Sid 🚀
@siddddhesh
🧑💻AI Engineer @IBM | ✍️ Tech Writer | Passionate about teaching & learning | Exploring AI, productivity, and writing 🌱 one insight at a time.
قد يعجبك
Agentic AI isn’t sci-fi, it’s here. Think: - Auto-ticket resolution - Self-healing infrastructure - Autonomous data enrichment The leap? Moving from reactive to proactive AI systems.
Some of my best AI projects weren’t just cod, they were collaboration Pairing with design, ops, and domain experts turned ‘good models’ into impactful products
Not all charts belong in Matplotlib. 📊 Plotly → interactive dashboards 📊 Seaborn → quick EDA visuals 📊 Altair → declarative charting Good visuals = better decisions
Slow Python code? Profile before you optimize import cProfile cProfile .run('my_function()') Guessing the bottleneck is slower than finding it
Data Scientists should learn systems design. Not to replace engineers, but to build AI that survives prod.
Writing tech blogs = explaining to your past self. - Keep intros short - Show working code early - Wrap with ‘next steps’ Your reader came for solutions, not suspense.
If your experiment isn't reproducible, it didn't happen. 💡 Use fixed random seeds 💡 Version control datasets 💡 Log env + library versions ML experiments = science, not art.
Feature engineering > fancy models. 90% of ML lift comes from: - Scaling numeric vars correctly - Encoding categories wisely - Creating domain-specific ratios The best Kaggle winners? Feature wizards. 🪄
Follow these to upgrade your timeline: Markets: @QuasarMarkets Finance: @qmbigbeat Investing: @_parrotfinance PE: @Nick4Pillars Coaching: @WealthAthlete Attorney: @john_jacob Business: @theonenicka Mindset: @MattHunterCoach Leadership: @leadwithchad
LLMs are expensive Not just in $$: - Latency (affects UX) - Carbon footprint - Prompt engineering overhead Smaller + smarter models might beat ‘bigger’ in real-world apps.
Let’s do a Data Science challenge: Given a dataset with 1M rows + 50 cols, find the 3 fastest ways to calculate median of col X in Python. Post your code ⬇️
Data Scientists, your go-to notebook tool? 📊 1️⃣ JupyterLab 2️⃣ VSCode + Jupyter Ext 3️⃣ Deepnote 4️⃣ Other (comment below!)
Writing docs for developers? Think API-first: - Start with usage examples - Show common pitfalls - Keep sentences under 20 words Docs are a dev tool, not a novel
Reading Research papers ≠ skimming formulas Breakdown method: 1️⃣ Skim intro & abstract 2️⃣ Identify problem & baseline 3️⃣ Understand method section 4️⃣ Jump to results & limitations Turn 15 pages → 15 mins
Top AI Papers of The Week (August 4-10): - CoAct-1 - ReaGAN - Agentic Web - Seed Diffusion - Efficient Agents - A Taxonomy of Hallucinations - Unified Retrieval Agent for AI Search Read on for more:
LLMs are moving from 'general chatbots' → domain-specialized copilots. Why? • Cost optimization • Accuracy in niche vocab • Easier eval metrics Enterprise AI in 2025 = smaller, smarter, task-focused models.
If you're not reading regularly, how can you expect to write well? You don't have to constantly be *writing* to improve. Reading is another form of practice; sort of like reviewing game tapes. You learn from studying others' approaches.
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