Space host | Content writer | Building @DBESTSPACES | Advocate @HackQuest_ | Creator @CoinMarketCap | Exploring AI & building in public.
web3
Joined February 2020
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Another milestone achieved with the @syscoin community.
On October 31st, I hosted a successful event in Lagos where we onboarded 100+ students, builders, and Web3 enthusiasts. Introducing them to what Syscoin is all about.
The meetup brought together amazing minds eager to learn, network, and explore the power of Syscoin’s ecosystem.
We had an educative session where attendees discovered how they can contribute to the ecosystem as content creators, community members, and more through @SuperDappAI.
Our speakers shared valuable insights on community building and how collaboration drives growth within the Web3 space.
A huge thank you to all the communities that partnered with me and @SyscoinAfrica to make this event a success:
➠ @BlockchainLASU
➠ @importapay_ng
➠ @HumbAfrica
➠ @morphscorner
➠ @globalAfriverse
And a special shoutout to syscoin for supporting the education of new Web3 enthusiasts and empowering them to take part in the ecosystem.
Let’s keep building the future of Syscoin together.
What’s your biggest takeaway about the syscoin ecosystem after attending the event?
DefiBNS retweeted
I've made up my mind never to give up on this boy.
Please my nephew needs help.
We need 16M for his heart surgery we have 3.6M now. 12.4M to go
Your 500,1000,2000 will put us a step ahead. 0030043880 access Oluwatoyin Ann Sogunro
Help us repost, quote and tag anyone who can help us.
@Wizarab10
@davido
DefiBNS retweeted
𝗠𝘆 𝗽𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝗼𝗻 𝗮𝗯𝗼𝘂𝘁 𝘁𝗵𝗲 𝗳𝘂𝘁𝘂𝗿𝗲 𝗼𝗳 𝗽𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝗼𝗻 𝗺𝗮𝗿𝗸𝗲𝘁𝘀:
"creating markets could become just as important as trading them."
In our world today, most of the attention goes to traders. Because they are seen as the one's who ↓
→ identifies the right outcome?
→ enters at the right price?
→ managed risk well enough to profit when the market resolved?
Now those questions will always matter.
But I think there's another role that deserves more attention: which is the market creator.
This is because, someone has to identify a question worth asking, turn it into a market, establish understandable rules, and give other people a reason to participate.
And that work shapes the opportunities available to everyone else.
So if the creation process is difficult, the ecosystem will naturally be limited by the markets a platform chooses to support.
And if creation becomes more accessible, users can also bring forward questions that reflect their own interests, communities, and areas of expertise.
That's why I'm impressed with what @xomarket and its XO 2.0 is bringing to the table.
The bigger idea is to make the process of creating, trading, and settling markets easier to navigate.
And the potential goes beyond simply increasing the number of markets available. Because It could expand the range of questions people can express through prediction markets in the first place.
N/B: liquidity will remain important. And so will transparent resolution rules and the ability to discover markets worth trading. Though incentives can help attract participation, but the long term test is whether the markets themselves provide a useful experience.
I also think AI could play an interesting role across this category, from helping people explore information to making market creation and discovery more accessible. So the important question is whether these tools improve the experience without compromising clarity or trust.
𝗠𝘆 𝘁𝗵𝗲𝘀𝗶𝘀 𝗵𝗲𝗿𝗲 𝗶𝘀 𝘀𝘁𝗿𝗮𝗶𝗴𝗵𝘁𝗳𝗼𝗿𝘄𝗮𝗿𝗱:
The next stage of prediction markets may be defined not only by how efficiently people trade, but by how many people can create meaningful markets of their own.
So if XO can help make that possible while maintaining the foundations that make markets useful, then it has an interesting opportunity to carve out a distinctive position in the category.
In essence, I'm watching this space closely 👀
One thing I find interesting about prediction markets is that some of the most compelling questions are also the easiest to overlook.
Because in our world today, everyone knows the obvious markets.
→ major elections,
→ big sporting events,
→ major crypto price movements,
→ and decisions that already attract attention from millions of people.
——————————🔻—————————
𝗕𝘂𝘁 𝗪𝗵𝗮𝘁 𝗔𝗯𝗼𝘂𝘁 𝗧𝗵𝗲 𝗦𝗺𝗮𝗹𝗹𝗲𝗿 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀?
→ Will a particular crypto community reach a milestone before the end of the month?
→ Which emerging narrative will gain traction next?
→ Will a specific project deliver on a promise its community has been debating for weeks?
——————————🔻—————————
These questions might not be important enough for a traditional platform to list. But within the right community, they can generate genuine interest, disagreement, and useful information.
And this is where user-created markets become interesting.
Instead of waiting for a platform to decide which questions deserve a market, users can potentially bring their own questions to the table.
|| @xomarket approach to market creation points toward this broader possibility: a prediction market ecosystem shaped not only by the platform, but also by the people who use it.
Though the opportunity isn't simply to have more markets. Rather it's to have markets that reflect the interests, knowledge, and conversations of different communities.
And that could literally make prediction markets feel less like a fixed catalogue of events and more like a living ecosystem of questions people actually care about.
Mind you, there are still important challenges to solve.
Because markets need clear rules, reliable resolution, sufficient liquidity, and a reason for traders to participate. Because more markets alone won't guarantee better markets.
But if the creation and settlement experience becomes easier, entirely new categories of markets could become possible.
And that's the part of XO I still find worth paying attention to.
Because the next interesting market might not be one everyone is already watching.
Rather, It might be a question someone in a small community decides to create.
DefiBNS retweeted
One thing I find interesting about prediction markets is that some of the most compelling questions are also the easiest to overlook.
Because in our world today, everyone knows the obvious markets.
→ major elections,
→ big sporting events,
→ major crypto price movements,
→ and decisions that already attract attention from millions of people.
——————————🔻—————————
𝗕𝘂𝘁 𝗪𝗵𝗮𝘁 𝗔𝗯𝗼𝘂𝘁 𝗧𝗵𝗲 𝗦𝗺𝗮𝗹𝗹𝗲𝗿 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀?
→ Will a particular crypto community reach a milestone before the end of the month?
→ Which emerging narrative will gain traction next?
→ Will a specific project deliver on a promise its community has been debating for weeks?
——————————🔻—————————
These questions might not be important enough for a traditional platform to list. But within the right community, they can generate genuine interest, disagreement, and useful information.
And this is where user-created markets become interesting.
Instead of waiting for a platform to decide which questions deserve a market, users can potentially bring their own questions to the table.
|| @xomarket approach to market creation points toward this broader possibility: a prediction market ecosystem shaped not only by the platform, but also by the people who use it.
Though the opportunity isn't simply to have more markets. Rather it's to have markets that reflect the interests, knowledge, and conversations of different communities.
And that could literally make prediction markets feel less like a fixed catalogue of events and more like a living ecosystem of questions people actually care about.
Mind you, there are still important challenges to solve.
Because markets need clear rules, reliable resolution, sufficient liquidity, and a reason for traders to participate. Because more markets alone won't guarantee better markets.
But if the creation and settlement experience becomes easier, entirely new categories of markets could become possible.
And that's the part of XO I still find worth paying attention to.
Because the next interesting market might not be one everyone is already watching.
Rather, It might be a question someone in a small community decides to create.
What if the next big evolution in prediction markets isn't just about trading, but about who gets to create the markets in the first place?
Now think about what YouTube did for video.
Before platforms like YouTube took off, creating and distributing content at scale was much harder. Because then you needed access, resources, and a way to reach an audience.
But then the model changed. Anyone could become a creator, and the audience decided what deserved attention.
And I think prediction markets have an opportunity to follow a similar path as well.
Though most people experience prediction markets from the trader's perspective: find a market, take a position, and wait for the outcome.
But what about the person who spots an interesting question before everyone else? I mean the person who understands a niche community, follows a developing trend, or sees a debate that deserves a market of its own?
Well that person should be able to create the market, too.
And this is exactly what makes @xomarket interesting to me.
The XO 2.0 thesis goes beyond simply giving people more markets to trade. Because it's about making it easier to create, trade, and settle markets, opening the door to a more creator-driven ecosystem.
Of course, letting anyone create markets is only one part of the equation. Discovery, liquidity, clear resolution rules, and trust still matter too. A platform needs to make the entire experience work, not just the creation process.
But if those pieces come together, the opportunity is bigger than adding another prediction market platform.
And also, It becomes a place where communities can turn their own questions into tradable markets.
YouTube made publishing accessible to more people. Could XO help make market creation accessible to more people, too?
Well from my own perspective, I think that's a solid thesis worth watching 👀
DefiBNS retweeted
What if the greatest threat to freedom isn't losing it overnight...
(:) ^^ ...but slowly accepting it's disappearance?
I created this video to explore why digital freedom matters, inspired by @durov message at the Oslo Freedom Forum.
Every message, every idea, and every voice deserves the chance to be heard.
Watch, share your thoughts, and if this message resonates with you, help more people see it.
#DigitalFreedom || #Telegram #PavelDurov || #Freedom || #Privacy || #HumanRights || #OsloFreedomForum
——————————🔻—————————
Beware of impersonators. Here's my X & TG username: @ItMe_Harry !!!
DefiBNS retweeted
Dear projects,
Your budget used to be $1K+ per KOL for paid marketing promotions.
Now, hosting a contest with a budget far below that makes me question how serious you are about scaling your product, TBH.
Because the distribution layer is already here.
Video creators who can make people care about your product.
Writers who can explain your product and its value.
Meme creators who can make your product relatable.
🔍 Researchers who can do deep dives into your product.
And many more talented creators ready to work.
The talent is here. The distribution is here.
Put your money where your mouth is.
GAMDON & Spenda are prime examples.
Creators are waiting.
DefiBNS retweeted
What if the next big evolution in prediction markets isn't just about trading, but about who gets to create the markets in the first place?
Now think about what YouTube did for video.
Before platforms like YouTube took off, creating and distributing content at scale was much harder. Because then you needed access, resources, and a way to reach an audience.
But then the model changed. Anyone could become a creator, and the audience decided what deserved attention.
And I think prediction markets have an opportunity to follow a similar path as well.
Though most people experience prediction markets from the trader's perspective: find a market, take a position, and wait for the outcome.
But what about the person who spots an interesting question before everyone else? I mean the person who understands a niche community, follows a developing trend, or sees a debate that deserves a market of its own?
Well that person should be able to create the market, too.
And this is exactly what makes @xomarket interesting to me.
The XO 2.0 thesis goes beyond simply giving people more markets to trade. Because it's about making it easier to create, trade, and settle markets, opening the door to a more creator-driven ecosystem.
Of course, letting anyone create markets is only one part of the equation. Discovery, liquidity, clear resolution rules, and trust still matter too. A platform needs to make the entire experience work, not just the creation process.
But if those pieces come together, the opportunity is bigger than adding another prediction market platform.
And also, It becomes a place where communities can turn their own questions into tradable markets.
YouTube made publishing accessible to more people. Could XO help make market creation accessible to more people, too?
Well from my own perspective, I think that's a solid thesis worth watching 👀
DefiBNS retweeted
I'm lying prostrate in the floor.
I'm so grateful to everyone for the support.
I'm beyond speechless at the support
Thank you.
For Accountability, here is a video showing the current level the funds have gotten to.
I'm very grateful
Any extra amount received over 5M will go towards purchase of After surgery items, like Wheelchair, Crutches and whatever medication is needed.
Also any extra amount will be channeled towards Physiotherapy and Recovery.
I'm in tears typing this, but not tears of pain but one of gratefulness and Appreciation
Things of Joy won't cease in your homes.
P.S. Sorry for any background noise and my poor voice.🙏
Please Everyone, I need your help.
From the accident, she has fractures in her hand, leg, and pelvic region. The doctor said the surgery will cost about ₦5 million. It is extensive and will be done in two phases.
First they will operate on her hand and leg. When she recovers from that, they will do the pelvic region.
It has been a painful experience. We have already spent so much on drugs and medications just to keep her stable this far. The pain is still there every day.
A lot of you have followed this from the day of the accident. You have seen the updates. You know what this has been like.
Please, if your heart can move you, even ₦1k, ₦2k, ₦5k, ₦10k, or ₦20k from you will help us. Every little bit carries weight right now.
Kuda Microfinance Bank
2004212740
Ekpo Richard Unwana
If you want to confirm or even visit to see how she is doing, please call or WhatsApp me on 08014616027.
Please help me share this too.
I need the support.
Thank you for reading this far.
DefiBNS retweeted
I LOVE MY FUCKING LIFE!!!
And tonight, we’re making more memories.
GAMDOM MAN IS GOING LIVE AT 8 PM WAT ON KICK💚
I need the whole X on the stream
THE WORLD IS OUR PLAYGROUND, AND WE CAME TO PLAY
GAMDOM MAN OUT! 🟢
kick.com/thegamdomman
DefiBNS retweeted
Vibe coded my parents' TV this afternoon!
It was showing a sponsored ad before you could even get to Netflix.
Gave Claude Code the model number, it found the TV on wifi, removed 25 Xiaomi packages and wrote a new launcher.
Looks like an Apple TV now lol
DefiBNS retweeted
If you learn AI Agent Engineering, you will never be unemployed again.
I’d do this.
Stage 1: Agent Architecture & State Machines
- Learn: Directed Acyclic Graphs (DAGs), cyclic workflows, LangGraph, Temporal dot io, state serialization.
- Practice: Build a multi-step agent using a state machine that can loop, branch and pause without using a fragile `while True` loop.
- Why: Chat loops crash in production. Real-world workflows require deterministic state management and crash recovery.
Stage 2: Tool Use & MCP (Model Context Protocol)
- Learn: JSON-RPC, strict Pydantic schemas, MCP server/client architecture, dynamic tool discovery.
- Practice: Write a raw MCP server from scratch exposing a local Postgres DB, handling auth and scoped permissions without using a wrapper SDK.
- Why: Agents need standardized, secure hands to do real work. Bespoke API wrappers do not scale across an enterprise.
Stage 3: Computer Use & GUI Automation
- Learn: DOM parsing, Set-of-Mark prompting, coordinate mapping, screenshot diffing, Playwright/Puppeteer.
- Practice: Build a vision-based agent that logs into a legacy web portal, navigates menus via screenshots and downloads a CSV.
- Why: 80% of enterprise software has no API. GUI agents unlock the legacy world that text-based agents cannot touch.
Stage 4: Advanced Memory & Context Engineering
- Learn: 3-tier memory (working, episodic, semantic), vector eviction policies, context compression, token budgeting.
- Practice: Build a memory engine that automatically compresses a 50-turn conversation into an episodic summary to prevent context overflow.
- Why: Infinite context causes hallucinations and OOM errors. Memory must be actively managed, scored, and evicted.
Stage 5: Multi-Agent Swarms & A2A Handoffs
- Learn: Agent-to-Agent (A2A) protocols, typed state passing, supervisor vs. peer-to-peer topologies, consensus voting.
- Practice: Build a 3-agent swarm (Researcher, Coder, Reviewer) that passes strictly typed JSON state, never raw text.
- Why: Single agents fail on complex tasks. Swarms divide and conquer, but raw text handoffs cause context drift and hallucinations.
Stage 6: Sandboxed Execution Environments
- Learn: E2B, WebContainers, gVisor, Docker-in-Docker, resource limits, network egress blocking.
- Practice: Build a code-interpreter agent that writes and executes Python in an isolated microVM with zero internet access.
- Why: Letting an LLM run `os.system()` on your host machine or production server is a catastrophic security risk.
Stage 7: Inter-Agent Security & Guardrails
- Learn: Indirect prompt injection, tool poisoning, sanitization proxies, least-privilege scoping.
- Practice: Build a middleware that strips malicious instructions from an email before the "Email Reader" agent passes it to the "Database" agent.
- Why: Multi-agent systems create internal attack surfaces. Agent A can be tricked into hijacking Agent B's tools.
Stage 8: Human-in-the-Loop (HITL) & Breakpoints
- Learn: Asynchronous approvals, webhook routing, Slack/Teams integrations, durable execution pauses.
- Practice: Build an agent that drafts a $10k invoice, serializes its state, and waits 3 days for a human to click "Approve" in Slack before resuming.
- Why: High-stakes automation requires human accountability. Agents must know how to pause, wait and resume cleanly.
Stage 9: Self-Correction & Reflection Loops
- Learn: Critic agents, traceback analysis, retry with context, compiler error parsing.
- Practice: Build a coding agent that reads its own stack traces, rewrites the failing code and retries up to 3 times before escalating to a human.
- Why: Agents that fail on the first try are useless. Agents that debug themselves are digital employees.
Stage 10: Trajectory Evals & CI Gates
- Learn: Step-level grading, golden DAGs, trajectory distance metrics, LLM-as-a-judge for tool calls.
- Practice: Build a CI pipeline that replays 100 agent trajectories and blocks the PR if the tool-call sequence deviates from the golden path.
- Why: Grading the final text output hides the fact that the agent hallucinated an API call or skipped a safety check to get there.
Stage 11: Graceful Degradation & Fallbacks
- Learn: Circuit breakers, API-to-browser fallbacks, model cascading, cost-kill switches.
- Practice: Build a routing layer where if the primary REST API 404s, the agent seamlessly spawns a headless browser to finish the job.
- Why: The real world is messy. Agents must adapt to broken environments and degraded APIs without crashing the workflow.
Stage 12: Public Agent Teardowns & Portfolios
- Learn: Architecture decision records (ADRs), cost-per-task analysis, latency profiling, public benchmarking.
- Practice: Publish a teardown of your multi-agent swarm, detailing the exact cost, failure rate, state-machine design and eval methodology.
- Why: Top AI labs hire Agent Engineers based on their system design judgment and production reality not their ability to import LangChain.
Bookmark and Repost!
DefiBNS retweeted
A drug shortage might affect more medications than the FDA shortage database lists, and hospitals may not realise how connected their supply chains are.
So I built Fontrel, an AI-powered pharmaceutical supply chain intelligence platform using @falkordb
When a drug goes into shortage, hospital pharmacies typically investigate that specific medication and look for alternatives.
But what happens when the same manufacturing plant produces several other drugs on the hospital's formulary
Those drugs could share supply-chain exposure, even if they come from different pharmaceutical companies.
Traditional shortage lists don't reveal these connections, making it difficult to identify hidden dependencies or assess alternative suppliers.
Fontrel traces drug shortages back to their manufacturing plants, identifies other medications sharing those facilities, and helps pharmacists find alternative products manufactured elsewhere.
It also remembers previous substitution decisions so hospitals can use that knowledge during future shortages.
How it works
Fontrel pulls real pharmaceutical data from openFDA, DailyMed, and FDA Drug Master File records.
It uses FalkorDB to connect drugs, ingredients, manufacturers, production facilities, suppliers, and shortages in a knowledge graph.
When a shortage is selected, an AI agent queries the graph to identify the manufacturing plant and other formulary drugs connected to it.
It identifies candidate alternatives matching the ingredients, strengths, dosage form, and route, while checking whether they share manufacturing facilities.
Pharmacists can approve or reject candidates, and Fontrel saves those decisions as institutional memory for future investigations.
FalkorDB's PageRank and connected-components algorithms help identify critical manufacturing plants and clusters of supply chain dependencies.
Here is a real example
Using FDA data from October 17, Fontrel traced a bupivacaine injection shortage to its manufacturing plant.
It discovered:
-69 other formulary products connected to the same facility.
-31 products without existing shortage notices.
-4 candidate alternatives with no shared manufacturing plant on record.
These connections would be difficult to identify from a shortage list alone.
Fontrel also shows the Cypher queries behind its findings, making the investigation transparent and auditable.
The demo below uses synthetic hospitals with real FDA pharmaceutical data. Candidate alternatives still require pharmacist review.
Fontrel was built for the FalkorDB Graph Hacks hackathon on @WeMakeDevs
Live app: fontrel.midelabs.xyz
Source code: github.com/mystiquemide/font…
Here is a 2-minute video walkthrough demonstrating how Fontrel uses FalkorDB
DefiBNS retweeted
Most people still treat crypto as something you only hold or trade.
But what if the stablecoins already sitting in your wallet could purchase things like;
→ mobile data,
→ virtual card, and
→ phone numbers without sending them to an exchange or a bank first?
Well that's basically what an innovative project called @GoFiCash is bringing to the table.
Walk with me 🧵⬇️
DefiBNS retweeted
Please Everyone, I need your help.
From the accident, she has fractures in her hand, leg, and pelvic region. The doctor said the surgery will cost about ₦5 million. It is extensive and will be done in two phases.
First they will operate on her hand and leg. When she recovers from that, they will do the pelvic region.
It has been a painful experience. We have already spent so much on drugs and medications just to keep her stable this far. The pain is still there every day.
A lot of you have followed this from the day of the accident. You have seen the updates. You know what this has been like.
Please, if your heart can move you, even ₦1k, ₦2k, ₦5k, ₦10k, or ₦20k from you will help us. Every little bit carries weight right now.
Kuda Microfinance Bank
2004212740
Ekpo Richard Unwana
If you want to confirm or even visit to see how she is doing, please call or WhatsApp me on 08014616027.
Please help me share this too.
I need the support.
Thank you for reading this far.
If you were the one that did a HIT AND RUN at Ogudu express, Lagos yesterday evening, just know that it was my wife you hit.
I FORGIVE YOU, I really do.
Getting the call and the news was heartbreaking and caused tears but I FORGIVE YOU.
I FORGIVE YOU
I'm grateful to and for the Man that kept her conscious rendering first aid till the Ambulance got there.
He even stayed and helped till midnight (few minutes ago).
I'm so grateful 🙏
DefiBNS retweeted
𝗔 𝗽𝗿𝗼𝗷𝗲𝗰𝘁 𝗶𝘀 𝗺𝗼𝗿𝗲 𝘁𝗵𝗮𝗻 𝗶𝘁𝘀 𝗳𝗲𝗮𝘁𝘂𝗿𝗲𝘀, 𝘄𝗵𝗶𝗹𝗲 𝗮 𝘁𝗼𝗸𝗲𝗻 𝗶𝘀 𝗺𝗼𝗿𝗲 𝘁𝗵𝗮𝗻 𝗶𝘁𝘀 𝘁𝗶𝗰𝗸𝗲𝗿.
So understanding how the pieces fit together is what makes exploring a project worthwhile.
I shared my thoughts on @truetradingai in the thread below. And this video also builds on that perspective,,,
thereby offering a different way to look at the project and its ecosystem.
So if you're exploring TRUE AI, I’d be interested in hearing what stands out to you most.
Though the thread goes deeper || While the video brings another perspective 🎥🧵⬇️
The easiest time to get excited about a crypto token is before it launches.
But the harder part is knowing what you're actually getting excited about.
Because behind every ticker, every countdown, and every launch announcement,,,
there's always an important story most people never stop to investigate.
And that's the story I wanted to understand... Walk with me🧵⬇️
