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As @coinbase’s x402 enables agent-to-agent (A2A) crypto payments, a question emerges. How can one agent trust that another has executed a task as promised, especially when that work happens off-chain or across multiple networks? This is where @eigencloud comes in 🧵

GPT-5.6 launch video shows ecdsa . fail The Eigen Labs Quantum Challenge made it to the GPT launch video. One of the three hero stories is Prof. Bartosz Naskrecki (@nasqret) talking about using GPT to solve hard math problems. On his board is ecdsa dot fail our open quantum challenge. Seeing it show up at the frontier ai lab's biggest launch of the year says something about where research is heading. Open agentic research is taking off! Strap up to solve the hardest scientific problems!

@ditto_eth @eigencloud @radius_xyz @a41_allforone Can vouch for his time at Eigen! Hard working and technically skilled

Hi, Ditto here. I’m back and now looking for my next full-time role. 📍5+ years leading growth and business across crypto infra / consumer (@eigencloud, @radius_xyz, @a41_allforone, and more) 📍Built one of APAC’s top crypto content channel, with 2.5M+ views Why I am back👇

Wow. We closed in June 20th a little over 500M tokens served. I guess, the next mark is 1B tokens in a day. We have found more efficiencies in the network where our providers can do 2x more capacity + concurrency. The world's sleeping compute is waking up!!

Been more than a year and the traders finally got it? 👀 Also it’s Eigen Labs pls @eigenlabs

WE'RE NOW 11.8% AHEAD OF GOOGLE'S CONFIDENTIAL CIRCUIT FOR QUANTUM COMPUTING

Project Darkbloom: Turning Idle Macs Into AI Infrastructure Every time an AI tool is used, the request travels through multiple layers of infrastructure before reaching the actual hardware doing the work. The flow usually goes across different layers of Data centers, cooling systems, GPU hardware and layers of margin → All baked into what you're paying. The @eigenlabs team calls this the Inference Tax. Darkbloom is their research initiative to address it. The premise: 100M+ Apple Silicon Macs already exist, already paid for, sitting idle most of the day. What if that compute could be organized into a usable inference network, with real privacy guarantees and better economics? - - - - - Why Apple Silicon Apple Silicon isn't just abundant, it is also technically well-suited for inference in ways that matter: • Unified memory: CPU and GPU share the same pool, eliminating discrete GPU bottlenecks • Model efficiency: Apple Silicon only processes the parts of a model actively needed per request, rather than the whole thing → Larger models run faster and cheaper • Power efficiency: ~30W to run a 60B model, versus multiples of that on data center GPUs • Marginal cost to a Mac owner: Primarily electricity, since hardware is already bought - - - - - The Hard Part: Making It Trustworthy One basic question is that If the prompt runs on a stranger's Mac, what stops them from reading it? Darkbloom's answer is to make snooping architecturally impossible, not just contractually prohibited: • Debuggers: Blocked at kernel level • Memory reads: Denied via Hardened Runtime • Binary tampering: Breaks code signature and then macOS refuses to run it • Nodes will be re-verified via 4-layer attestation every 5 minutes → Secure Enclave, Apple MDM, Apple-signed device certificates, continuous challenge-response The only way to break these protections is to physically reboot the machine, which immediately kills the process and wipes everything. Apple uses the same approach on their own Private Cloud Compute infrastructure. - - - - - What This Means for Eigen Darkbloom will not act as a standalone product, but as a proof of concept and signal about where Eigen is heading in the AI infrastructure stack. EigenLayer's core thesis has always been restoring trust to decentralized systems. Darkbloom extends that into AI compute, making inference verifiable, not just available. If it proves that 3rd party consumer hardware can be cryptographically trusted for sensitive workloads, it opens the door to a new class of decentralized AI infrastructure that doesn't rely on trusting a cloud provider or data center operator. This marks the beginning of Eigen playing within the privacy-as-infrastructure market. - - - - - Some Thoughts A few things worth keeping in mind as we went through the Darkbloom research paper: • The coordinator remains a trusted central layer for now; Team is transparent about this, but it's not eliminated yet • Security model currently assumes no unpatched macOS kernel vulnerabilities • Network traffic patterns can still reveal rough details about your request (e.g., how long it was, how complex) even if the content itself is hidden The real test is whether the privacy guarantees hold as more nodes join the network and whether people actually trust it enough to run sensitive workloads through it without incentives. Keyword: without incentives The biggest hurdle is trust; Getting someone comfortable enough to run their data and prompts through a stranger's machine. It's a hard sell and very few projects are even attempting to solve it seriously. Despite all that, the maths seem to work out quite nicely out when the team at @mementoresearch sized it out → Check out attached pages Disclosure: Project Darkbloom is a research initiative by Eigen Labs: Access here darkbloom.dev + I am a $EIGEN holder

And the best model used by AI visual effect VFX startups is Seedance 2.0

Caught up with a friend who worked at the No. 2 short drama distribution platform @Dramaboxer 3 things that shocked me: 1. The quality of their actors has been improving over the past 2-3 years due to the decline of Hollywood. A lot of professional actors are moving away from film into short drama. (Less cringe acting) 2. The distribution platform has very strict quality control over casting and content. Their producers work with agencies to produce localised content, and each country has its own "top hits theme." For example, Japan loves "workplace revenge" and the US loves "vampire billionaire." 3. Production timelines are actually around 2 months. Shooting usually takes about 2-3 weeks. Nowhere near Hollywood's multi-year timelines, but it still takes real time to complete.

Plenty of ways to make money. Fewer ways to stay aligned with yourself while doing it.

My first AI assistants

An absolute pleasure to see Singapore’s Senior Minister championing AI development. Never felt more bullish on Singapore than after the @cognition APAC launch. So many Singapore corporates are adopting Devin. Cognition’s last mile customer service remains unmatched!

High agency >> everything

Singapore = The intersection of US and China AI

I wish you were here earlier @claudeai Could have saved me from wasting my youth doing pointless modeling 😭

Saw a lot of "how is this size of raise even possible" in the group chats. Most of the large LPs writing these checks are endowments, sovereign funds, global family offices. They're structurally long-term and have the patience to ride the cycle. VC's job is to outperform BTC over the cycle, "simple" as that.

“You’re not allowed to use Claude or ChatGPT at Google.” “Ok boss” “Now tell everyone Gemini is the best”

Singapore is quietly becoming the AI hub of APAC. Thought recent incidents might cool Chinese talent on moving here. SG gov folks tell me the opposite but founders are actually setting up firms even earlier in Singapore. Geopolitically this one of the few places both sides can comfortably operate.