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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.

Nonzee

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47,316 Aufrufe • vor 1 Jahr •via X (Twitter)

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MegaHeroes | ∑:

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D2 Baseball

28,225 Aufrufe • vor 1 Jahr

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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:

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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

179,451 Aufrufe • vor 2 Monaten

Probably the hackiest "bypasses" I've ever pulled off in botting: The Shopify REST API was for a long time the best and fastest way to checkout on Shopify. High rate limits, no captchas, no checkpoint and easy to work with. This is what gave Cyber the edge at the very start in 2018 (Dashe days) The Shopify team (with Jean-Michel Lemieux at the helm) spent years plugging the holes to make sure it wasn't used during drops. Around 2020, Shopify released Shopify Pay, which allowed you to expedite checkouts on all stores by linking your details to a phone number. Turned out the payment tokens generated through there allowed you to use the payments API as you could 2 years back. We generated around 3k Shop Pay accounts with randomised details, load balanced them across Cyber tasks (as rate limits were per account) and dominated every single release (...until it got patched). We called it Fast2. 3 years later I noticed that the payments API could still be used with Shopify mobile apps using Apple Pay. Turns out you could still send the users' tokenised credit card data in the request and that would take precedence - and as long as a valid signed Apple Pay token generated in the last 2 minutes was also in the request data, the payment request would go through and bypass captcha and checkpoint. Generating those Apple Pay tokens was the challenge, and reversing iOS frameworks was not an option. So I repurposed an old iPhone with a custom sideloaded Apple Pay app that would submit the token to our servers when the flow completed. And by combining accessibility macros, developer sandbox mode to prevent using real credit cards, a voice activation trigger and a old pair of earphones taped onto the microphone - a makeshift Apple Pay token server was born and Fast2 (or was it Fast3?) was back. Jean-Michel Lemieux thank you for providing me with the longest, most multi faceted puzzle ever.

Probably the hackiest "bypasses" I've ever pulled off in botting: The Shopify REST API was for a long time the best and fastest way to checkout on Shopify. High rate limits, no captchas, no checkpoint and easy to work with. This is what gave Cyber the edge at the very start in 2018 (Dashe days) The Shopify team (with Jean-Michel Lemieux at the helm) spent years plugging the holes to make sure it wasn't used during drops. Around 2020, Shopify released Shopify Pay, which allowed you to expedite checkouts on all stores by linking your details to a phone number. Turned out the payment tokens generated through there allowed you to use the payments API as you could 2 years back. We generated around 3k Shop Pay accounts with randomised details, load balanced them across Cyber tasks (as rate limits were per account) and dominated every single release (...until it got patched). We called it Fast2. 3 years later I noticed that the payments API could still be used with Shopify mobile apps using Apple Pay. Turns out you could still send the users' tokenised credit card data in the request and that would take precedence - and as long as a valid signed Apple Pay token generated in the last 2 minutes was also in the request data, the payment request would go through and bypass captcha and checkpoint. Generating those Apple Pay tokens was the challenge, and reversing iOS frameworks was not an option. So I repurposed an old iPhone with a custom sideloaded Apple Pay app that would submit the token to our servers when the flow completed. And by combining accessibility macros, developer sandbox mode to prevent using real credit cards, a voice activation trigger and a old pair of earphones taped onto the microphone - a makeshift Apple Pay token server was born and Fast2 (or was it Fast3?) was back. Jean-Michel Lemieux thank you for providing me with the longest, most multi faceted puzzle ever.

Lucas

247,853 Aufrufe • vor 26 Tagen

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

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

79,779 Aufrufe • vor 4 Monaten

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

43,373 Aufrufe • vor 3 Monaten