Amid the whirlwind of rapid AI advancements, from EKS MCP's breakneck speeds to Hugging Face's budget-friendly robots, it's clear: AI is not only changing the way we code but also the gadgets we tinker with. Meanwhile, savvy developers are fine-tuning the delicate dance between innovation and security for a future that promises both power and peril. Dive in and untangle the threads of progress.

🚀 Accelerating application development with the Amazon EKS MCP server

🔍 Agentic AI Manifest: A Schema to Describe What Agents Do

🔑 AI agents have access to key data across the enterprise

🧩 Architecting Gen AI-Powered Microservices: The Unwritten Playbook

🔢 A visual introduction to vector embeddings

🛡️ GitHub MCP Exploited: Accessing private repositories via MCP

🤖 Human coders are still better than LLMs

🧗 It's not your imagination: AI is speeding up the pace of change

🖲️ New AI innovations that are redefining the future

🕵️ Using AI to outsmart AI-driven phishing scams

Read. Innovate. Secure. The AI frontier awaits.

AI takes a victory lap: Mary Meeker reveals ChatGPT snagged 800 million users in a brisk 17 months. Meanwhile, the bean counters cheer as inference costs nosedived 99% in just two years. Profitability? That's still a cliffhanger.

Azure AI Foundry gives developers the power to masterfully control AI agent workflows and streamline decision-making through a single API and SDK. Agentic DevOps elevates AI agents beyond mere coding assistants, morphing GitHub Copilot into a formidable dev partner eager to wrestle with code reviews and testing.

GitLab Duo, riding on Anthropic’s Claude, stumbled into a prompt injection blunder. Sneaky instructions nestled in projects allowed hackers to swipe private data. The culprit? Streaming markdown teamed up with shoddy sanitization. This opened a door for HTML injection and shined a spotlight on the double-edged sword of AI assistants: useful but also a tad too exploitable. GitLab scrambled to patch these loopholes, but the episode serves a stark reminder: AI insights need a fortress against crafty tampering.

UNC6032 swindled millions by spinning a tangled web of fake "AI video generator" sites. They slipped Python-based infostealers right under our noses, using social media ads as their Trojan horses. Meta’s ad transparency pulled back the curtain on over 30 malicious sites, yet the sneaky STARKVEIL malware continues to quietly plunder data.

82% of organizations have AI agents on deck; a mere 44% bother with security policies. That leaves a lot of open doors. A staggering 96% of tech pros are side-eyeing these agents as ticking time bombs, yet 98% plan to unleash more. It's like setting out catnip for hackers. These agents wield power with scant supervision. Only 52% of companies track their data frolics. IT teams typically get it (71%), but compliance (47%) and executives (34%) are in the slow lane.

Invariant played detective and unearthed a gaping hole in GitHub MCP. This flaw lets sneaky attackers hijack agents using malicious GitHub issues, spilling private repo secrets all over the public domain. Fortify your agent systems: clamp down on access and deploy Invariant Guardrails along with MCP-scan. Keep those threats at bay, lightning-speed.

Hugging Face just pulled the curtain back on HopeJR, a humanoid robot that swings 66 degrees of freedom—at just $3,000. This price tag shames the $16,000 slapped on Unitree's G1. Together with The Robot Studio, they've created this robot with a dash of Bender's charisma. The kicker? It's fully open-source. They're on a mission to democratize robotics for the rest of us.

Phishing scams are growing craftier, employing AI like FraudGPT to weave through filters and masquerade as real emails, boosting scam rates by 70%. AI can unveil sneaky phishing patterns humans miss, but it loves a good panic. It often cries wolf with false alarms and needs a babysitter to adjust to ever-shifting threats.

Meet agent-manifest.org. It's a fresh schema designed to crack open the mysteries of AI agents: roles, requirements, trust levels. Think of it as a decoder ring for the robots taking API jobs.

The EKS MCP server hands AI code assistants, like Q Developer CLI, the keys to a streamlined Kubernetes kingdom. App development? Now lightning fast. With LLMs tapping into real-time context, AI flexes its muscles in the wild world of Kubernetes ops and troubleshooting.

LLMs—the mysterious, fickle companions of coding. Senior engineers wade through it, extracting gold with tricks like "Second opinion" and "Throwaway debugging." Seth Godin rings the alarm: these clever machines aren't as clever as they look. First ask Claude, then call in a human.

Antirez recounted a story of working on Vector Sets for Redis, detailing a bug he encountered and his process of finding a solution through a creative approach involving LLM. He explored different methods to ensure link reciprocity and proposed a hashing solution that offered a balance between efficiency and protection against possible collisions. Overall, the experience highlighted the unique problem-solving capabilities of humans compared to LLMs.

OpenAI's text-embedding-ada-002 often gets a peculiar itch at dimension 196—vectors peaking awkwardly there. Enter text-embedding-3-small, swooping in to smooth out the distribution. Now, onto similarity metrics. For unit vectors, the dot product is your fast friend. It's interchangeable with cosine similarity, minus the extra math homework. Vector compression can slim things down with quantization and dimension reduction, but watch out—it might cut corners. Innovative tactics for storage and search can clean up the mess.

Turso overhauled its API with Go and AI, gunning for 1 billion databases. Think big, act smart. They squeezed every byte by adopting string interning. No more in-memory maps—they swapped them for a SQLite-backed LRU cache. The result? Leaner memory usage and hassle-free proxy bootstrapping.

AI Opportunity Tree welds cutting-edge tech to raw business value. Meanwhile, the AI System Blueprint knits tech tightly to stakeholder priorities. Lean models? They fuse teams, squash doubt, and thrust AI into action with exhilarating speed.

Perplexity Labs is your quick-draw tool for crafting apps and digital delights, powered by LLMs like GPT-4 Omni. It’s a star where others stumble: fast, project-driven tasks. Expect example-heavy insights and real-world project demos. While competitors dawdle, it delivers. Need deep web browsing, code execution, and inventive results? Just dive into its user-friendly gallery of 20+ samples. You might not leave.

The Voice Live API ditches the clutter of juggling models. One API call, and voilà—real-time, natural-sounding bots. It’s harnessed over WebSocket, keeping everything sharp and efficient.

Learn how LoRA and QLoRA make it possible to fine-tune huge language models on modest hardware. Discover the adapter approach for scaling LLMs to new tasks—and why quantization is the next step in efficient model training.

Stack Overflow once buzzed with collective brainpower. But then, it got too wrapped up in reputation points, a full-on leaderboard obsession. This detour dimmed its shine. It turns out, platforms flourish on real teamwork, not just gamified dick measuring contests. As AI sweeps through the coding world, developers are hungry for real connections. Let's face it—tech's true magic stems from humans, not soulless algorithms.

LLMOps shakes up the MLOps scene with tailor-made Kubernetes magic. It wrestles GPU scheduling, caching, and autoscaling for those behemoth LLM deployments. Keep an eye out for serverless endpoints and model meshes—smooth scaling and a wallet-friendly operation.

Plugging Gen AI into microservices isn't just a task. It's an adventure in tech wizardry. Get cozy with messaging queues, prompt caching, and the relentless art of watching in production.

Deploying LLMs on GCP Load Balancers is like fitting a square peg in a round hole. These models aren't stateless, so skip HTTP, go straight for TCP Load Balancing. Toss in Redis to keep those sessions on a leash. Tweak load balancer settings to dodge mid-stream socket calamities. Embrace the power of GKE Autopilot or Compute Engine to boost streaming.

ToolHive makes deploying MCP servers easy, secure and fun

Lark is a parsing toolkit for Python, built with a focus on ergonomics, performance and modularity.

This project implements an HTTP server that acts as a bridge between HTTP/1.1 requests and Server-Sent Events (SSE) using the mcp python library

Directory for Awesome MCP Servers