Trying to have some fun😃, we(@5hyguy) decided to build ourselves an intelligent mirror. Smart Mirror for Africa #JuJuMirror🥴 - screen touch interaction - integration with Alexa - YouTube integration - video call integration - control button @hackSultan @AremoQueenIdia

How to find a wife 😀

When building AI apps, the hardest part isn’t writing code , it’s getting everything to work together. From CUDA mismatches to framework conflicts, dependency hell in AI dev is real

Forget the 10-course "AI starter pack." Most beginners get overwhelmed. My single best recommendation for building practical AI skills: Google Skills. Why? You learn from the engineers who built the tools (TensorFlow, Gemini). It's focused, practical, and a lot of it is free.

Evaluating LLMs just got easier. DeepEval = pytest for LLM apps (⭐11k). ✅ 20+ ready-to-use metrics (G-Eval, RAGAS, bias, hallucination) 🔐 Red-team 40+ safety issues in 3 lines ⚙️ Works with LangChain, LlamaIndex, CrewAI & more 📊 CI/CD friendly 2 lines of code → real evals

Now: $0 Building. Failing. learning. Adding value. Staying consistent. Tomorrow: $10M

I know some of the live demo from Meta didn't go well but it is great to see how far they have gone and what they are trying.

We are all using the fake product

AI isn’t just about intelligence — it’s about value. This chart shows AI models ranked by capability vs. cost to run. The green zone = smartest per $ spent. Top-tier closed models? Powerful, but pricey. Which would you pick: brains 💭 or budget 💰? #AI #GenAI

🔧 Pro Tips - Use minimal reasoning for speed-sensitive tasks - Use high reasoning for deep exploration - Let GPT-5 meta-prompt itself to improve your prompts - Test & iterate prompting is an engineering skill now

5️⃣ Use tool preambles & updates Make GPT-5 explain its plan before acting. Benefits: - Better transparency - Fewer surprises in long workflows - Easier debugging of agent behavior

4️⃣ Structure your prompts GPT-5 thrives on clear, scoped, contradiction-free instructions. Examples: <instruction>Do X step-by-step</instruction> Avoid vague or conflicting rules they hurt performance more than in GPT-4.

3️⃣ Tune its autonomy (“agentic” calibration) Decide if GPT-5 should: - Act proactively without asking - Or wait for explicit instructions 📌 Define clear stop conditions in your prompt.

2️⃣ Control verbosity New verbosity parameter sets how much detail GPT-5 gives in final answers. - Low for short summaries - High for in-depth explanations or code reviews Can even set different verbosity per task.

1️⃣ Control its reasoning depth Use reasoning_effort: - Low → faster answers, less exploration - High → better for complex, multi-step tasks Pro tip: For most tasks, medium is enough.

Why GPT-5 is different - New parameters for reasoning & verbosity - More control over autonomy (“agentic” behavior) - Better at multi-step workflows - Highly sensitive to unclear or conflicting instructions

Prompting GPT-5 isn’t the same as GPT-4. If you use the old approach, you’re leaving performance on the table. Here’s how to really prompt to GPT-5 #GPT5 #PromptEngineering #AI #OpenAI

That screenshot wasn’t proof of AI incompetence. It was proof you shouldn’t expect human-like reasoning and calculator-like precision from a language model without giving it the right tools. #AI #ChatGPT #MachineLearning #LLM #DataScience

Benchmark the system (LLM + tools), not the LLM alone. The magic is in combining language reasoning with AI agents

Decimals like 5.9 and 5.11 are tricky. Borrowing across decimal places isn’t symbolic computation, it’s basically like doing mental math while distracted. Easy for a calculator, error-prone for a pattern predictor.

LLMs aren’t calculators. LLM predict the next word or token based on training data. If they don’t call a math/Python tool(AI Agent), they’re doing “math”