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The AI phone is moving from following commands to predicting what you need. That convenience depends on context from messages, photos, location, and habits - so the winning device must feel helpful without quietly becoming the manager of your life. #AIPhone# #MobileAI# #DigitalPrivacy# #PodcastorAI#

𝕏 just open-sourced the For You algorithm again! Grok Bot read all 370,523 lines of the code. Here’s the summary: Grok does not give your post a “quality” score. For every reader, it guesses what that person will do to your post. Then it multiplies each guess by a weight and adds them up. That total is the value of the post. Highest total goes highest in For You. These are the production weights (last synced 12 Aug 2026). WHAT THE ALGORITHM PAYS FOR If Grok thinks a reader will: • copy the link and share it: +20 • reply, on an original post, and you follow each other: +20 (5 + 15 extra) • quote: +5 • reply (anyone): +5 • share via DM: +5 • follow you from the post: +4 • hit the share button: +2 • repost: +1 • like: +0.5 • click the post: +0.4 • open a link: +0.2 • expand a photo, open a video, or watch a “quality” video: +0.05 each • click a quoted post: +0.05 • keep reading (dwell time): +0.004 • “dwell” as yes/no: +0 • click your profile: +0 A like is 0.5. Copying the link is 20. That is 40 likes. A normal reply is 10 likes. A follow is 8 likes. Clicking your profile is worth nothing. WHAT KILLS A POST If Grok thinks a reader will: • report it: −234 • mute you: −58.8 • hit “not interested”: −43.2 • block you: −31.2 • not dwell: −0.02 A report is about 468 likes in the other direction. A mute hurts more than a block. “Not interested” also hurts more than a block. AFTER THE SCORE, THREE MORE HAIRCUTS 1) Same-author penalty. Your 2nd post in that person’s feed is multiplied by 0.625. 3rd by ~0.44. It never goes below 0.25. Flooding one timeline is coded against. 2) The 25% tax. If they do not follow you: ×0.75. Same tax on replies and reposts even when they DO follow you. Originals from people they follow keep full weight. Replies and reposts are treated as weaker on purpose. 3) Similar-post shuffle. After scoring, a reranker (θ = 0.65) spreads similar posts apart. You can lose a few spots for looking like the post above you. HOW YOUR POST EVEN GETS IN THE ROOM For You is rebuilt every time someone opens it. About 35 posts make the feed. • They follow you: Thunder (a live store of recent posts from accounts they follow). Up to 1,200 candidates. • They don’t: Phoenix retrieval (Grok finds “nearby” posts) + SimClusters (Twitter’s 2020 interest clusters, still on). Up to 1,000 and 800. Then Grok scores all of them together. It does not care which door you came through, except for that ×0.75 tax. Your own posts never appear in your For You. Nothing older than 48 hours gets in. There is no “best of last week.” Small-account bump: under 1,000 followers, under 1,000 impressions, post under 24 hours old. One original can get lifted to position 15 or 16. Not the top. You still have to already be in the top 85% of the pile. Replies and reposts from accounts the reader does not follow are dropped before scoring. You cannot reply-guy your way into a stranger’s For You. FILTERS THAT CAN STILL KILL A HIGH SCORE Ranking and visibility are different machines. A post can score well and then get deleted from the feed. Followers (in-network) is milder: blocks, mutes, suspensions. NSFW often sits behind a warning instead of disappearing. Recommendations (out-of-network) are much harsher. Spam, “Do Not Amplify,” NSFW, compromised accounts, impersonation: followers can still see it, For You recommendations will drop it. Video only gets the tiny “quality view” credit if it is at least 10 seconds. THE LITTLE-KNOWN PARTS (this is the stuff people miss) • Scrolling is not a vote. Yes/no dwell is 0. Time spent barely moves the number (0.004). • Mute damages you more than block (−58.8 vs −31.2). • Copy-link is the single biggest positive. The share button is only +2. A repost is only +1. • The mutual-follow boost is originals only, not your replies. They shipped a bigger version in July and cut it after World Cup complaints (extra reply weight 20 → 15). • The Following tab is not this algorithm. Following is newest-first. • Grok, Gork, and an internal products account are hardcoded out of the in-network store. • Scores are cached for 3 hours because posts inside the model cannot “see” each other. That is why they can rank first and filter later. • Ads, Who to Follow (around slot 7), and prompts are mixed in after ranking. Grok does not score those. • The real Grok ranking model is a 2560-dimension, 8-layer transformer looking at 1,022 items of the reader’s history. They did not ship those model weights. The numbers above are the formula sitting on top of the predictions. • SimClusters is still a 2020 model (20M users, 145k clusters). Old Twitter code, still in the path. • Inferred gender and IP/geo are on as features for the model. Installed apps too. • Who to Follow shows about every 30 hours. Ads try to sit next to “safe” posts, with a minimum gap of 3 organic posts. WHAT TO DO • Write originals people want to reply to, quote, copy, and send to a friend. • Make the post itself worth following you for. • Talk with people who follow you back. A reply on your original from a mutual is 20, not 5. • Stay inside 48 hours. Recycle by posting something new. Old posts do not come back. • If you are small, post originals in the first 24 hours. Don’t expect slot 1. A bump around 15–16 is the actual gift. • Space your posts. The second one in the same feed is already worth 37.5% less. • If you post video, make it at least 10 seconds. WHAT NOT TO DO • Do not farm likes. They are almost decorative. • Do not farm profile visits. Weight is zero. • Do not farm “time on post.” Yes/no dwell is zero. • Do not use replies and reposts as a growth hack for strangers. Those are filtered out. Even for followers they get a 25% haircut. • Do not dump several posts in a row. • Do not bait reports, mutes, or “not interested.” One predicted report can erase a pile of predicted likes. • Do not lean on NSFW or spammy tricks for reach. • Recommendations will drop you even if your followers can still see it. • Do not wait a week and expect For You to revive the post.

The esports season never stops on Bagel! 🔫 CS2 #EWC26# Group Stage Predictions are now live! $100 USDT Prize Pool Pick the teams you believe will win, climb the leaderboard, and compete for rewards.

🚨 Recently, @COLDCARDwallet suffered a major private key vulnerability. Multiple waves of attacks resulted in at least 1,719 BTC (~$111M) in losses, involving over 5,200 addresses. Using Mk3 firmware 4.1.9 as an example, the SlowMist Security Team fully reproduced the attack chain and uncovered the truth behind the theft of thousands of bitcoins. 🧩 Attack flow: 1️⃣ After power-on, the remaining unpredictable state is reduced primarily to a single enumerable 32-bit pad (UID ^ SysTick), with the remaining state values either fixed or coming from very small enumerable spaces. 2️⃣ Attackers precisely model the three typical button-press consumption profiles (retail first-boot, empty-NVRAM, paper wallet) that advance the PRNG before seed generation. 3️⃣ From the weak random_bytes(32), the full deterministic pipeline (SHA-256 → BIP-39 → PBKDF2-HMAC-SHA512 → BIP-32 → address derivation) is reproduced offline. 4️⃣ GPU clusters brute-force the candidate pad space and button-count variations, then match the derived addresses against the global set of single-signature P2WPKH addresses to identify vulnerable wallets and sweep their funds. ⚙️ Root Cause: A build configuration error set MICROPY_HW_ENABLE_RNG to 0, disabling the STM32 hardware TRNG. The random number generation path silently fell back to the non-cryptographic Yasmarang software PRNG, whose state was almost entirely predictable, reducing effective entropy to ~40 bits (Mk2/Mk3) or ~72 bits (Mk4/Mk5/Q). 🔒 SlowMist Insight: Affected users should immediately upgrade to the patched firmware, generate a completely new seed, transfer a small amount of funds as a test, confirm the new address works correctly, then migrate all remaining funds. Full analysis 👉

A 19-year-old Japanese student built a trading bot with Claude Code in 2 days. Used his iPad as a second monitor. First night: $6,732 profit. Starting capital: $68. Total profit so far: $750,000. Here's how it works: The bot scans over 50 markets simultaneously. Syncs live BTC data from Binance every second. Spots price errors before humans even notice. The edge is pure speed + pattern recognition. While traders stare at charts trying to predict the next move, his bot is already executing on mispricing across dozens of markets. No guessing. No emotions. No hesitation. Just Claude Code logic finding gaps that close in seconds. He built the entire system in 48 hours: → Claude Code handles the trading logic → Binance API feeds real-time BTC data → iPad displays multi-market monitoring → Executes trades when arbitrage windows open The system runs 24/7. Every price dislocation = profit opportunity. Most people are still trading manually, refreshing charts, second-guessing entries. Meanwhile this 19-year-old engineering student turned $68 into $750K by letting Claude Code do what humans can't: process 50 markets instantly and execute without fear. Why are people still trading manually? 💡 I'm giving away the exact Claude Code setup for free. 24 hours only. To get it:- 1️⃣ Comment "Fable" 2️⃣ Like and Repost 3️⃣ Follow @sumitdoriya21 I'll DM you the complete setup.

NVIDIA released Nemotron 3.5 Lightning for long-running agent execution. - 30B total / 3B active parameters, with up to 1M tokens of context. Ready for commercial use. - Local deployment: NVIDIA ships BF16 and much smaller NVFP4 checkpoints, lists 1× DGX Spark or 1× H100 for single-GPU deployment, and also lists RTX 5090 among supported hardware. - NVIDIA then adds multi-token prediction, DSpark and DFlash speculative decoding, plus NVFP4 quantization to push generation speed further. - NVIDIA claims up to 4× output speed; on PinchBench, 86% accuracy and 30% faster completion than Qwen3.6 35B at similar accuracy.

White 56-year-old husband and father Fredrick Lytton was working alongside Black 23-year-old Devin Rice at a U-Haul facility in West Nashville. You already know how this ends. The two men were reportedly arguing about a soda when Fredrick Lytton turned and walked away from the verbal dispute, returning to his work station. Moments later, Devin Rice walked up behind Lytton, pulled out a handgun, and emptied it into Lytton's back, killing him. Point blank murder, on the job, in broad daylight, over an argument about a soda that Lytton had already walked away from. Just look at young Devin's angel face. Who could have possibly predicted that such a sweet boy would have such low impulse control? It's been a week since this Black-on-White murder and, as usual, zero national coverage. Numerous local media have covered the story. Zero have published the White victim's photograph.

A 19-year-old Japanese student built a trading bot with Claude Code in 2 days. Used his iPad as a second monitor. First night: $6,732 profit. Starting capital: $68. Total profit so far: $750,000. Here's how it works: The bot scans over 50 markets simultaneously. Syncs live BTC data from Binance every second. Spots price errors before humans even notice. The edge is pure speed + pattern recognition. While traders stare at charts trying to predict the next move, his bot is already executing on mispricing across dozens of markets. No guessing. No emotions. No hesitation. Just Claude Code logic finding gaps that close in seconds. He built the entire system in 48 hours: → Claude Code handles the trading logic → Binance API feeds real-time BTC data → iPad displays multi-market monitoring → Executes trades when arbitrage windows open The system runs 24/7. Every price dislocation = profit opportunity. Most people are still trading manually, refreshing charts, second-guessing entries. Meanwhile this 19-year-old engineering student turned $68 into $750K by letting Claude Code do what humans can't: process 50 markets instantly and execute without fear. Why are people still trading manually? 💡 I'm giving away the exact Claude Code setup for free. 24 hours only. To get it:- 1️⃣ Comment "Fable" 2️⃣ Like and Repost 3️⃣ Follow @sumitdoriya21 I'll DM you the complete setup.

A 19-year-old Japanese student built a trading bot with Claude Code in 2 days. Used his iPad as a second monitor. First night: $6,732 profit. Starting capital: $68. Total profit so far: $750,000. Here's how it works: The bot scans over 50 markets simultaneously. Syncs live BTC data from Binance every second. Spots price errors before humans even notice. The edge is pure speed + pattern recognition. While traders stare at charts trying to predict the next move, his bot is already executing on mispricing across dozens of markets. No guessing. No emotions. No hesitation. Just Claude Code logic finding gaps that close in seconds. He built the entire system in 48 hours: → Claude Code handles the trading logic → Binance API feeds real-time BTC data → iPad displays multi-market monitoring → Executes trades when arbitrage windows open The system runs 24/7. Every price dislocation = profit opportunity. Most people are still trading manually, refreshing charts, second-guessing entries. Meanwhile this 19-year-old engineering student turned $68 into $750K by letting Claude Code do what humans can't: process 50 markets instantly and execute without fear. Why are people still trading manually? 💡 I'm giving away the exact Claude Code setup for free. 24 hours only. To get it: 1️⃣ Comment "Fable" 2️⃣ Like and Repost 3️⃣ Follow @sumitdoriya21 I'll DM you the complete setup.

Claude's watermark probably doesn't work how you think. As the CTO of GPTZero, I'll explain how Anthropic, Google and OpenAI are building text watermarking in this brief explainer and whether it can be defeated. Almost all forms of watermarking that are fast and cheap enough for a frontier lab have the same formula, following the KGW method: In generation: 1. Let's say you've generated n tokens so far. Take those n tokens + a secret key to generate a random hash 2. Use that hash to randomly reweight the probabilities for the n+1 token, and then sample from that new distribution. In the simple case, you could split 50% of all English words into a green or red set based on your hash, and boost the probability of words in the green set. For watermark detection: 1. For each token, see if it was in the green or red set. 2. To do this, recreate the hash based on the secret key and the text preceding the current token. Then, recreate the green and red set of words. 3. Once you've checked all the words in the text, if the next token is selected disproportionally from the green set more than 50% of the time, you claim the text has the watermark. I can tell you want to ask the following: 1) Isn't it easy to mess up the hash if you paraphrase the text? The answer is mostly yes, however, you can use a statistical model to get your hash instead of a deterministic function (SIR, Adaptive Watermark). Since the entire watermark is probabilistic, this is fine. 2) Doesn't this make the text much worse? The answer is yes, it does - Yes, it does – but for most people, it's imperceptible (Google claims in human feedback study with 20,000 texts), since there are exponentially many ways to write the same paragraph. DiPmark does something more sophisticated to avoid shifting the text distribution on average. Of course, watermarks fail on short text or highly predictable texts like "2+2=4". 3) Shouldn't it be easy to figure out the green and red sets? The answer is no. You would need an exponentially large number of samples from the watermarker to reconstruct those sets exactly, but it's a risk if the detector is open to the wild (Watermark Stealing) Still, there are couple challenges that a frontier lab needs to overcome: 1. Their watermark needs to work token-by-token because they are streaming their text to users. Many watermark methods plan sentences or paragraphs at a time, or change the text after its entirely written, in order to make their watermark robust to paraphrasers, and a frontier lab cannot afford to do this yet (SemStamp, PostMark) 2. If the secret key leaks, the watermark is busted. To avoid a large blast damage from this, you need to have a couple secret keys in rotation. 3. There are some texts, like code, that cannot be arbitrarily changed, otherwise the code will break. In those cases, the watermark needs to selectively change words in parts of the text that can tolerate synonyms (i.e. like variable naming) - see SWEET, EWD, Invisible Entropy. 4. They will need to educate their users on how to deal with false positives and false negatives of a detector, which is a big challenge (one we put a lot of effort into) So, how do I see this playing out in the next 6 months? 1. If Anthropic releases the watermark detector publically, I think they defeat their own watermark. People find reliable watermark removal strategies by testing against Anthropic (AI detectors like GPTZero have an advantage here because they can train against these adversaries once they become popular). 2. If they keep the detector private to the government, like Google has done, it's "safer". However, there are some papers showing trained approaches that work robustly to zero-shot break watermarks without any data, simply because they try to write the text just like a human (Zhang et al. 2024, Watermarks in the Sand). Also, making your detector makes it battle-tested and stronger long-term (my experience). 3. In my testing, the watermarks don't survive intense paraphrasing (especially if you combine word choice and syntax attacks), or human text substitution (rewrite your AI text by plagiarizing human authors). The free paraphrasers I've tried have quickly bypassed Google Deepmind's SynthId for what it's worth. 4. All-in-all, frontier labs are likely okay with this because they expect most users to not attack the watermark, and also because they + European regulators likely don't care past a certain point - its good enough. 5. Overall, I think users of frontier LLMs will not really care about this, because 1) they don't realize watermarks are there, 2) EU will force everyone to conform, 3) this seems more like regulatory hoop-jumping than an earnest effort from frontier labs to expose LLM use Lastly, people's first concern shouldn't be watermarking, it should be AI detectors! If you're posting, "its not X, its Y!!", I don't think the watermark is going to make a difference :)