FLock2Moon

About FLock2Moon

What the FLock is the podcast channel of FLock.io, bringing you the latest insights, stories, and updates on sovereign AI, private deployment, and real-world enterprise AI adoption.

Publisher

FLock.io

Category

technology

Language

en

Explicit

No

First episode

17 Jun 2025

Latest episode

25 Aug 2026

Latest episodes

8 episodes in the feed.

1. 

   25 Aug 2026

   UNDP Unfiltered: An Insider's View on Decentralized AI, Sovereignty & Global Development

   Blockchain and AI often make headlines for hype and huge investments. But beyond the speculation, they are widening access to financial resources and innovation for underserved communities in developing economies. That’s exactly why FLock.io has worked with UNDP AltFinLab as an AI partner for the UNDP SDG Blockchain Accelerator, from Cohort 2 and now into Cohort 3, applying decentralised AI to tackle real-world problems across climate finance, supply chain, digital sovereignty, data governance, and more. In the newest episode of What the FLock, our Head of EMEA, Tiffany Wang, speaks with Robert Pasicko, Team Leader at UNDP AltFinLab, for a candid and unfiltered look at the work happening behind the scenes to advance the Sustainable Development Goals through blockchain and AI, and what it takes to turn emerging technology into measurable outcomes. Follow us for more insights, research, and product updates on decentralized AI, federated learning, and privacy-preserving AI infrastructure. FLock.io: 👉 X (Twitter): https://x.com/flock_io (https://x.com/flock_io) 👉 LinkedIn: https://www.linkedin.com/company/flock-io/ (https://www.linkedin.com/company/flock-io/) 🎧 Listen on Spotify: https://open.spotify.com/show/4n9VnAUu9cb5auZbEwvx9E 🌐 Learn more about us: https://www.flock.io/ and https://flockand.co/ UNDP AltFinLab: 👉 X (Twitter): https://x.com/UNDP_AltFinLab 👉 LinkedIn: https://www.linkedin.com/company/undp-altfinlab 🌐 Website: https://innovation.eurasia.undp.org/altfinlab/ The SDG Blockchain Accelerator Cohort 3 is now open for registration. Apply before Sep 30th, 2026: https://innovation.eurasia.undp.org/sdg-blockchain-accelerator-c3/ (https://innovation.eurasia.undp.org/sdg-blockchain-accelerator-c3/)

2. 

   6 Aug 2026

   Inside ClawBio: Open-Source AI Skills for Genomics Research and Clinical Practice

   "You cannot fail 1% of the time with patients." Manuel Corpas, Senior Lecturer at the University of Westminster and founder of ClawBio, joined our Head of EMEA, Tiffany Wang, on the latest episode of What The FLock to explain why the path from agentic AI to the clinic runs through validation, traceability, and reproducibility, not model capability alone. That's the idea behind ClawBio, an open library of bioinformatics skills for AI agents. What began as the winning project at the UK AI Agent Hackathon × OpenClaw at Imperial College London is now used by researchers across three continents, with 1,017+ GitHub stars, 43 contributors, 90 skills, and recognition in Nature (Vol. 653, May 2026). Frontier models are powerful, but biology operates on a different standard. A plausible answer isn't enough. It has to be checked against a validated method, every single time. Rather than relying on a model's pre-trained knowledge alone, ClawBio packages bioinformatics workflows into reusable "skills" that combine code, curated reference data, and defined procedures. Every answer follows a validated workflow instead of being generated from memory alone. That creates three properties clinical AI depends on: • Validation: Every result is checked against trusted methods and reference data, narrowing the gap between plausible and correct. • Traceability: Every output can be traced back to the exact code, data, and workflow that produced it, making results auditable. • Reproducibility: The same inputs produce the same outputs, allowing clinicians, researchers, and regulators to reproduce results with confidence. In one early gene-drug comparison, Manuel shares that accuracy improved from roughly 75–80% to 100% after the task was wrapped in a bioinformatics skill. The model itself didn't become smarter, the workflow became more reliable by stopping relying on the model's memory and grounding every answer in a reproducible process. The key takeaway: Domain-specific AI, especially in biology, won't be built by making models bigger alone. It will be built by creating systems that can validate every answer, show exactly how it was produced, and reproduce the same result every time. Where do you think the reliability bar should sit for AI in genomics? Watch the full conversation. Worth your time: 03:20–05:03 Why frontier models fall short in biology. 08:29–10:34 Validation, traceability, and reproducibility matter more than model capability in clinical AI. 11:45–15:50 How skill-guided workflows improved an early gene–drug comparison from ~75–80% accuracy to 100%. 15:50–18:56 17,000 evaluations across 16 models: what rigorous AI validation looks like in real-world science. 23:45–25:02 A candid discussion on private and local AI: what local processing can and cannot guarantee. 28:27–31:00 An open, transparent ecosystem is essential to democratizing agentic AI. —— Guest info: • Clawbio: https://github.com/ClawBio/ClawBio (https://github.com/ClawBio/ClawBio) • LinkedIn: https://www.linkedin.com/in/manuelcorpas (https://www.linkedin.com/in/manuelcorpas) • X: https://x.com/manuelcorpas (https://x.com/manuelcorpas) • Website: https://manuelcorpas.com/ (https://manuelcorpas.com/) Follow FLock.io on: • LinkedIn (https://www.linkedin.com/company/flock-io/): https://www.linkedin.com/company/flock-io/ • X (https://x.com/flock_io): https://x.com/flock_io Visit our website (www.flock.io): www.flock.io

3. 

   24 Jul 2026

   The City That Predicts Itself: Arakon on Predictive Intelligence and Sovereign AI

   Most governments run on hindsight. A road cracks, then someone fixes it. A tube delays, then commuters scramble to find another route. Two Imperial College London students think governments can do better. ⁠Dhruv Syam⁠ (https://www.linkedin.com/in/dhruv-syam/) and ⁠Viktor Smirnov⁠ (https://www.linkedin.com/in/viktorsmirnov24/) are building Arakon - a predictive intelligence layer for government that turns city data into a digital twin, allowing policymakers to test decisions before implementing them. The proof? They've segmented Hammersmith & Fulham into 17,000 road chunks, each scored for the likelihood and severity of future damage. That helps the council prioritise maintenance before small issues become costly disruptions. The same engine can scale beyond roads to bigger targets: tube closures, traffic jams, surge pricing. Transport for London might want to take a look. The clever part is that they don't do it by surveilling our data. The raw data never leaves the government's servers. Models are trained locally without sharing it out. That's federated learning, the technology FLock is providing. What starts as a pilot in Hammersmith could become a global public good. Built in the UK, not the Bay, it's another piece of the bigger picture behind the UK's sovereign AI ambitions. Timestamps: 00:00 - 03:45 | Meet Dhruv Syam & Viktor Smirnov: Reimagining government with predictive AI and city-scale digital twins. 03:45 - 09:40 | Why cities should be modelled as connected systems: Road maintenance, NHS pressure, and cross-domain signals reveal the second- and third-order effects of policy decisions. 09:40 - 14:43 | From potholes to predictions: How AI forecasts road deterioration before it happens. 14:43 - 18:40 | Beyond roads: Predicting traffic, tube closures, and city-wide disruption with the same AI engine while keeping sensitive government data secure. 18:40 - 32:10 | The case for sovereign AI in the UK, how federated learning enables it, and what makes FLock the right infrastructure for privacy-preserving public-sector AI.

4. 

   6 Jul 2026

   Your AI Agent Has a Backdoor: Tevy Kuch and Justin Lam on Closing It

   Most AI agent security assumes trust instead of proving it. Tevy Kuch and Justin Lam built Carapace to intercept every message an AI agent sends or receives and generate cryptographic proof that each security scan ran correctly, before the model ever sees the input. Tevy Kuch (https://www.linkedin.com/in/tevy-k-964892192/) — Data Scientist, Co-founder, Oneiris Mind Justin Lam (https://www.linkedin.com/in/justin-lam-b8306a223/)— Electrical and Electronics Engineer (Imperial College), CTO and Co-founder, Oneiris Mind Tevy and Justin met building Carapace for the Imperial College x FLock Blockchain for Good Alliance hackathon. Oneiris Mind, their primary venture, is a privacy-first EEG wearable — which is why the same hardware-rooted trust model shows up in both projects. Chapters (00:00) – Intro: guests Tevy Kuch and Justin Lam (00:40) – Introductions and the Imperial College hackathon (02:00) – What Carapace is and why Oneiris Mind needed it (04:15) – Why FLock's decentralized, trust-minimized network was a natural fit (06:30) – The four-vector attack model: prompt injection, credential infiltration, goal hijacking, absence of proof (09:00) – Why this is urgent: healthcare, finance, and auditable AI decisions (10:30) – Hardware trust: Intel SGX enclaves explained (13:00) – From hackathon prototype to manufactured EEG board (15:30) – Rebuilding Carapace's stack inside Oneiris Mind (18:00) – End-to-end security: EEG signals, wireless capture, and ARM TrustZone (21:30) – What FLock and BGA support could look like (24:00) – EEG data, brainwave states, and personalized models (28:00) – Shrinking the wearable: flex PCB and form factor Trust has to be provable, not assumed. Carapace exists because agent inputs were unverified and no one could confirm a security layer had actually run. Carapace runs three parallel cryptographic layers over every message, with zero code changes. It's built to route through OpenClaw agents without touching the underlying agent code. The architecture targets a specific four-vector attack model. Prompt injection, credential infiltration, goal hijacking, and the absence of cryptographic proof of execution. Software alone doesn't close the gap — hardware does. Intel SGX isolates encryption in a protected memory region even if the OS is compromised; ARM TrustZone does the equivalent on microcontrollers. The same problem shows up in Oneiris Mind's EEG wearable. Personal brain-wave data needs hardware-rooted security for the same reason agent decisions do — the founders built the device from the hardware layer up rather than sourcing proprietary hardware. Compliance is a real driver, not an afterthought. Frameworks like FCA and GDPR increasingly require audit trails that prove a scan ran, at a specific time, with a specific result. Key concepts Carapace: An AI agent security proxy that intercepts every message to and from an OpenClaw agent and routes it through three parallel cryptographic verification layers before the model sees the input. Four-vector attack model: The four failure modes Carapace defends against — prompt injection, credential infiltration, goal hijacking, and absence of cryptographic proof of execution. Intel SGX (Software Guard Extensions): A hardware enclave on x86 CPUs that isolates a protected memory region, keeping encrypted operations secure even if the operating system itself is compromised. ARM TrustZone: A hardware security extension on ARM microcontrollers that isolates peripherals into separate protected zones — the microcontroller-scale equivalent of SGX. Zero-knowledge proof (ZK-STARK): Lets a party prove a computation ran correctly without revealing the underlying data, so a security scan's result can be verified by any third party without trusting Carapace or FLock as a middleman. Oneiris Mind: Tevy and Justin's core venture — a privacy-first EEG wearable applying the same hardware-rooted trust model to personal brain-wave data.

5. 

   18 May 2026

   A FLock Love Story: Elizabeth Lui on Ecosystem, Decentralized AI, and Real-World Impact

   In this episode of What the FLock, host Sameeha Rehman speaks with Elizabeth Lui (https://x.com/elizabethluihy), Head of Ecosystem at FLock (www.flock.io), about her path from Hong Kong and political science into software engineering, blockchain, and decentralized AI. Elizabeth traces her journey through human rights work, university research, computational social science, and a move to the UK. She explains how she met FLock founder Jiahao (linkedin.com/in/jiahao7sun/?skipRedirect=true), joined as an early engineer, and now works across research, technical product management, and ecosystem partnerships. The conversation covers FLock’s mission to decentralize and democratize AI development, its products including AI Arena and federated learning on blockchain, and a UNDP pilot in the Dominican Republic that uses AI, smart contracts, and DAO governance to support rural women’s agricultural collectives with transparent, automated insurance payouts. Timestamps 00:00 — Intro: What the FLock, guest Elizabeth Lui 00:38 — Growing up in Hong Kong 01:20 — Arts, history, literature to politics at university 01:58 — Web3 as political systems 04:20 — NGO and early research career 05:35 — Computational social science and Python 06:23 — Twitter data for social science 07:35 — Move to the UK 09:18 — Oxford, work ethic, and research ambitions 13:08 — Tech bootcamp and software engineering in London 14:21 — Path toward blockchain engineering 15:21 — Meeting Jiahao and discovering early FLock 17:42 — Joining FLock as an early team member 18:19 — First impressions of the FLock team 24:00 — Explaining FLock to non-technical listeners 26:29 — Decentralized AI experience before and at FLock 28:11 — Research, TPM, and head of ecosystem 33:45 — UNDP x Dominican Republic pilot 37:49 — Hackathons, crowdsourced data, and AI Arena for niche models 42:31 — FLock’s next 3-5 years 45:21 — Rapid fire: AI, colleagues, Turing, yoga, and privacy 48:25 — UK health data leak discussion 51:03 — If FLock were a song Key Takeaways 1. Web3 is political, not only technical. Consensus, incentives, and punishment of bad actors are governance questions. Web3 implements them in code and protocols. 2. Decentralized does not automatically mean democratic. FLock aims for both: open infrastructure and broader participation, voice, and rewards in AI. 3. Privacy is underweighted in AI. Over-collection and poor handling of data, especially sensitive data like health records, can create harm that is hard to undo. 4. Niche, high-impact problems need different infrastructure. Large AI vendors may not build models for every local need. Decentralized training and incentives, such as AI Arena, can help fill that gap. 5. Real-world pilots matter. The Dominican Republic project connects on-the-ground data collection, fine-tuned models, transparent payouts, and community governance into one end-to-end use case. Key Terms Decentralized AI: AI development and training not controlled by a single central party. Democratic AI: An approach where more people can participate, govern, and benefit from AI development. Federated learning: Training models without centralizing sensitive raw data. AI Arena: FLock’s decentralized training platform with Kaggle (https://www.kaggle.com/)-like rewards via smart contracts. FL Alliance: Federated learning on blockchain, designed to be privacy-preserving across parties. DAO: On-chain governance for collectives, such as loan and insurance decisions. Links FLock (https://flock.io/)

6. 

   4 May 2026

   From Banking to FLock: Mo’s Journey into Sovereign AI

   Join us as we explore Mo Marikar's (linkedin.com/in/mohammedmarikar?originalSubdomain=uk) journey from a childhood fascination with programming to leading AI innovation at FLock. Discover insights on decentralizing AI, data privacy, and the future of sovereign AI in this in-depth interview. Chapters 00:00 Introduction to Flock and Its Unique Team 02:34 Mo Marikar's Early Life and Education 05:27 Career Beginnings at UBS and Transition to AI 08:34 Innovations in Wealth Management and AI Research 11:31 Building AI Capabilities at RBC 14:38 Data Privacy and Ethical Considerations in Banking 17:32 The Shift to Consumer Data Ownership 20:21 Mo's Journey to Flock and the Vision Behind It 22:59 Ambition and Career Reflections 25:58 Future Directions and Innovations at Flock 34:27 Navigating Corporate Life and Continuous Learning 36:33 Bridging Technology and Institutional Use Cases 41:24 Identifying Key Sectors for AI Implementation 47:45 The Future of Decentralized AI 51:07 Personal Aspirations and Career Reflections 54:36 Rapid Fire Round: Insights and Fun Questions —— Moderator: ⁠Sameeha⁠ (https://x.com/sameeha_rehman) Twitter: ⁠https://x.com/flock_io⁠ (https://x.com/flock_io) Discord: ⁠https://discord.gg/8NCwYn2Ype⁠ (https://discord.gg/8NCwYn2Ype) *The views and opinions expressed by guests on this podcast are their own and do not constitute any investment advice.

7. 

   20 Nov 2025

   FLock 2025 Earnings Call

   Recorded on Nov 18, 2025 — Live on 𝕏 (https://x.com/flock_io/status/1990755495435776241?s=46) In this session, FLock leadership breaks down the company’s 2025 performance and ecosystem milestones. Founder & CEO Jiahao Sun (@0x7SUN (https://x.com/0x7SUN)), CFO CosmeticFish (@cosmeticfish (https://x.com/cosmeticfish)), and Chief of Staff Sameeha Rehman (@sameeha_rehman (https://x.com/sameeha_rehman)) walk listeners through the year’s highlights, including: Network expansion across Training Nodes, Validators & Delegators Major strategic partnerships across AI, cloud, and Web3 ecosystems Growth of on-chain activity and protocol revenue Real-world adoption momentum in healthcare, government, finance & manufacturing What’s ahead for scaling decentralized AI globally Tune in for a concise breakdown of how FLock advanced DeAI from concept to real-world infrastructure in 2025 — and where the ecosystem is heading next.If you missed our earnings call yesterday, check out the full report and transcript here ↓ https://www.flock.io/blog/flock-2025-earnings-report-performance--growth-overview?preview=true (https://www.flock.io/blog/flock-2025-earnings-report-performance--growth-overview?preview=true)

8. 

   17 Jun 2025

   Baby4D: Turning Ultrasounds into Realistic Baby Photos with AI | EP1

   Dr. Ken, founder of Baby4D, discusses how their AI tool transforms 4D ultrasound scans into realistic baby portraits, allowing parents to connect with their unborn child in this interview episode.

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Spotify

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1 ratings

Podcast Authority Score: 48 / 100

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Engagement

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