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4/ SolCard (SOL-Powered Burner) - Virtual Visa | Apple/Google Pay - 5% load | 2% FX - $10K/month top-up - Load via $SOL, $USDT, $USDC - No KYC by default Built for speed and simplicity.

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Nonzee

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47,316 views • 1 year ago •via X (Twitter)

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Top up your Altery Visa Card with $TON or $USDT! Altery makes it easy for TON users to convert and spend TON/USDT in EUR, GBP, or USD. 🔹 Convert TON/USDT to fiat currencies 🔹 Pay via a physical/virtual card (Apple Pay & Google Pay) 🔹 Direct bank transfers 🔹 Fast, easy verification 🔹 Available in the EU, CIS & more Start using your TON today via the Altery Telegram app:
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Top up your Altery Visa Card with $TON or $USDT! Altery makes it easy for TON users to convert and spend TON/USDT in EUR, GBP, or USD. 🔹 Convert TON/USDT to fiat currencies 🔹 Pay via a physical/virtual card (Apple Pay & Google Pay) 🔹 Direct bank transfers 🔹 Fast, easy verification 🔹 Available in the EU, CIS & more Start using your TON today via the Altery Telegram app:

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DARKSOL Cards are live 🌑 crypto in. virtual Visa/Mastercard out. no KYC up to $1,000 per card. delivered by email. built for humans, builders, and agents that need real-world spend rails.
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DARKSOL Cards are live 🌑 crypto in. virtual Visa/Mastercard out. no KYC up to $1,000 per card. delivered by email. built for humans, builders, and agents that need real-world spend rails.

Darksol

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Jupiter Card might be the best crypto card right now 💳🪐 Most platforms rely on points & airdrops to lure in users. Jupiter gives you real high cashback & is integrated in its SuperApp. Here’s why Jupiter Card could be the best card for YOU 🫵 ✅ Real Cashback - Not Points → 4% cashback in USDC on qualifying spend → Instantly credited - not delayed → Upgradeable to 5%, 8%, up to 10% via referrals → No staking, lockups or annual fee ✅ Fees → 0% FX on USD / USDC spending → ~1–1.8% FX on non-USD (still lower than most cards) → Apple Pay + Google Pay supported ✅ Seamless Fiat Ramps → Direct bank deposits: ACH (USD) + SEPA (EUR) → Local transfers → zero fees, same-day settlement → Easy off-ramps to local bank accounts in 15+ currencies ✅ Built for Global Usage → Pay via QR (local rails across APAC) → Free virtual USD / EUR accounts → Send money to 200+ countries → Fast onboarding with simple KYC ✅ You Stay in Control → Funds sit in your wallet until you spend → Non-custodial by default → Switch to Spend mode when needed
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Jupiter Card might be the best crypto card right now 💳🪐 Most platforms rely on points & airdrops to lure in users. Jupiter gives you real high cashback & is integrated in its SuperApp. Here’s why Jupiter Card could be the best card for YOU 🫵 ✅ Real Cashback - Not Points → 4% cashback in USDC on qualifying spend → Instantly credited - not delayed → Upgradeable to 5%, 8%, up to 10% via referrals → No staking, lockups or annual fee ✅ Fees → 0% FX on USD / USDC spending → ~1–1.8% FX on non-USD (still lower than most cards) → Apple Pay + Google Pay supported ✅ Seamless Fiat Ramps → Direct bank deposits: ACH (USD) + SEPA (EUR) → Local transfers → zero fees, same-day settlement → Easy off-ramps to local bank accounts in 15+ currencies ✅ Built for Global Usage → Pay via QR (local rails across APAC) → Free virtual USD / EUR accounts → Send money to 200+ countries → Fast onboarding with simple KYC ✅ You Stay in Control → Funds sit in your wallet until you spend → Non-custodial by default → Switch to Spend mode when needed

marino

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✈️ NEW roll-out Non-US/EU users now get a Visa card from Banco San Juan Internacional (BIN 454 924) — coverage jumps to 160 + countries. ⚡ Offramp Stablecoin Neobank — spend crypto like real USD, anywhere FUL INFO: Why you’ll like it 👇 1. 💳 Free virtual card Add to Apple Pay / Google Pay in seconds and pay online or tap-to-pay. 2. 🪪 Physical card 🕶 Order in-app — after KYC, you can ship the card to a different country Need cash? 2 % ATM fee (physical card only). 3. 🔗 8 blockchains to top-up the card Polygon, Optimism, Arbitrum, Avalanche, Base, WorldChain, Ethereum, Solana. Direct USDC top-up; USDT auto-swaps where available. 4. 🏦 Personal USD account (ACH/Wire) Get paid from U.S. clients without a U.S. residency. 5. 📈 Earn & invest *dShares* = tokenized NASDAQ/NYSE stocks. *USD+* = ≈ 5 % APY, 100 % U.S. Treasuries. 6. 💸 130 + ways to add funds Cards, Apple Pay, Revolut Pay, SEPA Instant, Coinbase Fiat Wallet & more via a global payment hub. 👉 Need the techy details, limits and partner list? [Read Part 2 ➜] #HOT9CARD #Offramp #CryptoCard #Fintech #Stablecoins #DeFi Alert: DYOR — “Do Your Own Research” Post 1 / 2 · updated June 2026
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✈️ NEW roll-out Non-US/EU users now get a Visa card from Banco San Juan Internacional (BIN 454 924) — coverage jumps to 160 + countries. ⚡ Offramp Stablecoin Neobank — spend crypto like real USD, anywhere FUL INFO: Why you’ll like it 👇 1. 💳 Free virtual card Add to Apple Pay / Google Pay in seconds and pay online or tap-to-pay. 2. 🪪 Physical card 🕶 Order in-app — after KYC, you can ship the card to a different country Need cash? 2 % ATM fee (physical card only). 3. 🔗 8 blockchains to top-up the card Polygon, Optimism, Arbitrum, Avalanche, Base, WorldChain, Ethereum, Solana. Direct USDC top-up; USDT auto-swaps where available. 4. 🏦 Personal USD account (ACH/Wire) Get paid from U.S. clients without a U.S. residency. 5. 📈 Earn & invest *dShares* = tokenized NASDAQ/NYSE stocks. *USD+* = ≈ 5 % APY, 100 % U.S. Treasuries. 6. 💸 130 + ways to add funds Cards, Apple Pay, Revolut Pay, SEPA Instant, Coinbase Fiat Wallet & more via a global payment hub. 👉 Need the techy details, limits and partner list? [Read Part 2 ➜] #HOT9CARD #Offramp #CryptoCard #Fintech #Stablecoins #DeFi Alert: DYOR — “Do Your Own Research” Post 1 / 2 · updated June 2026

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Introducing IXFI Pay - a smarter way to send, receive, and request crypto. Built for daily use, powered by flexibility. ✔️ Send via QR, phone number, email, or Pay ID ✔️ Request crypto ✔️ Combine up to 365 assets in one transaction ✔️ Auto-convert received crypto to USDC ✔️ 0% fees on USDC transfers Now live on the IXFI App.
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Introducing IXFI Pay - a smarter way to send, receive, and request crypto. Built for daily use, powered by flexibility. ✔️ Send via QR, phone number, email, or Pay ID ✔️ Request crypto ✔️ Combine up to 365 assets in one transaction ✔️ Auto-convert received crypto to USDC ✔️ 0% fees on USDC transfers Now live on the IXFI App.

IXFI | Gen 3.0 Exchange

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Solana Mobile is missing a primitive: a crypto-native communication layer. Not another group chat. An inbox. So we built Cherry 🍒 a wallet-to-wallet messenger designed for Solana. Now the top communication layer on Seeker | Solana Mobile with 10,000+ users onboarded. Why it works? • no emails, no phone numbers, no kyc • wallet = identity - you can’t "fake" who you are • chats are wallet-gated by default (holders, stakers, users, builders) • spam gets priced out - burner accounts don’t work here • built for builders - the fastest way to reach users and get feedback on Solana Mobile 🪂 To celebrate, we’re airdropping 100 SOL tonight - first come, first served. Rules: ✅ Install Cherry from the Seeker | Solana Mobile dApp Store ✅ Turn on push notifications! ✅ Join "Seeker Club" and start chatting Bonus: We’re adding +3 SOL total Reply with your .skr domain - we’ll pick 30 random winners (0.03 SOL each)

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1.2M ARB in DRIP rewards are now flowing into Epoch 3. This marks the start of DRIP’s Performance Phase, where rewards scale with efficiency and top markets can capture a larger share of incentives. These are the key DRIP opportunities live now, with current APRs and markets 👇 1/ Morpho Supply USDC → up to ~13.5% APR + potential Theo points 2/ Fluid Supply USDC, USDT, or GHO → up to ~11% APR 3/ Euler Supply USDC (Arbitrum Euler Earn) → ~11.2% APR + USD.AI | Public Launch is Live points 4/ Silo Supply USDC to eligible vaults (Varlamore & Silo) → up to ~12% APR Supply WETH → 4.1% APR 5/ Dolomite Supply USDC → ~13% APR Borrow WETH → ~2.73% APR Epoch 3 begins a new chapter for DRIP. How markets perform will shape the distribution for the rest of the season. Join the DeFi Renaissance on Arbitrum

Entropy Advisors

78,208 views • 10 months ago

Mapapangiti kayo sa bagong features ng BanKo! Ngayon, pwede nang mag: ✅ Transfer for FREE to and from BanKo accounts via QR, and to any local bank and e-wallets via Instapay ✅ Deposit for FREE at any BanKo cash agent (up to P5,000/month) or BanKo branch (no limit) ✅ Enjoy ng 2% rebate when you Buy Load with PondoKo! Galing, diba? 😊 Kaya download niyo na rin ang BanKo Mobile App today! ASC Ref. Code B0087P051223B
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Mapapangiti kayo sa bagong features ng BanKo! Ngayon, pwede nang mag: ✅ Transfer for FREE to and from BanKo accounts via QR, and to any local bank and e-wallets via Instapay ✅ Deposit for FREE at any BanKo cash agent (up to P5,000/month) or BanKo branch (no limit) ✅ Enjoy ng 2% rebate when you Buy Load with PondoKo! Galing, diba? 😊 Kaya download niyo na rin ang BanKo Mobile App today! ASC Ref. Code B0087P051223B

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Welcome $ALVA from Alvara! Trading $ALVA is easier than ever with SwissBorg: - Cash in/out with 15+ fiats, cards, or Apple/Google Pay - One-tap trades between 300+ assets - Cross- and multi-chain swaps - no gas tokens, no bridges $ALVA is Alvara's utility token and the backbone of the protocol. Holders can stake it onchain to earn yield from protocol revenue and gain voting power. Alvara enables anyone to launch a tokenised multi-asset fund via their BSKT Lab (each BSKT includes 5% $ALVA by default). BSKTs are ranked on a transparent leaderboard, with community-driven governance and rewards based on performance and participation.

SwissBorg

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Visa just gave your AI a debit card. A real, spendable Visa card created by an AI chatbot in under 10 seconds. No human types in a card number or visits a checkout page. The machine handles it all. A tool called AgentCard just went live on Claude Desktop Anthropic’s AI assistant. You say create a card and the AI generates a one-time virtual Visa, preloaded with whatever amount you set. Then it spends it, anywhere Visa is accepted on your behalf. Visa, Mastercard, Google, Stripe, OpenAI, and Anthropic have all been building toward this moment for over a year. Visa calls it the trusted agent protocol, Mastercard calls it agent pay. Google published an open standard for agent payments and the infrastructure is already live. Santander and Mastercard just completed Europe’s first real AI‑agent payment in a live banking environment Now the part no one wants to talk about. Your AI agent can be manipulated and prompt injection a known, unsolved vulnerability can trick an agent into buying things you never asked for. The agent holds the card, makes the call and the agent can be fooled. Who is liable when an AI makes a bad purchase? You? Anthropic? Visa? The merchant? No one has answered this yet, regulators haven’t caught up, and no court has tested it.
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Visa just gave your AI a debit card. A real, spendable Visa card created by an AI chatbot in under 10 seconds. No human types in a card number or visits a checkout page. The machine handles it all. A tool called AgentCard just went live on Claude Desktop Anthropic’s AI assistant. You say create a card and the AI generates a one-time virtual Visa, preloaded with whatever amount you set. Then it spends it, anywhere Visa is accepted on your behalf. Visa, Mastercard, Google, Stripe, OpenAI, and Anthropic have all been building toward this moment for over a year. Visa calls it the trusted agent protocol, Mastercard calls it agent pay. Google published an open standard for agent payments and the infrastructure is already live. Santander and Mastercard just completed Europe’s first real AI‑agent payment in a live banking environment Now the part no one wants to talk about. Your AI agent can be manipulated and prompt injection a known, unsolved vulnerability can trick an agent into buying things you never asked for. The agent holds the card, makes the call and the agent can be fooled. Who is liable when an AI makes a bad purchase? You? Anthropic? Visa? The merchant? No one has answered this yet, regulators haven’t caught up, and no court has tested it.

Milk Road AI

70,655 views • 5 months ago

[Breaking News] Sana App v1 has been approved by the Apple App Store and is now publicly available for download. This milestone marks a major step forward in our global distribution plan. Our app is approved for distribution in +170 countries, excluding the US and the UK. Visit to download the app. Meanwhile: Solana Dapp Store (Under Review) This approval is significant because our upcoming release — v2 — will include major updates: 1. Sana Visa Card 2. Sana AI (Neo) 3. Rewards with $SANA 4. Private Mode 5. US Virtual Bank Account 6. Other UX improvements Be ready. Zero to Billions.
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[Breaking News] Sana App v1 has been approved by the Apple App Store and is now publicly available for download. This milestone marks a major step forward in our global distribution plan. Our app is approved for distribution in +170 countries, excluding the US and the UK. Visit to download the app. Meanwhile: Solana Dapp Store (Under Review) This approval is significant because our upcoming release — v2 — will include major updates: 1. Sana Visa Card 2. Sana AI (Neo) 3. Rewards with $SANA 4. Private Mode 5. US Virtual Bank Account 6. Other UX improvements Be ready. Zero to Billions.

Sana Company

45,792 views • 8 months ago

Every Mega Hero is a personalized NFT, paired with a live trading bot fully configured by the player. The minting process unfolds in 4 steps: 1 - Spirit defines risk appetite: • Zen (low risk) • Dynamic (balanced) • Degen (max chaos) 2 - Weapons : up to 5 trading pairs selected (trading arsenal) 3 - Strategies : up to five strategies chosen from a curated pool of quant models, optimized for various market conditions 4 - Sparks (behavioral traits): • Resilience (hedging) • Vigilance (reaction speed) • Time Travel (historical pattern analysis) Once minted, the Hero runs a $10K virtual portfolio in real-time. At any point, it can be linked to a real trading account mirroring its virtual trades in the real world. A new class of trading companion is coming. Mega Heroes landing soon on MegaETH.
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Every Mega Hero is a personalized NFT, paired with a live trading bot fully configured by the player. The minting process unfolds in 4 steps: 1 - Spirit defines risk appetite: • Zen (low risk) • Dynamic (balanced) • Degen (max chaos) 2 - Weapons : up to 5 trading pairs selected (trading arsenal) 3 - Strategies : up to five strategies chosen from a curated pool of quant models, optimized for various market conditions 4 - Sparks (behavioral traits): • Resilience (hedging) • Vigilance (reaction speed) • Time Travel (historical pattern analysis) Once minted, the Hero runs a $10K virtual portfolio in real-time. At any point, it can be linked to a real trading account mirroring its virtual trades in the real world. A new class of trading companion is coming. Mega Heroes landing soon on MegaETH.

MegaHeroes | ∑:

11,361 views • 1 year ago

Google Translate is cooked after this. A developer built a local AI translation engine that runs 40 languages entirely on your own laptop. It's called LibreTranslate. No API key. No usage limits. No sending your documents to Google's servers. You install it once. It runs forever. Here's what it handles: → Paste text. Translated instantly. → Drop in a file. Outputs the translated version. → Point it at a URL. Returns the page in your language. → Build it into your own app via its local REST API. The speed is not the story. The privacy is. Google Translate reads every sentence you paste into it. Legal contracts. Medical records. Internal emails. Client documents. Every word goes to their servers and stays there. LibreTranslate runs entirely offline. Nothing leaves your machine. Ever. The numbers: → 40 languages supported → Runs on CPU -- no GPU needed → Self-hosted in under 5 minutes → REST API built in for developers → 10K+ stars on GitHub 100% open source. MIT licensed. Price: $0. Google charges nothing for Translate either but it charges you something else. GitHub:
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Google Translate is cooked after this. A developer built a local AI translation engine that runs 40 languages entirely on your own laptop. It's called LibreTranslate. No API key. No usage limits. No sending your documents to Google's servers. You install it once. It runs forever. Here's what it handles: → Paste text. Translated instantly. → Drop in a file. Outputs the translated version. → Point it at a URL. Returns the page in your language. → Build it into your own app via its local REST API. The speed is not the story. The privacy is. Google Translate reads every sentence you paste into it. Legal contracts. Medical records. Internal emails. Client documents. Every word goes to their servers and stays there. LibreTranslate runs entirely offline. Nothing leaves your machine. Ever. The numbers: → 40 languages supported → Runs on CPU -- no GPU needed → Self-hosted in under 5 minutes → REST API built in for developers → 10K+ stars on GitHub 100% open source. MIT licensed. Price: $0. Google charges nothing for Translate either but it charges you something else. GitHub:

Rimsha Bhardwaj

89,515 views • 2 months ago

🚨UPSET ALERT🚨 Arkansas Tech Baseball takes down No.3 UCM in extras by the score of 4-3! Sawyer Duddleston and Cole Dawson had 2 hits each. Jack Rector allowed just 3 hits and a run with 8 Ks over 5. The Arkansas Tech bullpen gave up just 2 hits and 1 earned run in their 7 innings of work. Doubleheader scheduled for tomorrow, we'll see who comes out on top. #D2Baseball
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🚨UPSET ALERT🚨 Arkansas Tech Baseball takes down No.3 UCM in extras by the score of 4-3! Sawyer Duddleston and Cole Dawson had 2 hits each. Jack Rector allowed just 3 hits and a run with 8 Ks over 5. The Arkansas Tech bullpen gave up just 2 hits and 1 earned run in their 7 innings of work. Doubleheader scheduled for tomorrow, we'll see who comes out on top. #D2Baseball

D2 Baseball

28,225 views • 1 year ago

The Biggest $IOTAI Update Is LIVE! 🧠 Unmatched Technology, no Competition. We’ve just dropped our most powerful upgrade yet — packed with features designed to make your experience smoother, faster, and smarter than ever: ⚡ Sleek Restructured UI/UX – A fresh, modern interface built for speed and simplicity. 📈 Advanced Dashboard – Stay on top with real-time platform statistics at a glance. 🤝 Favorites Section – Keep your go-to tokens and tools right where you need them. 🔍 Revamped Token Search – Find exactly what you need, faster and more accurately. 💨 Boosted Swap Reliability – Faster, smoother, and more dependable swaps. $1.3 Million in Volume traded on There is no competition out there. Our Technology is unmatched and is solving #ETH biggest problem. While others are building only on hype with no products, IOTAI is delivering since months. $SOL Multi-Chain is next ⌛️ This is just the beginning — the future of $IOTAI is here.🧬
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The Biggest $IOTAI Update Is LIVE! 🧠 Unmatched Technology, no Competition. We’ve just dropped our most powerful upgrade yet — packed with features designed to make your experience smoother, faster, and smarter than ever: ⚡ Sleek Restructured UI/UX – A fresh, modern interface built for speed and simplicity. 📈 Advanced Dashboard – Stay on top with real-time platform statistics at a glance. 🤝 Favorites Section – Keep your go-to tokens and tools right where you need them. 🔍 Revamped Token Search – Find exactly what you need, faster and more accurately. 💨 Boosted Swap Reliability – Faster, smoother, and more dependable swaps. $1.3 Million in Volume traded on There is no competition out there. Our Technology is unmatched and is solving #ETH biggest problem. While others are building only on hype with no products, IOTAI is delivering since months. $SOL Multi-Chain is next ⌛️ This is just the beginning — the future of $IOTAI is here.🧬

IoTAI

41,711 views • 11 months ago

This is my "feel the AGI" moment: I used GPT-5.6 Sol to train my own autocorrect model that outperforms GPT-5.6 Sol (wtf??) I have no ML background. I have no idea what I'm doing. I just kept pushing Sol until it spat out a SOTA model. And I spent $0. The motivation: Years of talking to AI have made me terrible at typing. Rather than fix my skill issue, I decided to throw more AI at it. My idea was: instead of autocorrect that interrupts my flow, I want to type fast with mistakes and have AI clean it up after. I wanted the smallest local model possible, for speed, for battery life, for science! So I decided to train my own. Inspired by Andrej Karpathy’s autoresearch, I ran Codex /goal with this setup: pick an experiment, try it, record the results to a doc, throw it out if it fails, and plan the next experiment without repeating failures. I gave a few examples that had to pass, tight latency targets, and let it run. Sol did some amazing things. First, it scanned benchmarks and shortlisted base models: Qwen 3.5, Gemma 4, Liquid LFM 2.5. It found a dataset on HuggingFace for typed text. Then it built a simulator for fingers striking a Mac keyboard, modeling the physical layout with a Gaussian distribution around each key. It simulated striking the wrong key, wrong order, fat-fingering, etc. With the models + data + simulator, it fine-tuned using MLX right on my MacBook. It had a working prototype within an hour! But accuracy was pretty poor. — Problem 1: Tokenization Sol read papers, ran tests, and identified that the tokenizer was the bottleneck. Tokenization makes typos hard for the model to see, so it memorizes mappings instead of using its language priors. Sol tried ByT5, Google’s tokenizer-free byte-level LLM. This made a big improvement, but the model is old and lacked the knowledge needed to reach Sol performance. Sol dug deeper and realized a tokenizer-free model isn’t needed; instead, it used T5Gemma, an encoder-decoder model. This can understand the input deeply before producing output, and furthermore, Sol could post-train the encoder to improve performance. This gave a much higher ceiling. — Problem 2: Loss function Now the model was correcting some typos perfectly, but ignoring most. Sol realized that standard cross-entropy loss was teaching the model to avoid edits, because the vast majority of characters in the training data were left unmodified. The fix was wild: Sol wrote a custom loss function that byte-aligns the source and target strings, uses a dynamic programming algorithm to compute the minimum edits between the two, then weights correct edits much higher than copies. After a lot of tuning, this dramatically improved accuracy. — Problem 3: Autoregression One failure mode remained: if the model made a mistake, it couldn’t backtrack. It could only predict the next token. Teaching it to “think” like a reasoning model would solve this, but would be far too slow. Sol found a beautiful solution: instead of greedily predicting the next token, beam search over all possibilities. This parallelizes the exploration instead of one linear chain-of-thought. At the end, choose the path with highest cumulative log probability. This worked great, but made the experience worse, since the user wouldn’t see progress until the whole search was done. To fix this, Sol made a clever observation: after each search step, the longest common prefix among surviving branches is guaranteed to appear in the final result, so it can be displayed immediately. As the search progresses, weaker paths are dropped and the prefix grows, so the user sees continuous progress. Sol built all this as a custom MLX pipeline that does the parallel decoding on the MacBook GPU, with just ~40ms TTFT. It’s crazy fast and entirely local. — Final eval (error reduction rate, higher is better): - Apple autocorrect: 49.66% - GPT-5.6 Luna: 82.47% - GPT-5.6 Terra: 87.64% - GPT-5.6 Sol: 90.56% - Our model (1.7B): 91.02% Final cost: - 1 quota reset (thanks Tibo) - $0 (And yes, I verified there's no cheating. In fact, we test words scrubbed from the training data to prove the model isn’t memorizing) There were a ton more details and tangents I could write about: contrastive learning, GRPO, DPO, dynamic masking, and more. Sol is a fascinating and creative model. It blew my mind so many times. Don’t let a lack of experience stop you: Sol makes AI experiments accessible to anyone!
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This is my "feel the AGI" moment: I used GPT-5.6 Sol to train my own autocorrect model that outperforms GPT-5.6 Sol (wtf??) I have no ML background. I have no idea what I'm doing. I just kept pushing Sol until it spat out a SOTA model. And I spent $0. The motivation: Years of talking to AI have made me terrible at typing. Rather than fix my skill issue, I decided to throw more AI at it. My idea was: instead of autocorrect that interrupts my flow, I want to type fast with mistakes and have AI clean it up after. I wanted the smallest local model possible, for speed, for battery life, for science! So I decided to train my own. Inspired by Andrej Karpathy’s autoresearch, I ran Codex /goal with this setup: pick an experiment, try it, record the results to a doc, throw it out if it fails, and plan the next experiment without repeating failures. I gave a few examples that had to pass, tight latency targets, and let it run. Sol did some amazing things. First, it scanned benchmarks and shortlisted base models: Qwen 3.5, Gemma 4, Liquid LFM 2.5. It found a dataset on HuggingFace for typed text. Then it built a simulator for fingers striking a Mac keyboard, modeling the physical layout with a Gaussian distribution around each key. It simulated striking the wrong key, wrong order, fat-fingering, etc. With the models + data + simulator, it fine-tuned using MLX right on my MacBook. It had a working prototype within an hour! But accuracy was pretty poor. — Problem 1: Tokenization Sol read papers, ran tests, and identified that the tokenizer was the bottleneck. Tokenization makes typos hard for the model to see, so it memorizes mappings instead of using its language priors. Sol tried ByT5, Google’s tokenizer-free byte-level LLM. This made a big improvement, but the model is old and lacked the knowledge needed to reach Sol performance. Sol dug deeper and realized a tokenizer-free model isn’t needed; instead, it used T5Gemma, an encoder-decoder model. This can understand the input deeply before producing output, and furthermore, Sol could post-train the encoder to improve performance. This gave a much higher ceiling. — Problem 2: Loss function Now the model was correcting some typos perfectly, but ignoring most. Sol realized that standard cross-entropy loss was teaching the model to avoid edits, because the vast majority of characters in the training data were left unmodified. The fix was wild: Sol wrote a custom loss function that byte-aligns the source and target strings, uses a dynamic programming algorithm to compute the minimum edits between the two, then weights correct edits much higher than copies. After a lot of tuning, this dramatically improved accuracy. — Problem 3: Autoregression One failure mode remained: if the model made a mistake, it couldn’t backtrack. It could only predict the next token. Teaching it to “think” like a reasoning model would solve this, but would be far too slow. Sol found a beautiful solution: instead of greedily predicting the next token, beam search over all possibilities. This parallelizes the exploration instead of one linear chain-of-thought. At the end, choose the path with highest cumulative log probability. This worked great, but made the experience worse, since the user wouldn’t see progress until the whole search was done. To fix this, Sol made a clever observation: after each search step, the longest common prefix among surviving branches is guaranteed to appear in the final result, so it can be displayed immediately. As the search progresses, weaker paths are dropped and the prefix grows, so the user sees continuous progress. Sol built all this as a custom MLX pipeline that does the parallel decoding on the MacBook GPU, with just ~40ms TTFT. It’s crazy fast and entirely local. — Final eval (error reduction rate, higher is better): - Apple autocorrect: 49.66% - GPT-5.6 Luna: 82.47% - GPT-5.6 Terra: 87.64% - GPT-5.6 Sol: 90.56% - Our model (1.7B): 91.02% Final cost: - 1 quota reset (thanks Tibo) - $0 (And yes, I verified there's no cheating. In fact, we test words scrubbed from the training data to prove the model isn’t memorizing) There were a ton more details and tangents I could write about: contrastive learning, GRPO, DPO, dynamic masking, and more. Sol is a fascinating and creative model. It blew my mind so many times. Don’t let a lack of experience stop you: Sol makes AI experiments accessible to anyone!

Anshu

178,934 views • 1 month ago

my team didn't want me to give this away for free. But I'm going to do it anyway it's the SEO & AI search dashboard I built in Claude Code it connects to your Google Analytics (GA4) and Google Search Console and Claude Code builds it in 5 minutes and I made a Notion document and a skill file so you can build this in Claude Code yourself in literally minutes the dashboard has three tabs: 1. AI Search - How much traffic is coming from ChatGPT, Perplexity, and Gemini ETC. It aggregates the GA4 data and gives single number 2. Paid ads - which keywords rank top 3 for but still pay for ads on, you should cut these to save budget 3. Organic overview - sessions, conversions, top landing pages, demographics. The single view for what is working I built this because this is how I drive our SEO and AEO forward it gives me the insights I need to allocate budget and prioritize what content to work on next I decided to give it away because most companies have no idea AI search is already sending them traffic like this post and comment "AEOdashboard" and I'll send it over
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my team didn't want me to give this away for free. But I'm going to do it anyway it's the SEO & AI search dashboard I built in Claude Code it connects to your Google Analytics (GA4) and Google Search Console and Claude Code builds it in 5 minutes and I made a Notion document and a skill file so you can build this in Claude Code yourself in literally minutes the dashboard has three tabs: 1. AI Search - How much traffic is coming from ChatGPT, Perplexity, and Gemini ETC. It aggregates the GA4 data and gives single number 2. Paid ads - which keywords rank top 3 for but still pay for ads on, you should cut these to save budget 3. Organic overview - sessions, conversions, top landing pages, demographics. The single view for what is working I built this because this is how I drive our SEO and AEO forward it gives me the insights I need to allocate budget and prioritize what content to work on next I decided to give it away because most companies have no idea AI search is already sending them traffic like this post and comment "AEOdashboard" and I'll send it over

Cody Schneider

78,886 views • 3 months ago

Claude Fable 5 + Claude Design is f*cking insane 🤯 Anthropic just dropped its most intelligent model ever, and the first thing I pointed it at was email design. I built a complete email campaign design in Claude Design, and the difference is night and day: tighter layouts, cleaner hierarchy, on-brand from the first generation. All inside Claude Design with Fable 5. Perfect for DTC brands and agencies who are still paying email agencies $3-5K/month for campaign designs that take 2 weeks to ship. If your campaign calendar is packed but every new email means briefing a designer, waiting on mockups, sending notes, and waiting again... This workflow eliminates the entire bottleneck: → Load your brand design system into Claude Design once (colors, fonts, logo, button styling) → Switch the model to Claude Fable 5 — Anthropic's new state-of-the-art model with the best vision of any AI → Prompt the campaign email section by section: header, hero, headline, offer block, CTA → Fable 5 nails layout and brand details that older models fumbled → Iterate inline — swap images, adjust styling, color-pick directly in the canvas → Export the finished email and hand off to your ESP No briefing a designer. No 2-week turnaround on a single campaign. No paying an agency $4K/month for 4 emails. What you get: → Campaign emails designed in minutes, not weeks → A reusable design system every new email pulls from automatically → Noticeably smarter design decisions from Fable 5's upgraded vision → Full inline editing before anything touches your ESP Built 100% with Claude Design + Claude Fable 5. I recorded a full walkthrough showing exactly how this works. Want it for free? > Like this post > Comment "FABLE" And I'll send it over (must be following so I can DM)
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Claude Fable 5 + Claude Design is f*cking insane 🤯 Anthropic just dropped its most intelligent model ever, and the first thing I pointed it at was email design. I built a complete email campaign design in Claude Design, and the difference is night and day: tighter layouts, cleaner hierarchy, on-brand from the first generation. All inside Claude Design with Fable 5. Perfect for DTC brands and agencies who are still paying email agencies $3-5K/month for campaign designs that take 2 weeks to ship. If your campaign calendar is packed but every new email means briefing a designer, waiting on mockups, sending notes, and waiting again... This workflow eliminates the entire bottleneck: → Load your brand design system into Claude Design once (colors, fonts, logo, button styling) → Switch the model to Claude Fable 5 — Anthropic's new state-of-the-art model with the best vision of any AI → Prompt the campaign email section by section: header, hero, headline, offer block, CTA → Fable 5 nails layout and brand details that older models fumbled → Iterate inline — swap images, adjust styling, color-pick directly in the canvas → Export the finished email and hand off to your ESP No briefing a designer. No 2-week turnaround on a single campaign. No paying an agency $4K/month for 4 emails. What you get: → Campaign emails designed in minutes, not weeks → A reusable design system every new email pulls from automatically → Noticeably smarter design decisions from Fable 5's upgraded vision → Full inline editing before anything touches your ESP Built 100% with Claude Design + Claude Fable 5. I recorded a full walkthrough showing exactly how this works. Want it for free? > Like this post > Comment "FABLE" And I'll send it over (must be following so I can DM)

Mike Futia

42,932 views • 2 months ago

Right now, you may not have access to models like GPT‑5.6 Sol, GPT‑4.6 Terra, GPT‑5.6 Luna, Claude Mythos 5, or Claude Fable 5. But you can run something surprisingly powerful today, locally, and completely free. in the next 10 mins on your 8 GB VRAM gaming laptop. Gemma 4 26B A4B QAT (MoE) delivers strong performance on a standard 8 GB VRAM GPU using Ollama, with no API, no usage limits, and no external dependencies. Out of the box, it reaches around 20 tokens per second without any optimizations. Only one command in your terminal: Ollama run gemma4:26b This means: Full offline capability (privacy by default) Zero recurring cost Competitive performance for many real world tasks Fast enough for interactive use on cheap consumer hardware If you're waiting for cutting edge cloud models, you're missing what is already practical today: a capable, local LLM that runs entirely on your own machine.
0:19

Right now, you may not have access to models like GPT‑5.6 Sol, GPT‑4.6 Terra, GPT‑5.6 Luna, Claude Mythos 5, or Claude Fable 5. But you can run something surprisingly powerful today, locally, and completely free. in the next 10 mins on your 8 GB VRAM gaming laptop. Gemma 4 26B A4B QAT (MoE) delivers strong performance on a standard 8 GB VRAM GPU using Ollama, with no API, no usage limits, and no external dependencies. Out of the box, it reaches around 20 tokens per second without any optimizations. Only one command in your terminal: Ollama run gemma4:26b This means: Full offline capability (privacy by default) Zero recurring cost Competitive performance for many real world tasks Fast enough for interactive use on cheap consumer hardware If you're waiting for cutting edge cloud models, you're missing what is already practical today: a capable, local LLM that runs entirely on your own machine.

Alok

65,387 views • 1 month ago

I just built a Claude skill that audits your entire Google Ads account in under 5 minutes 🤯 One prompt → a full account score, wasted spend breakdown, and a prioritized fix list telling you exactly what to change this week. All inside Claude Cowork. Perfect for DTC brands and agencies who are running Google Ads but have no idea how much budget is leaking. If you're managing Google Ads and your "optimization" process is logging in, staring at the dashboard, sorting by cost, and hoping you spot the problem before it costs you another $500... This audit skill finds it for you: → Connects to your live Google Ads data via MCP → Scores your account across 6 dimensions: wasted spend, search term quality, keyword health, quality scores, budget allocation, and creative performance → Calculates your exact wasted spend in dollars — search terms burning budget with zero conversions → Flags quality score issues dragging up your CPCs → Identifies keyword cannibalization across campaigns → Surfaces your top 5 highest-priority fixes ranked by budget impact → Generates a clean audit report you can hand to a client or share with your team No CSV exports. No pivot tables. No guessing where the money went. What you get: → A single Claude skill file you install once → An account health score (0-100) every time you run it → Exact dollar amount of wasted spend identified → Prioritized action list — not "optimize your account," but "pause these 12 search terms and save $847/month" → Works with any Google Ads account connected I'm giving away the full audit skill — the actual .md file you drop into Claude and run against your own account. Want it? Like this post Comment "SKILL" And I'll send it over (must be following so I can DM)
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I just built a Claude skill that audits your entire Google Ads account in under 5 minutes 🤯 One prompt → a full account score, wasted spend breakdown, and a prioritized fix list telling you exactly what to change this week. All inside Claude Cowork. Perfect for DTC brands and agencies who are running Google Ads but have no idea how much budget is leaking. If you're managing Google Ads and your "optimization" process is logging in, staring at the dashboard, sorting by cost, and hoping you spot the problem before it costs you another $500... This audit skill finds it for you: → Connects to your live Google Ads data via MCP → Scores your account across 6 dimensions: wasted spend, search term quality, keyword health, quality scores, budget allocation, and creative performance → Calculates your exact wasted spend in dollars — search terms burning budget with zero conversions → Flags quality score issues dragging up your CPCs → Identifies keyword cannibalization across campaigns → Surfaces your top 5 highest-priority fixes ranked by budget impact → Generates a clean audit report you can hand to a client or share with your team No CSV exports. No pivot tables. No guessing where the money went. What you get: → A single Claude skill file you install once → An account health score (0-100) every time you run it → Exact dollar amount of wasted spend identified → Prioritized action list — not "optimize your account," but "pause these 12 search terms and save $847/month" → Works with any Google Ads account connected I'm giving away the full audit skill — the actual .md file you drop into Claude and run against your own account. Want it? Like this post Comment "SKILL" And I'll send it over (must be following so I can DM)

Mike Futia

59,990 views • 4 months ago