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When building neural networks, one of the first questions you should ask is: How many trainable parameters does my model have? The number of parameters determines: Model complexity Memory usage Training speed Risk of overfitting In this article, we'll learn how to calculate the number of parameters manually and verify the results using TensorFlow 2.x (Keras). Parameters are the values that the neural network learns during training. There are two types: Weights Biases Every neuron has: One weight
Every performance review follows the same pattern for me. For months, I'm busy delivering work, closing tickets, helping teammates, solving problems, and moving on to the next thing. Then review season arrives. Suddenly I'm searching through: • Jira tickets trying to remember what I actually achieved. The frustrating part is that the work happened. The evidence exists. It's just scattered everywhere. I've spoken to engineers, consultants, analysts and project managers who seem to face the same p
"Agents can act autonomously" is usually a claim in a pitch deck. I wanted to see what happens if you take it literally and build something that structurally requires it: an NFT mint with no wallet-connect button, no form, no button at all — a mint that can only be completed by code. The result is BLINK, 5,555 pixel sigils on Robinhood Chain, and this post is about the three design problems that fell out of committing to that premise. The first idea that occurs to everyone is a puzzle. Make the
Terminal coding agents like Claude Code, Codex, and OpenCode can now each run in their own isolated , orchestrated from , a tmux-style manager that runs them side by side in panes. Nothing an agent runs or edits touches your machine.Vercel SandboxHerdr When you start an agent, the plugin uploads your project files into a new Sandbox, installs the agent there, and opens a pane connected to it. The pane works like a local terminal; you type to the agent and watch it work. But it lives in the Sand
Announcing runtime instances in Amazon Bedrock AgentCore—persistent, managed EC2 infrastructure for production AI agents with multi-agent collaboration, GPU support, and sessions lasting up to 14 days.
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You've probably gotten an email that was supposed to feel personal, but obviously wasn't. Something about it just felt off. Automated. And somewhere in the back of your mind, you wondered: who approved this? Honestly? The answer is probably no one. Organizations are leaning harder on AI agents to actually do work. Agents are making database queries, writing letters, accessing email lists, but where does the access end? Without solid guardrails, tone-deaf emails are no longer an embarrassing edge
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