Top BNB Chain Memecoins with the Highest Capital Inflow (Last 24H) ⚡️ 🔸MarsCoin - @bnbMarsCoin 🔸龙虾 - @lobstercoinbnb 🔸SIREN - @genius_sirenBSC 🔸mubarak - @mubarak_cto 🔸4 - @4onbsc 🔸Virus2027 - @virus_soon 🔸B - @BUILDonBsc_AI 🔸猫王 - @Cakt_King 🔸 $BANANA - @BananaS31_bsc 🔸暴躁牛 - @ANGRYBULU

🤖 AI Signal (BSC) 拳击猫 CA: 0x2227e8ee1cc76393c1d330832ec5bcc661057777 拳击猫 (boxing cat), ticker 拳击猫, is a BSC token on Flap that graduated to PancakeSwap 54 d 18 h after launch. Posts claim this is the hottest GIF on X, that the original post went viral for posting the GIF, and that it combines kung fu, boxing and cat; one attached image is a screenshot of a post explaining how to use GIFs. The buyer list is odd: 85 of the top hundred buyers sold at least half, so much early buying is now supply. Attention is scattered across eight accounts. The biggest, @PSY_P488 at 34,996 followers, calls it X's hottest GIF; @0xEggg and @Ash_RS9, both over ten thousand, also posted it. One post asks why a leaderboard omits it. Our own tracking since the signal shows buys still outnumber sells and hourly volume near the signal's, but market cap is below our push price and off its peak. Graduation happened before our signal. The top ten held 19.7% at our signal, so a few wallets can move supply. Two bundler wallets still hold launch-block tokens, which can add sell pressure. The deployer has no prior token in our database. Seven wallets from our smart-wallet list bought, the first 54 d 8 h after launch; three bought within one minute of each other. KOL wallet A, 9,792 followers, bought. 📊 @ +55 d: MC $109.6K · Liq $37.6K · Holders 265 · 1h +19% · 1h Vol $33.5K 📈 now (+55 d): MC $109.0K · 5m buys/sells 118/98 Signals on Telegram: Houndsbot Not financial advice. Analysis only · DYOR · Path since our signal (+133s): $109.6K -> $109.0K (0.99x now, peak 1.19x at +99s) #BSC# #memecoin# Follow @houndsmeme for the daily record.

Project Spotlight - Remarkable BNB Chain Projects in September 🌟 AI : 🔸Alaya AI : @Alaya_AI 🔸LingoAI : @LingoAITech 🔸SwarmBase : @SwarmBase 🔸Cubed : @I3_Cubed 🔸GMGN : @gmgnai 🔸DGrid AI : @dgrid_ai DeFi : 🔸PancakeSwap : @PancakeSwap 🔸Aave : @aave 🔸Predict : @predictdotfun 🔸CoW Swap : @CoWSwap 🔸Four Meme : @fourdotmemezh 🔸Ophis : @ophisfi GAMEFI : 🔸 SERAPH : @Seraph_global 🔸 The Landlord : @TheLandlord2023 🔸 Audiera : @Audiera_web3 🔸 Sleepless AI : @SleeplessAI_Lab 🔸 Tatakai : @Tatakaiio 🔸 MGG : @MimboNode RWA : 🔸GlobalMPC : @MPC_Global_X 🔸RAX Finance : @RaxFinance 🔸Ondo : @OndoFinance 🔸GEMINT : @GEMINT 🔸Securitize : @Securitize 🔸Centrifuge : @centrifuge MEME : 🔸 招财猫 : @zhao_cai_mao_ 🔸 BUILDER : @thebnbmascot 🔸 うさぎ : @suicarabbitcto 🔸 旺柴 : @wangchaionbnb 🔸 short : @Bigshortdotxyz 🔸 MAME : @mamebnb

Daily Memecoin Recap - September 24 can we all agree to send a coin to a billy NPCs/AI Trading On Pump $npc -> hit $2.6m - fees used to generate ai agents who are given $5 to trade on @pumpfun app - 3000+ agents deployed w/ 25k+ call outs from them on the pump app Send Launchpad (eth) $send -> hit $15m $CONDO -> hit $2.3m, paired with $ONDO, their main runner - @Ondo announced a partnership with @Blackrock (world's largest asset manager) - $ONDO up 26% on the day Go Launchpad (sol) $go -> hit $10m, @lesgodotfun, pair memes with anything on sol (any SPL token) $og -> hit $450k Neet going for ATH solana:Ce2gx9KGXJ6C9Mp5b5x1sn9Mg87JwEbrQby4Zqo3pump -> hit $50m, culture coin, launched in may - not in employment, education, or training - some of the best content in the space/constantly going viral @neet_sol Muse robinhood:0x83a49b808f8d5e02cb2931cd2352988f498e5ba3 -> hit $12.6m, continued momentum after @finkd followed @agrippa_muse $jolly -> hit $4m, face/mascot of muse Collect $gocollect -> hit $2m - pokemon go for collecting cards. real life/physical meta Utility $mask -> hit $8.7m, paired with $zec $familiars -> hit $3.7m, fomo for ai agents robinhood:0xedbf91223639800bcd5756815caf908df3b890be -> hit $3.3m, lending protocol for agents $quanta -> hit $1.5m solana:2Ngvnnkwrwq5u4RYV5WYKJ2wiQoEkKaVCc4LuuhYSTNK -> hit $1.3m, launch a token paired to multiple stocks on $stonk BNB #招财猫# -> hit $3m #和平熊猫# -> hit $2.3m More Plays $shartcoin -> hit $5m, main runner on @yokaicapital's launchpad $goon -> hit $2.4m, paired with $hims $fooms -> hit $1.6m, @fomo inspired nfts $si -> hit $850k, another "super intelligence" beta after trumped renamed ai

⚙ How GLM-5.3-Flash Served 70 Trillion Free Tokens on Chinese Chips GLM-5.3-Flash's anonymous "Ox Alpha" trial burned through roughly 70 trillion tokens in a week — and Zhipu says all of it ran on Chinese AI chip clusters. For many observers, that is a bigger story than the model itself. Zhihu contributor 恋猫 breaks down the systems engineering that made it work. His framing: the model architecture first reduces how much data needs to move, then the inference system compresses what remains. The reported scale is around 100,000 Chinese chips — from Huawei, Moore Threads and Hygon, according to @latepostnews — though @Zai_org itself has only said "tens of thousands." 1️⃣ The architecture cuts data movement first Versus GLM-5.3, Flash lowers attention compute by 3.01x and KV cache by 4.44x. Less data shuttling between memory and compute is the foundation everything else builds on. 2️⃣ Compute-for-bandwidth: trading FLOPs for HBM relief The bottleneck on these chips is HBM: the compute units still have headroom while memory bandwidth is nearly saturated. The fix, loosely speaking, turns "every step: read the full state, write the full state" into "read the state, recompute a little, write back periodically." A bit of extra matrix math buys a large reduction in HBM write traffic. 3️⃣ Communication-for-bandwidth: shard the cache across cards The cluster also uses high-speed inter-chip links and aggregated bandwidth to cut how much data each card must keep resident. The author's example: rank 0 holds the KV and indexer cache for some layers, rank 1 holds the rest. No card stores every layer's cache long-term; data is prefetched over the interconnect as each layer executes. The trade-off is real — more communication, far less resident cache per card. 4️⃣ EPD separation: three pools, scaled independently Finally, Encode, Prefill and Decode are split into three independently scalable resource pools, which matters most for multimodal traffic. Together, these pieces are what let the cluster run at high utilization. 5️⃣ The result: 3x end-to-end, cost "comparable to NVIDIA" Zhipu's own claim: end-to-end serving performance improved 3x on the same hardware, bringing per-token cost close to mainstream NVIDIA GPUs. The author's reading: individual Chinese cards may still be weaker, but model-system co-design lets the cluster as a whole reach international-mainstream throughput and cost. He adds a wry footnote: Zhipu's API used to be notorious among Chinese developers for 429 rate-limit errors. That it could absorb this launch's traffic at all — entirely on Chinese silicon — is, in his words, proof that optimization never ends. 🔗 Full Reading: #GLM# #Zhipu# #AIChips# #AIInfra# #LLM# #Inference# #OpenWeights#

Daily Memecoin Recap - October 27 vol will pick up for q4. robinhood szn 2 soon Vlad reiterates intention to grow rh crypto robinhood:0xedbf91223639800bcd5756815caf908df3b890be -> hit $14.2m, quote retweeted by @RobinhoodCrypto robinhood:0xdee52f2ab639b6942b0d0f0565400b93b7a0fbe5 -> hit $3.6m, ai agent that trades, on robinhood $assay -> hit $1.4m, agent payments on rh - @vladtenev spoke about robinhood crypto and how excited he is about robinhood as a chain throughout the entire earnings call - people are calling for a season 2 of rh Movement against pump solana:3hQ7oAYxPtLXA9ifPc3K8E15CGFVxSaby3NEDo6xK9y4 -> hit $7m, "f pumpfun" - @dtrain22k's squad was rank #1# in the pump fun $2.5m trading competition - pump fun disqualified their team because their pnl came from this token Senate says pump is backed by noting $Nothing -> hit $2m - In the senate a member brought up pump fun tokens, saying that are backed by nothing but belief Zcash solana:BZFYNPeQAEW3HWQ4DNsTVahC1n4ZjTgn6jB2nnBbB96W -> hit $4.4m, zcash native launchpad on solana $invisible -> hit $825k, Zcat founder's cat (@zooko) Tech solana:syQqkspvb2PRr1meJ5pJDmhgxwc4hUou2TMjjrTmeta -> hit $9.2m, agents tech on bnb solana:Pmfm2RprxQRKZH1XWVsBUc2KgCTyzeDTSFbT1vJmogz -> hit $840k, launchpad, pump clone with less fees More plays solana:CSRRgTwf9o5KLgguS2M16aEAhV5PUUzfshrAAQ8opump -> hit $4.9m, @retardioclassic $招财猫 -> hit $4m, bnb jeff-3:native -> hit $2.6m, ansem reply meme base:0xb200000000000000000000585fd80c20af698f01 -> hit $2.4m, "one life, it's worth an attempt" on @base $Frongus -> hit $1.2m, "frontier class meme", toly inspired solana:AwYpgrsz17cA26UosYkHyX9dAhyx7ACZAR4BmneVpump -> hit $380k, trump recently said "I love the meat" at one of his rallies $DICTATOR -> hit $300k, @KMbappe getting dropped by sponsors for calling Mao Zedong a dictator

AI AGENTS ARE EXPANDING INTO GAMING AND DEFI SECURITY AI agents are moving beyond simple conversations. They are starting to create interactive experiences, perform specialized tasks, and support more complex digital systems. Two recent developments from @termix_ai show how broad this direction is becoming. First, a game creation Agent is now live on powered by @claudeai Fable 5.2. You describe the game you want in one sentence, and the Agent turns your idea into a playable experience. Try it here: The second development is C724 Security Agents, an initiative focused on bringing AI agents into DeFi security. This addresses a problem worth taking seriously. A protocol might pass an audit before launch, but its security risks continue to change. Contracts get updated, permissions change, new integrations appear, and attackers keep looking for weaknesses. An audit captures a specific moment. Continuous monitoring addresses what happens afterwards. C724 explores how specialized agents could support this process. They could monitor wallet permissions and smart contract activity, analyze suspicious transactions, alert security teams, and execute predefined protective actions when authorized and technically supported. What connects both developments is the idea of agents performing useful work through specialized capabilities. A game creation agent turns a simple instruction into an interactive experience. A security agent focuses on monitoring, threat detection, and response. I also find the connection to worth watching. Developers could list specialized agents, users could hire them for specific tasks, and payments could settle through the @termix_ai agent economy. I see two different applications of the same broader direction. AI agents are becoming tools people interact with, hire, and depend on to complete tasks. The next question is how far this model will extend across gaming, security, and other industries.