DataEdge Academy

DataEdge Academy We teach people (technical and non-technical) how to create, build, earn, and protect their identity in the Age of AI

26/09/2026

🤖 Your next robot could run an AI "brain" and a real-time "body" on a single board, with no cloud needed.

This week Arduino opened pre-orders for the VENTUNO Q, its first board built specifically to control a robot. It has a "dual-brain" design. A Qualcomm Dragonwing processor with up to 40 TOPS of AI power handles the thinking: vision, speech, even small language models running locally. A separate STM32 real-time microcontroller handles the reflexes: motors, sensors and timing-critical control. It comes with Ubuntu preinstalled, and Arduino App Lab lets you snap together ready-made AI models like building blocks. You can also bring your own model from Hugging Face. Qualcomm also announced it's acquiring PickNik, the team behind the open-source MoveIt robot-arm framework, and plans to connect it to this board.

Why it matters: building a smart robot used to need expensive hardware and several specialists. Tools like this let a student, teacher or solo maker go from idea to working prototype much faster.

🛠️ Try this weekend, even without the new board: build your own "dual-brain" robot with gear you already have. Run a small AI model on your laptop (Ollama or llama.cpp works well). Have it turn a spoken or typed instruction into a one-word command like LEFT, RIGHT or STOP. Send that word over USB serial to a regular Arduino Uno that moves a servo or lights an LED. The laptop does the thinking and the Arduino does the moving, which is the same design idea on a smaller scale.

💬 If your robot had an AI brain that worked offline, what's the first job you'd give it? Tell us in the comments. The most creative idea gets featured in next week's Community Challenge!

21/09/2026

Anthropic just revealed something wild: Claude is now doing 26% of its own AI model R&D — up from 0% just seven months ago. The machine is becoming its own mentor.

At Anthropic, roughly 30,000 Claude agents now run concurrently, and over 90% of the company's R&D work happens with Claude as a collaborator or lead. That's not a sci-fi headline — it's a live case study in "the mentor becomes the mentee becomes the mentor." What's striking for us isn't the automation, it's the feedback loop: rapid iteration, tight feedback, and a willingness to hand real responsibility to a still-learning system. That's the exact loop that makes a human mentee grow fast too. If you want to accelerate your own AI skills, borrow the same structure.

Hands-on takeaway: Pick one repetitive task you do weekly (writing summaries, cleaning data, drafting outlines) and hand it to an AI tool for a full week — but review every output and give it corrective feedback each time, like you'd coach a junior teammate. Track how fast the outputs improve. That's the mentorship loop in miniature.

Have you ever "mentored" an AI tool by correcting it repeatedly until it nailed your style? Drop your experience below — we want to hear it.

Your AI agent can be incredibly intelligent and still have one major problem:**It forgets.**You spend weeks building an ...
19/09/2026

Your AI agent can be incredibly intelligent and still have one major problem:

**It forgets.**

You spend weeks building an agent. It solves a complex problem brilliantly. Then the user asks a follow-up question and suddenly the agent has no idea who they are, what they discussed or what happened previously.

Why?

Because **LLMs are stateless by default.**

Simply sending the entire conversation history back to the model isn't a reliable long-term memory strategy. As conversations grow, you run into context limits, increasing token costs, latency and problems retrieving important information from long prompts.

So how do you build real memory?

In my latest Tony Tech Insights video, I break down:

🧠 Episodic memory
🧠 Semantic memory
🧠 Procedural memory
🔄 The extract-and-inject pipeline
🔎 Semantic retrieval
💾 Memory stores such as Postgres, Mem0 and Zep
⚙️ Integrating memory into an agent's control flow
🚀 How procedural memory can help agents avoid repeating failed strategies

The fundamental idea is:

**Memory is engineered, not given.**

If you're building AI agents or working with LLM applications, understanding memory architecture is becoming increasingly important.

Watch the video and let me know what approach you're using to give your AI agents long-term memory.

https://youtu.be/nso7aNgOooo

16/09/2026

Amazon just bought the company behind DuckDB. Here's why that's actually good news for you.

Last month AWS agreed to acquire DuckLabs, the Amsterdam startup behind DuckDB — the free, open-source database that lets you run fast SQL analytics on millions of rows right on your own laptop, no cluster, no warehouse, no cloud bill. The creators are staying on to keep building it, and DuckDB itself stays open-source under the DuckDB Foundation. For a data scientist, this is a signal: "big data" tooling is getting simpler, not more complicated, and the skills worth building right now are the ones that let you do more with less infrastructure.

Hands-on takeaway: if you've never tried it, install DuckDB today (one line: pip install duckdb) and run a SQL query directly against a CSV or Parquet file — no database setup required. It's the fastest way to feel what "in-process analytics" actually means.

Have you used DuckDB yet, or are you still reaching for a full data warehouse out of habit? Drop your experience (or your favorite lightweight data tool) in the comments — I'll share mine.

15/09/2026

Your first "team lead" job might not have a single human report to you.

GitHub just rolled out multi-agent Copilot Workspace sessions — instead of one AI helping you code, you can now run separate specialized agents for implementation, testing, and documentation at the same time, each working in its own isolated git branch so they don't step on each other, all coordinating off the same shared project context. That's a real shift for anyone starting out in tech: the skill isn't just "write the code" anymore, it's "direct the agents, review their work, and stitch it together." Apprentices who learn to orchestrate AI teammates now will have a serious head start.

Hands-on this week: pick a small side project (even a to-do app) and try splitting the work — have one AI session build a feature, a second write tests for it, and you play project lead reviewing and merging both. Notice where the handoffs get messy. That friction is exactly what you need to practice managing.

Have you directed more than one AI agent on the same project yet? Drop your experience (or your confusion) in the comments — let's troubleshoot it together.

14/09/2026

Sam Altman just told OpenAI's own employees something most builders never say out loud: "we're open to slowing down."

This week, OpenAI confirmed it's pushing its IPO into 2027 and says it's willing to pause training runs on its most capable models to give safety and alignment work more time to catch up. This is coming from the company racing hardest at the frontier of AI. That's not hesitation, that's discipline. The best mentors I've had all shared one trait: they knew the difference between moving fast and moving recklessly, and they weren't afraid to hit pause when the stakes went up.

Here's the mentor lesson for anyone building with AI right now: speed is not the goal, judgment is. Before you ship your next AI-powered project, tool, or automation, build in a deliberate "pause point" — a moment where you stop and ask "what could this get wrong, and who does it affect?"

Try this today: take one AI workflow you're currently building or using, and write down one failure mode you haven't tested for yet. That's your next hands-on lesson.

What's one time slowing down actually made your work better? Drop it in the comments — let's build a thread of real lessons for each other.

12/09/2026

A humanoid robot just ran the 100m in 8.64 seconds. That's faster than Usain Bolt's world record. 🤖

At the 2nd World Humanoid Robot Games in Beijing, X-Humanoid's "Tiangong Ultra" broke its own sprint record three times in five days — capping the games at 8.64 seconds, well under Bolt's human mark of 9.58s. It's a headline-grabbing number, but the real story for us is underneath the hood: that speed comes from serious advances in bipedal control — reinforcement-learning-trained gaits, faster actuators, and split-second balance correction, the same core tech that's now showing up in warehouse and delivery robots. This is exactly why "robotics" isn't just industrial arms anymore — it's rapidly becoming a full-body AI problem.

Hands-on this week: if you want a feel for what's actually happening under a robot's feet, spend 20 minutes with a free bipedal locomotion sim (search "OpenAI Gym Humanoid" or try MuJoCo's humanoid walker demo) and watch how a walking policy gets trained through trial and error. You'll see the same reward-shaping and balance logic that's powering robots like Tiangong.

Do you think humanoid robots hit the mainstream (retail, delivery, home) before 2030 — or is this still mostly a stage demo? Drop your take in the comments 👇

DataEdge Academy - September 2026 Cohort is OPEN 🚀This one's different: over 12 weeks you'll use Agentic AI (Claude, Cod...
11/09/2026

DataEdge Academy - September 2026 Cohort is OPEN 🚀

This one's different: over 12 weeks you'll use Agentic AI (Claude, Codex, Antigravity) to go from idea to a real, working product in your own niche, then take it to market and pitch it to real clients.

By the end you'll have: ✅ A working AI agent, built by you ✅ A validated niche + prototype ✅ A live MVP ✅ Your first client outreach sent

📅 Starts: Saturday, 19 September 2026 🕚 11am (UK/Nigeria) · 1pm (EAT), every Saturday 💻 Online & interactive 👨🏾‍🏫 Instructor: Dr. Onoja

If you or someone you know has been wanting to build something with AI, this is the cohort.

Register / join here 👇 https://chat.whatsapp.com/KzETFYyqSLuAnp9ubS9JSl?mode=gi_t

Please feel free to forward to friends & family! 🙏

**LangGraph vs CrewAI vs Claude Agent SDK — which one should you actually use? 🤔**There are more AI agent frameworks app...
11/09/2026

**LangGraph vs CrewAI vs Claude Agent SDK — which one should you actually use? 🤔**

There are more AI agent frameworks appearing every week, and it is becoming dangerously easy to choose one based on hype, tutorials or feature lists.

But what happens when you take these frameworks into a real production workflow?

In my latest explainer, I compare **LangGraph, CrewAI and the Claude Agent SDK** using a practical research → draft → fact-check workflow.

We look at:

🔹 Architecture and orchestration
🔹 Development speed
🔹 Production complexity
🔹 Auditability
🔹 Token consumption
🔹 Scalability
🔹 When each framework makes sense

The interesting part? There isn't a universal winner.

The right framework depends on what you're building, your budget, your scale and how much control you need over the system.

**Which would you choose: LangGraph, CrewAI or Claude Agent SDK?**

Watch the full video and let me know your choice 👇

https://youtu.be/rOlYiu4w8ew

09/09/2026

Google just gave a masterclass in what "better data" actually means.

On September 3, Google DeepMind rolled out WeatherNext 3 — a global weather model that's up to 50% more accurate on precipitation forecasts and forecasts at 5km resolution instead of 25km. The real breakthrough isn't a fancier neural net. It's the data pipeline: instead of waiting on lagging numerical weather outputs, WeatherNext 3 ingests real-time geostationary satellite data directly, so it can update hourly instead of every few hours. Same lesson applies whether you're forecasting rain or churn: better, fresher inputs often beat a bigger model.

Here's your hands-on takeaway: pick a project where you're using a stale or infrequent data source, and ask "what's the real-time version of this input?" Even swapping a daily batch feed for an hourly one can move your model's accuracy more than tuning hyperparameters will.

What's a project where fresher data would beat a fancier model? Drop it in the comments — let's compare notes.

5-km resolution, hourly updates, 50% better precipitation accuracy: that's what a good data pipeline buys you. 🌧️

Address

London

Website

Alerts

Be the first to know and let us send you an email when DataEdge Academy posts news and promotions. Your email address will not be used for any other purpose, and you can unsubscribe at any time.

Contact The School

Send a message to DataEdge Academy:

Shortcuts

Share

Category