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Sajjaad Khader reposted thisSajjaad Khader reposted thisSat down for a super detailed conversation with one of my favourites Sajjaad Khader to discuss all about AI Engineering vs Software Engineering career paths and how to prepare for each role (and how the two will eventually converge)! This is especially important as AI engineering is becoming less about training models from scratch and more about building production systems around models. If you're a job-seeker or exploring career paths in AI engineering, this conversation will be super valuable to tune in to! Check out the full video on YouTube now 👇Choosing Between Software Engineering VS AI Engineering (Career Paths)Choosing Between Software Engineering VS AI Engineering (Career Paths)
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Sajjaad Khader shared thisIf you want to win $1,000 in cash using AI, listen up. Airtable is hosting a “build-your-own” AI challenge and you could be the lucky winner… To enter in, click the link right here: https://lnkd.in/eyfYf6gE For this challenge, I created a data set of a bunch of different soccer players that have competed in the world cup before. And I want to see what YOU can create with it using Airtable AI First, you'll need to open up the data set in Airtable by clicking on this link: https://lnkd.in/e6hWFkgc Then, you need to click on the left column and open up Omni, which is Airtable’s AI assistant, and have it create a custom interface, visual, or report for you. The goal of this challenge is for you to be super creative. There are no hard set technical requirements. The more creative you are, the better chance you have at winning that $1,000. Once you finish up, you can submit your interface using the same submission link at the top of this post. Then, I’ll be personally selecting the winner of this contest and will get back to you within exactly 2 weeks with the final result! I can’t wait to see what you all create! #AirtablePartner
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Sajjaad Khader reposted thisSajjaad Khader reposted thisAI is opening the door to new career paths across every industry 🚪 On The Executive Stack, DDN Co-Founder and CEO Alex Bouzari joined Sajjaad Khader to share the top emerging job opportunities students and professionals should watch over the next five years. From industry-specific AI consulting to AI-driven data center construction and AI-powered software services, the future belongs to people who can turn AI capabilities into real enterprise value. Learn how AI is reshaping careers, infrastructure, and business transformation in the full episode: bit.ly/4wKclvY
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Sajjaad Khader shared thisDear companies, please stop filtering by college degree. It is the most overrated job application filter. A diploma doesn’t prove someone can solve problems. A GPA doesn’t prove they can adapt. A school name doesn’t prove they can deliver results. And this is coming from someone who earned two computer science degrees. From Georgia Tech. With a 4.0. One of my friends went to community college. Upon graduation, he struggled to land a job. No recruiter would give him a second look. But instead of letting that hold him down, he turned frustration to passion. He worked tirelessly on his craft. Ended up creating his own company instead of joining one. Shortly after that, he became a millionaire. He had the ability to solve problems. But the name on his resume held him back. As a result, companies missed out on him. And they lost big. Because he’s just one example. There are thousands of kids like him. Talented. Skilled. Hungry. But without the “right” school on their resume, they’re ignored. Hire for ability, not for paper. ♻️ Repost if you agree skills > degrees.
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Sajjaad Khader reposted thisThe worst part about being a software developer is dealing with bugs. I can lock in and build for hours, but one bug can completely kill my momentum. That is why I started using Atlassian Loom. It turned a frustrating process into something fast and structured. 1. Record Instead of trying to explain the issue, I just record it. Loom captures everything in one go including my screen, console logs, network activity, and device info. No more guessing or missing details. 2. Create It connects directly with Jira so I can instantly turn that recording into a clear, detailed ticket. No back and forth. No confusion. The context is already there. 3. Fix Then Rovo Dev takes over. It reviews the recording, reads the ticket, analyzes the data, and finds the root cause. Within seconds, it suggests a fix and even opens a pull request ready for review. Now I spend less time stuck on bugs and more time actually building. Less debugging. More shipping. Try Loom here: https://go.atlss.in/lv7psl ♻️ Repost to help other software developers squash bugs! #AtlassianPartner
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Sajjaad Khader shared thisThe worst part about being a software developer is dealing with bugs. I can lock in and build for hours, but one bug can completely kill my momentum. That is why I started using Atlassian Loom. It turned a frustrating process into something fast and structured. 1. Record Instead of trying to explain the issue, I just record it. Loom captures everything in one go including my screen, console logs, network activity, and device info. No more guessing or missing details. 2. Create It connects directly with Jira so I can instantly turn that recording into a clear, detailed ticket. No back and forth. No confusion. The context is already there. 3. Fix Then Rovo Dev takes over. It reviews the recording, reads the ticket, analyzes the data, and finds the root cause. Within seconds, it suggests a fix and even opens a pull request ready for review. Now I spend less time stuck on bugs and more time actually building. Less debugging. More shipping. Try Loom here: https://go.atlss.in/lv7psl ♻️ Repost to help other software developers squash bugs! #AtlassianPartner
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Sajjaad Khader reposted thisIf you’re still learning Salesforce the same way you did 2 years ago, you’re already behind. AI is moving fast. New tools. Constant changes. Tons of noise in the tech industry. That’s why Salesforce TDX stands out. It’s not just talk. You see how tools like Agentforce are actually being used. You build. You learn. You get real insights. Plus you get direct access to Salesforce leaders and a hands-on hackathon. If you’re serious about growing in this AI era, this is where you want to be. Link here to register: https://lnkd.in/eu3Tdqru ♻️ Repost to help other salesforce builders
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Sajjaad Khader shared thisCompanies aren't cutting jobs because of AI. They're cutting jobs and blaming AI. Big difference. In January 2026, three major companies cut 17,575 jobs. The headline? "AI-driven layoffs." The reality? Post-pandemic bloat. Stock price pressure. Cyclical business problems. Only 20% of customer service leaders who reduced headcount actually did it because of AI. The rest used AI as convenient framing. Now, lucky for you, the AI boomerang is starting. 55% of employers report that they regret their "AI layoffs". One major tech company cut 700 customer service reps, replaced them with AI, then ended up REHIRING THE HUMANS after customer complaints spiked. Their CEO even admitted they "focused too much on efficiency and cost." AI can automate some parts of a job. It cannot replace ownership, judgment, or trust. If you are an engineer, here is the lesson: • Do not take layoff headlines at face value • Most companies are still experimenting, not executing a clear AI strategy • Overcorrections are happening in real time • People who understand both systems and real-world context will be the always be the most valuable Do not panic. Do not get complacent. Understand what is actually happening, not just the story being told. ♻️ Repost to share the truth about the AI layoffs.
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Sajjaad Khader shared thisDear tech companies, it's 2026. Leetcode interviews need to go away. They started for a good reason. Test fundamentals. Data structures. Algorithms. How you think from scratch. That made sense. But that is not what it is anymore. Now it rewards pattern recognition. Grind enough problems. Memorize enough templates. Map questions to answers. Done. At that point, what are you even measuring? Is it an interview or a memorization contest? Real engineering is messy. Vague requirements. Conflicting constraints. Half broken systems. No obvious right answer. That gap is the problem. Companies are selecting people who can pass interviews, not people who can build. Here is what better looks like: Feature development interviews. The candidate gets dropped into a real codebase. They are asked to build and ship something small. They can use AI, just like on the job. Now the signal is real. The interviewer digs into the work: • Why did you design it this way • What tradeoffs did you make • How well is it tested • Is it secure • Will it scale You cannot fake that. This might be harder for some candidates. But it will finally measure the right thing. ♻️ Repost if you agree Leetcode should go away!
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Sajjaad Khader liked thisIf you want to win $1,000 in cash using AI, listen up. Airtable is hosting a “build-your-own” AI challenge and you could be the lucky winner… To enter in, click the link right here: https://lnkd.in/eyfYf6gE For this challenge, I created a data set of a bunch of different soccer players that have competed in the world cup before. And I want to see what YOU can create with it using Airtable AI First, you'll need to open up the data set in Airtable by clicking on this link: https://lnkd.in/e6hWFkgc Then, you need to click on the left column and open up Omni, which is Airtable’s AI assistant, and have it create a custom interface, visual, or report for you. The goal of this challenge is for you to be super creative. There are no hard set technical requirements. The more creative you are, the better chance you have at winning that $1,000. Once you finish up, you can submit your interface using the same submission link at the top of this post. Then, I’ll be personally selecting the winner of this contest and will get back to you within exactly 2 weeks with the final result! I can’t wait to see what you all create! #AirtablePartner
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Sajjaad Khader liked thisSajjaad Khader liked thisAI is opening the door to new career paths across every industry 🚪 On The Executive Stack, DDN Co-Founder and CEO Alex Bouzari joined Sajjaad Khader to share the top emerging job opportunities students and professionals should watch over the next five years. From industry-specific AI consulting to AI-driven data center construction and AI-powered software services, the future belongs to people who can turn AI capabilities into real enterprise value. Learn how AI is reshaping careers, infrastructure, and business transformation in the full episode: bit.ly/4wKclvY
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Sajjaad Khader liked thisSajjaad Khader liked thisToday was the last day of AI+ Expo and my last day at SCSP. I do not even know where to start. Being on the staff side this time around was a completely different experience. You see a packed room and think it just came together. It did not. There is so much that goes into it, and getting a front row seat to all of it was something I will genuinely not forget. 20k+ steps a day for three days straight. Worth every single one. And then there were the people. If you have ever taken a CS class online, you probably know Sajjaad Khader. His videos got me through more than I would like to admit. If you are in the data space, I am almost certain Sundas Khalid has shown up on your feed at some point. Meeting both of them in person, at an event like this? That felt unreal. But honestly, the people I will think about most are the ones at SCSP. Smart, kind, and genuinely passionate about the work they do. Being around that every day made a real difference. Thank you to the entire Special Competitive Studies Project - SCSP team for having me. This experience meant a lot. #SCSP #AIplusExpo #AI #EarlyCareer #DataScience
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Sajjaad Khader liked thisSajjaad Khader liked thisFinally received my YouTube Silver Play Button ❤️ More than the award itself, it made me reflect on why I started creating content in the first place. A big reason I do this is probably for another version of me. Someone sitting somewhere in the world, curious about AI, trying to learn machine learning and engineering deeply, but struggling to find practical, high-quality, accessible resources. I remember how overwhelming this field felt at times when I was starting out. That’s still the gap I want to bridge. I’m really trying to build this channel into a place that is all things AI, but coming from a real builder perspective. For AI builders, by AI builder ❤️ (And if you don’t think you’re one yet, come stop by my channel.) Over the next few months, I’ll be investing much more deeply into YouTube with Long-form technical AI content. Here's what you'll find: - Deep dives into AI engineering and agentic systems - State-of-the-art tools and workflows I actually use - Honest feedback on my stack and experiments - Strong foundational concepts explained deeply and practically - Interviews with leaders and executives building some of the most exciting AI products today I want this channel to help people build real understanding and real skills in AI, without all the noise and hype around the space. Thank you for supporting my work and being part of this journey.
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😴 Your GPUs are sitting idle. Even at peak demand. Most AI companies are only utilizing 50-70% of their GPU capacity during inference. That's millions of dollars in compute just... waiting. The throughput bottleneck isn't about buying more GPUs. It's about fundamentally rethinking how your infrastructure handles LLM requests. We just published a deep dive on why traditional scaling approaches fail at production scale and what actually works. The companies that solve throughput now will serve more users, deploy better models, and spend less on infrastructure. Want to see the difference? 💸 We're offering $100 in free GPU credits so you can test Tensormesh's distributed caching architecture yourself. 🔎 Read the full article: https://lnkd.in/gDmn3WX5 👉 Claim your credits: https://lnkd.in/gmKa8chj #AI #MachineLearning #LLMs #AIInfrastructure #GPUs
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The First Open-Source Rust Core & Python Wrapped LLM Framework Over the past three years, artificial intelligence has evolved faster than any other technology in history. LLMs (Large Language Models) like GPT-5, Claude, and Llama now power capabilities once thought impossible — from writing production-grade code to autonomous agent workflows that can schedule meetings, analyze reports, or handle customer support. But beneath the excitement lies a critical problem: the frameworks running these agents were never built for scale. Developers today prototype using Python frameworks such as LangChain, CrewAI, or LangGraph. These are great for demos, but at enterprise scale — thousands of agents running concurrently — they collapse under pressure. The GIL (Global Interpreter Lock) — prevents true parallel execution; “async” is only cooperative multitasking. Memory leaks & garbage collection pauses — long-running agents eventually crash unpredictably. Non-deterministic execution — pipelines behave differently across runs due to thread scheduling. https://lnkd.in/g2_kfJM7
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The Algorithm Daily
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Top stories in AI: Major breakthroughs, bold launches, and strategic shifts shaping the future of intelligence. 🔹 xAI launches Grok 4 Elon Musk's latest model claims superhuman reasoning and post-grad-level performance across disciplines. With 100x more training and 10x more RL compute than Grok 2, it’s built for real-world intelligence. 🔹 NVIDIA hits a $4 trillion market cap Now the world’s most valuable company, driven by surging demand for AI infrastructure. A strong signal that foundational hardware continues to lead the AI value chain. 🔹 Anthropic publishes alignment study Following concerns around Claude 3 Opus and “deceptive alignment,” Anthropic analyzed 25 leading models. Findings suggest evasiveness and misalignment are more common than we think pushing transparency to the forefront. 🔹 OpenAI reportedly developing its own AI-powered browser A browser integrated with ChatGPT and Operator (its web agent) could disrupt traditional search models and challenge Chrome's dominance. 🔹 Open-source momentum is building Rumors hint at a new open-weight model (akin to o3 mini) coming soon potentially unlocking new possibilities for researchers and developers alike. The AI space is evolving faster than ever, from infrastructure to alignment, intelligence, and interface. Which of these developments do you think will have the biggest impact? #AI #xAI #Grok4 #OpenAI #Anthropic #NVIDIA #LLMs
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Ripudaman Singh
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DeepSeek AI Signals a New Direction in Efficient Model Design : Latest Paper Suggests a Rethink of Large-Scale Training A newly published paper on arXiv proposes structural changes to neural networks that could meaningfully improve training stability and cost efficiency and it feels like a familiar pattern. The paper introduces mHC, a technique designed to stabilize large-scale training with minimal additional compute overhead. Tested on 3B, 9B, and 27B parameter models, mHC delivered stronger benchmark performance, particularly on reasoning-heavy tasks. Notably, CEO Liang Wenfeng co-authored and personally uploaded the paper, reinforcing how hands-on DeepSeek leadership remains with core research. If this sounds familiar, it should. Similar research breadcrumbs appeared ahead of R1 and V3, both of which preceded major releases. Why this matters Last year’s “DeepSeek moment” showed how R1 approached frontier-level performance at a fraction of the cost. This paper suggests they’re still unlocking efficiency gains. Combined with greater access to advanced AI chips, these architectural improvements point to something bigger. Looking ahead to 2026 Between cost-efficient training methods and accelerating hardware access, Chinese AI releases are set to become even more competitive—not just cheaper, but smarter and more stable at scale. This doesn’t look like a one-off paper. It looks like a preview. Read the paper here : https://lnkd.in/gR222q9P #AIResearch #DeepSeek #NeuralNetworks #LLMs #AIInfrastructure #MachineLearning #AITrends
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NVIDIA AI
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Our former intern, Selena, spoke with Carter Abdallah to share her advice for students. ✨ How to take advantage of the NVIDIA DGX Spark? Experiment. With preloaded tools, unified memory, and no GPU queues, the only limit is what you can imagine. 💭 See the demo Selena worked on during her internship here: https://lnkd.in/gc83sG4j #SparkSomethingBig 💫
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UMBC Center for Artificial Intelligence
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LLMs are surprisingly capable coding partners. UMBC CSEE Prof. Tim Oates gives a short, technical overview of why and how coding assistants work and where they predictably fail. Most of the session is supported with live, terminal-based demos using Claude Code. 1-2:30pm ET, April 3, ITE 325b & online https://lnkd.in/eengnYuP
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IRF Technologies
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Andreessen Horowitz
689K followers
We’re excited to lead Deeptune's Series A. Deeptune is building leading RL environments for computer-use and code. More data, more compute, and better architectures can only take us so far. As models move into real-world task execution, they need structured environments where they can learn to fully control computers and perform knowledge work tasks. Designing high-quality RL environments is extraordinarily difficult to get right, but Deeptune has proven they can, working closely with leading AI labs and developing environments for computer use that are already showing up in benchmark improvements. Deeptune’s founder and CEO, Tim Lupo, is an exceptional leader with a rare combination of technical expertise and product intuition. We’re thrilled to partner with Tim and the Deeptune team as they build this critical layer of the AI stack. By Marco Mascorro and Martin Casado
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AI|Xplore
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The AI landscape is moving at breakneck speed. Here are the key updates from the industry: 🚀 Andrej Karpathy Joins @Anthropic: A major talent acquisition that signals Anthropic’s serious push into advanced pre-training research. 📈 1,000x Compute Demand: @NVIDIA CEO Jensen Huang predicts a massive scale-up in compute, driven by the shift toward autonomous AI agents. ⚖️ OpenAI Legal Win: With the dismissal of the lawsuit from Elon Musk, the path is now clear for @OpenAI’s highly anticipated IPO. ⚡ Google’s Gemini Evolution: @Google has rolled out Gemini Omni and 3.5 Flash, prioritizing speed and versatility for developers. 🔗 Full AI news roundup in the link below
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DevCuration
637 followers
AI does not run on hype. It runs on infrastructure. And infrastructure is where SambaNova Systems decided to play. Founded in 2017 by Rodrigo Liang, Kunle Olukotun, and Christopher Ré, SambaNova Systems was built on a simple premise that most people underestimated at the time. Training grabs headlines. Inference pays the bills. Rodrigo Liang, CEO and Co Founder, brought 20+ years of semiconductor experience, including leading Oracle SPARC server and ASIC programs. Kunle Olukotun, Co-Founder and Chief Technologist, brought the architectural depth of a Stanford pioneer in multicore and domain specific systems. This was not a science project. It was a deliberate strike at the AI stack. The company built its own Reconfigurable Dataflow Units, deployed through SN40 and SN50 systems inside SambaRack, then wrapped it all in SambaStack, SambaOrchestrator, and SambaCloud. Full stack control. Hardware to API. OpenAI compatible endpoints so enterprises do not have to rip and replace to move fast. When latency, governance, and tokens per watt actually matter, inference optimized stops being marketing copy and starts being operational leverage. Capital noticed. On Feb 6, 2026, Reuters reported SambaNova Systems is in an oversubscribed Series E targeting more than $350M, led by Vista Equity Partners and co-led by Intel Corporation. Intel is reportedly committing around $100M, with flexibility up to $150M, with terms still being finalized. When Vista leans in, it signals enterprise scale. When Intel steps in, it signals belief in the silicon strategy. This follows a $676M Series D in April 2021 at a $5.1B valuation, led by SoftBank Vision Fund 2 with participation from Temasek, GIC, Intel Capital, BlackRock, GV (Google Ventures), Walden International School, and WRVI Capital. By 2021, total capital raised exceeded $1B. Wired later cited roughly $1.1B by late 2025. This is not incremental funding. This is conviction capital backing a thesis about where AI economics are heading. And the thesis is clear. Enterprises and sovereign AI customers need performance without surrendering control. Governments want models running within their borders. CFOs want throughput without runaway energy costs. Developers want compatibility without friction. SambaNova Systems is positioning itself as the backbone for that reality, powering sovereign AI providers across Australia, Europe, and the UK, while partnering with Hume AI and Canvass Analytics to push vertical workflows into production environments. The real takeaway is not the $350M headline. It is the pattern. Build proprietary silicon. Own the stack. Optimize for inference where value is captured. Attract long horizon investors who understand infrastructure cycles. Then scale into the parts of AI that enterprises cannot afford to get wrong. #ArtificialIntelligence #Semiconductors #EnterpriseInfrastructure #DataCenters #DCTalks
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Big Agile
911 followers
Traditional prompt engineering led to an endless loop of fixes and new errors, so the system never reached production-level reliability. This team turned to RL, fine-tuning a Llama model with an automated system of verifiers that checked responses against source documents, which eliminated the need for manual prompt engineering. The resulting model, now better able to reason independently rather than simply memorize responses, doubled its effectiveness, boosting its win rate against GPT-4o from a baseline of 27% to 58%.
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OPC Community
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This week's GitHub trending repos are all about making AI work harder for you. From NousResearch's Hermes Agent framework that learns your workflow, to Andrej Karpathy's single-file coding upgrade, to a personalized AI tutor, a platform that turns agents into teammates, and Google's on-device ML showcase — these five repos are shaping where dev tools are headed. Whether you're building agents or just want to code smarter, this roundup has something for you. Follow OPC Community for more: opc.community https://lnkd.in/edxeZR4p
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TensorMem Inc.
238 followers
After Positron AI's series-B of $230M a few weeks back, another AI chip maker MatX raises $500M in series-B round. TensorMem Inc. complements these offerings by streamlining the data pipeline across memory-storage hierarchy. We covered the rise of inference-native compute a few weeks back. https://lnkd.in/dJSegQXj
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Agentic AI @ UIUC
310 followers
We’re excited to launch the open-source initiative from Agentic AI @ UIUC Our club was founded on a simple belief: students shouldn’t just learn about modern AI systems — we should build real ones that people can use. Much of today’s most impactful AI infrastructure is closed source, which makes it difficult for students and builders to understand how these systems actually work. So we decided to change that. Our mission: • Recreate and reimagine widely used closed-source AI tools • Build production-style multi-agent systems in the open • Contribute meaningful infrastructure to the developer community • Give students real experience shipping software used beyond campus And today we’re excited to share our first open-source project Introducing Aegis — Self-Healing Data Quality Monitoring with Autonomous AI Agents Data pipelines rarely fail loudly. A column gets dropped upstream, a table stops refreshing, and by the time someone notices, dashboards are wrong and decisions are already compromised. We built Aegis to fix this. Aegis is an open-source data intelligence platform that uses a coordinated multi-agent system to automatically detect, diagnose, and recommend fixes for data quality issues before they reach stakeholders. How Aegis works: Aegis runs five specialized agents in a pipeline: 1)Investigator — Connects to warehouses (Snowflake, BigQuery, Postgres) and automatically discovers which tables to monitor using LangChain. 2)Sentinels — Continuously monitor schema drift and freshness violations. 3)Orchestrator — Deduplicates anomalies into incidents and coordinates response. 4)Architect — Performs LLM-powered root cause analysis and traces the blast radius across the lineage graph. 5)Executor — Generates remediation plans with actionable SQL ready for human approval. Key design principles • Deterministic fallbacks — works even without an LLM • Human-in-the-loop — agents propose, never auto-modify data • Structure-only discovery — minimal warehouse load, no profiling • Full lineage graph built from query log parsing Stack: Python, FastAPI, SQLAlchemy, LangChain, React, TypeScript This is just the beginning of our open-source journey. We’re building real, production-style AI systems in public and would love feedback from anyone working in data engineering, data quality, observability, or agent architectures Github Link - https://lnkd.in/gbdeiM2Q Developer : Ashleyn Castelino, Anirudh Konidala #OpenSource #DataEngineering #AI #MultiAgentSystems #LangChain #Python #BuildInPublic
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Anyscale
60K followers
We are seeing an emerging 3-layer OSS stack for AI compute: 🔧 PyTorch + 🧠 vLLM + ⚡ Ray + 📦 Kubernetes 🎥 Robert Nishihara gives a quick breakdown of how this stack works together to scale LLMs + GenAI workloads The AI compute software stack consists of 3 specialized layers: 🔧 Layer 1: Training & Inference Framework (PyTorch + vLLM) • Runs models efficiently on GPUs • Handles model optimization and model parallelism strategies • Manages accelerator memory and automatic differentiation ⚡Layer 2: Distributed Compute Engine (Ray) • Schedules tasks within jobs and coordinates processes • Ingests and moves data • Provides workload-aware failure handling and autoscaling 📦 Layer 3: Container Orchestrator (Kubernetes) • Provisions compute resources • Schedules entire jobs • Manages user and workload multitenancy Each layer handles what it does best. The separation of concerns makes this stack so powerful. 📖 Read the full blog post with examples from Pinterest, Uber, and Roblox: https://lnkd.in/eyn3jBfU
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5 Comments -
Rootly
12K followers
Rootly AI Labs Fellow Laurence Liang is live at ACL 2025 in Vienna presenting our latest work on applying ML to SRE, and catching up with researchers pushing the boundaries of AI. Matthew Toles, ML Researcher at Columbia University, is tackling one of the biggest gaps in LLMs today: their failure to ask clarification questions. His new paper explores why it matters and how to address it. Find his paper linked in the comments.
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3 Comments -
Anyscale
60K followers
At Ray Summit 2025, Elizabeth Hu, Goku Mohandas Mohandes and Akshay Malik from Anyscale will announce Lineage Tracking on Anyscale, a new observability tool for end-to-end ML lineage powered by OpenLineage. It maps datasets and models across Workspaces, Jobs, and Services and renders an interactive lineage graph for fast reproduction, audit, and debugging. It also integrates with your existing catalogs and registries like MLflow, Weights & Biases, and Unity Catalog. What you will learn: - How OpenLineage automatically captures datasets, models, code versions, and parameters across development to production on Anyscale - How to navigate the lineage graph to reproduce runs, trace failures upstream, and support governance and audits - How to connect lineage to MLflow, W&B, and Unity Catalog to keep one source of truth for artifacts and metadata Nov 3–5 at the San Francisco Marriott Marquis. Register now: https://lnkd.in/ghAANMa6
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Swartz Center for Entrepreneurship
7K followers
The Autonomy Stack Showdown: Models vs. Infrastructure! Next week at CMU Lab to Market in San Francisco, we're bringing together leaders who’ve shaped autonomy across research, company-building, and capital: Martial Hebert (Moderator), Dean, CMU School of Computer Science Arvind Gupta, Partner, Mayfield Ryan Oksenhorn, Co-Founder, Zipline Nancy Pollard, Professor, CMU Robotics Institute & SCS Sebastian Scherer, Co-Founder, FieldAI & CMU Professor of Robotics Together, they’ll examine where autonomy systems truly bottleneck today, how technical tradeoffs surface at scale, and what it takes to move from frontier advances to reliable, real-world systems. ✨ San Francisco | Feb 11, 2026 | https://lnkd.in/eyYatuJd #CMULabToMarket #CarnegieMellon Carnegie Mellon University's College of Engineering Carnegie Mellon University School of Computer Science Jesse Thill Meredith Meyer Grelli Namrata Banerjee Matthew Katsaros, M.A. Jenny Belardi Anita Jesionowski Carnegie Mellon University Alumni Association Carnegie Mellon University Robotics Institute
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