Brolaja

You don't need a perfect essay. All you need is an honest, specific, and a well-written one. So stop chasing perfect. Start chasing DONE.

PhD Students - Here are the 8 types of research gaps you must know. 1. 𝐊𝐧𝐨𝐰𝐥𝐞𝐝𝐠𝐞 𝐆𝐚𝐩 ↳ There is no or limited research on your topic. 2. 𝐄𝐯𝐢𝐝𝐞𝐧𝐜𝐞 𝐆𝐚𝐩 ↳ Your research contradicts existing research 3. 𝐌𝐞𝐭𝐡𝐨𝐝𝐨𝐥𝐨𝐠𝐢𝐜𝐚𝐥 𝐆𝐚𝐩 ↳ The existing research methods for your topic are insufficient. 4. 𝐄𝐦𝐩𝐢𝐫𝐢𝐜𝐚𝐥 𝐆𝐚𝐩 ↳ There is no or limited empirical data for your research topic. 5. 𝐏𝐫𝐚𝐜𝐭𝐢𝐜𝐚𝐥 𝐊𝐧𝐨𝐰𝐥𝐞𝐝𝐠𝐞 𝐆𝐚𝐩 ↳ There is a disconnect between theory and practice in your topic. 6. 𝐓𝐡𝐞𝐨𝐫𝐞𝐭𝐢𝐜𝐚𝐥 𝐆𝐚𝐩 ↳ Theoretical explanation for your topic is inadequate. 7. 𝐏𝐨𝐩𝐮𝐥𝐚𝐭𝐢𝐨𝐧 𝐆𝐚𝐩 ↳ Population is not fully or correctly represented in existing research. 8. 𝐃𝐚𝐭𝐚 𝐆𝐚𝐩 ↳ The existing data is insufficient to address the research question.

The big dilemma with teaching an "LLM course" is that it is really easy to get drawn into teaching the various technical things like efficiency tricks, attention variants, PPO vs GRPO, etc etc. But the real "meat" is not there, but in the data: data for pre-training, for mid-training, for SFT, for RL and for "reasoning", synthetic data, curated data, annotated data... cleaning, evaluating, improving, mixing, ... lots of stuff. but "data" is so much harder to teach: it is not "mathematic" or "algorithmic" like the technical things, and it is not clear what is the teachable thing there. it is also a lot less transparent than the technical topics, both because it is semi-secret, and also because it is also not appealing for publishing, for roughly the same reasons it is not appealing for teaching. so, what would you teach about data? what are the key lessons and insights one should know? any good papers or resources? good existing classes? blogs? hit me with what you have

#ThisWeekInUSNigeriaHistory in 2025, Nigerian chess master Tunde Onakoya made history by breaking the Guinness World Record for the longest chess marathon alongside American chess master Shawn Martinez in Times Square, New York. #DYK Tunde is a U.S. Government Exchange Program alumnus. He’s one of 14,000+ exchange alumni in Nigeria driving impact across U.S.–Nigeria life, culture, and collaboration. #Freedom250 Photo credit: Chess in Slums Africa

My name is Abdul Wahab. I am pleading with my Football Twitter (FT) family and everyone on this app to please RT. I am on the verge of losing a life-changing opportunity simply because of where I come from, and I have worked too hard to bottle it now. I grew up in extreme poverty in a small village called Kwabenantene in the Ahafo Region. I am the very first person in my family and my entire village to ever get a university education. My path was never easy. To survive undergrad, I literally had to drop out of classes mid-semester, pack my bags, and go back home to do heavy construction work just to raise enough money to survive the next term. I was laying bricks to pay for my textbooks. Despite having zero financial safety net, I poured my blood and sweat into my studies and graduated with First-Class Honours in Economics, ranking in the top 1% of my entire cohort of 435 students. But I didn’t just want to succeed alone; I went back to my village, gathered the youth, and started teaching and mentoring them. Because of that work, two students from my locality have now successfully made it into university too. I want to lift my people up. All this hard work finally paid off: I just received admission into the highly prestigious, integrated Masters/Ph.D. Economics program at Goethe University Frankfurt (GSEFM) in Germany! My tuition is officially 100% waived. However, I am facing a massive roadblock. Because the program is so competitive, I wasn't put directly into the fully-funded Ph.D. cohort right away. Instead, I was admitted to the MSQ track for Year 1. I will take the exact same rigorous classes alongside the Ph.D. students, and if I pass my Year 1 qualifying exams, I transition directly into the fully-funded Ph.D. I know I will pass those exams. The academics don't scare me. The finances do. My only problem right now is getting funding to cover my Year 1 living expenses in Frankfurt. If I can just survive these first 12 months, the Ph.D. funding takes over and my future is secured. To my FT family: you guys know my grind and my loyalty. If I get this lifeline, I promise I will proudly represent FT in every academic hall, conference, and research room I ever step into. I just need a chance to be in the room. I cannot let a lack of living expenses kill a dream I built with my bare hands. If you know of any scholarships, foundations, philanthropists, or anyone who can sponsor a village boy for just one year, PLEASE tag them or reach out. Please RT this until it reaches the right timeline. Let’s make this happen! 🌍📚🙏🏾

@J_activated @TheYorubaTimes I have a feeling you could get extra features like if the toilet is free, build in payment access......

@TheYorubaTimes This kind of public toilet directory can be simply built into Google Maps...it is supposed not to cost the Lagos State govt. much...this is how it is being done even in the developed world. Any junior IT student/graduate can set it up on Google Maps.

BREAKING 🇳🇬🧠: Lagos approves pilot proposal for geo mapped public toilet access app after Tokunbo Wahab meeting with UI graduates Adetokunbo Ogunnoiki and Ademola Gbadero, requests budget submission ahead of rollout under Cleaner Lagos initiative.

Hot take: RLHF isn't RL

First Nigerian to bring Patrice Evra to Oshodi underbridge to learn chess from the children and advocate for them🇳🇬❤️

A Stanford CS professor told his class something at the start of the semester that made half the students close their laptops. He said the skill that will separate the people who thrive in the next decade from the people who stall has almost nothing to do with coding. His name is Andrew Ng, and he has trained more machine learning engineers than almost anyone alive. Here is what he said, and why it changes how you should be learning right now. He said the bottleneck is no longer writing code. It is knowing which problems are worth solving in the first place. For thirty years, being a good engineer meant being able to build what someone else defined. In the world that is arriving, every engineer has infinite leverage to build almost anything, which means the person who picks the right thing to build now wins by orders of magnitude over the person who builds the wrong thing flawlessly. His framework for problem selection is deceptively simple. He calls it the three-question filter. The first question is whether the problem you are working on actually matters to someone who would pay for it or use it daily. Most students fail here. They work on projects that are interesting to them and nobody else, and then wonder why the portfolio produces no offers. The second question is whether the problem is still hard now that AI exists. If a single prompt to a hosted model solves it, the problem is no longer valuable to solve yourself. The interesting problems live in the gap between what AI can do alone and what it can do when combined with domain knowledge, careful system design, and data nobody else has access to. The third question is the one most people skip. Can you actually ship a working version in a week. Not a polished version. A crappy, embarrassing, actually-functional version. Ng said the number one predictor of which of his students ended up building something important was not talent. It was the willingness to ship something bad fast and then improve it in public. He said the students who kept tweaking in private for six months before showing anyone almost always produced worse final work than the students who shipped a broken version on week one and iterated based on real feedback. The people who are actually winning right now are not the ones with the best ideas. They are the ones who learned to pick problems that matter and ship solutions that barely work, before anyone else has even finished thinking about it.

🚨 BREAKING: NVIDIA proved backpropagation isn't the only way to build an AI. They trained billion-parameter models without a single gradient. Every AI you use today relies on backpropagation. It requires complex calculus, exploding memory, and massive GPU clusters. Meanwhile, an ancient, gradient-free method called Evolution Strategies (ES) was written off as impossible to scale. Until now. NVIDIA and Oxford just dropped EGGROLL. Instead of generating massive, full-rank matrices for every mutation, they split them into two tiny ones. The AI mutates. It tests. It keeps what works. Like biological evolution. But now, it does it with hundreds of thousands of parallel mutations at once. Throughput is now as fast as batched inference. They are pretraining models entirely from scratch using only simple integers. No backprop. No decimals. No gradients. We thought the future of AI required endless clusters of precision hardware. It turns out, we just needed to evolve.

Want to learn how to visualise and analyze spatial data in the social sciences? My #R #GIS courses are coming up in Apr-May 2026: Intro to using R as a GIS - No prior knowledge needed! Advanced R as a GIS - For those ready to dive deeper nickbearman.com/training-cours…

@ansem_edet Imagine a metro along the coastal highway

The coastal high way reduces travel time from Calabar- Lagos from 12-14hrs to about 6-7hrs assuming you’re driving safely On top of that you can get to Akwa Ibom in 50mins, Rivers in 2hrs, Bayelsa in 3, Delta state in 4, Edo in 5, Ondo in 6hrs, what does it do for the economies of the region and the country, realistic 8-15% GDP uplift in 15years

As an AI engineer. Please learn: - Python (deeply - it is still king in 2026) - Core ML/DL (transformers, attention, backprop, optimization, loss functions) - Frameworks (PyTorch 2.x / JAX - pick one deeply; understand both eventually) - Model architectures (LLMs, diffusion, multimodal, MoE basics) - Fine-tuning & PEFT (LoRA/QLoRA, adapters, full fine-tune trade-offs) - Data pipelines (cleaning, augmentation, tokenization, dataloaders, streaming) - Evaluation (benchmarks, perplexity, BLEU/ROUGE/BERTScore, human eval, RAGAS) - Serving & inference (vLLM, TGI, TorchServe, ONNX, TensorRT, quantization) - Prompt engineering + RAG + agents + tool calling patterns - MLOps (tracking experiments, versioning models/data, monitoring drift)

Yup. The scary part isn't that NNs rediscover biology. It's that biology might have converged on the only statistically efficient solution nature could find either. If there's really only one good factorization of natural images (and natural language), then we're not choosing architectures anymore… we're just discovering the same Platonic computer science over and over.

The model was traversing a plateau in the loss landscape and finally hit a region with steeper gradients. Or your learning rate scheduler kicked in. Or batch shuffling finally surfaced underrepresented examples. The real answer is: it depends, and the interviewer wants to see how many possibilities you can reason through.

I left out an important part of this story. This MT Scholar is BLIND. Yes, you read that right. He cannot see. In fact, it took us months of fighting with GRE before he could write the GRE exam because there was no accommodation for blind test takers in Ghana. Finally, we were able to secure a testing environment that would make him perform to his potential. He did very well in his GRE. This success is years in the making. And now, all the top schools in the US are gracing him with fully funded offers. We have already said no to Michigan, Chicago and Duke. It's now down to Princeton or Berkeley. Oh, let's say his name: Congratulations to Donaldson Kwame Ellis Larbie!

A lot of times these special kids are stuck in normal classes where they can't receive the special attention they need early enough. This causes this sort of butterfly effect.

If our children wins Gold Medals in International STEM Olympiad Grand Finale, they will all be eligible for full scholarships in top universities in the world. Education can change a child’s life forever.

Today we tried the Maths Thug of War at Educare HQ. Education can be fun, engaging and exciting 😁