Uzenabor Nelson- 🌶️.

Building

[Yasser @yasser_elsaid_]

Sharing your work is a good forcing function to make sure you're building something you're proud of.

If you're not proud enough to share your work then go and fix it until you are.

[Federico Simionato @fedesimio]

We've officially agreed to acquire Airtable for $1.285B! 😍

[Uzenabor Nelson- 🌶️. @NelsonUzenabor]

@the_boludada @TosinOlugbenga @flutterwave @FlwSupport @bachs_hq Is Bacs better than Monnify?

[Yewande @LionessAtEase]

Hmmm for my bootstrap founders who have been following me, you’d know I’ve chatted about this before. For distribution in Africa’s informal markets, you do not need to convince 10,000 people. You need just 1 cluster. 

Conquer 1 cluster first, move to the next cluster. By the time you get to the 4th cluster, the ripple effect has started.

The first to second month in the first cluster will be trial and error, listening and feedback, balancing strategies that reflect and balance ROI, revenue, expenses, etc. The third month is the same thing but you are already getting a hang of the mathematics and their ways. By the end of the 4th month you’re balancing. You need 3 - 5 field agents to get this done. 

Or you can just go the traditional route. Get a shop in the market. Hire 3 agents which one doubles as admin. Give them a target. Conquer there and move to the next market. And if you are that cracked, you can turn the shop to your own office that you open in the morning and close at night. Lead the team and be the admin/lead agent yourself. 

There is a neobank we are currently distributing. This one is even very interesting because the founder was very specific about his target numbers in target days. His team is running feasibility studies to see if what they envision can move. They just need the real target users in the monthly GMV to test the app for that time frame. 

I connected with 2 Phone store girls in Computer Village. One of them is a close acquaintance of mine. They pulled in 2 more of their friends who are also sales girls in big gadget stores. Just a small commission in getting bosses in their network to use the app. It’s like a side gig for them since it's just 3 months thing. 

Distribution is very tough and costly yeah but know that it is a cluster conquering thing. Once you earn the trust of 2 to 5 clusters, they will continue the work for you.

I think where founders are getting it wrong is in the misconception that distribution is a marketing function. I do not know about other continents in the world but I know that as far as Africa is concerned distribution is a social function.

Our people don’t adopt something because they discovered it. They adopt it because someone they know has tried it and that’s reduced the risk of them trying it too. This is why distribution is harder in Africa. You’re not just earning attention, you’re earning trust. 

Be sincere, the reason you as a digital savvy person started using OPay or moniepoint is because everybody is already using it. Yes or no? You reach there, Opay. You go there, Opay. Social function!  

You do not have funding like OPay or Chowdeck. You’re just a lone struggling founder who got intrigued with an infrastructure solution from one corner in Ajah Badore. That funds you are looking to pour on an ad that won’t likely reach your target audience who are busy 9-7 in the market. Employ 5 smart talkers inside that Ajah market. Place them on salary and commission based performance.

I usually tell founders on budget to base salary around minimum wage. This is Nigeria. You will find your people that will work with you at the cost you have. Gradually follow them through 1 cluster at a time.

[U.S. Mission Nigeria @USinNigeria]

Nigerian-American engineer & Harvard grad Yinka Ogunbiyi built HaloBraid, the world's first braid-assist robot, easing strain on stylists & saving salons time. $7M raised, 7,000+ salons waitlisted. American innovation, global impact. #AmericanExcellence in action.

Learn more: https://t.co/rThzLldE64

[Uzenabor Nelson- 🌶️. @NelsonUzenabor]

@ALOFFAWY Good! Exercise as well. I know you do that already

[Uzenabor Nelson- 🌶️. @NelsonUzenabor]

@rauchg https://t.co/Thys3gFQ2d - ai sdk, gpt 5.5 mini

[Uzenabor Nelson- 🌶️. @NelsonUzenabor]

@Jason I’m in

[YoungHoon Kim @yhbryankimiq]

THE HOLY SPIRIT IS THE HIGHEST INTELLIGENCE. 100%

[Mind and Glory 🎖 @mindandglory]

An intelligent man never lets urgency from others override the patience his strategy requires.

[Andrej Karpathy @karpathy]

LLM Knowledge Bases

Something I'm finding very useful recently: using LLMs to build personal knowledge bases for various topics of research interest. In this way, a large fraction of my recent token throughput is going less into manipulating code, and more into manipulating knowledge (stored as markdown and images). The latest LLMs are quite good at it. So:

Data ingest:
I index source documents (articles, papers, repos, datasets, images, etc.) into a raw/ directory, then I use an LLM to incrementally "compile" a wiki, which is just a collection of .md files in a directory structure. The wiki includes summaries of all the data in raw/, backlinks, and then it categorizes data into concepts, writes articles for them, and links them all. To convert web articles into .md files I like to use the Obsidian Web Clipper extension, and then I also use a hotkey to download all the related images to local so that my LLM can easily reference them.

IDE:
I use Obsidian as the IDE "frontend" where I can view the raw data, the the compiled wiki, and the derived visualizations. Important to note that the LLM writes and maintains all of the data of the wiki, I rarely touch it directly. I've played with a few Obsidian plugins to render and view data in other ways (e.g. Marp for slides).

Q&A:
Where things get interesting is that once your wiki is big enough (e.g. mine on some recent research is ~100 articles and ~400K words), you can ask your LLM agent all kinds of complex questions against the wiki, and it will go off, research the answers, etc. I thought I had to reach for fancy RAG, but the LLM has been pretty good about auto-maintaining index files and brief summaries of all the documents and it reads all the important related data fairly easily at this ~small scale.

Output:
Instead of getting answers in text/terminal, I like to have it render markdown files for me, or slide shows (Marp format), or matplotlib images, all of which I then view again in Obsidian. You can imagine many other visual output formats depending on the query. Often, I end up "filing" the outputs back into the wiki to enhance it for further queries. So my own explorations and queries always "add up" in the knowledge base.

Linting:
I've run some LLM "health checks" over the wiki to e.g. find inconsistent data, impute missing data (with web searchers), find interesting connections for new article candidates, etc., to incrementally clean up the wiki and enhance its overall data integrity. The LLMs are quite good at suggesting further questions to ask and look into.

Extra tools:
I find myself developing additional tools to process the data, e.g. I vibe coded a small and naive search engine over the wiki, which I both use directly (in a web ui), but more often I want to hand it off to an LLM via CLI as a tool for larger queries.

Further explorations:
As the repo grows, the natural desire is to also think about synthetic data generation + finetuning to have your LLM "know" the data in its weights instead of just context windows.

TLDR: raw data from a given number of sources is collected, then compiled by an LLM into a .md wiki, then operated on by various CLIs by the LLM to do Q&A and to incrementally enhance the wiki, and all of it viewable in Obsidian. You rarely ever write or edit the wiki manually, it's the domain of the LLM. I think there is room here for an incredible new product instead of a hacky collection of scripts.

[Jordi Hays @jordihays]

TBPN has been acquired by OpenAI

The world is changing quickly but TBPN will stay the same. Live every weekday just with a lot more resources.

Thank you to everyone that has been a part of this journey big or small. We are 17 months in and unironically just getting started.

[OpenAI @OpenAI]

Today, we closed our latest funding round with $122 billion in committed capital at an $852B post-money valuation.

The fastest way to expand AI’s benefits is to put useful intelligence in people’s hands early and let access compound globally.

This funding gives us resources to lead at scale. https://t.co/sY7YNUPSYO

[Yasser @yasser_elsaid_]

This is my playbook for bootstrapping an AI agent business to $9M ARR.

The most important thing is that you need something repeatable and scalable, something where if you do more of, you get more money. You need the equation where you can arbitrage every dollar you spend into more dollars on the other end.

Here is how you get there:

1. if you're in B2B, just do the B2B stuff.

self-serve is very hard to make work in B2B. it's so much easier to build a sales team, teach them the product, and let them sell it, instead of building a very intuitive platform and hoping people figure it out.

that's why all these bigger companies are mainly doing "book a demo with us." they charge customers a lot more because there's no public pricing, and they can set the product up for them. you cannot rely on a middle manager at a non-tech company to put in the effort to use your platform, even if it's extremely intuitive.

if you're bootstrapping, you can't hire a sales team on day one. so you need momentum from self-serve customers first. but the goal is to layer in sales as fast as possible, get on demo calls, set up the product for bigger customers, and invest in building an intuitive platform at the same time.

2. content is non-negotiable, even if you're sales-led.

good content gets you brand visibility and brand awareness, and that makes all the other channels work much more efficiently. paid ads work much better if people recognize your brand. if they click on your page and see content that people are engaging with, good quality content, it compounds everything.

here's what that looks like:

video: it depends on your ICP, but we all know video is hard to do, and that's a good thing because it makes the barrier to entry much higher. you can signal that you are a serious business if you do good quality video content.

be creative within video, but don't get too creative with the kinds of videos. the kinds of videos you should be doing are product videos and customer videos. that's it. you can be creative in telling your customers' story, you can be creative in launching a product, but don't do the stunt thing, the office content, the random skits. they can work, but you only do them after you do the things that you know will work.

hire a videographer in-house. agencies are so expensive (this is just a good rule of thumb).

text + personal brands: you need personal brands for everyone in the company. EGC (employee-generated content) needs to be a non-negotiable. everyone on the team posting at least twice a week.

3. warm outbound is the lowest-hanging fruit.

warm outbound = outbounding people who have already seen your product. people who interacted with your LinkedIn posts. people who visited your site but haven't signed up. people who created an account but never finished onboarding.

these people are the lowest-hanging fruit. email them, call them, put them in a sequence until they become customers. you can have very clear KPIs for your team on this.

4. cold outbound, if your ICP is big enough.

be good at writing cold emails and managing your own infrastructure. don't go through an agency. build a system where you can send emails profitably. if it works, send more. if that works, send more. scale it until it doesn't make sense to continue. also do this in-house if it's an important channel.

5. SEO and AEO are extremely important.

whenever I want to try a new product, I ask Claude. AI search is a non-negotiable channel now. you need to show up there.

that means a lot of Reddit, a lot of review websites, a lot of talking to blogs and backlinking sites to make sure they write what you want with the messaging you want.

6. expansion: be friends with your biggest customers.

get on a call with them. know them by name. they need to have your number. they need to be advocates for you. build community around the customer.

a lot of founders do not see their customers as friends or a community. they just see them as revenue. that's so bad. your customers need to enjoy spending time with you and talking with you.

7. pricing is the fastest lever.

you need to find a good sweet spot for packaging and pricing. incentivize people to spend more money and make sure it's a good deal for them. there's no shortcut, you talk to customers, see what they care about, see what they get a lot of value out of, and capture some of that value while making sure they're successful.

8. margins don't matter early on.

if you have a $10M ARR business but you spend $10M to run it, that's fine. you can always cut costs. revenue is the most important metric. it's easier to cut costs than to make more money, so in the beginning, focus on making more money.

That's how we built @chatbase to where it is today. Most of this will continue to scale with us as we go to 100M ARR.

[Yasser @yasser_elsaid_]

9M ARR 🥳

So happy!

@Chatbase is going to be a $100M ARR company.

Some days I feel it's inevitable, we're past the hardest part, it's almost too easy. Some days it feels too hard and I need a miracle.

Constantly moving between "I am a genius, how come no one is doing this" to "I don't know anything about anything".

Follow to watch the journey, you will never be this early.

[Uzenabor Nelson- 🌶️. @NelsonUzenabor]

@mehrab_build Check out https://t.co/LRKti0r5kk

Will appreciate it!

[ashe @ashebytes]

I have a theory that life meets you at your level of audacity

[Adams @Adams_Tech_AI]

Justine Musk on what it means to be married to a visionary like Elon Musk

Justine Musk is a Canadian author known as Elon Musk’s ex-wife and the mother of six of his children.

[Uzenabor Nelson- 🌶️. @NelsonUzenabor]

More messages shouldn’t mean more stress.

It should mean more opportunities.

That’s what AI agents are for.

Always-on.
Context-aware.
Closing while you sleep.

They turn conversations into qualified leads.