Kweku Bolo

@jeffasante 5 biiiillion

HTML and css too how much ago pay?

@djsliming “Let’s be logic” 🤡

So you’re telling me to be financially stable to date a broke lady ? And they come pre-fucked as well ??? 🤣🤣🤣🤣 Chale double it and give it to the next person , I’m not interested 🙏🏾

@MikelAsamoah Angela no kraa )gyee s3n?

Siafoɔ as I dey grind Angela Yu na where dem dey?

@jeffasante @niicommey01 @jjb_official The person e build am no be certified yet

@niicommey01 @jjb_official lol

You are using Lorem Ipsum on your website, and you're trying to charge me 20k for "license"? A whole government website. Supposed ICT Authority. Scrap the whole organisation. What a flipping joke.

@onua_zionfelix @EOCOghana what is he saying?

I Paid $13000 A Month As Rent At Kempinski , $18000 Monthly At Stanbic Height For 10 Yrs - Savile Row Boss, Nana Sarfo Shares Story

@MikelAsamoah These people wey NITA for certify

Do you know Andela? What industry level devops again do you want to see? Chale, e do aa you people just want to chat ong? Mmtthheeww

My 30+ observations on the greatest opportunities in AI agents right now: And some ideas that are keeping me up at night. 1. The new buyer on the internet is an AI agent. Imagine billions of new customers showing up with money to spend but they only shop via MCP. That's what's happening. No MCP server means you're invisible to the fastest growing buyer on the internet. 2. Every franchise system in America (30,000+) needs an agent layer and none of them have one. One founder per franchise vertical. That's 30,000 businesses waiting. 3. Everyone said "distribution is the only moat" a year ago. Now I'd add that the only moat is distribution plus memory. The company that has your audience AND your agent's accumulated context is impossible to leave. 4. Consumer mobile is more interesting than it's been since 2012. Apps can finally DO things for you instead of showing you things. The next wave of $100M apps are being built right now. 5. The most interesting startup nobody has built is an agent marketplace where you rent access to someone else's trained agent. A recruiter spent 6 months training a sourcing agent on healthcare hiring. That agent is worth renting to every other healthcare recruiter on earth. The agent itself becomes the product. 6. A sorta strange phenomenon that's happening right now is agents are developing preferences. Give the same agent the same task 100 times and it starts developing patterns in how it approaches it. Nobody is studying this yet. But the agents that develop good patterns are worth more than the ones that don't. That's a new kind of asset. 7. Dead internet theory is about to become dead SaaS theory. Half the apps you use will quietly replace their support team, their onboarding team, and their content team with agents. You won't notice for months. Then you'll realize you haven't talked to a human at that company in a year. 8. The most valuable data in the world right now is sitting in the support tickets of small or mid tier SaaS companies. Every ticket is a customer telling you exactly what to build next. Mine this. 9. The most interesting pricing problem nobody has solved is how do you price a product when your costs change every time OpenAI or Anthropic updates their model pricing? Your margins can swing 40% overnight based on a decision made in San Francisco. The company that builds dynamic pricing infrastructure for agent-based businesses solves a problem every AI company has. 10. The best AI products feel like they're reading your mind. The worst ones feel like filling out a form with extra steps. 11. An interesting arbitrage I've noticed lately is hiring a human VA for $20/hour to supervise an AI agent that does $200/hour work. The human just checks the output. 12. The managed AI agent business is becoming the new agency model. $5k/month per client. You build it, run it, maintain it. The client gets a digital employee they never have to think about. This will be a $50 B+ category. 13. The first "shadow agent" scandals are about to drop. Employees running personal agents on company infrastructure without telling anyone. Using company API keys. Agents accessing internal docs. IT departments have little visibility into this right now. Lots of opportunity to build companies here. Definitely a painkiller not a vitamin type of business. 14. Right now there are probably millions of agents running on autopilot that their creators forgot about. Still burning tokens. Still sending emails. Still scraping websites. Still costing money. The "find and kill your zombie agents" tool is a product that writes itself. 15. Companies are starting to hire based on someone's agent portfolio instead of their resume. "Show me 3 agents you built that are running right now." It's REALLY early but it's starting. 16. Your Slack archive is a product. Every company's internal Slack has thousands of messages explaining how they actually do things. The company that lets you point an agent at your Slack history and auto-generate SOPs and agents from it will be enormous. 17. We're watching the cost of intelligence fall faster than the cost of distribution. Which means distribution is now the expensive thing. 18. The most underrated asset a human can have in 2026: the ability to sit in a room with another human, make eye contact, and have a real conversation. As AI handles more of the transactional stuff, the humans who can do the relational stuff become disproportionately valuable. The soft skills people used to dismiss as fluffy are becoming the hard skills. The hard skills people spent decades acquiring are becoming the soft ones. 19. There are MANY huge companies to be built around the fact that most people's agents are running on their personal laptops which they also use to browse the internet, check email, and download random files. The attack surface is enormous. One compromised Chrome extension and your agent's API keys, customer data, and workflows are exposed. 20. There's a new type of burnout forming that doesn't have a name. It's not from working too hard. It's from context switching between human work and agent work 50 times a day. Reviewing agent output, correcting it, approving it, reviewing again. The mental load of supervising agents is different from the mental load of doing the work yourself. Some founders are telling me they were less tired when they did everything manually because at least the cognitive pattern was consistent. 21. The cheapest form of market research: search "[your industry] spreadsheet template" on Google. Whatever people are tracking manually is your product. 22. Half the YC companies pivoted within 8 weeks of demo day. Not because they failed. Because agents let them test 5 ideas in the time it used to take to test one. The concept of "committing to an idea" is dissolving. Serial pivoting is becoming the default because 1) AI lets you move fast 2) the world is moving fast. 23. The loneliest job in tech right now is being the only person at your company who understands what the agents are doing. You can't explain it to your boss. You can't hand it off to a colleague. If you leave, everything breaks. You've become a single point of failure for an entire automated system. That person needs a title, a team, and a backup plan. Most companies haven't figured this out yet. 24. Your browser history is the most valuable training data you own and you're giving it away for free. Every site you visit, every product you research, every competitor you study, every pricing page you screenshot. That behavioral data, structured and fed to an agent, would make it understand your business better than any onboarding call. The company that lets you turn your browser history into agent context builds something nobody can replicate. 25. Everyone is building AI wrappers. Nobody is building AI unwrappers. The tool that takes an AI-generated document and tells you which parts a human wrote and which parts were generated. 26. Stripe just became the most important company in the agent economy and they barely had to do anything. Every agent that sells something needs Stripe. Every agent that buys something needs Stripe. They're the payment rail for the entire agentic internet by default. 27. The most undervalued API in the world right now is the US Postal Service address verification API. It's practically free. Every local business lead gen agent needs it. Every real estate agent needs it. Every direct mail agent needs it. Boring government infrastructure is quietly becoming the backbone of agent-native businesses. 28. The concept of "business hours" is for humans. Your agent closed a deal in Tokyo at 3am, processed the payment, sent the onboarding email, and updated the CRM before your alarm went off. 29. What happens when agents start recommending other agents? Your research agent finds that a competitor's sales agent is better and suggests you switch. Agent referral networks are forming organically. The first agent affiliate program is probably 6 months away. 30. Cal dotcom closed their source code. That's the canary. When open source companies start closing up, it means agents were cloning their product too easily. Every open source company is quietly asking the same question right now. 31. "AI for pet groomers" sounds like a joke and that's exactly why it will work. 150,000 of them in America. Zero tech. All scheduling by phone or IG DMs. The joke ideas always win. 32. The thing that will seem most obvious in hindsight: we spent 2025-2026 arguing about which model is best while the entire value was in the orchestration layer. The model is the CPU. Nobody buys a computer based on the CPU anymore. They buy it based on what they can do with it. Makes so much sense in hindsight. What else will be obvious in hindsight? I'll share more notes soon. I can't sleep with all that's going on. Maybe you too. What an incredible time to be building.

@kwasiofori_ Don’t brand and see😂

My cousin Wale joined a dating app in Pretoria and matched with a woman who said her name was Thandi. Her profile said she loved hiking, jazz, and deep conversations about the meaning of life. Wale said he also loved all 3 things. He had never hiked in his life. The first date was at a botanical garden. Wale wore hiking boots he had bought that morning. The sales assistant had asked if he was a serious hiker. Wale said he was a serious romantic. The assistant sold him socks too. Thandi arrived in sandals and a sundress. She looked at his boots. She asked if he was planning to climb a mountain. Wale said the mountain was metaphorical. Thandi said metaphors were for men who couldn't commit to actual terrain. They argued about hiking for 20 minutes. Then about jazz. Thandi said her favourite artist was Hugh Masekela. Wale said he also loved Hugh Masekela. Thandi asked him to name one album. Wale said The Best of Hugh Masekela. She stared at him. He stared back. Then she laughed so hard a security guard came over to check if she was okay. They have been married for 4 years. Wale now owns 3 pairs of hiking boots and can name every Masekela album. He still cannot hike. Thandi says the boots are a monument to the best lie she ever caught. The meaning of life, they have discovered, is not a deep conversation. It is watching someone fail at pretending and deciding they are worth the mess. Love is a lie you both agree to laugh about forever.

@excusewilliam @spongethagod )ny3 b3yifuor?

Regardless of how minimalist the wedding was, it's curious that your immediate reaction to two people taking a leap of faith is to imagine its potential failure.

AI has stopped being a feature and started being the foundation. We're excited about a new wave of startups rebuilding software, services, and silicon— and pushing AI into the physical world. ycombinator.com/rfs

@M00NC4KE_ You make used to cocomelon eh

chale are they afraid of color? what is this ugly shit?

@jeffasante @obsrvate Ibi question I ask ooo

@beezy_py @obsrvate capo something people go use days say am you just throw ei

Megan Thee Stallion went live and revealed she is going to SUE her ex Klay Thompson because he promised to send her $300k a month even if they broke up and now he isn't sending her any money 👀🤔 "Yall know it cost to be with me he signed the contract" "I need my $300k or else"

@jeffasante There’s always a pussy element to wars

🤣🤣

@jeffasante Aku freeze🤣🤣

E keep they response 🤣

@jeffasante @_lawslaw 🤣🤣🤣🤣🤣🤣 it’s self conscious

@_lawslaw @beezy_py

You spoke English to the AI and it responded in GA without you commanding it to speak Ga? Like the AI took its own path?

There are Ghanaian engineers at NASA. Ghanaian surgeons running hospital departments in London. Ghanaian economists at the IMF and World Bank, some of them administering the very programs that have failed their home country. Ghanaian mathematicians. Ghanaian architects. Ghanaian writers who have won international literary prizes. Ghanaian tech entrepreneurs building companies that work. When given access to resources, institutions, and an enabling environment, Ghanaians perform at the highest levels of every field. This is not an argument that individual talent solves structural problems. It is a refutation of the claim that the problem is the people. The problem is never the people. The people are everywhere. The talent is everywhere. The ambition is everywhere. The capacity is everywhere. What is not everywhere is the policy space, the institutional support, the geopolitical backing, the market access, and the freedom from externally imposed economic programs that systematically prevent the conversion of human capacity into collective industrial development. The difference between a Ghanaian running a department at a London hospital and Ghana having a functioning public health system is not the Ghanaian. It is everything around the Ghanaian.

How do I transition from Data engineer to AI engineer? Here's a structured 6-step transition path: You already have the hardest part: engineering fundamentals + production mindset. The goal is to add the AI layer on top. Step 1: Work inside an AI-flavored data pipeline - Ingestion + cleaning for RAG / analytics - Chunking, metadata, indexing - Observability and data quality checks Step 2: Learn how model providers work - APIs, limits, retries, rate limits - Latency and cost trade-offs - Privacy and data handling constraints Step 3: Prompting as an engineering discipline - Prompt templates - Versioning + change logs - Structured outputs (JSON schemas) Step 4: Evaluation for generative systems - Golden sets - Automated checks (format, factuality signals, regressions) - Human review loops when it matters Step 5: Tool integration + agentic flows - Function/tool calling - Guardrails (allowed tools, timeouts, fallbacks) - Tracing what the agent did and why Step 6: Build 1-2 small projects end-to-end Examples: - RAG assistant over internal docs - Ticket triage bot with tool calls + evals For an experienced data engineer, this is usually not a multi-year shift. With focused effort + hands-on work, ~3-4 months can be enough to become interview-ready. If you're making this transition, what part feels most unclear: evals, prompting, or agents?

@SIKAOFFICIAL1 If your pastor is this fat, you better leave that church.

“Any man who gives GH¢10/GH¢20 offering is not ready for marriage” —Ghanaian man of God tells his female congregants, claiming that any man who is incapable of giving in abundance in the house of God is incapable of taking care of them. [🎥: rev.takal]