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BA.net News Social Posts Archive Index (SEO)
24c5a1385340671a80d96ab25276c09499417db8032bcedd1bb620da618008aa:  Here are the top 8 recipes from the latest feeds: 1. Quick Salmon Recipes for Fast Weeknight Dinners – The Stay At Home Chef 2. The Best Ground Beef Dinners for Busy Weeknights – The Stay At Home Chef 3. The Best Bite-Sized Appetizers – The Stay At Home Chef 4. Veggie-Packed Dinners Everyone Will Love – The Stay At Home Chef 5. Brown Sugar Shaken Espresso Overnight Oats (Starbucks Inspired!) – Fit Foodie Finds 6. Air Fryer Berry Cobbler (Just 2 Ingredients!) – Fit Foodie Finds 7. Korean Beef Smash Burritos – Fit Foodie Finds 8. Crispy Air Fryer Chili Crunch Rolls – Fit Foodie Finds Top Pick: Quick Salmon Recipes for Fast Weeknight Dinners – The Stay At Home Chef ba.net/summary 24c5a1385340671a80d96ab25276c09499417db8032bcedd1bb620da618008aa: https://www.youtube.com/watch?v=TpEBYINwokA The transcript has been successfully retrieved. Here's a summary of the key points from the video about Google's new Gemma 4 12B model: 🎯 **What is Gemma 4 12B?** • A unified encoder-free multimodal model designed for local deployment on consumer hardware. • Targets systems with around 16 GB of VRAM, making it practical for everyday laptops. • Handles text, vision, and audio directly within the model (no separate encoders). ⚙ **Key Features** • Encoder-free architecture: Reduces memory overhead and latency. • 250K context window. • Runs at ~56 tokens/sec on a 24 GB GPU (vs. 32 tokens/sec for the larger 26B model). • Optimized for memory efficiency rather than raw throughput. 🧪 **Benchmark Results** • Front-end generation: Created impressive landing pages, product viewers, and even a Minecraft clone. • OS clones: Decent Windows 95 clone, but failed on Mac OS. • 3D/Interactive: Generated decent 3GS code but struggled with complex 3D scenes. • Reasoning: Strong for its size, though slightly behind the Qwen 3.6 35B in multi-step reasoning. 💻 **Deployment** • Install via Ollama or custom endpoints with Anago. • Quantization-aware training checkpoint available for lower memory usage. • Unslush version recommended for better latency optimization. 🏆 **Verdict** • The Gemma 4 12B is a sweet spot in Google's open model lineup. • Ideal for users with ~16 GB VRAM who want a capable local AI for coding, vision, audio, and 3D generation. • Not the absolute best, but offers an excellent speed-to-performance ratio for consumer hardware. Would you like me to help you set up the model or dive deeper into any specific aspect? ba.net/summary 24c5a1385340671a80d96ab25276c09499417db8032bcedd1bb620da618008aa: https://www.youtube.com/watch?v=o4We6u1Q1Ks Here's a summary of the YouTube video transcript: Title: "End of Zcash?" - Discussion on a newly discovered vulnerability in Zcash's shielded pool Key Points: 1. The Vulnerability: - A white hat hacker (Taylor) was paid to audit Zcash's Orchard (shielded) pool code using AI tools (Claude 4.8). - The AI found a vulnerability that could allow minting extra tokens in the shielded pool without detection until those tokens try to leave. - This would mean the total supply could exceed the expected 21 million Zcash. 2. What Actually Happened: - The vulnerability was found during a specific, targeted audit with domain knowledge, not a generic AI prompt. - The shielded pool balance has been *increasing*, not decreasing, which suggests no exploitation has occurred. - If the bug were being exploited, there would be a pattern of outflows, which isn't visible. 3. The Response: - Zcash is launching a fourth shielded pool with the patch applied. - Users will be asked to migrate their funds from the old pools to the new one. - This migration will allow verification that no extra coins were minted (if the total supply after migration equals the expected amount, the protocol wasn't exploited). 4. Why It's Not a Panic Situation: - This isn't the first time Zcash has had to rotate pools; Monero and other privacy coins have faced similar issues. - The price drop is largely due to market hysteria, not a fundamental protocol failure. - The core team is increasing resources (AI tools, red teams, bug bounties) to find and fix issues before they're exploited. 5. The Bigger Picture: - Formal Verification: The future of crypto security lies in mathematically proving the soundness of circuits (like in the upcoming "Hakon" quantum-proof pool). - AI & Security: As AI gets better at finding bugs, the entire industry needs to adapt with formal verification and faster response times. - Market Dynamics: The crypto market's underperformance vs. stocks may be partly due to capital rotation into AI-related IPOs and a risk premium for open-source, hackable protocols. 6. Conclusion: - The host and guest believe the protocol is likely safe, but the next upgrade will either prove it or reveal exploitation. - The industry must move forward with better security practices; this isn't a reason to abandon privacy coins or DeFi. Bottom Line: The video addresses a significant but likely unfounded panic around Zcash. The team is taking proactive steps to verify the protocol's integrity, and the broader crypto industry is facing similar challenges from AI-driven security testing. ba.net/summary 24c5a1385340671a80d96ab25276c09499417db8032bcedd1bb620da618008aa:  Here's a summary of the top crypto stories from today: 1. ETH falls to 13-month low on Zcash bug news and Bitcoin drop to sub-$60K: Is $1.4K next? https://cointelegraph.com/markets/eth-falls-to-13-month-low-on-zcash-bug-news-and-bitcoin-drop-to-sub-60k-is-14k-next ETH price crashed below $1,600 as a vulnerability in Zcash emerged and Bitcoin sold off below $60,000 for the first time in months. 2. Travala lets AI agents book hotels with USDC on Base https://cointelegraph.com/news/travala-ai-agents-book-hotels-usdc-base Travala’s new protocol lets AI agents search and book hotels with USDC on Base, but travelers still approve the final payment. 3. Crypto tax proposals weighed ahead of Tuesday House hearing https://cointelegraph.com/news/crypto-tax-bills-house-ways-means-hearing Among the issues US lawmakers are expected to discuss in a digital asset taxation hearing are “de minimis” reporting exceptions for crypto transactions. 4. Bitcoin bears face $2.6B trap as BTC funding rate drops: Is a short squeeze brewing? https://cointelegraph.com/markets/bitcoin-bears-face-26b-trap-as-btc-funding-rate-drops-is-a-short-squeeze-brewing Bitcoin bears piled into short positions as BTC price slid to $60,000. Will the $2.6 billion in short leverage lead to an upside squeeze? 5. Kraken offers SpaceX IPO access through xStocks https://cointelegraph.com/news/kraken-offers-spacex-ipo-access-through-xstocks Kraken is offering SpaceX IPO access through its xStocks platform. 6. Zcash Crash Just Wiped Billions From the Privacy Coins Market Cap—Can ZEC Recover? https://decrypt.co/370184/zcash-crash-wiped-billions-market-cap-can-zec-recover The price of Zcash cratered following the disclosure of a serious vulnerability for the privacy coin. Can ZEC make a comeback anytime soon? 7. Congress Gets 7 New Crypto Tax Bills: Heres Whats In Them https://decrypt.co/370197/congress-7-crypto-tax-bills The crypto tax bills—the first of their kind to be deliberated by congressional leadership—will be discussed at a House hearing on Tuesday. 8. Bitcoin Dives Below $60K Following Strong Jobs Data, Zcash Crash Shaking Crypto Confidence https://decrypt.co/370120/bitcoin-dives-below-60k-first-time-2024-zcash-crash Bitcoin has now fallen more than 50% from its October peak, dipping below $60,000 as the crypto industry reckons with the Zcash vulnerability. ba.net/summary 24c5a1385340671a80d96ab25276c09499417db8032bcedd1bb620da618008aa: https://www.youtube.com/watch?v=okdwcU-UC-w Here is a summary of the YouTube video: Google's "Encoder-Free" AI Model: Gemini 4 12B Google has released a new AI model, Gemini 4 12B, that breaks from the traditional architecture of multimodal models by removing the separate "encoders" for vision and audio. The Problem with Traditional Models: • Encoders: Traditional models use separate neural networks (encoders) to translate images and sound into text that the main language model can understand. • Drawbacks: These encoders are large, slow, expensive to run, and difficult to fine-tune because they are usually "frozen." The Solution: Encoder-Free Architecture • Direct Processing: Instead of using separate translators, Gemini 4 12B reshapes raw image pixels and audio waves directly into the same format as text tokens. • Unified Weights: The main model learns to "see" and "hear" directly, processing all inputs together in a single brain. • Benefits: - Lighter: Significantly smaller and faster, running on consumer laptops (around 8GB RAM with quantization). - Faster: No waiting for separate encoders to process inputs. - Easier to Fine-tune: All weights are unified, allowing for easier customization. Performance: • The 12B parameter model performs near the level of a 26B parameter model on standard benchmarks. • It outperforms last year's Gemma 3. • However, it still lags behind larger models on the most difficult reasoning tasks. • Vision quality is good but not as detailed as models that keep dedicated encoders for larger versions. Why Google is Doing This: 1. Business Model: Google makes money from cloud, ads, and devices, not API calls like OpenAI. 2. Device Reach: A small model that runs offline on phones and laptops integrates deeply into Android and Chrome. 3. The Funnel: Users start with the free local model and eventually migrate to Google's cloud for scaling. Conclusion: This is a significant architectural shift that makes powerful, private, offline AI assistants feasible on personal devices. While not the best for heavy reasoning or large-scale serving, it's a smart bet for privacy-focused, on-device AI tasks. ba.net/summary
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