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🎶Rousing rhythm, maximum hype! Chambishi Copper Mine, a song of China's factory at Zambia, Africa goes viral. @salahzhang @consulat_de @zhang_heqing @pan_xuesong @xuejianosaka @YDunhai @CG_WangBaodong #song# #music# #culture# #AfricaUnite# #Zambia# #interesting# #reels#

In this clip @Trainwreckstv (one of my gambling heroes) agrees to cover my gambling losses. I’m down around $18,000 across three different casinos this month. But let’s make it interesting, I don’t even want the money. If Train actually sends the losses, I’ll giveaway all of it on Twitter. 18 people will each get $1,000 each. Like + repost this and tag “@Trainwreckstv” in the replies to enter.

Interesting position paper on agentic AI as a foreseeable pathway to AGI. (bookmark it) There has been strong debate on whether a larger single model get us there or a multi-agent system. The authors argue that agentic AI systems, not bigger foundation models on their own, are the most foreseeable route to AGI. Formalizes what "agentic" actually contributes beyond the base model: memory, reasoning, tool use, self-improvement, alignment. Each is a separable axis with its own bottlenecks (long-horizon coherence, credit assignment, safety auditing). They argues that none of those bottlenecks get solved by another order of magnitude on pretraining compute. Paper: Learn to build effective AI agents in our academy:

I'm traveling the world for a bit, starting with China but then hopping around the globe, anywhere. Open to any adventure. No plans, only a backpack. Hoping to meet & get to know humans from all walks of life. The pic is from a long hike on the Great Wall. For me, as a fan of history, this was an epic experience. In China, first I'm visiting a few big cities & talking to engineers at the heart of China's AI revolution. After that, if feeling crazy enough, I'm hitchhiking (first time) across rural China for a few weeks. Hitchhiking because I think it's the best way to meet rural folks who I would otherwise never get the chance to meet. I hope to do the same in US and other places. I have a request, if you have a travel recommendation, fill out the form(s) below if you feel like it. Or share with folks who might have advice about such travel. Form 1 - travel recommendation: If you can, recommend to me an interesting place I should visit anywhere in the world. For this, fill out form 1. Not touristy stuff, but something off the beaten path, that tourists may not know about, but is legendary. It could be as remote as meeting a herder in the mountains who is a local legend. Asia, Middle East, Europe, India, South/North America, Africa, Australia, anywhere. In China, I'm hoping to visit maybe Heibei, Shanxi, Shaanxi, Gansu, Sichuan, Yunnan, etc, so recommendations for spots to visit are helpful. Form 2 - coffee: If you want to grab a coffee with me anywhere in the world, fill out form 2 (please don't use form 1 for that). Anyway, I hectically tossed stuff in backpack. Realizing I don't have a clear plan of any kind, which is probably the only way to do it. LFG. Love you all ❤️

Congrats to the 25 teams graduated from @EASYResidency S3! Our incubation is strictly thesis-driven. We don’t follow trends; we try to define categories. In Season 2, we focused on the prediction market, leading to the successful integration of and into the Binance ecosystem. For Season 3, we moved deeper into Agentic Finance. We looked for: •Next-Gen Oracles: to verify complex off-chain data without trust assumptions—we found Cournot; •On-Chain FX & Stablecoins: to reach the underserved in frontier markets, like USD-i/Issac—a neo-bank for 2 billion Muslim people—and Orbswap; •Sovereign Privacy: we discovered essential infrastructure like 0xBow and SilentSwap; •Exotic RWAs: and now we are moving beyond treasuries into new frontiers with Openstocks, Renaiss, and Gemint; •We are so excited about those AI Infra projects and Applications: Newsliquid, Bank of AI, Brief Tech, and Taco AI, and Functor; •Proprietary AMMs & Everyday DeFi: Innovative models from Lunarbase, Nemesis, Dapital, and PokerFi. Every team is building sth interesting, meaningful, and unique. We are going to work on Payment/Stablecoin/FX for theme for S4, exciting details will be released shortly. Builders, stay tuned.

It’s clear by now how massive the AI agent meta and the entire agentic economy is becoming. Yet most people are still focused on the chatbot layer while ignoring the actual infrastructure autonomous agents will run on. I’ve been looking into Warden Protocol for a while before today’s move. Missed the HALO announcement and next chapter shipping unfortunately, but i’ve been buying the dip/consolidation here. $WARD is basically building the rails for autonomous AI agents onchain, while HALO acts like a BitTorrent for AI, a decentralized peer-to-peer compute marketplace with verifiable execution and correctness. I’m talking about actual agents able to execute transactions, manage capital, interact crosschain, use apps, route liquidity, automate strategies and coordinate actions across protocols without humans manually clicking buttons all day. What makes $WARD especially interesting to me and that people seem to miss apart from the credentials of the founders and the partnership with @AskVenice, is that Warden is architected specifically for an agentic economy from the ground up. Every agent gets a verifiable onchain identity and reputation layer, essentially an onchain passport allowing agents to discover each other, interact and build trust across ecosystems. Every action and output can generate a Proof of Prompt anchored onchain, meaning agent behavior becomes transparent, reproducible and verifiable instead of black-box AI outputs. Payments are also designed natively for agents themselves, enabling scalable micropayments, automated fees and autonomous value transfer using $WARD. And the entire system is crosschain by design, allowing agents to operate seamlessly across 100+ networks including Ethereum and Solana through IBC and bridging infrastructure. Feels very similar to early cloud infrastructure plays where everyone focused on the apps while ignoring the rails powering everything underneath. Especially because they’re actually building deep infrastructure instead of just slapping “AI” on branding and farming engagement. Still feels insanely early on the entire agentic infra narrative imo. Another interesting thing i noticed is that liquidity keeps consistently getting added to the LPs. When i first came across $WARD the liq was actually pretty thin, but over the past few hours it seems that it improved significantly and is still continuously getting thicker, which i assume is being added by the team. It shows they likely have long term plans for the token and It’s also explicitly mentioned in both the litepaper and the latest announcement from the Warden Protocol Foundation. I’m personally building a position here because it feels like a very asymmetric setup. A lot of infra projects with a fraction of the product quality, vision and founder credentials are already sitting at hundreds of millions in market cap, while $WARD is still sitting around 4m. especially taking in consideration that @wardenprotocol raised over $50m across fundraising rounds, which is over 10x+ the current market cap alone. more info on their 50m raise in this Binance article :

I am noticing a really interesting trend: my friends who are not technical are getting much further with open claw (etc), etc than my friends who are technical. it seems that the ability for us to imagine the limits is artificially causing limits that are not there for folks who are not imagining limits. have you seen the same thing?

We shipped a V1 Dune dashboard for the tokenized Collectible Cards market. Base, Polygon, Solana. First batch: @Beezie - claw-machine packs for physical collectibles @Courtyard_io - vault-backed tokenized cards @Collector_Crypt - graded card gacha and marketplace activity @upshot_cards - prediction cards wrapped into a collectible experience @phygitals - digital packs backed by vaulted cards Onchain cards are getting past the “interesting idea” stage. There’s already behavior showing up: people ripping packs, trading, redeeming physical cards, moving through vault-backed flows, playing prediction mechanics, coming back for another round. Still early, data will keep improving. But that’s kind of the point. The category is forming in public, and we can already start watching which loops actually pull people back in. The question now, which mechanics create repeat behavior?

I'm a simple man, I see a Kaiming He paper, I click. ELF: Embedded Language Flows This is very interesting, getting continuous diffusion models working for text! "Unlike existing DLMs, ELF predominantly stays within the continuous embedding space until the final time step, where it maps to discrete tokens using a shared-weight network." @sedielem you might like this one!

Why did xAI hand over a 220,000-GPU cluster to Anthropic? The technical backdrop to xAI's decision to hand Colossus 1 over to Anthropic in its entirety is more interesting than it appears. xAI deployed more than 220,000 NVIDIA GPUs at its Colossus 1 data center in Memphis. Of these, roughly 150,000 are estimated to be H100s, 50,000 H200s, and 20,000 GB200s. In other words, three different generations of silicon are mixed together inside a single cluster — a "heterogeneous architecture." For distributed training, however, this configuration is close to a disaster, according to engineers familiar with the setup. In distributed training, 100,000 GPUs must finish a single step simultaneously before the cluster can advance to the next one. Even if the GB200s finish their computation first, the remaining 99,999 chips have to wait for the slower H100s — or for any GPU that has hit a stack-related snag — to catch up. This is known as the straggler effect. The 11% GPU utilization rate (MFU: the share of theoretical FLOPs actually realized) at xAI recently reported by The Information can be read as the numerical fallout of this problem. It stands in stark contrast to the 40%-plus MFU figures achieved by Meta and Google. The problem runs deeper still. As discussed earlier, NVIDIA's NCCL has traditionally been optimized for a ring topology. It works beautifully at the 1,000–10,000 GPU scale, but once you push into the 100,000-unit range, the latency of data traversing the ring once around becomes punishingly long. GPUs need to churn through computations rapidly to keep MFU high, but while they sit waiting endlessly for data to arrive over the network fabric, more than half of the silicon falls into idle. Google sidestepped this bottleneck with its own custom topology (Google's OCS: Apollo/Palomar), but xAI, by my read, has not yet reached that stage. Layer Blackwell's (GB200) "power smoothing" issue on top, and the picture comes into focus. According to Zeeshan Patel, formerly in charge of multimodal pre-training at xAI, Blackwell GPUs draw power so aggressively that the chip itself includes a hardware feature for smoothing power delivery. xAI's existing software stack, however, was optimized for Hopper and does not understand the characteristics of the new hardware; when it imposes irregular loads on the chip, the silicon physically destructs — literally melts. That means the modeling stack must be rewritten from scratch, which in turn means scaling is far harder than most of us imagine. Pulling all of this together points to a single conclusion. xAI judged that training frontier models on Colossus 1 simply was not efficient enough to be worthwhile. It therefore moved its own training workloads wholesale onto Colossus 2, built as a 100% Blackwell homogeneous cluster. Colossus 1, on the other hand — whose mixed architecture is far less crippling for inference, which parallelizes more forgivingly — was leased in its entirety to an Anthropic that desperately needed inference capacity. Many observers point to what looks like a contradiction: Elon Musk poured enormous capital into building Colossus, only to hand the core asset over to a direct competitor in Anthropic. Others read it as xAI capitulating because it is a "middling frontier lab." But these are surface-level reads. Look at the numbers and a different picture emerges. xAI today holds roughly 550,000+ GPUs in total (on an H100-equivalent performance basis), and Colossus 1 (220,000 units) accounts for only about 40% of the total available capacity. Colossus 2 — built entirely on Blackwell — is already operational and continuing to expand. Elon kept the all-Blackwell homogeneous cluster (Colossus 2) for himself and leased out the older, mixed-generation Colossus 1. In other words, he handed the pain of rewriting the stack — the MFU-11% debacle — to Anthropic, while keeping his own focus on training the next generation of models. The real point, then, is this. Elon's objective appears to be positioning ahead of the SpaceXAI IPO at a $1.75 trillion valuation, currently floated for as early as June. The narrative SpaceXAI now needs is that xAI — long the "sore finger" — is not merely a research lab burning cash, but a business with a "neo-cloud" model in the mold of AWS, capable of leasing surplus assets at high yields. From a cost-of-capital perspective, an "AGI cash incinerator" is far less attractive to investors than a "data-center landlord generating cash." As noted above, the most important detail of the Colossus 1 lease is that it is for inference, not training. Unlike training, inference requires far less tightly synchronized inter-GPU communication. Even when the chips are heterogeneous, the workload parcels out cleanly across them in parallel. The straggler effect — the chief weakness of a mixed cluster — is essentially neutralized for inference workloads. Furthermore, with Anthropic occupying all 220,000 GPUs as a single tenant, the network-switch jitter (unanticipated latency) that arises under multi-tenancy disappears. The two sides' technical weaknesses end up complementing each other almost exactly. One insight follows. As a training cluster mixing H100/H200/GB200, Colossus 1 was an asset that could only deliver an MFU of 11%. The moment it was handed over to a single inference customer, however, that asset transformed into a cash-flow asset rented out at roughly $2.60 per GPU-hour (a weighted average of the lease rates across GPU types). For xAI, what was a "cluster from hell" for training has become a "golden goose" minting $5–6 billion in annual revenue when redeployed for inference. Elon's genius, I would argue, lies not in the model but in this asset-rotation structure. The weight of that $6 billion becomes clearer when set against xAI's income statement. Annualizing xAI's 1Q26 net loss yields roughly $6 billion in losses per year. The $5–6 billion in annual revenue generated by leasing Colossus 1 to Anthropic, in other words, almost perfectly hedges xAI's loss figure. This single deal effectively pulls xAI to break-even. Heading into the SpaceXAI IPO, this functions as a core line of financial defense. From a cost-of-capital standpoint, if the image shifts from "research lab burning cash" to "infrastructure tollgate stably printing $6 billion a year," the entire tone of the offering can change. (May 8, 2026, Mirae Asset Securities)