KITE AI

Meet Your Muse: the entry review is now public 🪁 Thank you for your patience. Reviewing the entries took longer than the original Oct 7 schedule, and winners have not been drawn yet. Check your entry status and the reason for the review result. All original submissions, including duplicates and excluded records, are listed here: docs.google.com/spreadsheets… ⏰ Review-error appeals close 48 hours after this post. We will reply below with the exact deadline in PT and UTC. The appeal process and draw rules are below. 👇

Thanks to everyone who joined Kite's community lunch in Singapore! 🇸🇬 We’d love to connect with more builders, teams, and people exploring AI payments, exchange ideas, and find ways to work together. If that sounds like you, let’s keep the conversation going. 🪁 @BroLeon @0xAA_Science @agintender @Tintinx2021 @GuHype @EudemoniaCC @Ru7Longcrypto @sal_hotpot666 @stambouli_o1 @Ccweb3hub @hanajin @CryptoKris666 @Chloeppan @edisonxu888 @yueya_eth @0xBeyondLee @zolo_gg @raychief6 @vael0x @scottshics @Cindyshi0907 @withhazelhu @Henryleemr @token2049 #token2049

Excited to join @MemeCore_M’s MAGICORE as a partner! 🪁✨ We have some free tickets for the Kite community 🎟️ Want to join us? Leave a comment below and we’ll DM you with the details. ⚠️ We’ll only DM commenters for this event’s ticket giveaway, and only from this account. Watch out for impersonators. We’ll never ask for payment, seed phrases, or private keys.

Poki is distributing invitations for our Proof of AI event. So if you want one, leave a comment 🙌

From chips and models to agents, AI keeps moving fast. The Kite team visited @AlibabaGroup's Apsara Conference in Hangzhou, China, where the lineup included @nvidia, @intel, @Meta and @AMD. Which AI shift feels most real to you right now? 🪁

AI agents are reshaping industries. How can students prepare for the opportunities ahead? Our APAC Lead @0xLaughing will join students and industry speakers at “AI Unlock: Students in the Agent Era” to discuss AI, Web3, and career development. Hosted by @tbablockchain, @trondao, NTU Fintech Club, and @GoKiteAI. Speaker lineup: ▷ Shih-Wei Liao | Associate Professor, Dept. of CSIE & GINM, NTU ▷ @gobananas929 | Operations Manager, TRON ▷ @Alvin0617 | Founder, CryptoWesearch ▷ @0xfomor | Social Lead, TrueNorth AI ▷ @0xLaughing | APAC Lead, Kite September 8, 18:30 to 21:30 (UTC+8) Room 102, 1F, Xinsheng Lecture Building, National Taiwan University Join us to explore opportunities in the agent era. 🪁 luma.com/r3dy7inv

Trusted agent payments start with authority that can be verified and constrained. At @SlowMist_Team × @MetaEraCN’s “Through the Mist, Into Trust” event in Hong Kong, our APAC Lead @0xLaughing joined the Agent Payments panel on the shift from human payments to agent payments. The wider program also brought together perspectives from @osldotcom, @plumenetwork, @selat_ai, and @DogPay_. ▷ Identity: which agent is acting ▷ Authority: what it can do, and within which limits ▷ Accountability: how every payment is verified and risks managed These are the controls that turn agent payments into trusted payments. 🪁

Global asset allocation is being reshaped by the convergence of digital finance and AI. At @Techub_News’ "Future Insights: New Opportunities in Global Asset Allocation" forum in Hong Kong, our APAC Lead @0xLaughing joined a panel on the intersection of digital finance, AI, public markets, and prediction markets. ▷ Capital is moving across more connected market structures ▷ Digital financial infrastructure makes ownership and settlement programmable ▷ AI agents need verifiable identity and scoped authority to participate safely Thank you to @Techub_News for convening the discussion, alongside institutions including @futufriends, @HSKChain, and @zr_securities. 🪁

AI’s future is shaped not only by what models can do, but by the conversations that move the industry forward. At @binance Clubhouse Bali during @CoinfestAsia, the Kite team connected with the people building that future: ▷ Our Head of Ecosystem @Henryleemr joined a panel to share Kite’s perspective on the AI industry. ▷ He also sat down one-on-one with @jessicasmw, Global Media & Content Lead at Binance, for an interview. ▷ On the ground, our APAC Lead @0xlaughing connected with builders and community members throughout the event. Thank you to @binance and everyone who made time to connect. 🪁

Building through every market cycle takes more than conviction. It takes real conversations with the people moving the industry forward. On August 20 and 21, Kite will join @binance Clubhouse Bali as a sponsor during @CoinfestAsia 2026. The Kite team will take part in keynote sessions and panel discussions, connecting with practitioners, users, and community members on the ground to explore how the industry can keep building through every cycle. Register: luma.com/93geox3v See you in Bali. 🪁

Strong foundation models do not automatically solve the last mile of real-world service work. This AI on Air clip is excerpted from a recent episode on the @NoPriorsPod channel, and the speaker is Melisa Tokmak @melisatokmak, founder and CEO of @Netic_AI. ▷ The question can labs do this echoes the older can Google do this question: they can handle some core competencies, but they are not investing in everything; for essential services, she sees the leading labs as strong businesses and partners rather than a direct competitive risk. ▷ Enterprises are not asking for rapid experimentation: OpenAI builds products fast and also kills them fast, while Silicon Valley often says Anthropic and Claude pulled ahead in coding assistants through focus, yet the enterprise side shows roughly 20 products, which is hard for industries that need dependable service to trust. ▷ Researchers often want the most generalizable answer, as if AGI will later explain how to serve essential services; but millions of people bring different worries, accents, contexts, and reasons to return, so models alone are not enough, and the harnesses, orchestration, software, and product built on top all have to be excellent.

LLMs do eventually learn concepts, but only after exhausting every other option, a striking contrast with how humans abstract concepts from very little data. This AI on Air clip is excerpted from a recent episode on the Redpoint AI show/channel @Redpoint, featuring Lukasz Kaiser @lukaszkaiser, co-author of the Transformer paper and former researcher at Google Brain @GoogleResearch and @OpenAI, reflecting from firsthand experience on the gap between Transformers and human learning. ▷ With chain of thought, RL and tools, this next-word predictor can already do things that would have seemed unbelievable two years ago, and spending hours daily discussing hard problems with a coding assistant and getting real implementations has become a reality. ▷ His analogy: like the saying about doing the right thing only after exhausting all other options, LLMs need a trillion tokens to absorb every surface-level pattern, and only when those fail to explain something do they finally learn the concept, while humans often get concepts from far less data, sometimes even making them up. ▷ Both sides have actually grown stronger: Transformers keep improving, yet the case for something beyond them has strengthened too, with a number of labs now pursuing post-Transformer architectures and seeing interesting results, and he admits he still doesn't know who wins.

For computer-use agents, reliability has less to do with clicking the right coordinates every time and more to do with understanding what the user actually wants. In this AI on Air clip, excerpted from a recent episode on the Latent Space Podcast @latentspacepod show, the speaker is Danielle Perszyk, a researcher at Amazon AGI Lab @amazon. ▷ Two years ago the plan was to meet the models where they were: get atomic interactions like clicking and scrolling reliable, then let developers string them into workflows for repetitive tasks, which was a big unlock at the time. ▷ Hitting the right button and screen coordinates may be close to solved, but that is not what reliability is. When booking travel, choosing a layover or a direct flight changes how you think, and the goal itself unfolds and gets refined as you interact with the computer. ▷ What separates a capable executive assistant from an AI agent is that the assistant models the user's mind, decomposing not just the task but preferences and intentions. Reliability is ultimately about modeling the user's mind, and that shift reframes what it is we are building.

As AI gets stronger, how much knowledge still belongs in your own head? This AI on Air clip is excerpted from a recent episode on the @ycombinator show, featuring Patrick Collison @patrickc, co-founder of @stripe, on what college students should still learn and derive from first principles. ▷ His mental model is cache: Jeff Dean's famous numbers every programmer should know are a reminder that L1 cache, RAM, and the network differ radically in bandwidth and latency; knowledge works similarly, because asking an agent to compute or look something up is far slower than retrieving it from cognitive L1 cache. ▷ Even granting the models their full capabilities, neuronal lookups should stay much faster for a long time, since the brain allows far more round trips than muttering to a voice interface, whispering, or typing out a query. ▷ Revealed preference points the same way: companies like Stripe and the labs still place an enormous premium on cognitive ability, so renouncing that training before evidence shows those benefits are saturated would be premature.

AI answer engines take content from the entire web but no longer send traffic back, hollowing out the foundation of the content economy. In this AI on Air clip, excerpted from a recent episode on the @Bankless show, the speaker is Matthew Prince @eastdakota, co-founder and CEO of @Cloudflare. ▷ The ads-based business model of the internet is approaching its end game: ad blockers had already pushed CPMs into a slow decline, but over the last two years AI has caused a near step-function drop in ad value. ▷ Google's AI overviews, ChatGPT, and Claude are not search engines but answer engines: they strip mine the web and hand users the answer directly, so no one clicks the links. Without eyeball traffic there is no ad revenue, and content creation is either retreating behind paywalls or shutting down entirely. ▷ For users this saves time, but these AI tools run on paid subscriptions. For people in the global south who cannot afford them, the internet is getting smaller, not bigger.