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

👉 LinkedIn: 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/

"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

• LinkedIn: https://www.linkedin.com/in/manuelcorpas

• X: https://x.com/manuelcorpas

• Website: https://manuelcorpas.com/

Follow FLock.io on:

• LinkedIn: https://www.linkedin.com/company/flock-io/

• X: https://x.com/flock_io

Visit our website: www.flock.io

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 and ⁠Viktor Smirnov⁠ 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.

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 — Data Scientist, Co-founder, Oneiris Mind

Justin Lam — 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

1. 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.
2. 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.
3. The architecture targets a specific four-vector attack model. Prompt injection, credential infiltration, goal hijacking, and the absence of cryptographic proof of execution.
4. 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.
5. 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.
6. 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.

In this episode of What the FLock, host Sameeha Rehman speaks with Elizabeth Lui, Head of Ecosystem at FLock, 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, 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

1. Decentralized AI: AI development and training not controlled by a single central party.
2. Democratic AI: An approach where more people can participate, govern, and benefit from AI development.
3. Federated learning: Training models without centralizing sensitive raw data.
4. AI Arena: FLock’s decentralized training platform with Kaggle-like rewards via smart contracts.
5. FL Alliance: Federated learning on blockchain, designed to be privacy-preserving across parties.
6. DAO: On-chain governance for collectives, such as loan and insurance decisions.

Links

FLock

Join us as we explore Mo Marikar's 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.

Started as an intern at UBS at 18 through a social mobility charity, became one of the bank's earliest promoted associate directors during the 2008 credit crisis, then moved to RBC in 2013 where he built a hybrid data/sales practice and hired the Royal Bank of Canada's first AI engineer in wealth management. That engineer was Jiahao Sun — FLock's founder.

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

Key takeaways

1. Most people don't know what they're compromising. Governments and enterprises are adopting AI tools without understanding where their data goes — or that local alternatives already exist on their current hardware.
2. Healthcare data has no undo. Once a genome is in the wild, there is no password reset. The privacy stakes in healthcare AI are categorically different from other sectors.
3. Nations are ceding sovereignty by accident. Widespread adoption of foreign AI for critical infrastructure quietly transfers leverage to another state in any future dispute.
4. Competitors can collaborate safely with federated learning. Banks can jointly train fraud models without sharing customer data; defense allies can pool threat intelligence without revealing asset locations.
5. You probably don't need new hardware. The average office machine runs at 5% capacity. Distributed inference can extend AI capability across a nation's existing infrastructure without a significant lift in power or water use.
6. The privacy paradox is real. People cite privacy concerns in every conversation — then click a $100 Amazon voucher to connect their Facebook account.

Key concepts

• Federated learning (FL): Training AI models across distributed data sources without centralizing the raw data — the architecture that lets RBC-style banks share fraud signals without exposing customers.
• Distributed inference: Running model inference across multiple devices rather than a central server; Mo's argument that existing office hardware is already sufficient.
• Sovereign AI: AI infrastructure owned and operated within a nation's or organization's own control, not dependent on foreign cloud providers.
• Flockit: FLock's federated learning toolkit, already deployed at several institutions.
• FL Alliance: FLock's privacy-preserving cross-party federated learning framework.

——

Moderator: ⁠Sameeha⁠

Twitter: ⁠https://x.com/flock_io⁠

Discord: ⁠https://discord.gg/8NCwYn2Ype⁠

#SovereignAI #FederatedLearning #DataPrivacy #FLock

*The views and opinions expressed by guests on this podcast are their own and do not constitute any investment advice.

Recorded on Nov 18, 2025 — Live on 𝕏

In this session, FLock leadership breaks down the company’s 2025 performance and ecosystem milestones.

Founder & CEO Jiahao Sun (@0x7SUN), CFO CosmeticFish (@cosmeticfish), and Chief of Staff Sameeha Rehman (@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

Baby4D, the AI tool powered by FLock.io that’s changing the pregnancy journey. It transforms 4D ultrasound scans into lifelike baby portraits, helping parents connect with their baby before birth. Founder Dr. Ken shares the story, the tech behind it, and what’s next.

Baby4D Intro Video

00:30 – Introduction

FLock.io — the world’s first decentralized AI model training platform for transforming ultrasound scans into lifelike baby images.

01:40 – What is Baby4D?

02:48 – The Inspiration: A Father’s Story

04:30 – The Tech Behind Baby4D

06:05 – Accuracy vs. Emotion

07:35 – Real User Reactions

08:55 – How It Works: User Experience

10:08 – Ethics and Data Privacy

11:30 – Powered by FLock.io’s Decentralized AI

12:25 – What’s Next for Baby4D?

13:35 – From the UK to the World

——

Moderator: Sameeha

Twitter: https://x.com/flock_io

Discord: https://discord.gg/8NCwYn2Ype

*The views and opinions expressed by guests on this podcast are their own and do not constitute any investment advice.