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

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#

AI is fundamental to everything Lockheed Martin does, says its CEO Jim Taiclet. Ed Ludlow caught up with the defense tech company to discuss how it's implementing AI into its roadmap and the company's long-standing relationship with Nvidia ahead of Bloomberg's Defense Tech Special this Friday at 11 am ET/8 am PT