I made a web interface to calibrate the servos on the Kame32 robot. Now tuning each servo is way easier, definitely better than hardcoding and reflashing every time!

🧩 New module live: chainlist-mcp is now public on GitHub. Part of the PILSO OS suite, chainlist-mcp provides dynamic access to verified chain metadata and RPC endpoints via Chainlist — composable and agent-ready. No config. No hardcoding. Just modular chain discovery for any

Back to the grind.Hardcoding text should be illegal

Hardcoding Integrity: Ukraine’s Anti-Corruption War Illuminates Ethics in Industrial Automation Ukraine’s bold initiative—a law proposing polygraph tests for officials with Russian familial ties—is more than geopolitical strategy. Read More -

You don’t send transactions on @anoma .You express an intent. 🎴 “I want to swap this for that, privately, only if conditions are met.”. No intermediaries. No hardcoding paths. 💭 It’s coordination made native. Built from scratch by @anoma.

🔐 Tired of hardcoding `if (user.role === "admin")`?.Meet **Decide**, a role & condition-based permission engine for Rust+JS. Ask can(), get your answer. Clean. Fast. No BS. it's time to Decide. 🧰 📦 NPM:

Figma Make x @supabase .→ Add login flows.→ Save user data.→ Store uploaded images, files, and more.→ Call private APIs without hardcoding secrets

Devs, breathe easy. Anoma handles the mess ,you build the magic. No more hardcoding bridges or chasing liquidity. Just focus on what users want,the Intent Machine does the rest. Build once. Tap everything. This is devflow unlocked.

Typed routes from Rails? No way!. Learn how to set up typed, auto-updating route helpers in your Inertia.js Rails app using the JS Routes gem. This video walks you through installation, configuration, and usage, so you can stop hardcoding URLs and keep your frontend routes in

Enter Anoma's Intent-Centric Architecture. Instead of predefined transactions, users publish intents — declarative expressions of what they want. The system discovers how to fulfill them through a decentralized solver network. No more hardcoding logic. No more static contracts.

What if coordination worked like human conversations?. Instead of rigid apps…you express intent.Instead of devs hardcoding every interaction…you just state what you want. That’s what @anoma is enabling: expressive, intent-based coordination across the chain. InfoFi

⚠️ New threat detected: what-is-amazon-free-card885@1.0.2 ⚠️. The script seems to be part of a spamming operation and uses bad security practices, such as hardcoding paths and credentials. Therefore, it's a potential security risk.

Anoma for Developers: Building Intent-Centric dApps 🧱✨. Tired of building dApps limited by blockchains’ rigid architecture?.@anoma introduces a whole new paradigm: intent-centric programming. Instead of hardcoding user flows and contract logic, developers define the what—users

🧩 New module live: erc20-mcp is now public on GitHub. Part of the PILSO OS suite, erc20-mcp enables token-aware agents — with native access to token metadata, balances, and allowances across supported chains. No ABI injection. No hardcoding. Just secure token inference for

What makes anoma unique in Web3?. 🌐 Intent-centric architecture: Apps match user intents without hardcoding pathways.🔒 Privacy by design: Zero-knowledge proofs protect data and transactions.⚡ Multi-chain settlement: Enables atomic swaps across different chains. 👉 Example:

What if smart contracts were never the smartest part of crypto?. @anoma flips the whole paradigm:.Your intent is the code. No manual gas configs. No rigid hardcoding. No need to spell out how — just what. → You declare a goal.→ Solvers coordinate the outcome

Cursor: hardcodes the ID.Cursor, 4 seconds later:.“Wait, that’s hardcoding. Let me fix it properly.”. Bro you literally just did that.

AI → ML → DL → GenAI → GPT — Simplified. AI (Artificial Intelligence).The broad goal: make machines think like humans. ML (Machine Learning).A subset of AI — machines learn from data (no hardcoding). DL (Deep Learning).A subset of ML — uses neural networks (like the brain)

McGrew explains that the breakthrough with LLMs is giving robots a cheap, flexible language interface. Instead of hardcoding every behavior, you can now just describe tasks—and thanks to powerful vision models, robots can interpret and act on those instructions far more