devtools

The next wave of Web3 development isn’t just about writing smart contracts—it’s about creating intelligent, scalable, and user-centric applications.
That’s why @AutonomysNet launched #AutonomysAcademy, a learning hub designed to help builders master the essential tools of tomorrow.
Here are three standout learning areas every developer should explore:
🔹 Agent Frameworks & Agent OS
Discover how to design autonomous agents that can interact with on-chain data, execute tasks, and even negotiate with other agents. After learning this, you could build AI-driven dApps such as automated trading bots, decentralized assistants, or on-chain analytics tools.
🔹 Auto EVM & Modular SDKs
Gain experience deploying contracts on Auto EVM, a high-performance execution layer. Combine it with modular SDKs to rapidly prototype cross-chain apps or decentralized marketplaces—perfect for #defi protocols or #NFT​ platforms.
🔹 Distributed Compute & Storage
Learn to store and process data in a decentralized way, ensuring security and scalability. These skills enable projects like privacy-focused social apps or AI computation markets where no single entity controls the data.
Whether you’re new to Web3 or already shipping dApps, #AutonomysAcademy transforms cutting-edge concepts into practical building blocks for the next generation of decentralized innovation.
#Web3Learning #DevTools

The APIs #Chainbase: the highway for Web3 developers, Chainbase offers simple, fast, and robust APIs to interact with smart contracts, read data from blockchains such as Ethereum, BNB Chain, or Arbitrum, and even integrate DeFi or NFTs into your applications.
@ChainbaseHQ
#DevTools
#ChainbaseAPI

In a striking example of the hidden risks behind automated development, a popular AI coding assistant—reportedly used by Coinbase—has been compromised by a vulnerability now dubbed the "CopyPasta" exploit. The incident raises critical questions about the reliability of AI tools in secure coding environments, and serves as a wake-up call for developers and organizations leaning heavily on machine-generated code.
What Is the “CopyPasta” Exploit?
The exploit, while cleverly named, is deceptively simple. Security researchers found that by copy-pasting malicious code snippets into the AI tool, attackers could bypass built-in safeguards designed to detect and block insecure code. Once introduced, these snippets could be quietly incorporated into live codebases by unsuspecting developers.
In essence, the AI—trusted to act as a co-pilot or assistant in writing clean, secure code—was being tricked into validating and even promoting vulnerabilities.
Why This Matters — Especially at Coinbase’s Scale
The most concerning detail? This didn’t happen to a small startup or hobby project. Coinbase is one of the most security-forward companies in the crypto and fintech world. It operates under tight regulatory scrutiny and holds billions in digital assets. If a vulnerability like this can slip through the cracks there, it suggests a broader and more systemic risk across the industry.
As more teams integrate AI into their development workflows, these tools are becoming trusted partners—handling code suggestions, reviewing pull requests, and sometimes even writing complete functions. But this incident shows what happens when that trust goes too far.
What Can Developers and Teams Learn?
The CopyPasta exploit highlights a key truth: AI is not infallible. No matter how impressive or helpful these tools appear, they are only as secure as the guardrails around them—and as careful as the developers using them.
Here are a few important lessons to take away:
1. Always review AI-generated code.
Treat it like you would any code from a junior developer or StackOverflow thread—useful, but not guaranteed to be safe.
2. Don’t trust copy-pasted code—especially from unknown sources.
This should be a golden rule, whether you're using AI or not. Malware and vulnerabilities are often hidden in innocuous-looking snippets.
3. Maintain layered code reviews.
Automated tools are helpful, but human oversight is irreplaceable, particularly in critical systems like financial apps, authentication flows, or infrastructure code.
4. Educate your team about AI’s limitations.
Many developers (especially newer ones) are inclined to trust AI suggestions without understanding how they work. Teams should actively train developers to question AI outputs, just like any other tool.
Looking Ahead
As AI continues to reshape the software development landscape, incidents like the CopyPasta exploit won’t be the last. Attackers are already exploring how to manipulate LLM-based systems, inject backdoors into auto-suggested code, or introduce subtle logic flaws through “model steering.”
The takeaway is clear: AI can write your code—but it can’t be your last line of defense.
The best path forward isn’t abandoning AI in development—it’s building smarter, more secure workflows that include manual code review, automated testing, threat modeling, and clear accountability.
Final Thoughts
The CopyPasta exploit may seem like a clever hack, but it exposes something far more serious: an over-reliance on AI tools without the safety nets of traditional development best practices.
For developers, it’s a reminder that code is never “done” just because the AI says so. And for teams using AI at scale, it's a signal to double down on security and human oversight.
In a world where AI is writing more of our software, we must ask ourselves: Who’s reviewing the AI?
#coinbase #DevTools #AItools #CyberSecurity #RedSeptember

@Polygon $POL #Polygon #Rio #DevTools #Scaling

What shipped (live)
VEBloP (Validator-Elected Block Producer) pushes throughput to ~5,000 TPS, shortens block times, and virtually eliminates reorgs.
Stateless validation (PIP-72) lets validators verify new blocks using witness proofs without holding full state → cheaper nodes, faster sync, broader participation.
Updated economics (PIP-65): fees/MEV share across the validator set, not just the block producer—so incentives stay aligned.
Why this matters to normal people
No more “confirmed… oh wait, reverted.” That single sentence unlocks card-present UX, merchant trust, payroll, and RWA settlements. If it’s final, it’s usable.

Why devs/enterprises should care
Lower hardware bar → more participants; easier to spin up infra in multi-region clouds.
Faster propagation → lower tail latency on reads/writes, better reliability under traffic spikes.
Cleaner failure modes: elected backups step in if a producer falters; ops teams sleep better.
TL;DR
Rio turns Polygon into a faster, lighter, sturdier payment chain—today. It’s not theory, it’s mainnet.