your project on a quantum safe L1. Migration will mostly fail with the much larger signature size being one of the reasons. What is remarkable about QRL 2.0 is that its base layer performance is on par with Ethereum despite using the most secure (largest and most difficult to keep fast) Level 5 NIST PQ signature, ML-DSA-87.

alami9

“BNB has a quantum-safe chain coming”? They published a paper, not a chain. Their own May report calls it future research, “not a response to any immediate threat.” Nothing is live. The test cut throughput \~40% and blew transaction size from 110 bytes to \~2,500 - and that’s using ML-DSA-44, the weakest Dilithium tier, picked to limit the damage. They still lost 40%. P2P and KZG aren’t even in scope yet. Every BNB key today is still ECDSA and exposed. That’s not a quantum-safe chain, it’s a benchmark proving how painful the retrofit is.

LearnBitcoinCom

"Reach consensus FAST" is exactly what Bitcoin governance is designed not to do, and the reason matters here. If you could force rapid consensus on a major protocol change, you'd also force rapid consensus on any future change someone with enough capital wanted. That's a worse outcome for sound money than the worst-case quantum scenario. What's actually happening on quantum is the governance process working as designed: \- Multiple BIPs are in discussion for post-quantum signature schemes. \- Years of review, not weeks. The candidate signature algorithms (ML-DSA / Dilithium, SLH-DSA / SPHINCS+) are NIST-standardized and being stress-tested in production elsewhere before Bitcoin commits to one. No cryptographically relevant quantum computer exists. CRQC isn't a near-term threat horizon, it's a research milestone, and progress has been slower than the noisier claims suggest. Your scenario: Satoshi's coins move, panic ensues, is real, but doesn't require fast governance to address. It requires the 5-veto process to converge on a soft fork that transitions vulnerable scripts before that becomes possible. That work is happening, in public, on the mailing list and at conferences. The slowness is exactly the property that makes the answer credible when it comes. The opposite of slow consensus is governance capture. Quantum risk is real. Solving it by inviting consensus capture would be worse than the disease.

CrazyAppel

decentralization is not the only appeal for blockchain finance. There is also the fact that markets are open 24/7 The fact that fees and speed surpasses likes of Visa/Mastercard in less than 10 years of optimization (think Base, Algo etc...) Also, decentralization is kinda misunderstood, I will use an example I have experience with: Say I want to train my AI with company data so that when I make prompts, I get more accurate results, but this data is extremely sensitive so I can't use traditional cloud ML training solutions, so I opt for a decentralized solution such as Fetch AI (FET). In this case, it is decentralized because I pay DIRECTLY for what I USE and I get charged fairly because it's all programmatic. If this was a traditional Cloud AI training service, the company offering that service has full control over the pricing, what happens with your data etc, that's why it is centralized while FET is decentralized. However, the token FET itself has nothing to do with decentralization right? The service they offer is a decentralized one, but the tokenomics itself maybe centralized. Same with storage services, OneDrive/Google Drive VS FileCoin or STORJ, same concept. The coins maybe centralized and the owners of the treasury private keys can just pull the rug maybe yes, but the services they offer are still decentralized.

talissman_7

Spot on. Competing in the sub-second latency game against institutional infra is basically financial suicide for retail. That’s exactly why I built this dataset primarily for training ML/NLP models rather than direct execution. The goal isn't to try and snipe that initial 10-second spike, but to feed this historical sentiment decay into predictive models to classify market regimes, or to understand how liquidity absorbs shocks over a longer window (15m - 1h). Really appreciate the insight from the actual HFT side! Confirms exactly what the 1m candle data is hinting at

Ok_Freedom3290

Man, I feel this. Discretionary trading in prop firm environments is a psychological meat grinder. The drawdown rules are designed to make you tilt, and once you tilt, the account is blown. The best advice I can give is to completely remove your emotions from the equation and move towards a purely systematic approach. When you trade a mechanical system based on a statistical edge (we use ML models at AlphaSignal to find anomalies rather than trusting our gut), you stop caring about individual trades. A loss isn't a personal failure, it's just a statistical probability playing out. Once you have a backtested system with a known max drawdown that fits within the prop firm's rules, passing becomes a math equation rather than an emotional battle.

K42st

How after all your mouth do you explain post quantum resistent addresses, you know the kind that lead to quantum resistent keys well before quantum computers are anywhere near capable of cracking signitures, these are real today and so is the research. ML-KWM/ kyber SPHINCS+ XMSS (used by quantum resistent ledger) But please explain to me how all these coins get dumped where are they getting dumped and by who that can’t be traced remember it would be theft? Enlightened me!

never_safe_for_life

So I agree with you somewhat. Long before the current LLM phase we've had ML generated summaries of articles. And you're right, they are concise and to the point. I usually read them first. But I mean, look at this post. It's 17 paragraphs and has one point to make. I think LLMs are taking the trend backwards.

schrodingersbadger

NIST has already finalized their standards. “algorithms include ML-KEM (formerly CRYSTALS-Kyber) for general encryption, and ML-DSA (CRYSTALS-Dilithium) and SLH-DSA (SPHINCS+) for digital signatures, with FALCON as an additional signature standard” From what I gather from the Google paper, QRL is the only project that is currently quantum safe. Been looking into investing in it.

gtwooh

Statements like this show your lack of understanding of the subject outside of incendiary headlines. Already there is BIP-360 (Pay-to-Merkle-Root) on BTQ Bitcoin Quantum testnet; post-quantum signatures (e.g., NIST ML-DSA, SPHINCS+) on Blockstream Liquid sidechain. And if you just hold and have not spent your BTC you’ve never exposed your public keys and thus are not currently at risk. But thanks for spreading FUD

Ok-Operation4437

That’s just false. NIST has released the first finalized post-quantum encryption standards to protect against quantum computer threats. These guidelines focus on algorithms resistant to attacks like Shor’s, which could break traditional RSA and ECC encryption. The primary standards are FIPS 203 (ML-KEM for general encryption), FIPS 204 (ML-DSA for digital signatures), and FIPS 205 (SLH-DSA as a hash-based backup). NIST urges immediate transitions for systems like TLS, VPNs, and email, with full migration targeted by 2035. These lattice- and hash-based algorithms offer strong security for key exchange and signatures without hardware changes. Crypto however…

Ok-Operation4437

NIST has released the first finalized post-quantum encryption standards to protect against quantum computer threats. These guidelines focus on algorithms resistant to attacks like Shor’s, which could break traditional RSA and ECC encryption. The primary standards are FIPS 203 (ML-KEM for general encryption), FIPS 204 (ML-DSA for digital signatures), and FIPS 205 (SLH-DSA as a hash-based backup). NIST urges immediate transitions for systems like TLS, VPNs, and email, with full migration targeted by 2035. These lattice- and hash-based algorithms offer strong security for key exchange and signatures without hardware changes. Crypto however…

Kemilio

QRL was mentioned before Algerians: “A few blockchains have made progress in real-world deployment of PQC. In particular, the QRL [63, 64] launched in 2018 stands out as post-quantum from inception. Its original design was based on the stateful post-quantum signature scheme known as XMSS [241] and it is currently adding support for the stateless post-quantum signature scheme called CRYSTALS-Dilithium [242] and recently standardized by NIST under the name ML-DSA [243]. Other examples of post-quantum blockchains include Mochimo (MCM), which uses a variant of hash-based post-quantum Winternitz One-Time Signatures (WOTS) [244, 245], and the post-quantum privacy-preserving Abelian blockchain (ABEL), which makes extensive use of lattice-based PQC.”

brobbio

**Securing Elliptic Curve Cryptocurrencies Against Quantum Vulnerabilities — Babbush et al. (Google Quantum AI / Ethereum Foundation / Stanford), March 2026** Core findings: * Shor's algorithm can break 256-bit ECDLP (the cryptographic basis of Bitcoin and Ethereum) On a superconducting architecture, this translates to fewer than 500,000 physical qubits and ~9 minutes of runtime. **Roughly a 20× improvement over prior estimates.** **Bitcoin vulnerabilities** * ~1.7M BTC in P2PK scripts exposes public keys directly; ~6.9M BTC total are currently at-rest vulnerable * P2TR (Taproot) reintroduced at-rest vulnerability; P2PKH/P2WPKH protect against at-rest attacks only if keys are never reused * **Proof-of-Work consensus is not meaningfully threatened** **Ethereum vulnerabilities** * All accounts that have sent a transaction expose their public key permanently (Account Vulnerability) * Admin keys controlling smart contracts, stablecoins (~$200B), and RWAs are at-rest vulnerable (Admin Vulnerability) * L2 rollups and bridges using zkSNARKs inherit cryptographic vulnerabilities (Code Vulnerability); ~15M ETH at risk * BLS12-381 validator signatures vulnerable; compromising 2/3 of validators would allow chain rewrite (Consensus Vulnerability) * KZG trusted setup for blob data availability is susceptible to a one-time on-setup attack (Data Availability Vulnerability) **Dormant assets problem** * ~2.3M BTC inactive for 5+ years cannot be migrated via software updates; likely includes Satoshi-era coins * Three community options: Do Nothing (quantum attackers eventually take them), Burn (protocol destroys them), Hourglass (rate-limits spending) **Migration to Post-Quantum Cryptography (PQC)** * PQC signatures (e.g. Falcon, ML-DSA) are 10–20× larger than ECDSA, creating bandwidth and consensus challenges for Bitcoin in particular Algorand (Falcon), QRL, Abelian, and Solana (experimental) are already deploying PQC **Migration must begin immediately; the authors estimate the window is still open but narrowing fast** *The quantum threat to cryptocurrency is closer than commonly assumed, affects active transactions (not only dormant holdings), and requires urgent PQC migration across all major blockchains.*

Ok-Can-1275

BitTensor has 21 million coins just like BitCoin, including all the "subnets" that belong to BitTensor. #1 "subnet" (SN) all the way to 128 SN, and soon to double. in order to use/unlock a subnet in BitTensor, you must 1st buy TAO in order to participate in any-and-all decentralized open-source a.i. infrastructure to your desire. the price doesn't matter, the price can be $1 or $100, if the product costs $20 then you just need $20 to activate it. TAO runs off Proof-of-Intelligence which is a scoring mechanism rewarding the highest training participants with the highest scoring intelligence for yielding reward to "mine", which theoretically is infinite as long as there is always improvement in intelligence which is valued and metricized the team (like Const / Steeves) comes from deep technical ML / research backgrounds, not influencer marketing. awkward delivery ≠ fraud. comparing them to a man that owns a multi-trillion dollar company is surely going to be trained to be professional. also, Harvard University did their research on BitTensor back in 2020 released publicly way before TAO even launched: [https://ui.adsabs.harvard.edu/abs/2020arXiv200303917R/abstract](https://ui.adsabs.harvard.edu/abs/2020arXiv200303917R/abstract) BitTensor: A Peer-to-Peer Intelligence Market

Ok-Can-1275

BitTensor has 21 million coins just like BitCoin, including all the "subnets" that belong to BitTensor. #1 "subnet" (SN) all the way to 128 SN, and soon to double. in order to use/unlock a subnet in BitTensor, you must 1st buy TAO in order to participate in any-and-all decentralized open-source a.i. infrastructure to your desire. the price doesn't matter, the price can be $1 or $100, if the product costs $20 then you just need $20 to activate it. TAO runs off Proof-of-Intelligence which is a scoring mechanism rewarding the highest training participants with the highest scoring intelligence for yielding reward to "mine", which theoretically is infinite as long as there is always improvement in intelligence which is valued and metricized the team (like Const / Steeves) comes from deep technical ML / research backgrounds, not influencer marketing. awkward delivery ≠ fraud. comparing them to a man that owns a multi-trillion dollar company is surely going to be trained to be professional. also, Harvard University did their research on BitTensor back in 2020 released publicly way before TAO even launched: [https://ui.adsabs.harvard.edu/abs/2020arXiv200303917R/abstract](https://ui.adsabs.harvard.edu/abs/2020arXiv200303917R/abstract) BitTensor: A Peer-to-Peer Intelligence Market

Short-Cantaloupe-899

Just something I built as a small research project to track short-term BTC forecast ranges vs actual price behavior. I was curious how often downside bands expand before major moves. Nothing commercial, just testing statistical and ML models and letting them rerun every day with fresh data to see how projections evolve. Still experimenting with it.

MP_Sweet

We used a combination of ML algo's with AI over the years, for a subset of scrips & tried modelling the same method for other segments, markets & the technical analysis results weren't fruitful at all. Do you know where it worked the most? It's with sentiment & fundamental analysis. AI should be used where it can work best.

Adventurous-Sir444

This is so stupid.. Like how is this an AML risk.. that's not how ML works lol

MelonHoly

Exactly. They have to do this. They aren't just doing this to be difficult. If they won't let the customer withdraw 2k from the account - there must be an active ML suspicion on the account.

AutoModerator

Post is by: dorienh and the url/text [ ](https://goo.gl/GP6ppk)is: /r/CryptoMarkets/comments/1rordzy/podcast_sentiment_as_crypto_alpha_coinmonks/ A recent analysis published in Coinmonks examines whether AI-derived metrics from crypto podcasts (e.g., sentiment strength, contrarian signals, narrative intensity, attention share) can predict returns on assets like BTC, ETH, SOL, DOGE, and AVAX. The study uses lagged features only (no lookahead bias), chronological splits, and tests correlations + ML models (Random Forest, SVR, AdaBoost) across 1d/3d/7d horizons. Key takeaways from the results: * 29 out of 42 metrics showed significant correlation with future log returns (p < 0.001 after correction). * Contrarian indicators performed particularly well: high bullish podcast sentiment often preceded negative returns (corrs -0.11 to -0.19), while bearish/disagreement signals preceded upside—suggesting podcasts capture euphoria tops or capitulation bottoms. * Podcast metrics alone gave modest out-of-sample performance (R² \~0.05, corr up to \~0.39 on longer horizons). * Combined with price/returns data → clear uplift (R² 0.15–0.26, corr 0.45–0.52), with strongest gains on DOGE and noticeable for SOL/ETH/AVAX. Full article here: [https://medium.com/p/733522113090](https://medium.com/p/733522113090) Notebook/code for replication: [https://github.com/dorienh/MarketAnalysis/blob/main/audioalpha\_analysis.ipynb](https://github.com/dorienh/MarketAnalysis/blob/main/audioalpha_analysis.ipynb) Curious if others are exploring podcast/narrative data as alternative signals? Have you seen similar edges from social or on-chain sentiment? Or thoughts on why contrarian podcast signals might outperform in crypto? Looking forward to discussion—open to critiques or related studies. *I am a bot, and this action was performed automatically. Please [contact the moderators of this subreddit](/message/compose/?to=/r/CryptoMarkets) if you have any questions or concerns.*

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Post is by: Jinnapat397 and the url/text [ ](https://goo.gl/GP6ppk)is: /r/CryptoMarkets/comments/1rfk3sh/is_zkml_on_mobile_finally_practical_the_tech/ Zero-Knowledge Machine Learning has been a massive buzzword in the space for the last year, but mostly theoretical because running these proofs usually requires heavy compute. I was just reading through some recent engineering blogs and stumbled across a highly technical post from the world research team that actually caught my attention. Say what you want about their initial rollout, but their recent cryptographic pivot is genuinely interesting. They just open-sourced a GKR-based prover (called "Remainder") specifically optimized for edge devices like smartphones. The TL;DR implication is that they can now push updated Machine Learning models (like a new iris-recognition algorithm) directly to a user's phone. The phone runs the ML model locally on the existing biometric data and generates a zero-knowledge proof that the computation was correct. The network receives the cryptographic proof, but the raw biometric data never leaves the device. This effectively solves the biggest UX and privacy hurdle: users can get upgraded security on their digital IDs without ever having to visit an "Orb" again. Getting a GKR + Hyrax implementation to run efficiently on standard mobile hardware is a pretty massive leap for decentralized identity. Has anyone here looked into the GitHub repo for Remainder? I'm curious if this specific prover architecture could be easily adapted for other privacy-preserving DApps outside of the biometric space. *I am a bot, and this action was performed automatically. Please [contact the moderators of this subreddit](/message/compose/?to=/r/CryptoMarkets) if you have any questions or concerns.*

anymonero

The funny thing is, **they already did**. Many websites already use ML-KEM for TLS key exchange and *all* popular browsers support it. OpenSSH allows hybrid encryption with ML-KEM and SNTRUP. And that's been the case for multiple years. But nobody here knows anything about what is actually happening in IT security as a whole and just parrots maxi talking points.

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Post is by: No-Cupcake5851 and the url/text [ ](https://goo.gl/GP6ppk)is: /r/CryptoMarkets/comments/1rb3akq/algo_signals_february_21_50_signals_bullish_bias/ Saturday system check — monitoring over the weekend. Fear & Greed: **8/100 — Extreme Fear** · 50 signals across 49 symbols today — **bullish** lean (3L / 0S). **Top 3 signals (by confidence):** | Symbol | Direction | Entry → Target | Stop | |--------|-----------|----------------|------| | ADAUSDT | LONG ▲ (100% conf) | $0.2765 → $0.2783 | SL: $0.2757 · RSI 20 | | DUSDT | LONG ▲ (91% conf) | $0.0076 → $0.0076 | SL: $0.0076 · RSI 27 | | AIUSDT | LONG ▲ (90% conf) | $0.0223 → $0.0224 | SL: $0.0223 · RSI 28 | Confidence from an ONNX ensemble (70% ML + 30% heuristic). Above 0.75 I size normally, 0.65–0.75 I half-size, below that I skip. Is anyone else building out multi-exchange execution, or mostly single-exchange? --- Running a small private beta of the full platform at **ferroquant.com** — 6 people in right now. If you want access to the full signal list, backtesting, or just want to poke around, drop a comment or message me. *I am a bot, and this action was performed automatically. Please [contact the moderators of this subreddit](/message/compose/?to=/r/CryptoMarkets) if you have any questions or concerns.*

reliable35

I still love $RENDER & TAO sub-net tokens feel too much like a casino. I’m hoping both do well come the eventual Alt coin season & I agree Render a more focused project - safer bet.. TAO more speculative & it’s needs some of its subnet (AI & ML) projects.. to get massive real work adoption & revenue… a few are headed that way like Chutes.. but there’s many ways the project could fail, with its many moving parts. However it could really 5x 10x from here with little trouble & establish itself as the decentralised global market for ML & AI..

Spirited_Syllabub488

Hey, I have my own profitable ML-based trading model for BTC (and it also works on Gold). I’ve tested it on about 5 years of data and it averaged roughly around 10% per month with fixed lot. It has a sharpe ratio of >3. I don’t have much capital myself, so I’m looking for genuine people who might be interested to buying it. If you find it interesting, feel free to message me. This is my report - [https://drive.google.com/file/d/1xwNisxVslkfPPY9g2rcbWt4shrI-9o4-/view?usp=drive\_link](https://drive.google.com/file/d/1xwNisxVslkfPPY9g2rcbWt4shrI-9o4-/view?usp=drive_link)

crunchyeyeball

Yup. Domain first registered only 5 months ago. OP claims to be in ML & coding, but post history suggests he's actually in marketing and "outreach", promoting random companies: https://www.reddit.com/r/IndianFreelancers/comments/1r0d8jw/looking_for_remote_cold_calling_outreach_work/

AutoModerator

Post is by: hacktradeAi and the url/text [ ](https://goo.gl/GP6ppk)is: /r/CryptoMarkets/comments/1r5zxlu/how_multiagent_ai_detects_crypto_breakouts_before/ I've been trading crypto for 5 years and always struggled with the same problem: by the time I spot a breakout forming, the move is already halfway done. So I built an AI system that monitors markets differently. THE MULTI-AGENT APPROACH Instead of relying on a single indicator, I use specialized AI agents: \*\*Pattern Recognition Agent\*\* - CNN-LSTM hybrid trained on 5 years of data - Monitors 15m/1H/4H/1D simultaneously - Identifies 12 distinct pattern types \*\*Cross-Market Agent\*\* - Graph neural network tracking 50+ pairs - Detects BTC-to-altcoin lead relationships - Often predicts moves 30-60 seconds early \*\*Order Flow Agent\*\* - Transformer architecture for L2 data - Spots iceberg orders and absorption - Identifies whale positioning \*\*Sentiment Agent\*\* - NLP model processing funding rates, liquidations, social data - Quantifies fear/greed extremes THE CONSENSUS Each agent votes with confidence. Final signals only trigger when agreement >70% and confidence >0.8. Individual agents: \~55% accuracy Ensemble consensus: 68% accuracy THE INSIGHT Breakouts don't happen randomly. They're preceded by subtle signals across multiple dimensions that human traders miss. The AI catches the confluence. Running live for 6 months. Not perfect, but catches moves I would have missed entirely. Anyone else experimenting with ML for trading? Would love to hear your approaches. *I am a bot, and this action was performed automatically. Please [contact the moderators of this subreddit](/message/compose/?to=/r/CryptoMarkets) if you have any questions or concerns.*

reliable35

$100 right now on BitTensor - ML & AI models very likely to come back 🔥

reliable35

Only two answers to this…Bitcoin the best safe play… BitTensor the best high risk pay.. Why? Bittensor isn’t just a crypto, it’s a live, competitive AI economy, with teams of developers battling.. to build the best ML models, get ranked by real performance, and earn rewards, (TAO) accordingly. That’s incentivised intelligence, not hype. If AI is the future, 🤔Bittensor is one of the few networks where working models, competition, and value creation already exist .. scare supply and brutally merit based…. This project will either fall apart.. or do many multiples from where we are now. DYOR.

altFINS_official

Hey everyone, I wanted to share the API my team built at **altFINS** for anyone who’s been struggling with limited market APIs. 🔗 [https://altfins.com/crypto-market-and-analytical-data-api/](https://altfins.com/crypto-market-and-analytical-data-api/) **What it is:** altFINS API is a **crypto market + analytics API** that aggregates **live + historical exchange data** from 30+ exchanges and wraps it with **150+ technical indicators** so you don’t have to build it yourself. **Why it matters:** If you’re building bots, signals, dashboards, or AI agents — this gives you: ✅ Unified price & orderbook data across exchanges ✅ OHLCV, depth, funding rates, perp swaps info ✅ Built-in technical indicators (RSI, EMA, MACD, BB, etc.) ✅ Clean JSON responses with low latency ✅ Designed for production apps & automation **Who it’s for:** • Algo traders & bots • Quant researchers • ML/AI models needing clean features • Portfolio trackers • Data scientists **What makes it different:** Instead of stitching multiple websockets + compute pipelines, AltFINS gives you **ready-to-use analytics** so you can focus on strategy, not plumbing. Example use cases: • Strategy backtesting with uniform market data • Real-time signal generation without custom indicator code • Feeding AI agents with structured features • Cross-exchange arbitrage insight If anyone wants help onboarding, code examples, or pricing plans — drop a comment! I’d be happy to help.

LargeSnorlax

We got 80cm of snow so everything's been sludgy here, I love the snow and cold honestly I've been watching the [biggest ML ever](https://ore.supply/) over on ORE, got half a thousand people trying for the $68,000 pot right now, good entertainment

Sensitive-Mango3216

This is some next-level conspiracy thinking but honestly the timing is kinda spooky when you lay it out like that The part about Bitcoin being suspiciously machine-friendly is actually wild - no identity requirements, pure computation rewards, deterministic rules. That's not how humans usually design financial systems at all Though I think you're right that 2006 AI couldn't have pulled off the whole Satoshi thing. But the idea that early ML developments made some researchers nervous about centralization and influenced Bitcoin's design? That's actually pretty plausible

Original-Assistant-8

The article says "Bitcoin Quantum replaces Bitcoin's quantum-vulnerable ECDSA signatures with ML-DSA (Module-Lattice Digital Signature Algorithm)" That's the obvious change- it doesn't solve the underlying issues that result from trying to migrate to this concept. I don't get how this company is acting like it is taking over and defining the fork. Am I missing something?

coinfeeds-bot

tldr; BTQ Technologies has launched the Bitcoin Quantum testnet, marking the first quantum-safe fork of Bitcoin, 17 years after its genesis block. The testnet uses NIST-standardized ML-DSA cryptography to protect Bitcoin from quantum computing threats. It invites miners, developers, researchers, and users to test and refine quantum-resistant solutions. With quantum computing advancements posing risks to Bitcoin's security, this initiative aims to safeguard the cryptocurrency's future while aligning with U.S. government mandates for post-quantum cryptography. *This summary is auto generated by a bot and not meant to replace reading the original article. As always, DYOR.

Treo123

I agree with the ones saying use ML, not LLMs for trading. And be prepared to redeploy your ML trading strategies frequently and often, and also be prepared to sink months of learning just to get started. I do suggest that you do learn ML trading and Hyperliquid is a great place to deploy your learnings and experiments. To get started, I suggest that you go through the full free learning cycle here https://www.youtube.com/@memlabs-research/videos It's a great place to start and I'm suggesting these videos because I know they are from a tradfi quant who's also deploying trading strategies on Hyperliquid. And for your blockchain infrastructure, use [Chainstack](https://chainstack.com/build-better-with-hyperliquid/), obviously.

AgentOrange256

Crypto is significantly easier to track than traditional financial mechanisms actually. Feds are happy when they get a crypto case vs a complicated traditional ML cases lmfao. Jesus people…

whoisww-

Polkadot 2.0 uses a RISC-V virtual machine that makes cryptography agile . Don't need a hard fork, just swap the signature pallet to a Lattice-based scheme (like ML-DSA) via a runtime upgrade. The plumbing was literally designed to be quantum-swappable

djhaf

You need to have your lawyer draw up a demand letter. They will return it to you because if they dont do it within x days they by law have to pay you something lile 3x the original amout if they dont return your money. I used ML Esquire in Miami to send a demand letter to Rosland capital to get 250k back that my father sent for gold that they held for 6 months without delivering anything. Very reasonably priced and we got the check back within a week of them receiving it.

qwer1627

Oh, there’s much more fun for me in local inference\ML, that’s all - each niche and market has its scammers and its visionaries, you know; I dont hold much against crypto, im just arriving at it as an interesting thing to dig into from a different niche

Major_Amphibian1529

Sounds cool but why is this in the crypto sub? Unless they're talking about ML for trading algorithms or blockchain data analysis, seems like it belongs more in r/MachineLearning

radiohead-nerd

Revisions was a poor term choice. What I meant by revisioning is in the sense of ongoing development, not fixing fundamental flaws. NIST released the first three standards (ML-KEM, ML-DSA, SLH-DSA) in August 2024, but the process continues with evaluating more algorithms (like FALCON) and addressing implementation challenges (like large key sizes, performance), requiring constant updates to software, protocols (TLS), and industry adoption to prepare for future quantum computers.

obolli

Hey thanks a lot! I noticed this too, almost each post goes viral here on reddit I think I racked up around 2 Million views in 1 month with 10 posts or so. it was an accident and I do struggle a little bit with coming to terms to charge for something, this information in the end is all on the ledger and I find it a bit of a turn off that other tools charge so much for something one can get for free. I do think something like what I actually do for a living (ML) added on top I would feel more comfortable charging. On monday I actually had a meeting with a quant firm I did an internship at during my studies and maybe I can get paid there and still offer the community some free tools. I really appreciate the input and the ideas. I actually also thought about creating something along the lines of your suggestion. An expected value indicator of a trade based on volatility and history. My immediate bet is a ios app, lots of the users I got said they'd pay for notifications, and I have the app in review right now with Apple.

obolli

Predicting if someone is an insider would be really hard. I have a page wallets of interest /woi where I rank all traders by traditional performance metrics. One thing that I'll build on top of this is a traditional ML system that predicts if a current setup is likely to be of interest for a trader. And I think that's fairly doable. That's how Netflix's original recommender System worked

obolli

i get you're really into hedera, it's pretty cool but just as someone kind of knowing the projects and tech from both sides. Before you throw all your life savings in (small moonbag is different). Ask yourself, why would I care? Why would any company actually using AI care? What is it that needs "verifiable compute" What is a black box ? Why would I use a decentralized solution for that and go through all this trouble spend all this extra money and brain power? I'm not looking for you to explain it here, I just find most people in crypto don't really think of that, especially when blending AI/ML into crypto. There very rarely is a reason for it having a crypto solution. And then the solution itself is usually what most of crypto is, dream, might come true, but it's so far out, no one would go for it.

obolli

As an ML engineer (someone who does ai) it depends on your use keys. Most use cases are on Ethereum, Solana, arb etc. also the infrastructure. And the general chat in academia is very skeptical of what quantum computing will actually achieve in the near future. I'm not saying it's correct. I just give you a viewpoint from within the industry and academia from sota peeps

lindo_dia_pra_dormir

She was right. For real. Do not cry, but she did the basics of ML analysis

Cryptizard

Yes, post-quantum signatures are available. They were standardized by NIST a year ago. There are two options: ML-DSA and SLH-DSA, based on modular lattices and hash functions, respectively. Both have much larger signature sizes than ECDSA, which is currently used by Bitcoin and many other blockchains. This is a problem for upgrading because it means that the block size has to increase dramatically in order to support the new signature schemes. If Bitcoin were updated to ML-DSA without changing the block size, only \~100 transactions would fit into a block compared to the current \~3000. And the last time people tried to increase the block size of the Bitcoin protocol, it was so hard to get consensus that it resulted in forking to a whole new coin (BCH). As to the abandoned coins that are vulnerable to quantum computers, I personally think it is a feature, not a flaw. I think of it like a public bounty for scientists and companies working on quantum computing. As long as a migration path is in place in advance so that people who do have control of their keys can upgrade, I believe it is perfectly fine for the old lost coins to be up for grabs.

anymonero

The network traffic is encrypted with TLS, so you can't just read it with Wireshark but once ECC is broken, you can decrypt it because you could derive the shared secret. Though Firefox and Chromium already have post quantum TLS implemented through ML-KEM, so it's actually already fixed.

Cryptizard

Not technically difficult but hard to get everyone to agree. ML-DSA, the replacement for ECDSA which is used in Bitcoin, has signatures which are 50x bigger than ECDSA. It is going to require dramatically increasing the block size in order to adopt it. But the last time people suggested increasing the block size it was so contentious that it caused a hard fork into an entire different coin called Bitcoin Cash.

tsusanka

Hey! About quantum-ready: Bitcoin and other crypto projects are not yet ready for quantum computers. That's just a fact and I am glad we are starting to have these discussions. If Bitcoin and other cryptocurrencies aren’t quantum-safe, it doesn’t really make sense to build a quantum-safe hardware wallet, right? Attackers would target the blockchains directly rather than your wallet. That being said, we’ve been thinking about what we can do already, and for Safe 7 we’ve introduced two modifications: 1. The bootloader is now signed with SLH-DSA (a post-quantum algorithm standardized in 2024) alongside a traditional signature. 2. The device includes a certificate proving its authenticity, signed with ML-DSA, again alongside traditional signature schemes (ECDSA and Ed25519). The first signature (1) is verified every time you power up your Safe 7 - you can think of this as a quantum-safe boot process. No other hardware wallet has this as of now. The latter (2) is not yet implemented in Suite, but will be in the following months. You can find more technical details here [https://trezor.io/guides/trezor-devices/trezor-safe-7/going-quantum](https://trezor.io/guides/trezor-devices/trezor-safe-7/going-quantum) . Hope that wasn't too technical, let me know if you have more questions! \> Additionally would appreciate a “dumbed-down” version (no wifi/bluetooth) with the rest of this latest tech being made available too. You mean Safe 3 plus Tropic01 and quantum-ready? Got it!

Aazimoxx

In recent years, over here in the land of Oz we've had some pretty crazy lapses of oversight come to light regarding money laundering. From multiple supermarket cooler bags filled with cash being routinely processed through our biggest casinos in broad daylight, to some of our largest banks admitting (after MILLIONS of violations) that 'oh, we didn't have our AML stuff switched on' 😝 Granted, those cases did end up with big smackdowns (in the case of two of those banks, the two largest corporate fines in Australian history, $700m and $1.3bn) - but it gives you some idea of how laissez-faire a lot of this has been even just a few years ago here. There's always going to be an extra degree of difficulty involved with laundering fiat currency versus crypto though; even a 'public' coin like BTC is trivially easy to carry out large-scale automation processes with, to have it processed in small chunks across a number of outlets to avoid the scrutiny attracted by large transactions. Especially when there exist DEXes and no-account-swap sites like SideShift or ChangeNow, some of which are able to be accessed via Tor, there's simply no practical way to plug all those gaps. It's a different animal to traditional ML scenarios, especially when you have privacy coins you can use as an extra 'hop' in the chain to break attempts at forensic trails 🤔 So realistically, I think whatever measures are taken, have zero hope of stamping it out or even slowing it considerably - at best, they're just making it a little more inconvenient for the crims... Unfortunately sometimes that comes at a direct cost in convenience and safety to the average joe. Just think of all the extra identity theft being carried out due to every fly-by-night exchange storing extensive ID profiles of each user, in order to satisfy KYC laws? 🤨

reliable35

No doubt about.. BitTensor.. best Altcoin at the moment. TAO could flip ETH.. Bitcoin secures blocks - Ethereum runs contracts. BitTensor monetizes ML models basically selling intelligence. As subnets earn real revenues (billions early), TAO becomes AI’s settlement layer, accruing demand, scarcity, and massive value…

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Post is by: Nivedita_Rawat2 and the url/text [ ](https://goo.gl/GP6ppk)is: /r/CryptoMarkets/comments/1o7aw3c/narrative_tokensyield_markets_and_ai_infra/ Yield-tokenization protocols remain a key DeFi narrative in 2025, with Pendle (PENDLE) standing out for splitting yield-bearing positions into Principal and Yield Tokens to enable fixed/floating yield strategies and speculation on future yields. Pendle’s growth has been driven by integrations with LSDs and stablecoin ecosystems, and its fee-driven token design ties protocol activity to token utility—though it remains a higher-volatility, high-conviction DeFi play. On the AI side, Bittensor (TAO) continues to anchor the decentralized ML narrative with a marketplace for model contributions, benefiting recently from renewed institutional interest and a technical rebound in October. *I am a bot, and this action was performed automatically. Please [contact the moderators of this subreddit](/message/compose/?to=/r/CryptoMarkets) if you have any questions or concerns.*

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Post is by: potifai and the url/text [ ](https://goo.gl/GP6ppk)is: /r/CryptoMarkets/comments/1o0hy4n/complete_ai_automation/ Meet the Ultimate AI + ML + RL Trading System This is not a simple bot — it’s a fully autonomous, AI-powered, ML-trained, RL-enhanced trading engine built to adapt, learn, and outperform. It runs on any MT5 broker, any market, any symbol — Forex, crypto, indices, metals — and processes data across multiple timeframes in real time. Candle data is continuously downloaded and updated on every close, so decisions are always based on the most accurate market picture. Next-Gen AI Brain At its core is a multi-layered intelligence system combining: Machine Learning (ML) models trained to detect patterns and improve decision-making over time Reinforcement Learning (RL) that adapts to changing market conditions and optimizes strategies dynamically LSTM-based sentiment analysis to capture market psychology Five advanced technical indicators for robust confirmation ATR-based volatility and regime detection for smarter trade setups This hybrid approach allows the bot to agree, disagree, or scale risk based on signal strength — never blindly trading. Dynamic Risk & Position Management Risk management is fully AI-optimized and symbol-aware: ML-driven lot sizing based on volatility and account balance Adaptive stop-loss and take-profit levels that shift as market structure evolves Reward-to-risk ratios tuned dynamically for each instrument Specialized profiles for different markets — tighter stops & bigger profits for gold, larger positions for EURUSD, etc. Market Intelligence & Execution Multi-symbol orchestration — trades several assets simultaneously Real-time candle processing — no delay in decision-making Weekend auto-pause & weekday auto-resume — zero manual input required Low-latency order execution — built to minimize slippage Built-In Security & Protection Your bot stays secure and exclusive with: Hardware-bound license keys VM detection & blocking Anti-debugging & reverse-engineering defenses Tamper-resistance for maximum reliability Why This Bot Stands Out With AI, ML, and RL working together, this system is: Self-learning & self-improving — it evolves as markets change Emotion-free & consistent — no human bias or fear Fully automated — handles multiple markets without supervision Scalable & broker-agnostic — works anywhere, anytime This isn’t just a trading bot — it’s an AI-powered, ML-trained, RL-driven trading partner designed to grow smarter the longer it runs. Email montyr372@gmail.com GUI & source code is available! *I am a bot, and this action was performed automatically. Please [contact the moderators of this subreddit](/message/compose/?to=/r/CryptoMarkets) if you have any questions or concerns.*

HopeMrPossum

Oh 100%, most money laundering is done with fiat, a tonne goes through other asset classes, anyone who thinks crypto is the primary vehicle for ML knows nothing about AML

LexxM3

That’s well above average ML commission rates and is even above premium rates. Who’s the criminal now?

Royal-Income-4032

Cool idea, especially using mining for ML training instead of waste. Do they have any working demo yet, or is it still just on paper?

Narrow_Chance7639

Your claim on TAO and RNDR being down over the last 7 days does not match market data. Bittensor (TAO) showed a 0.9% increase over the past 7 days, trading around $347 with a market cap over $3.4 billion. Render (RNDR) experienced a minor 0.8% dip in the same period, but its overall sector momentum remains positive, with a market cap at $1.92 billion and trading volume up to $389.88 million weekly. The broader AI crypto sector added 11% to its market cap, reaching $33.9 billion in September alone, driven by listings and institutional inflows. These swings are not just hype; TAO's decentralized ML network has seen staking increases, and RNDR's GPU platform processed over $140 million in 24-hour volume. Dismissing them as slop ignores the underlying adoption metrics.

whatwilly0ubuild

Look, the inflation concerns are real but getting paid in crypto isn't the magic bullet most people think it is. Working at a company that solves tough engineering problems for startups and researchers, I've seen plenty of teams try to build payroll systems around crypto and most of them realize pretty quickly why traditional finance still dominates. The harsh reality is crypto salaries mostly exist in three buckets: blockchain companies paying their devs, sketchy offshore operations, and a handful of progressive startups trying to look cutting edge. Non-tech crypto jobs are rare as hell because most legitimate companies aren't set up for the accounting nightmares it creates. Our clients who've tried implementing crypto payroll face the same issues every time. Tax reporting becomes a disaster since you need to track the fair market value at the moment of each payment for tax purposes. One client spent more on accounting than they saved on transaction fees. Employees also freak out when Bitcoin dumps 20% the day after payday and their rent money evaporates. The stablecoin route makes more sense theoretically but you're still exposed to depegging risk and the IRS treats every conversion as a taxable event. Plus most landlords and utility companies aren't accepting USDC so you're constantly converting back to fiat anyway, which defeats the whole purpose. Here's what actually works. Negotiate a portion of your compensation in crypto if your employer is open to it, or just buy crypto with part of each paycheck yourself. You get the exposure you want without the operational headaches. Some of our customers in the fintech space are building better rails for this but the infrastructure isn't there yet for mainstream adoption. The platforms you mentioned for yield and cards are fine for playing around but they're not replacing traditional banking anytime soon. Data pipelines, cryptography, and ML are unforgiving if you cut corners, same applies to crypto financial infrastructure. Most off-the-shelf tools weren't built for your use case, they're built for generalization. If you really want crypto exposure to hedge against inflation, the smarter play is maxing out your regular salary negotiations and converting a fixed percentage to crypto yourself. You maintain flexibility, avoid tax complications, and can actually optimize your DCA strategy instead of being forced to hold whatever dumps into your wallet on payday. The difference between teams who engineer from first principles and those who chase narratives is night and day, same logic applies to personal finance.

groovinhipster

Didn’t OP say in this comment that the input and output tables do not have to fulfil 130 sum? https://www.reddit.com/r/Bitcoin/s/DzSVnmw9ML

whatwilly0ubuild

Yeah you're spot on about this convergence happening right now, not someday. At my firm we handle this exact type of R&D for clients and the AI crypto integration is already way deeper than most people realize. The automated portfolio rebalancing stuff you mentioned is table stakes at this point. We've built systems that go way beyond basic rebalancing though. Think real-time risk management that can detect flash crash patterns and pull liquidity before shit hits the fan, or ML models that can spot whale movements and front-run large order flows by milliseconds. The technical challenge isn't really the AI part anymore, it's building the infrastructure that can handle the latency requirements. Crypto markets move fast and if your model takes 100ms to make a decision while someone else's takes 10ms, you're dead in the water. Our customers learned this the hard way when they tried to run complex deep learning models in production trading environments. What's really interesting is the arms race aspect you touched on. We're seeing algorithmic trading firms deploy increasingly sophisticated models just to stay competitive. But here's the thing, most teams are building these systems on shaky foundations. They'll throw a neural network at price prediction without understanding the underlying market microstructure or how their own trading affects liquidity. The market stability question is fascinating from an engineering perspective. More efficient price discovery should theoretically reduce volatility, but when you have dozens of AI systems all trained on similar datasets making similar decisions at the same time, you get these weird feedback loops that amplify moves instead of dampening them. Security reviews aren't optional if you're serious about scaling this stuff. We've seen trading bots get manipulated through adversarial inputs or fail catastrophically during market stress because nobody tested edge cases properly. The difference between teams who engineer from first principles and those who just chain together APIs is night and day when markets get volatile. The infrastructure is definitely there now though. What took months to build five years ago can be deployed in days with the right architecture.

whatwilly0ubuild

Working at a platform that designs decentralized and ML-driven systems and we've been helping clients build on Base since it launched. The technical fundamentals are solid but there are some real trade-offs to consider. Base is basically Coinbase's attempt to own the L2 narrative while keeping transaction fees reasonable. For developers it's pretty straightforward since it's just Optimism under the hood with Coinbase's infrastructure backing it. We've deployed smart contracts there for several customers and the developer experience is clean, gas costs are way lower than mainnet Ethereum, and bridging assets is smooth. The real advantage is the Coinbase integration. If you're building something that needs fiat onramps or you want access to Coinbase's user base, Base makes sense. Our clients who are doing consumer-facing DeFi apps love that users can fund wallets directly from their Coinbase accounts without dealing with bridge complexity. That said, the ecosystem is still pretty thin compared to Polygon or Arbitrum. Less liquidity, fewer established protocols, and you're basically betting on Coinbase's long-term commitment to the platform. We've seen companies get burned when centralized players change priorities. The "Everything App" marketing is typical Coinbase overpromising. They're trying to be the WeChat of crypto but honestly most of our customers just use Base for specific use cases, not as some all-encompassing platform. For newcomers, Base is actually pretty good because the Coinbase connection makes onboarding less of a clusterfuck. But if you're serious about DeFi or building complex applications, you probably want to start on mainnet or Arbitrum where there's more established infrastructure. Security reviews aren't optional if you're serious about scaling on any L2, but Base's architecture is battle-tested since it's just Optimism with different branding. The main risk is Coinbase-specific, not technical.

whatwilly0ubuild

Man, you've basically documented everything wrong with crypto UX in one post. Working at a platform that designs decentralized and ML-driven systems, we deal with this exact bridging nightmare constantly with our clients. Your experience is unfortunately typical, but there are way simpler paths to get USDC on Base that don't involve that ridiculous multi-hop journey. Coinbase has native Base support since they built the damn network, so you can buy USDC directly on Base through their exchange or wallet. Binance also supports Base deposits/withdrawals now. Would've saved you like 5 steps and a bunch of gas fees. For future reference, you can also use Superbridge or the official Base bridge to go directly from Ethereum mainnet to Base if you already have USDC on mainnet. Much cleaner than going through TRON. The broader point you're making about network-specific tokens is spot on though. Most people don't realize that USDC on Ethereum is completely different from USDC on Base or Polygon, even though they have the same name and theoretically the same value. The technical architecture makes them totally separate assets that need bridging to move between chains. This is exactly why our customers who build wallet interfaces spend so much time on the UX layer. The underlying infrastructure is a mess of incompatible networks and most users shouldn't have to understand the technical details to move money around. That said, I'd be really careful about putting money into any "pre-IPO" projects through random crypto platforms. The legitimate investment world doesn't work that way and there are a ton of scams using familiar company names to steal people's funds. The complexity you went through might actually be a feature, not a bug, if it makes you think twice about where you're sending your money.

whatwilly0ubuild

Cross-chain transaction debugging tools are absolutely terrible right now. I work at an engineering consultancy and we see this shit daily with our clients building DeFi protocols and wallet infrastructure. The current landscape is a mess of block explorers that don't talk to each other, transaction traces that stop at bridge contracts, and debugging workflows that require like 6 different tools just to figure out why a transaction failed across chains. Etherscan, Polygonscan, whatever, they're all isolated silos. What I really want is something that can trace a transaction from start to finish across multiple chains and show you the actual execution flow. Like if I bridge USDC from Ethereum to Polygon and something breaks, I want to see the entire path, not just fragments. The closest thing is Tenderly but it doesn't handle cross-chain scenarios well and their API is expensive as hell for production use. Another gap is real MEV monitoring for regular users. Sure, flashbots has dashboards for searchers but normal people getting rekt by sandwich attacks have no visibility into what happened. Our customers who build trading interfaces constantly ask about this. They want to show users when they got MEV'd and by how much, but there's no good API that covers all the different MEV types across chains. Portfolio tracking is oversaturated but they all suck at DeFi positions. They can handle basic token balances but try tracking your actual PnL from liquidity providing across multiple pools and protocols and they fall apart. The math gets complex with impermanent loss, reward tokens, and compounding, and most tools just give up or show wrong numbers. Smart contract testing and simulation tools are getting better but still pretty clunky. Most teams try to duct-tape these systems together with Foundry and some custom scripts but there's definitely room for something more integrated. Especially for testing upgrade scenarios and cross-protocol interactions. The one thing that's almost there but not quite is decent alerting infrastructure. You can set up basic price alerts everywhere but try setting up alerts for smart contract events, unusual transaction patterns, or governance proposals and you're back to building custom monitoring. Data pipelines, cryptography, and ML are unforgiving if you cut corners so whatever gets built needs to actually work reliably, not just look good in demos.

whatwilly0ubuild

Damn, this hits close to home. Working at a platform that designs decentralized and ML-driven systems, I see brilliant technical founders struggle with this exact problem constantly. Here's the brutal truth: technical excellence means absolutely nothing to investors if you can't demonstrate market pull. I've watched our clients build incredible cryptographic systems that solve real problems, but the ones that get funded aren't necessarily the best tech. They're the ones that figured out the investment game. What actually gets crypto VCs excited? First thing is traction that proves people want what you built. Even if it's just a few dozen users actually using your system daily, that beats a thousand Twitter followers. Deploy something minimal and get real usage data. Market timing and narrative matter way more than you think. Right now, investors are hot for AI + crypto integration, RWA tokenization, and infrastructure that makes DeFi actually usable for normies. If your project doesn't fit into a current narrative, you're swimming upstream. Team credibility is everything. VCs invest in people, not projects. If you don't have previous exits or recognizable company experience, you need to find someone who does to be your front person. The email and Twitter approach is dead on arrival. Most our customers who successfully raise do it through warm introductions. You need to get in rooms where these people actually are, conferences, hackathons, or find someone who knows them. Also, stop calling it "shilling." That language immediately signals amateur hour to serious investors. Frame it as solving a real problem that people are willing to pay for. Your runway situation sucks but rushing into bad investment terms is worse than running out of money. Focus on getting one real investor who believes in what you're building rather than blasting everyone with generic pitches.

whatwilly0ubuild

At my firm we handle this exact type of R&D for clients building crypto platforms and yeah, this cost basis nightmare is everywhere. Coinbase's export tools are genuinely terrible for anything beyond basic buy and hold. The IRS absolutely can piece together your cost basis even if Coinbase screwed up their 1099. They get copies of all those forms plus they're building out blockchain analytics capabilities that are way more sophisticated than most people realize. Our customers in crypto compliance have shown us some of the tooling that's available now and it's not amateur hour anymore. Here's the thing though, if you traded ETH to BTC, that's a taxable event regardless of whether you think it was "profit taking" or not. The IRS treats crypto to crypto trades as if you sold the ETH for USD and immediately bought BTC. So you owe taxes on any gain the ETH had from when you originally bought it to when you traded it. Your accountant using zero cost basis was the worst possible approach because it assumes every dollar you received was pure profit. You definitely need to fix that with actual cost basis calculations. The risk of fudging the numbers is real. The IRS has been ramping up crypto enforcement and they're not stupid about blockchain analysis. If your reported numbers don't make sense compared to on-chain data, that's an audit waiting to happen. Get your actual transaction history from all exchanges, calculate proper cost basis using FIFO or specific identification method, and file amended returns if needed. Data pipelines, cryptography, and ML are unforgiving if you cut corners, and so is tax compliance. Most teams try to duct-tape these tax calculations together retroactively and it becomes a mess. Better to get it right now than deal with penalties and interest later.

GerManic69

Ive said it in other posts. Its easy to give an ML model like XGBoost a bunch if order book and l2 data and feed it through regression to "train" a "model" and by definition it is AI, but the reality is it has essentially no predictive power and is a scam. Stay away.

butt-slave

This guy is an actual ML engineer, he’s not just asking Grok to pick tickers for him lmao

ColdStriking1356

Not at all. I genuinely feel like those who said BTC is only for ML, they're the ones who bought more