Join us

ContentUpdates from Levelop.dev...
Link
@ninaddesai shared a link, 1 year, 1 month ago
Staff Engineer, Infracloud

Eliminating Observability Vendor Lock-in with OpenTelemetry: A Hands-On Demo

OpenTelemetry Prometheus Docker Elastic Python

Struggling to switch from Prometheus to Elasticsearch without rewriting your app? This hands-on guide shows how OpenTelemetry decouples your observability backend with zero app changes. Includes working Docker-based examples and step-by-step guidance.

Prometheus visualizing myapp_requests_total metric via OpenTelemetry and Docker-based Python app
Link
@faun shared a link, 1 year, 2 months ago
FAUN.dev()

Trump administration considering broader DeepSeek ban

DeepSeek—at one time, the darling of chatbot innovation in China—now finds itself under the unforgiving hammer of a US ban. The reason? Sketchy ties with China's military. Toss in the troubling bit about the60,000 Nvidia chipsit's hoarding—20,000 of those should've been off-limits—and you've got a r.. read more  

Trump administration considering broader DeepSeek ban
Link
@faun shared a link, 1 year, 2 months ago
FAUN.dev()

How to use any Python AI agent framework with free GitHub Models

GitHub Modelsdishes out no-cost access to models that mirror OpenAI's magic, but with a twist—easy integration with Python. Just snag a Personal Access Token and dive in. Swap models faster than you change socks. 📈.. read more  

How to use any Python AI agent framework with free GitHub Models
Link
@faun shared a link, 1 year, 2 months ago
FAUN.dev()

Understanding RAG: Retrieval Augmented Generation Essentials for AI Projects

Retrieval-Augmented Generation (RAG) turns Large Language Models into knowledge-sniffing bloodhounds.It fetches real-time intel to crush those pesky hallucinations and refresh its smarts on demand. Why stick with static models when RAG gives your AI brains a live data feed? Real-time accuracy withou.. read more  

Understanding RAG: Retrieval Augmented Generation Essentials for AI Projects
Link
@faun shared a link, 1 year, 2 months ago
FAUN.dev()

Inside the CodeBot: A Gentle Introduction to How LLMs Understand Nullability

LLMs get nullability. The more you train them, the sharper they become. Pythia, with her heftier brain, deciphers nullability faster, thanks to top-notch inference tricks... read more  

Inside the CodeBot: A Gentle Introduction to How LLMs Understand Nullability
Link
@faun shared a link, 1 year, 2 months ago
FAUN.dev()

Meta Sought Funds for Llama AI Model Development from Amazon and Microsoft

Metaasked rivals likeMicrosoftfor cash to handle its soaring AI expenses. Bold move, right? Say hello toLlama 4—a beast with next-gen scalability. Think 10 million token contexts and a slickMixture-of-Expertsdesign. Legal drama over training data could crank up costs, butMetaplays it smart, pushing .. read more  

Meta Sought Funds for Llama AI Model Development from Amazon and Microsoft
Link
@faun shared a link, 1 year, 2 months ago
FAUN.dev()

Microsoft AI CEO: ‘It’s Smarter to Be 6 Months Behind’ — Here’s Why

Microsoftplays it cool with an "off-frontier" AI strategy, sidestepping heavyweights likeOpenAI. It's a cost-cutting, reliability-boosting move. Even with deep pockets sunk intoOpenAI,they're building pint-sized brainiacs with theirPhi project. The grand plan? Stand-alone strength by 2030... read more  

Link
@faun shared a link, 1 year, 2 months ago
FAUN.dev()

Gemini 2.5 Flash with ‘thinking budget’ rolling out to devs, Gemini app

Gemini 2.5 Flashbursts into the scene with a sparkling new feature: a "thinking budget." This lets developers fine-tune token-based reasoning anywhere from 0 to a whopping 24,576, cranking up accuracy without gouging your pockets. Catch it in preview onGoogle AI StudioandVertex AI. The model handles.. read more  

Gemini 2.5 Flash with ‘thinking budget’ rolling out to devs, Gemini app
Link
@faun shared a link, 1 year, 2 months ago
FAUN.dev()

Introducing OpenAI o3 and o4-mini

Creating a degree19odd-power polynomial with a linear coefficient of-19is not your usual algebra homework. Get cozy withT19(x), because factorization demands finesse here. Aim to break it down into at least three stubbornly irreducible pieces. The trick? Jugglingnon-linear factorsto dodge any slip i.. read more  

Introducing OpenAI o3 and o4-mini
Link
@faun shared a link, 1 year, 2 months ago
FAUN.dev()

What the heck is MCP and why is everyone talking about it?

Picking the right AI model forGitHub Copilotis like matchmaking. It's about the project's quirks, and balancing razor-sharp accuracy with processing muscle... read more  

What the heck is MCP and why is everyone talking about it?
Levelop is an interview preparation platform designed specifically for working software engineers (typically with 2–6 years of experience) who want to land jobs at top-tier tech companies.

Instead of just handing you endless lists of problems or passive videos to watch, Levelop uses an active, AI-guided approach to help you build the right mental models for tough technical interviews.

Here is how it works:

Two Specialized AI Mentors: * Orion (Coding AI): Instead of just telling you that your code is wrong, Orion steps in when your code fails, maps out where your knowledge gap is, and guides you to fix it yourself.

Aurora (System Design AI): Rather than making you watch a 40-minute video, Aurora has a live conversation with you to explain foundational system design concepts before you even start drawing on the canvas.

Sprint-Based Practice: You practice in structured loops called "sprints," which combine both Data Structures & Algorithms (DSA) and system design problems.

Actionable Feedback Loop: At the end of every sprint, you receive a detailed report. It scores your technical skills, gives you a behavioral profile, and ranks the exact weaknesses you need to focus on during your next sprint.

In short, it is a smart, interactive practice arena that focuses on actively fixing your specific weaknesses rather than just tracking how many hours you spend studying.